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Accurately reconstructing high-resolution (HR) images remains challenging in scenarios where HR observations cannot be captured due to optical, hardware, or cost constraints. To address this limitation, we introduce an image super-resolution (SR) framework that reconstructs HR content solely from multiple low-resolution (LR) measurements, without relying on any HR reference images. The proposed method formulates a unified degradation model that describes how HR pixels contribute to LR observations under subpixel shifts and anisotropic downsampling. Based on this model, we develop an adaptive search algorithm capable of identifying the minimal and most informative combination of LR images required to equivalently represent the latent HR image. The selected LR images are then used to construct a solvable linear system whose solution directly yields the HR pixel values. Experiments conducted on the USAF 1951 resolution target demonstrate that the proposed approach improves Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) by 27.33% and 44.64%, respectively, achieving a resolvable spatial frequency of 228 line pairs per millimeter. In semiconductor chip inspection, PSNR and SSIM increase by 22.36% and 40.38%. These results verify that the proposed LR-combination-based strategy provides a physically interpretable and highly practical alternative for applications in which HR reference images cannot be obtained.

Ammonia (NH 3 ) is pivotal to the fertilizer industry and one of the most commonly produced chemicals 1 . The direct use of atmospheric nitrogen (N 2 ) had been challenging, owing to its large bond energy (945 kilojoules per mole) 2 , 3 , until the development of the Haber–Bosch process. Subsequently, many strategies have been explored to reduce the activation barrier of the N≡N bond and make the process more efficient. These include using alkali and alkaline earth metal oxides as promoters to boost the performance of traditional iron- and ruthenium-based catalysts 4 – 6 via electron transfer from the promoters to the antibonding bonds of N 2 through transition metals 7 , 8 . An electride support further lowers the activation barrier because its low work function and high electron density enhance electron transfer to transition metals 9 , 10 . This strategy has facilitated ammonia synthesis from N 2 dissociation 11 and enabled catalytic operation under mild conditions; however, it requires the use of ruthenium, which is expensive. Alternatively, it has been shown that nitrides containing surface nitrogen vacancies can activate N 2 (refs. 12 – 15 ). Here we report that nickel-loaded lanthanum nitride (LaN) enables stable and highly efficient ammonia synthesis, owing to a dual-site mechanism that avoids commonly encountered scaling relations. Kinetic and isotope-labelling experiments, as well as density functional theory calculations, confirm that nitrogen vacancies are generated on LaN with low formation energy, and efficiently bind and activate N 2 . In addition, the nickel metal loaded onto the nitride dissociates H 2 . The use of distinct sites for activating the two reactants, and the synergy between them, results in the nickel-loaded LaN catalyst exhibiting an activity that far exceeds that of more conventional cobalt- and nickel-based catalysts, and that is comparable to that of ruthenium-based catalysts. Our results illustrate the potential of using vacancy sites in reaction cycles, and introduce a design concept for catalysts for ammonia synthesis, using naturally abundant elements. Ammonia is synthesized using a dual-site approach, whereby nitrogen vacancies on LaN activate N 2 , which then reacts with hydrogen atoms produced over the Ni metal to give ammonia.

Brain atlases are fundamental to understanding the topographic organization of the human brain, yet many contemporary human atlases cover only the cerebral cortex, leaving the subcortex a terra incognita. We use functional MRI (fMRI) to map the complex topographic organization of the human subcortex, revealing large-scale connectivity gradients and new areal boundaries. We unveil four scales of subcortical organization that recapitulate well-known anatomical nuclei at the coarsest scale and delineate 27 new bilateral regions at the finest. Ultrahigh field strength fMRI corroborates and extends this organizational structure, enabling the delineation of finer subdivisions of the hippocampus and the amygdala, while task-evoked fMRI reveals a subtle subcortical reorganization in response to changing cognitive demands. A new subcortical atlas is delineated, personalized to represent individual differences and used to uncover reproducible brain–behavior relationships. Linking cortical networks to subcortical regions recapitulates a task-positive to task-negative axis. This new atlas enables holistic connectome mapping and characterization of cortico–subcortical connectivity.This work by Tian and colleagues unveils the extraordinarily complex layout of the human subcortex by identifying 27 new functional regions that organize hierarchically across four scales and adapt to changing cognitive demands.

Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and feature weight estimation need to be reliable to ensure that important connections and circuits with high predictive utility can be reliably identified. We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults (n=400). Despite achieving modest prediction accuracies (r=0.2–0.4), we find that feature weight reliability is generally poor for all predictive models (ICC< 0.3), and significantly poorer than predictive models for overt biological attributes such as sex (ICC≈0.5). Larger sample sizes (n=800), the Haufe transformation, non-sparse feature selection/regularization and smaller feature spaces marginally improve reliability (ICC< 0.4). We elucidate a tradeoff between feature weight reliability and prediction accuracy and find that univariate statistics are marginally more reliable than feature weights from predictive models. Finally, we show that measuring agreement in feature weights between cross-validation folds provides inflated estimates of feature weight reliability. We thus recommend for reliability to be estimated out-of-sample, if possible. We argue that rebalancing focus from prediction accuracy to model reliability may facilitate mechanistic understanding of cognition with machine learning approaches.

M n+1 AX n phases are a large family of compounds that have been limited, so far, to carbides and nitrides. Here we report the prediction of a compound, Ti 2 InB 2 , a stable boron-based ternary phase in the Ti-In-B system, using a computational structure search strategy. This predicted Ti 2 InB 2 compound is successfully synthesized using a solid-state reaction route and its space group is confirmed as P 6 ¯ m2 (No. 187), which is in fact a hexagonal subgroup of P6 3 /mmc (No. 194), the symmetry group of conventional M n+1 AX n phases. Moreover, a strategy for the synthesis of MXenes from M n+1 AX n phases is applied, and a layered boride, TiB, is obtained by the removal of the indium layer through dealloying of the parent Ti 2 InB 2 at high temperature under a high vacuum. We theoretically demonstrate that the TiB single layer exhibits superior potential as an anode material for Li/Na ion batteries than conventional carbide MXenes such as Ti 3 C 2 . Two-dimensional materials are promising for electrochemical storage and conversion, but are somewhat limited in composition. Here the authors use a computational strategy to predict the existence of a layered boride material, which they synthesize and demonstrate prospective for use as an anode material.

In addition to increasing the expression of programmed death-ligand 1 (PD-L1), tumor cells can also secrete exosomal PD-L1 to suppress T cell activity. Emerging evidence has revealed that exosomal PD-L1 resists immune checkpoint blockade, and may contribute to resistance to therapy. In this scenario, suppressing the secretion of tumor-derived exosomes may aid therapy. Here, we develop an assembly of exosome inhibitor (GW4869) and ferroptosis inducer (Fe 3+ ) via amphiphilic hyaluronic acid. Cooperation between the two active components in the constructed nanounit induces an anti-tumor immunoresponse to B16F10 melanoma cells and stimulates cytotoxic T lymphocytes and immunological memory. The nanounit enhances the response to PD-L1 checkpoint blockade and may represent a therapeutic strategy for enhancing the response to this therapy. PD-L1 is frequently expressed on the surface of cancer cells and can be excreted from cancer cells in exosomes. Here, the authors generate a nanotherapy that combines an inhibitor of exosome production and an inducer of ferroptosis, enhancing the response to immune checkpoint blockade therapy.

