Browse Articles

Chiral peptidoglycan mimics target bacterial wall biosynthesis for pathogen intervention

Imaging the sub-moiré potential using an atomic single electron transistor

Evaluation of standard, black-box, and bayesian RSM-SVR models in the semi-arid area of south-eastern Iran for predicting soil chemical properties

Decoding the substrate specificity landscape of a promiscuous enzyme through multi-substrate mutational scanning

Optical characterization analysis of surface modified-plastics (SMP) induced by atmospheric cold plasma system

Benchmarking EGF signaling pathway inference using phosphoproteomics and kinase-substrate interactions

Abstract Signaling pathways are useful models for interpreting molecular data, but their coverage has long been constrained by classic biochemistry methods. The growing corpus of kinase-substrate interactions, coupled to phosphoproteomics improvements, pave the way to revisit classic signaling pathways. In this study, we explore context-specific signaling pathway inference from phosphoproteomics and kinase-substrate networks. Focusing on epidermal growth factor (EGF), we conduct a meta-analysis and generate three datasets representing the most comprehensive characterization of the EGF response to date. We infer kinase-kinase pathways and compare them to different ground truth sets. Literature-curated networks consistently yield the highest recovery of ground-truth interactions, with modest gains from network propagation methods. Up to 90% of interactions are absent from current ground truth sets, indicating many unexplored interactions supported by data and knowledge. Our results demonstrate the limitations of traditional views on signaling pathways and point to opportunities for generating better mechanistic hypotheses.

AI tools can design genomes. Will they upend how life evolves?

Enhancing the classification of spectrally similar land use/land cover classes using transfer learning in arid regions

Abstract Accurate mapping of Land Use/ Land Cover (LULC) in arid and semi-arid areas is essential for managing urban planning, monitoring environmental risks, and preserving the ecosystem balance. Conventional classification techniques provide inaccurate mapping of LULC in such areas due to the spectral similarity behavior of built-up and bareland classes. To address this challenge, semantic segmentation techniques have been recently employed to enhance classification accuracy. Meanwhile, in the case of imbalanced datasets, these models still exhibit limited generalization ability. To overcome this limitation, this study proposes a transfer learning approach to enhance the classification accuracy between built-up and bareland classes in the Nile Delta in Egypt, utilizing an imbalanced dataset. Four transfer learning models were employed, including Resnet50-Unet, Resnet50-FPN, Resnet50-PSPNet and Unet and their results were compared with the Maximum Likelihood classifier. The results showed that Resnet50-FPN achieved the highest F1-score of (0.877), followed by Resnet50-Unet (0.8705), Resnet50-PSPNet (0.852), Unet++ (0.792) and Maximum Likelihood (0.725). These findings highlight the effectiveness of transfer learning in enhancing LULC classification performance compared to the conventional Maximum Likelihood classifier. The superior performance of Resnet50-FPN is due to its multi-scale feature extraction capability, which enables more effective representation of LULC classes while preserving the spatial information of remotely sensed images. This indicates that the ResNet50-FPN classifier can be employed in different contexts to differentiate between built-up and bareland areas.

Population genomics reveals association of transposable elements variants with climatic adaptation in wild Amur grape

Light-assisted drying enables vaccine stabilization and supports cold-chain-independent distribution

Universal cryogenic transfer of liquid metal particles in polymers for wafer-scale stretchable integrated electronics

Abstract Gallium-based liquid metals (LMs) are promising materials for stretchable electronics due to their metallic conductivity and deformability. However, the fabrication of large-area stretchable integrated electronics using LMs on various polymers remains challenging due to their high surface tension, fluidity, and poor wettability. Current techniques, such as selective wetting and lift-off processes, face limitations related to substrate compatibility and Ga/metal alloying, hindering their applicability in integrated electronic systems. To address these challenges, we developed a high-resolution top-down etching-based photolithography combined with a universal cryogenic transfer method for transferring patterned LM particles (LMPs) in various polymer substrates. The cryogenic environment modifies the interfacial bonding between the LMPs and substrates, resulting in a universal transfer. The resulting liquid metal particle network embedded polymer (LNEP) exhibits high electrical conductivity (~1.71 × 10⁶ S/m), stability, and strain-insensitive performance across various polymers. This process is scalable to large-area fabrication, overcoming the limitations of existing LM patterning techniques. Leveraging this approach, we demonstrated the use of LNEP ranging from skin-conformal wearable sensors to hybrid stretchable circuits and implantable devices, demonstrating the universality of the method. This technique establishes a scalable pathway for stretchable electronics in advanced applications.

Novel magnetically retrievable SnO2/Fe3O4/WSe2 heterojunction for the photocatalytic degradation of tinidazole through a synergistic strategy for environmental remediation

Tumor-informed circulating tumor DNA stratifies recurrence risk and survival in anal squamous cell carcinoma

Music is not a universal language — but it can bring us together when words fail

How a protein repurposes vitamin B12 as a light sensor

An interactive genetic algorithms system customizes robot appearance via cognitive noise filtering under embodied constraints

Defect-evolved quadrupole higher-order topological nanolasers

Abstract Topological photonics have been garnering widespread interest in engineering the flow of light with topological ideas. Strikingly, the recent introduction of higher-order topological insulators has generalized the fundamental framework of topological photonics, endowing counterintuitive strong confinement of light at lower-dimensional boundaries, thus unlocking exciting prospects for the exploration of topological phenomena in fresh routes as well as the design of topology-driven nanoscale light sources. Here, we revealed the photonic quadrupole topological phases can be activated by defect evolution and performed experimental demonstrations of associated nanoscale lasing operation under this paradigm. The quadrupole higher-order topological nanocavity is constructed by two topologically distinct photonic crystal slabs with opposite directions of defect evolution. Stable single-mode emission and low lasing threshold in telecom C-band are achieved at room temperature of the defect-evolved quadrupole topological nanolaser. This work reveals new possibilities for photonic quadrupole topological phase transition, providing an intriguing route toward light confinement and modulation under the topological framework.

Why every scientist needs a librarian

Design and simulation of a p-type dual interbridge treeFET with comprehensive DC, analog/RF, and linearity analysis for CMOS circuit applications

Atmospheric CO2 drawdown during the Emeishan flood basalt volcanism