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Achieving robust joint utilization of multidomain characteristics and deep-network features while maintaining a high jamming-recognition accuracy with limited samples is challenging. To address this issue, this paper proposes a multidomain characteristic-guided multimodal contrastive recognition method for active radar jamming. This method involves first thoroughly extracting the multidomain characteristics of active jamming and then designing an optimization unit to automatically select effective characteristics and generate a text modality imbued with implicit expert knowledge. The text modality and involved time-frequency transformation image are separately fed into text and image encoders to construct multimodal-feature pairs and map them to a high-dimensional space for modal alignment. The text features are used as anchors and a guide to time-frequency image features for aggregation around the anchors through contrastive learning, optimizing the image encoder’s representation capability, achieving tight intraclass and separated interclass distributions of active jamming. Experiments show that compared to existing methods, which involve directly combining multidomain characteristics and deep-network features, the proposed guided-joint method can achieve differential feature processing, thereby enhancing the discriminative and generalization capabilities of recognition features. Moreover, under extremely small-sample conditions (2～3 training samples for each type of jamming), the accuracy of our method is 9.84% higher than those of comparative methods, proving the effectiveness and robustness of the proposed method.

Abstract Background An adaptive design allows modifying the design based on accumulated data while maintaining trial validity and integrity. The final sample size may be unknown when designing an adaptive trial. It is therefore important to consider what sample size is used in the planning of the study and how that is communicated to add transparency to the understanding of the trial design and facilitate robust planning. In this paper, we reviewed trial protocols and grant applications on the sample size reporting for randomised adaptive trials. Method We searched protocols of randomised trials with comparative objectives on ClinicalTrials.gov (01/01/2010 to 31/12/2022). Contemporary eligible grant applications accessed from UK publicly funded researchers were also included. Suitable records of adaptive designs were reviewed, and key information was extracted and descriptively analysed. Results We identified 439 records, and 265 trials were eligible. Of these, 164 (61.9%) and 101 (38.1%) were sponsored by industry and public sectors, respectively, with 169 (63.8%) of all trials using a group sequential design although trial adaptations used were diverse. The maximum and minimum sample sizes were the most reported or directly inferred (n = 199, 75.1%). The sample size assuming no adaptation would be triggered was usually set as the estimated target sample size in the protocol. However, of the 152 completed trials, 15 (9.9%) and 33 (21.7%) had their sample size increased or reduced triggered by trial adaptations, respectively. The sample size calculation process was generally well reported in most cases (n = 216, 81.5%); however, the justification for the sample size calculation parameters was missing in 116 (43.8%) trials. Less than half gave sufficient information on the study design operating characteristics (n = 119, 44.9%). Conclusion Although the reporting of sample sizes varied, the maximum and minimum sample sizes were usually reported. Most of the trials were planned for estimated enrolment assuming no adaptation would be triggered. This is despite the fact a third of reported trials changed their sample size. The sample size calculation was generally well reported, but the justification of sample size calculation parameters and the reporting of the statistical behaviour of the adaptive design could still be improved.

Abstract Background Ovarian granulosa cells (GCs) play crucial roles in follicular growth and development. Their normal function is influenced by various factors, including oxidative stress, which is a significant factor. Afamin protein is a vitamin E-specific binding protein that acts as a vitamin E carrier in follicular fluid. Although the mechanism of the protective effect of afamin on human ovarian GCs is still unclear, there is evidence it has an antioxidant effect in neuronal cells. Methods In this study, we investigated the protective effects of afamin proteins on testosterone propionate (TP)-induced ovarian GCs using a human ovarian tumor granulosa cell line (KGN). Results The results showed that afamin reduced TP-induced oxidative stress in KGN cells by decreasing the levels of oxidative damage markers, enhancing the activity of antioxidant enzymes, and exerting a protective effect on GCs. Supplementation with afamin repaired mitochondrial dysfunction by improving mitochondrial membrane potential damage and ATP levels. It counteracted TP-induced apoptosis by decreasing the activity of Caspase-3 and upregulating the expression of the anti-apoptotic gene (BCL-2) while downregulating the expression of the pro-apoptotic gene BCL-2-associated X protein (BAX). In addition, afamin regulated the expression of genes related to ovarian steroid hormone synthesis, ameliorating the endocrine dysfunction observed in TP-induced KGN cells. Conclusion Therefore, Afamin proteins may serve as important complementary factors that protect GCs from other types of damage, such as oxidative stress, and may help improve ovarian follicle quality and female reproductive function.

Abstract Dental implant restoration shows an effective method for the rehabilitation of missing teeth. The failure rate of periodontal implants in patients with chronic periodontitis is associated with periodontal flora, inflammation, and long-term periodontal bone resorption caused by chronic periodontitis. However, the therapeutic effects of dental implant restoration on inflammation in patients with chronic periodontitis have not addressed. The purpose of this study is to evaluate the risk indicators for inflammation, bone loss and implant failure in patients with chronic periodontitis. A total of 284 patients with dental implant restoration were recruited and divided into periodontally healthy patients (n = 128) and chronic periodontitis patients (n = 156). Periodontal indices including probing depth (PD), sulcus bleeding index (SBI), plaque index (PLI), gingival bleeding (GIL) and bleeding on probing (BOP) were compared in two groups. Inflammatory cytokines including tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-1 (IL-1), matrix metalloproteinase-2 (MMP-2) and matrix metalloproteinase-9 (MMP-9) levels at baseline, 6 and 12 months after surgery, and the implant survival rate at 12 months after surgery, as well as the risk factors associated with failure of dental implant were also assessed. Outcomes demonstrated that patients in the chronic periodontitis group had higher values of periodontal indices than those in the periodontally healthy group. All inflammatory parameters in the chronic periodontitis group were higher than those in the periodontally healthy group and negatively associated with the chronic periodontal index (CPI) in chronic periodontitis patients. Chronic periodontitis patients had higher the prevalence of mucositis and peri-implantitis than patients with healthy periodontium. Implant diameter, length and design was associated with the risk of implant failure for chronic periodontitis patients receiving dental implant. The cumulative implant failure rate and incidence of implant fractures for chronic periodontitis patients at 12 months after surgery were 12.10% and 7.23% (p