Single-atom catalysts (SACs) have attracted significant attention because they exhibit unique catalytic performance due to their ideal structure. However, maintaining atomically dispersed metal under high temperature, while achieving high catalytic activity remains a formidable challenge. In this work, we stabilize single platinum atoms within sub-nanometer surface cavities in well-defined 12CaO·7Al 2 O 3 (C12A7) crystals through theoretical prediction and experimental process. This approach utilizes the interaction of isolated metal anions with the positively charged surface cavities of C12A7, which allows for severe reduction conditions up to 600 °C. The resulting catalyst is stable and highly active toward the selective hydrogenation of nitroarenes with a much higher turnover frequency (up to 25772 h −1 ) than well-studied Pt-based catalysts. The high activity and selectivity result from the formation of stable trapped single Pt atoms, which leads to heterolytic cleavage of hydrogen molecules in a reaction that involves the nitro group being selectively adsorbed on C12A7 surface. Stabilize the active metal single atoms under harsh conditions is critical for the development of single atom catalysts. Here the authors report a nanoporous crystal, 12CaO·7Al 2 O 3 , that can firmly stabilize Pt single atoms in its surface cavities for efficient catalytic hydrogenation of nitroarenes.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by degeneration of dopaminergic neurons associated with dysregulation of iron homeostasis in the brain. Ferroptosis is an iron-dependent cell death process that serves as a significant regulatory mechanism in PD. However, its underlying mechanisms are not yet fully understood. By performing RNA sequencing analysis, we found that the main iron storage protein ferritin heavy chain 1 (FTH1) is differentially expressed in the rat 6-hydroyxdopamine (6-OHDA) model of PD compared with control rats. Our present work demonstrates that FTH1 is involved in iron accumulation and the ferroptosis pathway in this model. Knockdown of FTH1 in PC-12 cells significantly inhibited cell viability and caused mitochondrial dysfunction. Moreover, FTH1 was found to be involved in ferritinophagy, a selective form of autophagy involving the degradation of ferritin by ferroptosis. Overexpression of FTH1 in PC-12 cells impaired ferritinophagy and downregulated microtubule-associated protein light chain 3 and nuclear receptor coactivator 4 expression, ultimately suppressing cell death induced by ferroptosis. Consistent with these findings, the ferritinophagy inhibitors chloroquine and bafilomycin A1 inhibited ferritin degradation and ferroptosis in 6-OHDA-treated PC-12 cells. This entire process was mediated by the cyclic regulation of FTH1 and ferritinophagy. Taken together, these results suggest that FTH1 links ferritinophagy and ferroptosis in the 6-OHDA model of PD, and provide a new perspective and potential for a pharmacological target in this disease.

This paper is concerned with designing a chaotic encryption system to generate the nonlinear component, substitution box (S-box), of a block cipher system. Many existing S-boxes generation methods employ a single or complicate chaotic systems to yield S-boxes. All of these chaotic systems are integral and have promoted the development of the theoretical research of chaotic S-boxes. However, it is difficult to implement the integral chaotic S-box generation systems that are appropriate for practical engineering applications. In this paper, a six-dimensional fractional Lorenz–Duffing chaotic system and O-shaped path scrambling algorithm (FLDSOP) is developed to yield an S-box with good dynamic characteristics. First, FLDSOP leverages a six-dimensional fractional Lorenz–Duffing chaotic system to construct a preliminary S-box. Second, it designs an O-Shaped path scrambling scheme to disturb the order of elements in the obtain S-box. Experimental results have shown that the chaotic S-box produced by the proposed FLDSOP algorithm can effectively resist to multiple types of cryptanalysis attacks.

The barrier layer in Cu technology is essential to prevent Cu from diffusing into the dielectric layer at high temperatures; therefore, it must have a high stability and good adhesion to both Cu and the dielectric layer. In the past three decades, tantalum/tantalum nitride (Ta/TaN) has been widely used as an inter-layer to separate the dielectric layer and the Cu. However, to fulfill the demand for continuous down-scaling of the Cu technology node, traditional materials and technical processes are being challenged. Direct electrochemical deposition of Cu on top of Ta/TaN is not realistic, due to its high resistivity. Therefore, pre-deposition of a Cu seed layer by physical vapor deposition (PVD) or chemical vapor deposition (CVD) is necessary, but the non-uniformity of the Cu seed layer has a devastating effect on the defect-free fill of modern sub-20 or even sub-10 nm Cu technology nodes. New Cu diffusion barrier materials having ultra-thin size, high resistivity and stability are needed for the successful super-fill of trenches at the nanometer scale. In this review, we briefly summarize recent advances in the development of Cu diffusion-proof materials, including metals, metal alloys, self-assembled molecular layers (SAMs), two-dimensional (2D) materials and high-entropy alloys (HEAs). Also, challenges are highlighted and future research directions are suggested.