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Hierarchically structured chiral luminescent materials hold promise for achieving efficient circularly polarized luminescence. However, a feasible chemical route to fabricate hierarchically structured chiral luminescent polycrystals is still elusive because of their complex structures and complicated formation process. We here report a biomimetic non-classical crystallization (BNCC) strategy for preparing efficient hierarchically structured chiral luminescent polycrystals using well-designed highly luminescent homochiral copper(I)-iodide hybrid clusters as basic units for non-classical crystallization. By monitoring the crystallization process, we unravel the BNCC mechanism, which involves crystal nucleation, nanoparticles aggregation, oriented attachment, and mesoscopic transformation processes. We finally obtain the circularly polarized phosphors with both high luminescent efficiency of 32% and high luminescent dissymmetry factor of 1.5 × 10 −2 , achieving the demonstration of a circularly polarized phosphor converted light emitting diode with a polarization degree of 1.84% at room temperature. Our designed BNCC strategy provides a simple, reliable, and large-scale synthetic route for preparing bright circularly polarized phosphors. Chiral emitters with high photoluminescence quantum yield are desirable for use in circularly polarized LEDs. The authors demonstrate the transfer of chirality from nanoscale copper iodide clusters to microscale chiral luminescent polycrystals by non-classical crystallization.

Polyphenols are the largest group of phytochemicals with important biological properties. Their presence in conveniently available low-cost sources, such as agri-food by-products, has gained considerable attention in their recovery and further exploitation. Retrieving polyphenols in a green and sustainable way is crucial. Recently, deep eutectic solvents (DESs) have been identified as a safe and environmentally benign medium capable of extracting polyphenols efficiently. This review encompasses the current knowledge and applications of DESs and assisted technologies to extract polyphenols from agri-food by-products. Particular attention has been paid to fundamental mechanisms and potential applications in the food, cosmetic, and pharmaceutical industries. In this way, DESs and DESs-assisted with advanced techniques offer promising opportunities to recover polyphenols from agri-food by-products efficiently, contributing to a circular and sustainable economy.

Co-based metal–organic framework (MOF), a kind of porous crystal material composed of Co ions and organic linkers, is a common type of MOF. It not only has the intrinsic properties of MOF, such as structural diversity, functional adjustability, and high surface area, but more importantly, it contains Co metal species, which are considered by many reports to be active catalytic centers for many reactions. Meanwhile, metal catalysts always received wide and sustained attention. The combination of the two types of catalysts can enhance the catalytic performance and achieve the “1 + 1 > 2” effect. In this review, we mainly overview the synthesis methods of Co-based MOF-derived metal catalysts from nano- to atom- level and their applications in the catalysis field in recent years and put forward our own views and prospects for this research direction.

Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to build a real-time crop diseases and pests video detection system in the future, a deep learning-based video detection architecture with a custom backbone was proposed for detecting plant diseases and pests in videos. We first transformed the video into still frame, then sent the frame to the still-image detector for detection, and finally synthesized the frames into video. In the still-image detector, we used faster-RCNN as the framework. We used image-training models to detect relatively blurry videos. Additionally, a set of video-based evaluation metrics based on a machine learning classifier was proposed, which reflected the quality of video detection effectively in the experiments. Experiments showed that our system with the custom backbone was more suitable for detection of the untrained rice videos than VGG16, ResNet-50, ResNet-101 backbone system and YOLOv3 with our experimental environment.

The recent explosion of large volume of standard dataset of annotated images has offered promising opportunities for deep learning techniques in effective and efficient object detection applications. However, due to a huge difference of quality between these standardized dataset and practical raw data, it is still a critical problem on how to maximize utilization of deep learning techniques in practical agriculture applications. Here, we introduce a domain-specific benchmark dataset, called AgriPest, in tiny wild pest recognition and detection, providing the researchers and communities with a standard large-scale dataset of practically wild pest images and annotations, as well as evaluation procedures. During the past seven years, AgriPest captures 49.7K images of four crops containing 14 species of pests by our designed image collection equipment in the field environment. All of the images are manually annotated by agricultural experts with up to 264.7K bounding boxes of locating pests. This paper also offers a detailed analysis of AgriPest where the validation set is split into four types of scenes that are common in practical pest monitoring applications. We explore and evaluate the performance of state-of-the-art deep learning techniques over AgriPest. We believe that the scale, accuracy, and diversity of AgriPest can offer great opportunities to researchers in computer vision as well as pest monitoring applications.