Abstract Objective This paper investigates the relationships between physical activity (PA), school bullying, emotion regulation self-efficacy (ERS), and interpersonal relationship distress (IRD) among junior high school students. It also examines the underlying mechanisms of school bullying to provide insights into reducing adolescent bullying and to lay the groundwork for preventing and controlling aggressive behaviors. Methods A survey was conducted on 484 students (240 males, 12.18 ± 0.8 years) from 4 secondary schools using the Physical Activity Rating Scale (PARS), Emotional Management Self-Efficacy Scale (EMSS), Interpersonal Relationship Distress Scale (IRDS), and Campus Bullying Scale (CBS) to examine the effects among the variables. A stratified random sampling method was used to select the sample, and data were collected with a structured questionnaire. The data were analyzed using SPSS 24.0 and AMOS 24.0 statistical software. The analysis included Pearson correlation analysis, structural equation modeling, and bias-corrected percentile Bootstrap methods. Results (1) PA negatively predicts IRD, which in turn has an indirect effect on bullying (PA → IRD → Bullying), ES = -0.063. Additionally, EM and IRD act as mediators between PA and school bullying (PA → EM → IRD → Bullying), ES = 0.025. (2) PA negatively predicts IRD, which has an indirect effect on being bullied (PA → IRD → Being bullied), ES = -0.044. EM and IRD serve as chain mediators between PA and being bullied (PA → EM → IRD → Being bullied), ES = -0.071. Conclusion PA can positively predict bullying, but it can be mitigated through EM to reduce IRD, thereby decreasing the occurrence of campus bullying and being bullied.

Abstract Dynamic tracking of spinal instrumentation could facilitate real-time evaluation of hardware integrity and in so doing alert patients/clinicians of potential failure(s). Critically, no method yet exists to continually monitor the integrity of spinal hardware and by proxy the process of spinal arthrodesis; as such hardware failures are often not appreciated until clinical symptoms manifest. Accordingly, herein, we report on the development and engineering of a bio-adhesive metal detector array (BioMDA), a potential wearable solution for real-time, non-invasive positional analyses of osseous implants within the spine. The electromagnetic coupling mechanism and intimate interfacial adhesion enable the precise sensing of the metallic implants position without the use of radiation. The customized decoupling models developed facilitate the precise determination of the horizontal and vertical positions of the implants with incredible levels of accuracy (e.g.,

Abstract China faces challenges in reaching its carbon neutrality goal by the year 2060 to meet the Paris Agreement and improving air quality simultaneously. Dramatic nitrogen emission reductions will be brought by this ambitious target, yet their impact on the natural ecosystem is not clear. Here, by combining two atmospheric chemistry models and two process-based terrestrial ecosystem models constrained using nationwide measurements, we show that atmospheric nitrogen deposition in China’s terrestrial land will decrease by 44–57% following two emission control scenarios including one aiming at carbon neutrality. They consequently result in a pronounced shrinkage in terrestrial net ecosystem production, by 11–20% depending on models and emission scenarios. Our results indicate that the nitrogen emission reductions accompanying decarbonization would undermine natural carbon sinks and in turn set back progress toward carbon neutrality. This unintended impact calls for great concern about the trade-offs between nitrogen management and carbon neutrality.

Abstract Tumour detection with high selectivity and sensitivity is crucial for delineating tumour margins and identifying metastatic foci during image-guided surgery. Optical nanoprobes with preferential tumour accumulation is often limited by inefficient amplification of biological signals. Here, we report the design of a library of hydrophobic core-tunable ultra-pH-sensitive nanoprobes (HUNPs) for orthogonally amplifying tumour microenvironmental signals on multiple tumour models. We find that tuning the hydrophobicity of nanoparticle core composition with non-ionizable monomers can enhance cellular association of HUNPs by more than ten-fold, resulting in a high cellular internalization efficiency of HUNPs with up to 50% in tumours. Combining high tumour accumulation and high cell internalization efficiency, HUNPs show orthogonally amplified fluorescence signals, permitting the precise locating and delineating margins between malignant lesions and normal tissues with high contrast-to-noise ratio and resolution. Our study provides key strategies to design nanomedicines with high intracellular bioavailability for cancer detection, drug/gene delivery, and therapy.

Rainfall is a common trigger for landslide reactivation, as it raises groundwater levels and reduces bedrock or soil shear resistance. This study focuses on the Kualiangzi landslide in the southern region of Sichuan Province, China. Real-time monitoring of groundwater levels and rainfall from July 2013 to September 2016 is analyzed. Groundwater table increments, considering groundwater drainage rate, were calculated using the water-table fluctuation and master recession curve method and the response time of the groundwater table to rainfall events was estimated using the cross-correlation function. Results reveal that groundwater level declines from tension troughs to landslide fronts in the rainy season, with a significant positive correlation between the groundwater level in the tension trough and landslide surface displacement. Evaluated spring elevations for groundwater discharge range from 410 m to 440 m, which is in agreement with the actual spring elevations (390–423 m). Lag times of groundwater response to rainfall decreases with cumulative rainfall of the rainy periods. In the middle part of the landslide, two responses between rainfall and groundwater levels indicate two water movement pathways: Vertical cracks or fractures resulting from the slow landslide movement, and matrix pore space in unconsolidated sediment. Variations in peak values of the cross-correlation function suggest early dominance of the uniform matrix flow and later dominance of preferential flow during the rainy period.

To address the challenges of fracture recognition in the CT scanning images of coal or rock, particularly the interference of gangue and the recognition of fractures at different scales, proposed and implemented a network model for coal-rock fracture extraction based on deep learning (MCSN). According to the U-Net architecture, the model utilized its encoder-decoder structure and skip connections to segment the fracture structure images from complex coal-rock body images. Firstly, the fracture structures in the CT scanning images were annotated manually using the internal scan images captured by a coal industrial CT scanning system. And the annotated original data was augmented to create a coal-rock CT fracture dataset. Subsequently, to make the extraction network of main features have a stronger extraction capability of fracture structure features, the weights of a pre-trained VGG16 model were transferred to the U-Net encoder through a transfer learning technique. Simultaneously, the decoder part of the U-Net model was improved using the deep separable dilated convolutional modules (DCAC) and residual modules to effectively boost the recognition capability of fracture structures in the CT images, demonstrating a superior segmentation accuracy and robustness. To validate the effectiveness of the coal-rock fracture extraction network model proposed, the results obtained by the MCSN were compared with those of classical convolutional neural networks and threshold segmentation methods. Experimental comparisons revealed a significant advantage of the model proposed in both qualitative and quantitative analyses. The proposed model, employing a multi-scale fusion strategy, demonstrated the capability to effectively extract fractures in complex coal-rock images, thereby enhancing the efficiency and accuracy of fracture identification. The model was applied to the identification of fractures in roadway surrounding rock based on borehole imaging. Fracture extraction was performed through the analysis of borehole videos and planar unfolded images collected by a borehole imaging instrument. The results from both sources were cross-validated to obtain an accurate distribution of fractures in roadway surrounding rock. Furthermore, the model provides guidelines for the injection and sealing of boreholes, increasing volume fraction of coal seam gas extraction.

