Browse Articles

Uncovering the cytogenetic hallmarks of resident and expanded natural killer cells from patients with acquired aplastic anemia

Local, but not circulating, complement C3 shapes immune checkpoint blockade efficacy by controlling myeloid cell infiltration

Statistical AI Enables Precise Screening of Multielement Catalysts

Intravascular behavior of cutaneous polynucleotide injectables following intra-arterial exposure

Cryo-EM structures of human FANCJ reveal the mechanism of G-quadruplex unwinding and disease-associated mutations

Abstract Guanine-rich nucleic acid sequences can fold into G-quadruplex (G4) structures that regulate DNA replication, transcription, and translation. Fanconi anemia group J helicase (FANCJ) resolves G4 structures at stalled replication forks. Despite its central role in genome maintenance, the molecular basis of G4 recognition and unwinding by FANCJ has remained unclear. Here, we report cryo-EM structures of human FANCJ bound to a G4-containing DNA substrate and ATPγS. The structures reveal direct engagement of the G4 by the Fe–S domain. Structure-guided mutagenesis demonstrates that this interface is essential for G4 binding and unwinding. The structures further capture open and closed conformational states linked to ATP hydrolysis, providing a mechanism for directional translocation along 5′ ssDNA and progressive G4 unwinding. Together, these findings establish the structural basis of G4 recognition by FANCJ and provide mechanistic insights into how disease-associated mutations linked to Fanconi anemia and breast cancer impair helicase function.

Intrinsic Stabilization of High-Order Van Hove Singularity in 2D Rashba Superconductor

Evaluation of stroke-associated risk alleles in a multi-ethnic Singaporean ischemic stroke cohort

Gαq activation of free fatty acid receptor 4 suppresses metabolic dysfunction by disrupting Nr1h3-PPARγ axis

Discovering interpretable drug formulation behavior patterns via a mechanistic-augmented conditional variational autoencoder

Abstract Formulation composition, processing conditions, and their combined effect on drug solubility and particle size are not fully understood. This is due to a complicated network of interactions dependent on the conditions, which are usually investigated by trial-and-error testing. A question that has been left practically unanswered is whether the experimental formulation data can still find patterns of structured and interpretable behavior that are beyond mere prediction. This paper tries to answer whether the latent generative modeling can structurize formulation knowledge in a continuous, regime-aware manner. 114 niosome formulation samples were mined systematically from 17 publications based on the PRISMA framework. Inputs to model the encapsulated drug efficiency and particle size were 11 drug, formulation, and processing variables. To develop a structured latent understanding of formulation behavior, a mechanistic-augmented conditional variational autoencoder was used. The discovered latent space became a continuum with regimes overlapping, smooth changes, and feature, response relationships being dependent on the context. In a sense, formulation behavior can be considered as a structured latent landscape that can be used for regime-aware analysis and the generation of hypotheses in data-driven formulation research.

Microbial single-cell transcriptomics links gut microbiota functional states to metabolic changes in male mice

Carbene Analogues of Group 15: Reduction of s-Hydrindacene-Based Chloropnictogenium Ions To Access an Antimony Hydride Monocation and a Trinuclear Bismuth Dication

Deconvolution of the Mg-Related blue band in GaN via selective chemical treatments

Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control

pH-Dependent Vibrational Dynamics Drives Excited-State Quenching in the Phycobiliprotein Complex PC645

AI-driven feature descriptor design using CNNs and transfer learning for enhanced image retrieval accuracy and efficiency

CenSegNet: a generalist high-throughput deep learning framework for centrosome phenotyping at spatial and single-cell resolution in heterogeneous tissues

Abstract Centrosome abnormalities (CA) are a hallmark of epithelial cancers, yet their spatial complexity and phenotypic heterogeneity remain poorly understood due to limitations in conventional image analysis. Here we present CenSegNet (Centrosome Segmentation Network), a modular deep learning framework for high-throughput segmentation of centrosomes and epithelial architecture, enabling accurate and generalisable centrosome phenotyping at spatial and single-cell resolution across imaging modalities and tissue contexts. Applied to tissue microarrays comprising 911 breast cancer cores from 127 patients, CenSegNet enables large-scale, spatially resolved quantification of numerical and structural CA. We show that these CA subtypes are mechanistically uncoupled, exhibiting distinct spatial distributions, age-dependent dynamics, and associations with tumour grade, hormone receptor status, genomic alterations and nodal involvement. Structural CA are associated with overall survival, whereas discordant CA profiles at tumour margins correlate with local tumour aggressiveness and stromal remodelling. These findings establish CenSegNet as a scalable platform for spatially resolved centrosome phenotyping, enabling systematic investigation of centrosome biology and its dysregulation in cancer and other epithelial diseases.

Interfacial Reverse Charge Transfer Enabling Near 100% Selectivity of Glycerol Photooxidation into Formate over Schottky’s Au/TiO ₂

A methodology for qualitative and quantitative assessment of compressional deformation from growth strata: the Dibei structural zone, Kuqa Depression, Northwestern China

Fluctuation-driven mass-selective transport in dynamic nanopores

Abstract Precise molecular separation is essential in nanotechnology. However, static designs based on pore size and host-guest interactions often fail for species with nearly identical properties. To overcome this, we elucidate how nanopore dynamics govern transport and separation by establishing a general, predictive framework. We demonstrate that diffusion in a fluctuating periodic potential is maximized at an optimal fluctuation rate. Critically, this rate depends on molecular mass, translating subtle mass differences into significant kinetic disparities. By integrating this framework with quantum-chemical energy landscapes for archetypal soft porous crystals, we identify two factors governing selectivity: the magnitude of energy-barrier fluctuations and the alignment between nanopore dynamics and the energy-fluctuation rate that maximizes selectivity. These results establish nanopore dynamics as a key design dimension for controlling separation by tuning structural dynamics via ligand substitution or external fields, providing a theoretical foundation for the rational design of separation materials.

Efficient Light-Driven CO ₂ Capture and Reversible Release Enabled by Metastable Photoacid-Decorated Metal–Organic Frameworks