This study presents an approach that integrates compressed sensing technology with two-dimensional hyperchaotic coupled Fourier oscillator systems (2D-HCFOS) to address the challenge of slow encryption speeds in agricultural unmanned aerial vehicles (UAVs). The primary challenge in enhancing encryption speed lies in the limited capacity inherent in traditional chaotic-based systems and the computational complexity of their processes. The 2D-HCFOS utilizes a complex two-dimensional hybrid chaotic system, which significantly enhances the security of agricultural UAV image data. Notably, the image encryption process is performed on a personal computer connected to the drone, ensuring efficient processing. By integrating advanced Fourier series and nonlinear coupled oscillators, the model surpasses existing chaotic-based methods, improving both the pseudo-randomness and robustness of encryption. Additionally, incorporating Bonouille functions into the discrete cosine transform (DCT) domain results in a sparser measurement matrix, which is essential for efficient encryption on personal computers. The effectiveness of 2D-HCFOS in securely encrypting agricultural drone images has been rigorously validated through simulations and analytical evaluations using sophisticated row, rotation, and matrix encryption techniques. The improved security performance is further verified by comparative analysis. Compared with other models, the Lyapunov index of 2D-HCFOS is 15.1039, and the sample entropy is 2.4987, indicating that it possesses superior chaotic performance and encryption reliability.

MicroRNA319 (miR319) is one of the first characterized and conserved microRNA families in plants and has been demonstrated to target TCP (for TEOSINTE BRANCHED/CYCLOIDEA/PROLIFERATING CELL FACTORS [PCF]) genes encoding plant-specific transcription factors. MiR319 expression is regulated by environmental stimuli, suggesting its involvement in plant stress response, although experimental evidence is lacking and the underlying mechanism remains elusive. This study investigates the role that miR319 plays in the plant response to abiotic stress using transgenic creeping bentgrass (Agrostis stolonifem) overexpressing a rice (Oryza sativa) miR319 gene, Osa-miR319a. We found that transgenic plants overexpressing Osa-miR319a displayed morphological changes and exhibited enhanced drought and salt tolerance associated with increased leaf wax content and water retention but reduced sodium uptake. Gene expression analysis indicated that at least four putative miR319 target genes, AsPCF5, AsPCF6, AsPCF8, and AsTCP14, and a homolog of the rice NAC domain gene AsNAC60 were down-regulated in transgenic plants. Our results demonstrate that miR319 controls plant responses to drought and salinity stress. The enhanced abiotic stress tolerance in transgenic plants is related to significant down-regulation of miR319 target genes, implying their potential for use in the development of novel molecular strategies to genetically engineer crop species for enhanced resistance to environmental stress.

AbstractAccumulating evidence has shown that inflammation, the gut microbiota, and neurotransmitters are closely associated with the pathophysiology of depression. However, the links between the gut microbiota and neurotransmitter metabolism remain poorly understood. The present study aimed to investigate the neuroinflammatory reactions in chronic restraint stress (CRS)-induced depression and to delineate the potential links between the gut microbiota and neurotransmitter metabolism. C57BL/6 mice were subjected to chronic restraint stress for 5 weeks, followed by behavioural tests (the sucrose preference test, forced swim test, open field test, and elevated plus maze) and analysis. The results showed that CRS significantly increased interleukin-1 beta (IL-1β), interleukin-2 (IL-2), interleukin-6 (IL-6), and tumour necrosis factor α (TNFα) levels and decreased brain-derived neurotrophic factor (BDNF) expression, accompanied by the activation of IkappaB-alpha-phosphorylation-nuclear factor kappa-B (IκBα-p-NF-κB) signalling in the mouse hippocampus. In addition, the neurotransmitter metabolomics results showed that CRS resulted in decreased levels of plasma 5-hydroxytryptamine (5-HT), dopamine (DA), and noradrenaline (NE) and their corresponding metabolites, and gut microbiota faecal metabolites with the 16S rRNA gene sequencing indicated that CRS caused marked microbiota dysbiosis in mice, with a significant increase in Helicobacter, Lactobacillus, and Oscillibacter and a decrease in Parabacteroides, Ruminococcus, and Prevotella. Notably, CRS-induced depressive behaviours and the disturbance of neurotransmitter metabolism and microbiota dysbiosis can be substantially restored by dexamethasone (DXMS) administration. Furthermore, a Pearson heatmap focusing on correlations between the microbiota, behaviours, and neurotransmitters showed that Helicobacter, Lactobacillus, and Oscillibacter were positively correlated with depressive behaviours but were negatively correlated with neurotransmitter metabolism, and Parabacteroides and Ruminococcus were negatively correlated with depressive behaviours but were positively correlated with neurotransmitter metabolism. Taken together, the results suggest that inflammation is involved in microbiota dysbiosis and the disturbance of neurotransmitter metabolism in CRS-induced depressive changes, and the delineation of the potential links between the microbiota and neurotransmitter metabolism will provide novel strategies for depression treatment.