Abstract Purpose Bone and joint tuberculosis (BJTB) is a distinct variant of tuberculosis in which clinical diagnosis often leads to relative misdiagnosis and missed diagnoses. This study aimed to evaluate the diagnostic accuracy of the targeted nanopore sequencing (TNPseq) assay for BJTB patients in China. Method The study enrolled a cohort of 163 patients with suspected BJTB. Diagnostic testing was performed using the TNPseq assay on samples including punctured tissue, pus, and blood. The diagnostic accuracy of the TNPseq assay was then compared with that of the T-SPOT and Xpert MTB/RIF assays. Result TNPseq exhibited superior performance in terms of accuracy, demonstrating a sensitivity of 76.3% (95% CI: 71.0-81.6%) and a specificity of 98.8% (95% CI: 93.5–100%) in clinical diagnosis. When evaluated against a composite reference standard, TNPseq demonstrated a sensitivity of 74.4% (95% CI: 69.3–79.5%) and a specificity of 98.8% (95% CI: 93.7–100%). These results exceed the performance of both the T-SPOT and Xpert MTB/RIF tests. Notably, TNPseq demonstrated high specificity and accuracy in puncture specimens, with a sensitivity of 75.0% (95% CI: 70.2–79.8%) and a specificity of 98.3% (95% CI: 92.7–100%), as well as in pus samples, with a sensitivity of 83.3% (95% CI: 78.6–88.1%) and a specificity of 100% (95% CI: 100–100%). Additionally, TNPseq facilitated the detection of mixed infection scenarios, identifying 20 cases of bacterial-fungal co-infection, 17 cases of bacterial-viral co-infection, and two cases of simultaneous bacterial-fungal-viral co-infection. Conclusion TNPseq demonstrated great potential in the diagnosis of BJTB due to its high sensitivity and specificity. The ability of TNPseq to diagnose pathogens and detect drug resistance genes can also guide subsequent treatment. Expanding the application scenarios and scope of TNPseq will enable it to benefit more clinical treatments.

Abstract The lockbolt structure is essential in railway wagons, and a scientific lockbolt layout can ensure uniform load distribution, thereby preventing failure. However, current engineering lacks layout optimization methods that address multidimensional failure modes. This paper presents a new lockbolt structure layout optimization method based on submodel, parametric models, and a multi-strategy integrated NSGA-III (MSNSGA-III), adhering to the DVS EFB 3435-2 standard. This method simultaneously optimizes the number and spacing of lockbolts to prevent tensile, bearing, shear, and other static failure modes under specified load conditions. The proposed method was applied during the design phase of a container flatcar. Optimization results indicate that, compared to NSGA-III, this method achieves the best IGD and HV values across multiple complex test functions, demonstrating superior performance in solving complex Pareto front optimization problems. Additionally, the optimized lockbolt structure's safety margins increased by a maximum of 59.81%, passing the full vehicle strength test and significantly enhancing resistance to multidimensional failure modes. These results highlight the method's significant practical application value in addressing the optimization of railway wagon lockbolt structures under complex multidimensional failure modes.

Abstract Background: The rise of network pharmacology has led to the widespread use of network-based computational methods in predicting drug target interaction (DTI). However, existing DTI prediction models typically rely on a limited amount of data to extract drug and target features, potentially affecting the comprehensiveness and robustness of features. In addition, although multiple networks are used for DTI prediction, the integration of heterogeneous information often involves simplistic aggregation and attention mechanisms, which may impose certain limitations. Results: MSH-DTI, a deep learning model for predicting drug-target interactions, is proposed in this paper. The model uses self-supervised learning methods to obtain drug and target structure features. A Heterogeneous Interaction-enhanced Feature Fusion Module is designed for multi-graph construction, and the graph convolutional networks are used to extract node features. With the help of an attention mechanism, the model focuses on the important parts of different features for prediction. Experimental results show that the AUROC and AUPR of MSH-DTI are 0.9620 and 0.9605 respectively, outperforming other models on the DTINet dataset. Conclusion: The proposed MSH-DTI is a helpful tool to discover drug-target interactions, which is also validated through case studies in predicting new DTIs.

Abstract Forest soils store substantial amounts of carbon in various soil organic matter (SOM) components due to high plant litter inputs and active microbial turnover. However, the variations in plant- and microbial-derived SOM components in surface and subsurface forest soils across a wide geographic scale remain poorly understood. This study investigated the SOM components from aboveground and belowground plant inputs and fungal and bacterial necromass in surface (soil0–5 cm) and subsurface (soil5–10 cm) soils across 32 forest sites in China and analyzed their relationships with climate and edaphic factors. Compared to soil0–5 cm, soil5–10 cm exhibited lower soil organic carbon content and cutin biomarker concentration but higher concentrations of fungal necromass carbon and lignin phenols. Higher mean annual precipitation led to higher concentrations of cutin and suberin biomarkers in soil0–5 cm and soil5–10 cm, respectively. Higher soil organic carbon content was associated with lower plant-derived lignin biomarkers, higher lignin oxidation degrees, and increased microbial necromass-derived amino sugars across sites, highlighting the pivotal role of microbial necromass in SOM stabilization. Additionally, both fungal and bacterial necromass decreased with increasing mineral weathering across sites. These insights improve the understanding of environmental drivers of source-specific carbon storage in forest soils. Graphical Abstract

