Browse Articles

Structural insights into light-gating of potassium-selective channelrhodopsin

Author Correction: Normal tissue radioprotection by amifostine via Warburg-type effects

Proton-electron coupling and mixed conductivity in a hydrogen-bonded coordination polymer

Investigating risk factors for migraine in Syrian women: a cross-sectional case-control study

A self-powered soft triboelectric-electrohydrodynamic pump

Establishment of an animal model for monkeypox virus infection in dormice

Cloned airway basal progenitor cells to repair fibrotic lung through re-epithelialization

Isotemporal substitution of sedentary time with different physical activity intensities and sleep in obesity parameters across eight latin American countries

Diffusion-programmed catalysis in nanoporous material

Abstract In the realm of heterogeneous catalysis, the diffusion of reactants into catalytically active sites stands as a pivotal determinant influencing both turnover frequency and geometric selectivity in product formation. While accelerated diffusion of reactants can elevate reaction rates, it often entails a compromise in geometric selectivity. Porous catalysts, including metal-organic and covalent organic frameworks, confront formidable obstacles in regulating reactant diffusion rates. Consequently, the chemical functionality of the catalysts typically governs turnover frequency and geometric selectivity. This study presents an approach harnessing diffusion length to achieve improved selectivity and manipulation of reactant-active site residence time at active sites to augment reaction kinetics. Through the deployment of a thin film composed of a porous metal-organic framework catalyst, we illustrate how programming reactant diffusion within a cross-flow microfluidic catalytic reactor can concurrently amplify turnover frequency (exceeding 1000-fold) and enhance geometric selectivity ( ~ 2-fold) relative to conventional nano/microcrystals of catalyst in one-pot reactor. This diffusion-programed strategy represents a robust solution to surmount the constraints imposed by bulk nano/microcrystals of catalysts, marking advancement in the design of porous catalyst-driven organic reactions.

Associations between phenol and paraben exposure and the risk of developing breast cancer in adult women: a cross-sectional study

Micro Immune Response On-chip (MIRO) models the tumour-stroma interface for immunotherapy testing

Mechanical properties and mechanism of damage and deterioration of coal under cyclic loading

Direct probing of energy gaps and bandwidth in gate-tunable flat band graphene systems

Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning

Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction

Nitrogen enrichment and vascular plant richness loss reduce bryophyte richness

Abstract Grasslands’ high diversity is threatened by land-use changes, such as nitrogen fertilization, leading to productive but low-richness, fast-growing plant communities. Bryophytes are a key component of grassland diversity and react strongly to land use. However, it is unclear whether land-use effects are direct or mediated by changes in vascular plants. Increases in vascular plant cover are likely to decrease bryophyte abundance through light competition. Whether changes in vascular plant composition and richness also play a role remains unclear. We sampled bryophytes in a factorial grassland experiment manipulating nitrogen fertilization, fungicide, species richness, and functional composition of vascular plants crossed with moderate disturbances by weeding. Disturbance increased bryophyte richness and modulated treatment effects. In contrast to previous studies reporting indirect negative fertilization effects via increasing vascular plant productivity and reduced light levels, nitrogen fertilization directly reduced bryophyte cover and species richness, possibly because of toxic effects. Low vascular plant richness and dominance of fast-growing species reduced bryophyte richness. This might be because of decreased structural and resource niche heterogeneity in species-poor communities. Our results highlight novel mechanisms by which land-use intensification can affect bryophytes and suggest that a loss of vascular plant richness might have cascading effects on other taxonomic groups.

Author Correction: Targeting adipocyte ESRRA promotes osteogenesis and vascular formation in adipocyte-rich bone marrow

Survival and risk factors for metastatic colorectal cancer patients with a history of prior malignancy

Publisher Correction: The NIN transcription factor coordinates CEP and CLE signaling peptides that regulate nodulation antagonistically

Quantum-inspired K-nearest neighbors classifier for enhanced printer source identification in forensic document analysis

Abstract Document source identification in printer forensics focuses on determining the source printer of a document by analyzing characteristics such as printer model, serial number, defects, or unique artifacts. This is crucial in forensic investigations involving counterfeit documents or anonymous threats. However, identifying consistent patterns across different printers remains challenging, especially when perpetrators attempt to obscure these artifacts. Machine learning models in this field must identify discriminative features that differentiate printers while minimizing noise. In particular, choosing an appropriate distance metric for K-Nearest Neighbors (KNN) classifiers is critical and requires experimentation. This study proposes a quantum-inspired approach to improve KNN’s performance in printer source identification. By exploring alternative number of neighbors (K), quantum-inspired computing can optimize feature space calculations, even in noisy conditions. This allows the system to iteratively refine and select the optimal K value based on classification performance, ensuring that the best K is identified for the specific dataset and task. The system utilizes the Grey Level Co-occurrence Matrix (GLCM) for feature extraction, which is robust to changes in rotation and scale. Experimental results demonstrate that the Quantum-inspired KNN (QKNN) classifier outperforms classical KNN, achieving higher accuracy in identifying subtle printing artifacts, even under variable conditions.