Donor-derived infections (DDIs) have become a significant cause of infection in organ transplant recipients. Elaborating on the species, distribution, and resistance of donor-derived pathogens (DDPs) holds important implications. A retrospective cohort study included 302 deceased donors and their corresponding 464 kidney transplant recipients and 175 liver transplant recipients. We detected DDPs in preservation fluid (PF) using both conventional culture and mNGS, and subsequently analyzed the incidence of DDIs after transplantation. 89.4% (270/302) of donors had positive cultures. Predominant multidrug-resistant organism included HLAR- , CRAB, CRKP, CRPA, MRS and ESBL- . Compared with conventional culture, mNGS exhibited superior sensitivity for detecting bacteria and fungus in PF, with shorter turnaround time (p < 0.001). The incidences of DDIs in kidney and liver transplant recipients were 16.6% (77/464) and 19.4% (34/175) respectively. The recipients with DDIs were associated with elevated serum creatinine or total bilirubin levels, increased infection events, higher risks of graft loss, elevated mortality, and longer length of hospital stay (p < 0.05). Multidrug-resistant organism are prevalent in deceased donors, with PF contamination primarily originating from donors. Integration of mNGS into donor screening protocols enables timely antimicrobial intervention, potentially improving transplant outcomes.

Background Thyroid cancer (THCA) has become increasingly common in recent decades, and women are three to four times more likely to develop it than men. Evidence shows that estrogen has a significant impact on THCA proliferation and growth. Nevertheless, the effects of estrogen-related genes (ERGs) on THCA stages, immunological infiltration, and treatment susceptibility have not been well explored. Methods Clinicopathological and transcriptome data of patients with THCA from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were cleaned before consensus clustering. Differential expression analysis was performed on the genes expressed between THCA and paraneoplastic tissues in TCGA, and Wayne analysis was performed on the ERGs obtained from the Gene Set Enrichment Analysis MsigDB and differentially expressed genes (DEGs). Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were used to identify the set of estrogen-related differentially expressed genes (ERDEGs) associated with progression-free intervals (PFI) and to establish a prediction model. Receiver operating characteristic curves were plotted to calculate the risk scores and PFI status to validate the predictive effect of the model. Enrichment analyses and immune infiltration analyses were performed to analyze DEGs between the high- and low-risk groups, and a nomogram plot was used in the risk model to predict the PFI of THCA. Results The expression of 120 ERDEGs differed significantly between the two groups ( P < 0.05). Five (CD24, CAV1, TACC1, TIPARP, and HSD17B10) of the eight ERDEGs identified using univariate Cox and LASSO regression were validated via RT-qPCR and immunohistochemistry analysis of clinical tissue samples and were used for clinical staging and drug sensitivity analysis. Risk-DEGs were shown to be associated with immune modulation and tumor immune evasion, as well as defense systems, signal transduction, the tumor microenvironment, and immunoregulation. In 19 of the 28 immune cells, infiltration levels differed between the high- and low-risk groups. High-risk patients in the immunotherapy dataset had considerably shorter survival times than low-risk patients. Conclusion We identified and confirmed eight ERDEGs using a systematic analysis and screened sensitive drugs for ERDEGs. These results provide molecular evidence for the involvement of ERGs in controlling the immunological microenvironment and treatment response in THCA.