Abstract Plastic pollution is an emerging environmental issue, with microplastics and nanoplastics raising health concerns due to bioaccumulation. This work explored the impact of polystyrene nanoparticle (PS-NPs) exposure during prepuberty on male reproductive function post maturation in rats. Rats were gavaged with PS-NPs (80 nm) at 0, 3, 6, 12 mg/kg/day from postnatal day 21 to 95. PS-NPs accumulated in the testes and reduced sperm quality, serum reproductive hormones, and testicular coefficients. HE staining showed impaired spermatogenesis. PS-NPs disrupted the blood-testis barrier (BTB) by decreasing junction proteins, inducing inflammation and apoptosis. Transcriptomics identified differentially expressed genes related to metabolism, lysosome, apoptosis, and TLR4 signaling. Molecular docking revealed Cordycepin could compete with polystyrene for binding to TLR4. Cordycepin alleviated oxidative stress and improved barrier function in PS-NPs treated Sertoli cells. In conclusion, prepubertal PS-NPs exposure induces long-term reproductive toxicity in male rats, likely by disrupting spermatogenesis through oxidative stress and BTB damage. Cordycepin could potentially antagonize this effect by targeting TLR4 and warrants further study as a protective agent. This study elucidates the mechanisms underlying reproductive toxicity of PS-NPs and explores therapeutic strategies.

In the title molecule, C11H11BrO3, the dihydroindene moiety is essentially planar but with a slight twist in the saturated portion of the five-membered ring. The methoxy groups lie close to the above plane. In the crystal, π-stacking interactions between six-membered rings form stacks of molecules extending along the a-axis direction, which are linked by weak C—H...O and C—H...Br hydrogen bonds. A Hirshfeld surface analysis was performed showing H...H, O...H/H...O and Br...H/H...Br contacts make the largest contributions to intermolecular interactions in the crystal.

In ship detection through remote sensing images, optical images often provide rich details and texture information; however, the quality of such optical images can be affected by cloud and fog interferences. In contrast, Synthetic Aperture Radar (SAR) provides all-weather and all-day imaging capabilities; however, SAR images are susceptible to interference from complex sea clutter. Cooperative ship detection combining the advantages of optical and SAR images can enhance the detection performance of ships. In this paper, by focusing on the slight shift of ships in a small neighborhood range in the prior and later temporal images, we propose a method for cooperative ship detection based on neighborhood saliency in multisource heterogeneous remote sensing images, including optical and SAR data. Initially, a sea-land segmentation algorithm of optical and SAR images is applied to reduce interference from land regions. Next, single-source ship detection from optical and SAR images is performed using the RetinaNet and YOLOv5s models, respectively. Then, we introduce a multisource cooperative ship target detection strategy based on the neighborhood window opening of single-source detection results in remote sensing images and secondary detection of neighborhood salient ships. This strategy further leverages the complementary advantages of both optical and SAR heterogeneous images, reducing the possibility of missing ship and false alarms to improve overall detection performance. The performance of the proposed method has been validated using optical and SAR remote sensing data measured from Yantai, China, in 2022. Compared with existing ship detection methods, our method improves detection accuracy AP50 by ≥1.9%, demonstrating its effectiveness and superiority.

Abstract Retrosynthesis is a crucial task in drug discovery and organic synthesis, where artificial intelligence (AI) is increasingly employed to expedite the process. However, existing approaches employ token-by-token decoding methods to translate target molecule strings into corresponding precursors, exhibiting unsatisfactory performance and limited diversity. As chemical reactions typically induce local molecular changes, reactants and products often overlap significantly. Inspired by this fact, we propose reframing single-step retrosynthesis prediction as a molecular string editing task, iteratively refining target molecule strings to generate precursor compounds. Our proposed approach involves a fragment-based generative editing model that uses explicit sequence editing operations. Additionally, we design an inference module with reposition sampling and sequence augmentation to enhance both prediction accuracy and diversity. Extensive experiments demonstrate that our model generates high-quality and diverse results, achieving superior performance with a promising top-1 accuracy of 60.8% on the standard benchmark dataset USPTO-50 K.

Abstract Multiferroic Ba2CuGe2O7 was anticipated as a potential member of the exciting group of materials hosting a skyrmion or vortex lattice because of its profound Dzyaloshinskii–Moriya interaction (DMI) and the absence of single ion anisotropy (SIA). This phase, however, could not be evidenced and instead, it exhibits a complex incommensurate antiferromagnetic (AFM) cycloidal structure. Its sister compound Ba2MnGe2O7, in contrast, is characterized by a relatively strong in-plane exchange interaction that competes with a non-vanishing SIA and the weak DMI, resulting in a quasi-two-dimensional commensurate AFM structure. Considering this versatility in the magnetic interactions, a mixed solid solution of Cu and Mn in Ba2Cu1−x Mn x Ge2O7 can hold an interesting playground for its interactive DMI and SIA depending on the mixed spin states of the transition metal ions towards the skyrmion physics. Here, we present a detailed study of the micro- and macroscopic spin structure of the Ba2Cu1 − x Mn x Ge2O7 solid solution series using high-resolution neutron powder diffraction techniques. We have developed a remarkably rich magnetic phase diagram as a function of the applied magnetic field and x, which consists of two end-line phases separated by a potentially quantum-critical phase at x = 0.57. An AFM conical structure at zero magnetic field is demonstrated to persist up to x = 0.50. Our results provide crucial information on the spin structure and magnetic properties, which are necessary for the general understanding and theoretical developments on multiferroicity in the frame of skyrmion type or frustrated AFM lattice where DMI and SIA play an important role.

Abstract In this study, we investigated the status and influencing factors of coronavirus disease 2019 (COVID-19) vaccination in patients with chronic obstructive pulmonary disease (COPD). A questionnaire on COVID-19 vaccination in patients with COPD was developed. The clinical characteristics, COVID-19 vaccination status, other relevant vaccinations, and vaccination status of the patients with COPD were collected anonymously. Logistic regression analysis was used to analyze the factors influencing COVID-19 vaccination in patients with COPD. There were 1898 returned questionnaires, of which 1874 were valid. The proportion of patients who completed the COVID-19 vaccination program was 78.60%. Factors influencing the COVID-19 vaccination rate were: the age of individuals who were 75–85 years old and > 85 years old, acute exacerbation 3–4 times in the previous year, comorbid cardiovascular and endocrine system diseases, failure to take regular medication for COPD, application of non-invasive ventilation machines, believing that their current health condition has deteriorated, believing that the current COVID-19 vaccine is not safe, medical staff not specifying whether they would recommend vaccination against COVID-19, medical staff not recommending the COVID-19 vaccine, and fear of adverse reactions and aggravation of COPD. Patients with COPD had a high COVID-19 vaccination rate in China, whereas patients with pneumonia, influenza, and herpes zoster had a low vaccination rate. Improving the patients’ understanding of the safety and effectiveness of the vaccine and promoting effective communication between medical staff and patients would help increase the vaccination rate of patients with COPD.

