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Genomic landscape of drug binding and pharmacogenetic variation across diverse populations using SNPdrug3D
Inhibition of the QPCT–PDIA4 axis rescues ΔF508 and N1303K CFTR in cystic fibrosis
Switchable annulation paths to diverse N-bridged bicyclic scaffolds via ligand-directed dicarbonylation
Nek1 defines a branch of centriolar microtubule length control parallel to CP110-Cep97
Abstract Centrioles maintain a characteristic length throughout the cell cycle, which is essential for the accurate functioning of centrosomes and ciliogenesis. The CP110-Cep97 complex acts as a cap on the distal end of the centriole, restricting microtubule extension. However, whether CP110-Cep97 alone or in conjunction with additional players regulates this process remains unclear. In this study, we identify the kinase Nek1 (NIMA-related kinase 1) as a key factor that works with the CP110-Cep97 complex to control centriole length. Nek1 localizes alongside CP110 and Cep97 at the distal end of the centriole and interacts with Cep97 and Cep78. Loss of Nek1 induces pronounced centriolar microtubule hyperelongation without displacement of the CP110-Cep97 complex, indicating that Nek1 restricts centriole extension through a distinct mechanism. Co-depletion of Nek1 and CP110, but not Cep78, further enhances the hyperelongation phenotype, demonstrating that Nek1 and CP110 pathways act in parallel to maintain centriole length in cycling cells. Notably, Nek1 is removed from the basal body during ciliogenesis in a Cep78-dependent manner, thereby linking Cep78 to the spatial regulation of Nek1 activity. Together, these findings establish Nek1 as an important safeguard that works with the CP110-Cep97 complex to ensure the structural integrity of centrioles.
Ammonia mediated electrostatic adsorption synthesis of paired subnanoclusters with enhanced hydrogen oxidation performance
Imaging spectroscopy reveals spike-like repeating radio burst pairs in the solar corona
Full vision adaptation in mixed-light conditions enabled by dynamic water adsorption/desorption
Abstract Mimicking the human eye’s ability to autonomously adapt to diverse and mixed illumination conditions remains a fundamental challenge in artificial vision systems. Although substantial progress has been made in materials and device engineering, current adaptive vision architectures still depend heavily on complex circuitry or algorithms and are typically restricted to uniform illumination owing to the strong intensity-dependence of photosensitivity. Here, this work presents a highly adaptive TiO₂/PEDOT:PSS photomemristor that leverages the tunable conductivity of PEDOT:PSS together with the optoelectronic response of TiO₂. The photothermal effect dynamically modulates the water absorption/desorption equilibrium in PEDOT:PSS, enabling reversible suppression or enhancement of photosensitivity under bright or dim illumination, respectively. By combining with artificial neural networks (ANNs), the artificial vision system based on TiO₂/PEDOT:PSS photomemristor arrays achieves a high accuracy of 91.3% in image recognition under mixed-light conditions—without the need for complex circuitry or algorithms. This work may establish a new approach for designing autonomous, efficient, and high-performance neuromorphic vision systems to advance the development of autonomous driving and humanoid robots.
Supercharged ferritin nanocages enable universal cytosolic protein delivery
Abstract Efficient intracellular protein delivery represents an essential prerequisite for protein-based biotechnologies and therapeutics targeting intracellular components. However, this process is limited by multiple factors, including nonspecific protein binding, insufficient cellular uptake, inefficient endosomal escape, and inadequate cytosolic protein release. Here we show that by engineering fully recombinant supercharged protein nanocages, we achieve exceptionally high cellular uptake using a strategy we term ‘supercharged interface engineering’. By incorporating unnatural amino acids bearing phenylboronic acid groups, we develop a representative protein nanocage, pFn + . Simply mixing pFn+ with protein cargoes forms a noncovalent complex possessing enhanced cellular uptake efficiency, robust endosomal escape capability, and excellent biocompatibility. Notably, this system successfully delivers functional gene-editing tools and therapeutic antibodies in female mouse models. These findings indicate that pFn+ represents a promising platform for enhancing the cytosolic delivery of protein cargoes. Moreover, the proposed supercharged interface engineering strategy is valuable for advancing next-generation intracellular protein delivery systems.
Historical and projected trends of long-term nitrogen dynamics from China’s mariculture
Highly sensitive X-ray responsive molecular switches
Adaptive Riemannian optimization for multi-scale diffeomorphic matching
Abstract Image matching is a fundamental task in quantitative biomedical and biological image analyses, enabling researchers to compare, integrate, and interpret imaging data across subjects, time points, modalities, and experimental conditions. Existing state-of-the-art registration methods are slow due to inefficient implementations and poor convergence rates because of the ill-conditioned nature of the optimization problem. Deep learning methods offer fast inference but require extensive training time, substantial inference memory, and fail to generalize across long-tailed distributions or diverse image modalities, necessitating costly retraining. We address these challenges by proposing FireANTs, a training-free, GPU-accelerated, multi-scale adaptive Riemannian optimization algorithm for fast and accurate dense diffeomorphic image matching. FireANTs more than doubles the speed of the community standard ANTs registration tool on a CPU, and is two orders of magnitude faster on a GPU. On the GPU, FireANTs performs competitively with deep learning methods on inference runtime while consuming up to 10 × less memory. FireANTs demonstrates robustness on a wide variety of matching problems across modalities, species, and organs, without any domain-specific training or tuning. Our framework allows hyperparameter grid search studies with less resources and time compared to traditional and deep learning registration algorithms alike.
Scg2 drives corticospinal circuit reorganization with spinal premotor interneurons and astrocytes for motor recovery after stroke in mice
Charge density wave in a band insulator
Ground squirrel coprolites preserve complex archives of ancient environmental DNA over 700,000 years
Passive subambient cooling and atmospheric water nexus
Model predictive task sampling for efficient and robust adaptation
CLPX acquires an iron-sulfur cluster to sustain mitochondrial proteostasis in cancer cells
Intrinsic neuronal diversity as a substrate for cortical area specialization in primate vision
Abstract In primates, primary visual cortex (V1) circuits support early visual processing, whereas in the lateral prefrontal cortex (LPFC), they support higher-order computations. Here, we characterized the intrinsic electrophysiological (e)-properties and morphology of excitatory (EXC), fast-spiking inhibitory (FSI), and non-fast-spiking inhibitory (NFSI) neurons in layers I-III of V1 and LPFC, of a small primate, the common marmoset. EXC and FSI neurons exhibited broader action potentials in LPFC than in V1. Compared to V1, LPFC EXC neurons exhibited reduced excitability, whereas FSI neurons showed increased excitability. NFSI neurons showed fewer area-specific differences. Notably, EXC and FSI neurons displayed increased bursting in LPFC compared to V1. Morphologically, LPFC-EXC neurons exhibited longer dendritic arbors, whereas LPFC-FSI neurons showed greater axonal complexity relative to V1. Our findings demonstrate area-specific functional and anatomical diversification of major neuronal types in layers I-III of the primate neocortex and provide an open-access resource for studies of neocortical cell types.