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As the cornerstone of AI generated content, data drives human-machine interaction and is essential for developing sophisticated deep learning agents. Nevertheless, the associated data storage poses a formidable challenge from conventional energy-intensive planar storage, high maintenance cost, and the susceptibility to electromagnetic interference. In this work, we introduce the concept of metasurface disk, meta-disk, to expand the capacity limits of optical holographic storage by leveraging uncorrelated structural twist. We develop a physical twisted neural network to describe the optical behavior of the meta-disk and conduct a comprehensive lateral error analysis, where the meta-disk stores large volumes of information through internal structural multiplexing. Two-layer 640 µm x 640 µm meta-disk is sufficient to store over hundreds of high-fidelity images with SSIM of 0.8. By harnessing advanced three-dimensional (3D) printing technology, optical holographic storage is experimentally demonstrated with Pancharatnam-Berry metasurfaces. Our technology provides essential backing for the next generation of optical storage, display, encryption, and multifunctional optical analog computing. Meta-disk utilizes structural multiplexing to significantly enhance optical holographic storage capacity, enabling the storage of numerous high-fidelity images. The technology offers potential applications in optical storage and optical computing.

Emotion matching prediction between music and video segments is essential for intelligent mobile sensing systems, where multimodal affective cues collected from smart devices must be jointly analyzed for context-aware media understanding. However, traditional approaches relying on single-modality feature extraction struggle to capture complex cross-modal dependencies, resulting in a gap between low-level audiovisual signals and high-level affective semantics. To address these challenges, a dual-driven framework that integrates perceptual characteristics with objective feature representations is proposed for audiovisual affective matching prediction. The framework incorporates fine-grained affective states of audiovisual data to better characterize cross-modal correlations from an emotional distribution perspective. Moreover, a decoupled Deep Canonical Correlation Analysis approach is developed, incorporating discriminative sample-pairing criteria (matched/mismatched data discrimination) and separate modality-specific component extractors, which dynamically refine the feature projection space. To further enhance multimodal feature interaction, an Attention and Feature-Consistency-Loss-Based Multi-Task Learning Network is proposed. In addition, a feature-consistency loss function is introduced to impose joint constraints across dual semantic embeddings, ensuring both affective consistency and matching accuracy. Experiments on a self-collected benchmark dataset demonstrate that the proposed method achieves a mean absolute error of 0.109 in music–video matching score prediction, significantly outperforming existing approaches.

Bioproduction of renewable chemicals is considered as an urgent solution for fossil energy crisis. However, despite tremendous efforts, it is still challenging to generate microbial strains that can produce target biochemical to high levels. Here, we report an example of biosynthesis of high-value and easy-recoverable derivatives built upon natural microbial pathways, leading to improvement in bioproduction efficiency. By leveraging pathways in solventogenic clostridia for co-producing acyl-CoAs, acids and alcohols as precursors, through rational screening for host strains and enzymes, systematic metabolic engineering-including elimination of putative prophages, we develop strains that can produce 20.3 g/L butyl acetate and 1.6 g/L butyl butyrate. Techno-economic analysis results suggest the economic competitiveness of our developed bioprocess. Our principles of selecting the most appropriate host for specific bioproduction and engineering microbial chassis to produce high-value and easy-separable end products may be applicable to other bioprocesses. Esters can be used as fuels and specialty chemicals for food flavoring, cosmetic and pharmaceutical industries. Here, the authors systematically engineer clostridia, including discovery and deletion of prophages to increase strain stability, for the production of butyl acetate and butyl butyrate from corn stover at low cost.

The trace concentration of H 2 in most ecosystems after the Earth’s oxidation has long caused the neglect of hydrogenotrophic denitrification for nitrogen loss. Here, we find that the interspecies hydrogen transfer between cyanobacteria and symbiotic bacteria within cyanobacterial aggregates is an undiscovered pathway for nitrogen loss. Cyanobacteria in aggregates can actively generate H 2 under the diel cycle as an electron donor for neighboring hydrogenotrophic denitrifiers. The hydrogenotrophic denitrification in engineered cyanobacterial aggregates accounts for a nitrogen removal rate of 3.47 ± 0.42 mmol l −1 day −1 . This value is nearly 50% of the heterotrophic denitrification rate, which far exceeds the general concept of the trace role. We find that H 2 -evolving cyanobacteria and hydrogenotrophic denitrifiers coexist in 84% of the 63 globally distributed cyanobacterial aggregates, where bloom colonies and phototrophic mats from hot springs are identified as potential hotspots. We suggest that interspecies hydrogen transfer within cyanobacterial aggregates is possibly responsible for the excessive nitrogen loss rate during cyanobacterial blooms where cyanobacterial aggregates persist. Nitrogen loss in aquatic ecosystems has long focused on heterotrophic denitrification. This study shows that interspecies hydrogen transfer between cyanobacteria and symbiotic hydrogenotrophic bacteria in aggregates can drive hydrogenotrophic denitrification, a pathway for nitrogen loss.

Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem. Accurately classifying individual shrub vegetation species to understand their spatial distributions and to effectively monitor species diversity in the Gobi ecosystem is essential. High-resolution remote sensing data create vegetation type inventories over large areas. However, high spectral similarity between shrublands and surrounding areas remains a challenge. In this study, we provide a case study that integrates object-based image analysis (OBIA) and the random forest (RF) model to classify shrubland species automatically. The Gobi region on the southern slope of the Tian Shan Mountains in Northwest China was analyzed using readily available unmanned aerial vehicle (UAV) RGB imagery (1.5 cm spatial resolution). Different spectral and texture index images were derived from UAV RGB images as variables for species classification. Principal component analysis (PCA) extracted features from different types of variable sets (original bands, original bands + spectral indices, and original bands + spectral indices + texture indices). We tested the ability of several non-parametric decision tree models and different types of variable sets to classify shrub species. Moreover, we analyzed three main shrubland areas comprising different shrub species and compared the prediction accuracies of the optimal model in combination with different types of variable sets. We found that the RF model could generate higher accuracy compared with the other two models. The best results were obtained using a combination of the optimal variable set and the RF model with an 88.63% overall accuracy and 0.82 kappa coefficient. Integrating OBIA and RF in the species classification process provides a promising method for automatic mapping of individual shrub species in the Gobi region and can reduce the workload of individual shrub species classification.

Fish products are consumed by human beings as a high-quality protein source. However, overfishing, and pollution puts out an urgent call to seek a new strategy to substitute fish protein for secure eco-sustainability. Plant-based fish analogs, which mimic the structure, texture, and flavor of fish meat products, are a rapid-growing segment of the food products. The purpose of this review is to discuss the feasibility and potential strategies for developing plant-based fish analog. The nutritional properties, especially the protein quality of plant-based fish analogs, were discussed. Furthermore, a thorough comparison was made between fish and terrestrial animal muscle structures, including both macroscopical and microscopical structures. Potential processing technologies for producing plant-based fish analogs from plant proteins and approaches for the characterization of the fish analog structures were elaborated. Comparing all the current processing techniques, extrusion is the predominately used technique in the current industry. At the same time, 3D-printing and electrospinning have shown the prominent potential of mimicking fish muscle structure as bottom-up approaches. Finally, key challenges and future research were discussed for the potential commercialization of plant-based fish analogues. The primary focus of this review covers the innovative works that were indexed in the Web of Science Core Collection in the past five years.

As the global population approaches 10 billion by 2050, the critical need to ensure food security becomes increasingly pronounced. In response to the urgent problems posed by global population growth, our study adds to the growing body of knowledge in the field of alternative proteins, entomophagy, insect-based bioactive proteolysates, and peptides. It also provides novel insights with essential outcomes for guaranteeing a safe and sustainable food supply in the face of rising global population demands. These results offer insightful information to researchers and policymakers tackling the intricate relationship between population expansion and food supplies. Unfortunately, conventional agricultural practices are proving insufficient in meeting these demands. Pursuing alternative proteins and eco-friendly food production methods has gained urgency, embracing plant-based proteins, cultivated meat, fermentation, and precision agriculture. In this context, insect farming emerges as a promising strategy to upcycle agri-food waste into nutritious protein and fat, meeting diverse nutritional needs sustainably. A thorough analysis was conducted to evaluate the viability of insect farming, investigate insect nutrition, and review the techniques and functional properties of protein isolation. A review of peptide generation from insects was conducted, covering issues related to hydrolysate production, protein extraction, and peptide identification. The study addresses the nutritional value and global entomophagy habits to elucidate the potential of insects as sources of peptides and protein. This inquiry covers protein and hydrolysate production, highlighting techniques and bioactive peptides. Functional properties of insect proteins’ solubility, emulsification, foaming, gelation, water-holding, and oil absorption are investigated. Furthermore, sensory aspects of insect-fortified foods as well as challenges, including Halal and Kosher considerations, are explored across applications. Our review underscores insects’ promise as sustainable protein and peptide contributors, offering recommendations for further research to unlock their full potential.