Abstract Accurate and reliable prediction of porosity forms the foundational basis for evaluating reservoir quality, which is essential for the systematic deployment of oil and gas exploration and development plans. When data quality of samples is low, and critical model parameters are typically determined through subjective experience, resulting in diminished accuracy and reliability of porosity prediction methods utilizing gated recurrent units (GRU), a committee-voting ensemble learning (EL) method, and an enhanced particle swarm optimization (PSO) algorithm are proposed to optimize the GRU-based porosity prediction model. Initially, outliers are eliminated through box plots and the min–max normalization is applied to enhance data quality. To address issues related to model accuracy and high training costs arising from dimensional complexity, substantial noise, and redundant information in logging data, a committee-voting EL strategy based on four feature selection algorithms is introduced. Following data preprocessing, this approach is employed to identify logging parameters highly correlated with porosity, thereby furnishing the most pertinent data samples for the GRU model, mitigating constraints imposed by single-feature selection methods. Second, an improved PSO algorithm is suggested to tackle challenges associated with low convergence accuracy stemming from random population initialization, alongside the absence of global optimal solutions due to overly rapid particle movement during iteration. This algorithm uses a good-point set for population initialization and incorporates a compression factor to devise an adaptive velocity updating strategy, thereby enhancing search efficacy. The enhanced PSO algorithm’s superiority is substantiated through comparison with four alternative swarm intelligent algorithms across 10 benchmark test functions. Ultimately, optimal hyper-parameters for the GRU model are determined using the improved PSO algorithm, thereby minimizing the influence of human factors. Experimental findings based on approximately 15,000 logging data points from well A01 in an operational field validate that, relative to three other deep learning methodologies, the proposed model proficiently extracts spatiotemporal features from logging data, yielding enhanced accuracy in porosity prediction. The mean squared error on the test set was 7.19 × 10–6, the mean absolute error stood at 0.0082, and coefficient of determination reached 0.99, offering novel insights for predicting reservoir porosity.

Abstract Objective it was to explore the influence of the postoperative pain management mode on the psychological state, quality of life (QOL), and nursing satisfaction of late patients in the intensive care unit (ICU) and improve the nursing effect of late patients in the ICU. Methods seventy patients who were admitted to the postoperative ICU for gastric cancer and received treatment in our hospital from March 2021 to May 2022 were selected. The patients were assigned into a research group and a control (Ctrl) group according to a random number table, with 70 cases in each group. The Ctrl group received routine nursing intervention, while research group received nursing intervention based on routine nursing intervention with postoperative pain management mode and received psychological care. Good communication was established with the patients, and the postoperative pain assessment was well conducted. The general information, state-trait anxiety (STAI) score, World Health Organization’s Quality of Life Instrument (WHO QOL-BREF) score, and care satisfaction were compared. Results the general information differed slightly, such as sex, age, and ward type, between groups, with comparability (P > 0.05). S-AI scores (13.15 ± 1.53 vs. 16.23 ± 1.24) and T-AI scores (14.73 ± 3.12 vs. 18.73 ± 3.16) in research group were inferior to those in Ctrl group (P

Abstract Background This study aims to analyze the pathogenic gene in a Chinese family with non-syndromic hearing loss and identify a novel mutation site in the TNC gene. Methods A five-generation Chinese family from Anhui Province, presenting with autosomal dominant non-syndromic hearing loss, was recruited for this study. By analyzing the family history, conducting clinical examinations, and performing genetic analysis, we have thoroughly investigated potential pathogenic factors in this family. The peripheral blood samples were obtained from 20 family members, and the pathogenic genes were identified through whole exome sequencing. Subsequently, the mutation of gene locus was confirmed using Sanger sequencing. The conservation of TNC mutation sites was assessed using Clustal Omega software. We utilized functional prediction software including dbscSNV_AdaBoost, dbscSNV_RandomForest, NNSplice, NetGene2, and Mutation Taster to accurately predict the pathogenicity of these mutations. Furthermore, exon deletions were validated through RT-PCR analysis. Results The family exhibited autosomal dominant, progressive, post-lingual, non-syndromic hearing loss. A novel synonymous variant (c.5247A > T, p.Gly1749Gly) in TNC was identified in affected members. This variant is situated at the exon–intron junction boundary towards the end of exon 18. Notably, glycine residue at position 1749 is highly conserved across various species. Bioinformatics analysis indicates that this synonymous mutation leads to the disruption of the 5' end donor splicing site in the 18th intron of the TNC gene. Meanwhile, verification experiments have demonstrated that this synonymous mutation disrupts the splicing process of exon 18, leading to complete exon 18 skipping and direct splicing between exons 17 and 19. Conclusion This novel splice-altering variant (c.5247A > T, p.Gly1749Gly) in exon 18 of the TNC gene disrupts normal gene splicing and causes hearing loss among HBD families.

Abstract Noise impairs the performance of inverse synthetic aperture radar (ISAR) motion compensation, which induces severe defocusing under low signal‐to‐noise ratio environments. To overcome this issue, a novel similarity‐oriented (SO) method with a two‐domain denoising strategy is proposed. A PIxEl similarity‐oriented (PIE‐SO) denoising method designed for range‐Doppler (RD) domain and a modified RAnge Profile Similarity‐Oriented (RAP‐SO) denoising method designed for high‐resolution range profile (HRRP) matrix are included in the presented framework. Firstly, the PIE‐SO method directly performs a two‐dimensional fast Fourier transform on dechirp processed echo data to form a coarsely focusing ISAR image in the RD domain. Then the focusing image is separated from the noise background by virtue of pixel similarity, after which the noise is preliminarily removed. Subsequently, the coarsely denoised image is transformed into the HRRP matrix. Considering the range profile similarity impaired by noise is restored by the PIE‐SO denoising, a Laplacian regularised‐weighted nuclear norm proximal (LR‐WNNP) operator is proposed. The proposed modified RAP‐SO method, that is, the LR‐WNNP operator, exploits the low‐rank property of the HRRP matrix and the local similarity of adjacent HRRPs to reduce the residual noise. As a result, ISAR imaging quality is significantly improved. Comprehensive experiments illustrate the effectiveness and superiority of the presented method.

Abstract Saline-sodic stress restricts the absorption of zinc by rice, consequently impacting the photosynthesis process of rice plants. In this experiment, Landrace 9 was selected as the test material and the potting method was employed to investigate the influence of ZnO nanoparticles (ZnO NPs) on zinc absorption and chlorophyll fluorescence in rice grown in saline-sodic land. The research findings demonstrate that the application of ZnO NPs proves to be more advantageous for the growth of rice in saline-sodic soil. Notably, the application of ZnO NPs significantly decreases the levels of Na+ and MDA in rice leaves in saline-sodic soil, while increasing the levels of K+ and Zn2+. Additionally, ZnO NPs enhances the content of chloroplast pigments, specific energy flux, quantum yield, and the performance of active PSII reaction center (PI ABS) in rice leaves under saline-sodic stress. Furthermore, the relative variable fluorescence (W K and V J) and quantum energy dissipation rate (φ Do) of rice are also reduced. Therefore, the addition of ZnO NPs enhances the transfer of electrons and energy within the rice photosystem when subjected to saline-sodic stress. This promotes photosynthesis in rice plants growing in saline-sodic land, increasing their resistance to saline-sodic stress and ultimately facilitating their growth and development.

Abstract Background The energy/protein imbalance in a low-protein diet induces lipid metabolism disorders in late-phase laying hens. Reducing energy levels in the low-protein diet to adjust the energy-to-protein ratio may improve fat deposition, but this also decreases the laying performance of hens. This study investigated the mechanism by which different energy levels in the low-protein diet influences liver lipid metabolism in late-phase laying hens through the enterohepatic axis to guide feed optimization and nutrition strategies. A total of 288 laying hens were randomly allocated to the normal-energy and normal-protein diet group (positive control: CK) or 1 of 3 groups: low-energy and low-protein diet (LL), normal-energy and low-protein diet (NL), and high-energy and low-protein diet (HL) groups. The energy-to-protein ratios of the CK, LL, NL, and HL diets were 0.67, 0.74, 0.77, and 0.80, respectively. Results Compared with the CK group, egg quality deteriorated with increasing energy intake in late-phase laying hens fed low-protein diet. Hens fed LL, NL, and HL diets had significantly higher triglyceride, total cholesterol, acetyl-CoA carboxylase, and fatty acid synthase levels, but significantly lower hepatic lipase levels compared with the CK group. Liver transcriptome sequencing revealed that genes involved in fatty acid beta-oxidation (ACOX1, HADHA, EHHADH, and ACAA1) were downregulated, whereas genes related to fatty acid synthesis (SCD, FASN, and ACACA) were upregulated in LL group compared with the CK group. Comparison of the cecal microbiome showed that in hens fed an LL diet, Lactobacillus and Desulfovibrio were enriched, whereas riboflavin metabolism was suppressed. Cecal metabolites that were most significantly affected by the LL diet included several vitamins, such as riboflavin (vitamin B2), pantethine (vitamin B5 derivative), pyridoxine (vitamin B6), and 4-pyridoxic acid. Conclusion A lipid metabolism disorder due to deficiencies of vitamin B2 and pantethine originating from the metabolism of the cecal microbiome may be the underlying reason for fat accumulation in the liver of late-phase laying hens fed an LL diet. Based on the present study, we propose that targeting vitamin B2 and pantethine (vitamin B5 derivative) might be an effective strategy for improving lipid metabolism in late-phase laying hens fed a low-protein diet.

Selective activation of Pt(IV) prodrugs within tumors has emerged as a promising strategy in tumor treatment. Although progress has been made with photo- and ultrasound-activated Pt(IV) prodrugs, concerns remain over the non-specific activation of photosensitizers (PS) and the potential for phototoxicity and chemical toxicity. In this study, a sequential dual-locked Pt(IV) nano-prodrug that can be activated by both the acidic tumor microenvironment and light was developed. The Pt(IV) prodrug was prepared by conjugating PS-locked Pt(IV) to a polymeric core, which was then chelated with metallo iron to lock its photoactivity and form a metallo-nano prodrug. Under acidic tumor microenvironment conditions, the metallo-nano prodrug undergoes dissociation of iron, triggering a reduction process in oxaliplatin under light irradiation, resulting in the activation of both chemotherapy and photodynamic therapy (PDT). Additionally, the prodrug could induce metallo-triggered ferroptosis and polarization of tumor-associated macrophages (TAM), thereby enhancing tumor inhibition. The dual-lock strategy employed in a nanoparticle delivery system represents an expansion in the application of platinum-based anticancer drugs, making it a promising new direction in cancer treatment.

Understanding how environmental factors influence the responsiveness of crop yield to growing season precipitation (GSP) can reduce the risk of yield fluctuations, ensuring stable crop production. This study involved the cultivation of rainfed potatoes at three locations within the climatic transition zone of Northwest China. We employed stepwise linear regression and machine learning techniques to pinpoint the key environmental factors influencing potato tuber yield and the yield-precipitation relationship. The slope of the water-limited yield potential relationship for potatoes was determined to be 172.1 ​kg ​ha−1 ​mm−1, with an intercept at 121.2 ​mm. The potato tuber yield exhibited an upward trend with increasing GSP but declined once the precipitation exceeded 400 ​mm. However, GSP alone explained up to 30% of the variability in potato tuber yield. Factors such as soil moisture at planting, maximum temperatures during the tuber stolon initiation and bulking stages, diurnal temperature fluctuations at maturity, and excessive precipitation events during the growing season significantly influenced potato tuber yield, and consequently, the relationship between yield and GSP. Conversely, climatic factors accounted for more than 63% of the variation in potato tuber yield, with the multiple linear regression model yielding the best results. This was especially evident when the yield-precipitation relationship was categorized into two groups based on the amount and distribution of GSP, maximum temperature, and radiation levels. This analysis suggested that preventing unnecessary water evaporation when precipitation is low, improving drainage when precipitation is high, and planting potato on an optimal date can advance potato production.