Chlorogenic acid (ChA)—one of the most abundant polyphenol compounds in the human diet—exerts anti-inflammatory activities. The aim of this study was to investigate the effect of ChA on gut microbiota in ulcerative colitis (UC). Colitis was induced by 2.5% dextran sulfate sodium (DSS) in C57BL/6 mice, which were on a control diet or diet with ChA (1 mM). The histopathological changes and inflammation were evaluated. Fecal samples were analyzed by 16S rRNA gene sequencing. ChA attenuated several effects of DSS-induced colitis, including weight loss, increased disease activity index, and improved mucosal damage. Moreover, ChA could significantly suppress the secretion of IFNγ, TNFα, and IL-6 and the colonic infiltration of F4/80+ macrophages, CD3+ T cells, and CD177+ neutrophils via inhibition of the active NF-κB signaling pathway. In addition, ChA decreased the proportion of Firmicutes and Bacteroidetes. ChA also enhanced a reduction in fecal microbiota diversity in DSS treated mice. Interestingly, ChA treatment markedly increased the proportion of the mucin-degrading bacterium Akkermansia in colitis mice. ChA acted as the intestine-modifying gut microbial community structure, resulting in a lower intestinal and systemic inflammation and also improving the course of the DSS-induced colitis, which is associated with a proportional increase in Akkermansia.

This study systematically characterized the key clinical features and influencing factors of patients with T-LGLL-PRCA, aiming to provide evidence to improve clinical diagnostic and therapeutic strategies. Clinical characteristics were retrospectively compared between patients with T-LGLL-PRCA (n=15) and those without PRCA (T-LGLL-Non-PRCA, n=25). Risk factors for the development of T-LGLL-PRCA were evaluated using univariate and multivariate logistic regression analyses. This study retrospectively included a total of 40 patients with T-LGLL, of whom 15 were classified into the T-LGLL-PRCA group, accounting for one-third of the entire cohort. The median age of patients in the T-LGLL-PRCA group was 68 years, with a relatively high proportion aged ≥65 years, and the majority were female. The most common symptoms included fatigue, dizziness, and palpitations. The major comorbidity was thrombocytopenia. Positivity for EBV and ANA was frequently observed. Mutations in TET2 exon 11 and exon 3 were the most frequently detected genetic variants. At the molecular level, clonal rearrangements of the TCRβ and TCRγ genes were most commonly observed. Moreover, a substantial proportion of patients displayed a TCR γδ immunophenotype. Significant differences between groups were observed in circulating T-LGL count, Hemoglobin (HB), Hematocrit (HCT), Reticulocyte Count (RNC), Erythropoietin (EPO) levels >750 mIU/mL, and Immature Reticulocyte Fraction (IRF) (  < 0.05). Univariate logistic regression suggested that HB ( :0.926;  = 0.003), HCT ( :0.837;  = 0.006), EPO levels>750 mIU/mL ( :7.071;  = 0.008), RNC ( :0.682;  = 0.027), and IRF ( :0.857;  = 0.007) were associated with T-LGLL-PRCA(  < 0.05). Multivariate analysis identified RNC ( :0.590;  = 0.048) as the sole independent influencing factor (  < 0.05). RNC is an independent influencing factor for the development of T-LGLL-PRCA.

RNA degradation using ribonuclease targeting chimeras (RiboTACs) is a promising approach for cancer therapy. However, potential off-target degradation is a serious issue. Here, a RiboTAC is designed for tumor microenvironment triggered activation. The tumor microenvironment activated RiboTAC (TaRiboTAC) incorporates two pre-miR-21 binders, a near-infrared fluorophore IR780, an RGD targeting peptide and a phenylboronic acid caged ribonuclease recruiter. The caged ribonuclease recruiter is embedded in the molecule and exposed in acidic pH, the phenylboronic acid cage is removed by H 2 O 2 making the TaRiboTAC responsive to the acidic and high H 2 O 2 levels in the tumor microenvironment. It is shown the TaRiboTAC targets tumor tissue and degrades pre-miR-21. The degradation of pre-miR-21 by TaRiboTACs significantly increases the radiotherapeutic susceptibility of cancer cells achieving efficient suppression of human lung adenocarcinoma A549 tumors in living mice. Off-target effects have limited RNA degradation approaches. Here, the authors develop a tumor microenvironment-activated ribonuclease targeting chimera (RiboTAC) demonstrating H 2 O 2 and acid activated degradation of pre-miR-21, which was effective in restoring radiosensitivity in lung cancer.