Aiming at the problem of cutting roof and retaining roadway on the right third working face of 16#, east second mining area of Jixian Coal Mine in Shuangyashan city, the mechanical model of surrounding rock of cutting roof and retaining roadway is established, and the theoretical calculation formula of key parameters such as cutting height and cutting angle is given; the influence of roof cutting parameters (roof cutting height and angle) on the stress distribution and deformation characteristics of the surrounding rock is further analyzed. The numerical simulation results show that the greater the roof cutting height is, the better the relief effect is; with the increase of the cutting angle, the side stress of solid coal shows an increasing trend, which also creates certain conditions for the caving of gangue. The similar simulation results show that when the coal seam is mined to a certain distance, the upper rock stratum in the goaf collapses at the same time, and the rock mass in the slit range and caving rock mass form a hinged joint, that is, the side filling of the roadway is basically completed, and the structure of “low cantilever beam + high hinged rock beam” is formed above the roadway. The engineering practice show that the cutting height is 8.0 m (2.39 m medium sandstone), the cutting angle is 10°, and the spacing of the drilling holes is 600 mm. When it is about 60 m away from the working face, the roof subsidence is 168 mm, the solid coal wall displacement is 212 mm, the deformation of the gangue wall is almost 0, and the bottom heave is 120 mm.

In order to improve the Hoek-Brown strength criterion not considering the influence of intermediate principal stress on rock strength, the evolutions of rock strength were investigated according to the results of the rock true triaxial compression test. The intermediate principal stress coefficient was introduced to quantify the effect on rock strength. Considering the relationships between the parameter of the Hoek-Brown strength criterion and the rock stress levels, the modified Hoek-Brown strength criterion for the true triaxial stress was then established with the Lagrange interpolation method. The spatial envelope characteristics of the modified strength criterion were analyzed. The best-fitting errors of seven rocks with true triaxial test results were finally compared with the three other three-dimensional strength criteria to verify the rationality of the modified strength criterion. The research results indicate that the rock strength increases gradually with the increase of minimum principal stress, and increases first and then decreases with the increase of intermediate principal stress, which presents a significant interval effect. The modified strength criterion not only has the advantage of the Hoek-Brown strength criterion in the nonlinear characteristics of the meridian plane but also describes the basic characteristics of rock strength in stress space. The spatial envelope surfaces for the modified strength criteria with the linear and quadratic interpolation are the non-equilateral hexagonal pyramidal surfaces and conical surfaces, which can meet the requirement of continuous smoothness in the tension-compression meridian plane interval. The modified strength criterion can better predict the true triaxial strength of rocks and reasonably reflect the influence of intermediate principal stress on the rock strength. The modified strength criterion with the quadratic interpolation improves the prediction accuracy of rock strength by about 1.2 to 2.0 times compared to the criterion with linear interpolation. The modified strength criterion has good agreement with the true triaxial test results of different hard and brittle rocks compared with other true triaxial strength criteria, which shows the applicability and reliability of the modified strength criterion.

Abstract METTL3 is the catalytic subunit of the methyltransferase complex, which mediates m6A modification to regulate gene expression. In addition, METTL3 regulates transcription in an enzymatic activity-independent manner by driving changes in high-order chromatin structure. However, how these functions of the methyltransferase complex are coordinated remains unknown. Here we show that the methyltransferase complex coordinates its enzymatic activity-dependent and independent functions to regulate cellular senescence, a state of stable cell growth arrest. Specifically, METTL3-mediated chromatin loops induce Hexokinase 2 expression through the three-dimensional chromatin organization during senescence. Elevated Hexokinase 2 expression subsequently promotes liquid-liquid phase separation, manifesting as stress granule phase separation, by driving metabolic reprogramming. This correlates with an impairment of translation of cell-cycle related mRNAs harboring polymethylated m6A sites. In summary, our results report a coordination of m6A-dependent and -independent function of the methyltransferase complex in regulating senescence through phase separation driven by metabolic reprogramming.

Abstract In recent decades boreal wildfires have occurred frequently over eastern Siberia, leading to increased emissions of carbon dioxide and pollutants. However, it is unclear what factors have contributed to recent increases in these wildfires. Here, using the data we show that background eastern Siberian Arctic warming (BAW) related to summer Russian Arctic sea-ice decline accounts for ~79% of the increase in summer vapor pressure deficit (VPD) that controls wildfires over eastern Siberia over 2004-2021 with the remaining ~21% related to internal atmospheric variability associated with changes in Siberian blocking events. We further demonstrate that Siberian blocking events are occurring at higher latitudes, are more persistent and have larger zonal scales and slower decay due to smaller meridional potential vorticity gradients caused by stronger BAW under lower sea-ice. These changes lead to more persistent, widespread and intense high-latitude warming and VPD, thus contributing to recent increases in eastern Siberian high-latitude wildfires.

Abstract The synthesis of constrained 12-membered rings is notably difficult. The main challenges result from constraints during the linear peptide cyclization. Attempts to overcome constraints through excessive activation frequently cause peptidyl epimerization, while insufficient activation of the C-terminus hampers cyclization and promotes intermolecular oligomer formation. We present a β-thiolactone framework that enables the synthesis of cyclo-tetrapeptides via direct aminolysis. This tactic utilizes a mechanism that restricts C-terminal carbonyl rotation while maintaining high reactivity, thereby enabling efficient head-to-tail amidation, reducing oligomerization, and preventing epimerization. A broad range of challenging cyclo-tetrapeptides ( > 20 examples) are synthesized in buffer and exhibits excellent tolerance toward nearly all proteinogenic amino acids. Previously unattainable macrocycles, such as cyclo-L-(Pro-Tyr-Pro-Val), have been produced and identified as μ-opioid receptor (MOR) agonists, with an EC50 value of 2.5 nM. Non-epimerizable direct aminolysis offers a practical solution for constrained peptide cyclization, and the discovery of MOR agonist activity highlights the importance of overcoming synthetic challenges for therapeutic development.

Abstract To scavenge energy from imperceptible vibrations, this paper investigates the broadband response and output performance of a microvibrational piezoelectric energy harvesting system with mechanical stopper. The energy harvesting system comprises a cantilever beam made of piezoelectric material, which is affixed with a coil at its unbound end and a mechanical stopper. The coil is placed in a magnetic field to provide an ultra‐low level excitation. The electromechanical model is derived according to force integration method (FIM) and Hertz's contact theory, and numerical simulations are undertaken to evaluate the influence of the excitation level, and the gap on the performance. For the linear counterpart without stopper, experimental results indicate the system generates a peak power of 24.12 μW with matched resistance under excitation with a level of 0.003 N and a frequency of 200.3 Hz. When a polydimethylsiloxane (PDMS) stopper is introduced, the vibration of the piezoelectric beam exhibits an obvious nonlinearity with an amplitude of micron scale. Increasing the excitation level and decreasing the gap could efficiently broaden the response bandwidth. Experimental results demonstrate that a copper stopper with larger elastic modulus results in a wider response frequency range, and the half‐power bandwidth could reach 37.1 Hz under excitation with a level of 0.003 N.

Abstract Background Emerging evidence suggests that alterations in BCAA metabolism may contribute to the pathogenesis of sarcopenia. However, the relationship between branched-chain amino acids (BCAAs) and sarcopenia is incompletely understood, and existing literature presents conflicting results. In this study, we conducted a community-based study involving > 100,000 United Kingdom adults to comprehensively explore the association between BCAAs and sarcopenia, and assess the potential role of muscle mass in mediating the relationship between BCAAs and muscle strength. Methods Multivariable linear regression analysis examined the relationship between circulating BCAAs and muscle mass/strength. Logistic regression analysis assessed the impact of circulating BCAAs and quartiles of BCAAs on sarcopenia risk. Subgroup analyses explored the variations in associations across age, and gender. Mediation analysis investigated the potential mediating effect of muscle mass on the BCAA-muscle strength relationship. Results Among 108,017 participants (mean age: 56.40 ± 8.09 years; 46.23% men), positive associations were observed between total BCAA, isoleucine, leucine, valine, and muscle mass (beta, 0.56–2.53; p

Abstract Background Esophageal cancer represents a significant public health concern; however, reliable diagnostic and prognostic markers have not been established. This study aimed to investigate the clinical value of plasma D-dimer levels in patients with esophageal cancer. Methods Overall, 120 patients with esophageal cancer who underwent radical surgical resection at our department between January 2019 and 2020 were included (esophageal cancer group). Plasma D-dimer levels were measured preoperatively and on postoperative days 1 and 14. Additionally, 60 healthy participants (control group) with measured plasma D-dimer levels were included. The preoperative D-dimer levels and positive D-dimer test rates were compared between the groups. The 3-year survival rate in patients with esophageal cancer was calculated using the Kaplan–Meier method. Results Preoperative D-dimer concentration in the esophageal cancer group was (0.65 ± 0.859 µg/mL) significantly higher than that in the control group (0.32 ± 0.369 µg/mL). The positivity rate in the esophageal cancer group (35.0%, 42/120) was significantly higher than that in the control group (15%, 9/60). D-dimer concentrations were significantly higher 1 day postoperatively than preoperatively. Conversely, D-dimer concentrations were significantly lower 14 days postoperatively than preoperatively. Patients in the esophageal cancer group with plasma D-dimer concentrations ≤ 0.5 µg/mL had significantly higher 3-year survival rates than those with higher concentrations. In the logistic multivariate analysis, tumor pathological stage and preoperative plasma D-dimer levels were independent prognostic factors of 3-year survival rates in patients with esophageal cancer. Conclusion Plasma D-dimer concentrations are clinically valuable in esophageal cancer diagnosis, postoperative recurrence monitoring, and prognosis prediction.

Abstract Economic productivity depends on reliable access to electricity, but the extreme shortage events of variable wind-solar systems may be strongly affected by climate change. Here, hourly reanalysis climatological data are leveraged to examine historical trends in defined extreme shortage events worldwide. We find uptrends in extreme shortage events regardless of their frequency, duration, and intensity since 1980. For instance, duration of extreme low-reliability events worldwide has increased by 4.1 hours (0.392 hours per year on average) between 1980–2000 and 2001–2022. However, such ascending trends are unevenly distributed worldwide, with a greater variability in low- and middle-latitude developing countries. This uptrend in extreme shortage events is driven by extremely low wind speed and solar radiation, particularly compound wind and solar drought, which however are strongly disproportionated. Only average 12.5% change in compound extremely low wind speed and solar radiation events may give rise to over 30% variability in extreme shortage events, despite a mere average 1.0% change in average wind speed and solar radiation. Our findings underline that wind-solar systems will probably suffer from weakened power security if such uptrends persist in a warmer future.

Abstract Fermenting Chinese medicinal herbs could enhance their bioactivities. We hypothesized probiotic-fermented gastrodia elata Blume (GE) with better potential to alleviate insomnia than that of unfermented, thus the changes in chemical composition and the insomnia-alleviating effects and mechanisms of fermented GE on pentylenetetrazole (PTZ)-induced insomnia zebrafish were explored via high-performance liquid chromatography (HPLC) and mass spectroscopy-coupled HPLC (HPLC-MS), phenotypic, transcriptomic, and metabolomics analysis. The results demonstrated that probiotic fermented GE performed better than unfermented GE in increasing the content of chemical composition, reducing the displacement, average speed, and number of apoptotic cells in zebrafish with insomnia. Metabolomic investigation showed that the anti-insomnia effect was related to regulating the pathways of actin cytoskeleton and neuroactive ligand-receptor interactions. Transcriptomic and reverse transcription qPCR (RT-qPCR) analysis revealed that secondary fermentation liquid (SFL) significantly modulated the expression levels of neurod1, msh2, msh3, recql4, ercc5, rad5lc, and rev3l, which are mainly involved in neuron differentiation and DNA repair. Collectively, as a functional food, fermented GE possessed potential for insomnia alleviation.

Abstract African swine fever (ASF) is a highly contagious disease of domestic and wild pigs caused by African swine fever virus (ASFV). The mortality rate associated with ASF is remarkably high, almost approaching 100%. Since the introduction of ASF into China in 2018, its rapid spread has caused marked economic losses in the country’s swine industry. To date, there are no safe and effective commercial vaccines or antiviral drugs against ASF; thus, there is an urgent need to develop novel prevention and control strategies. Traditional Chinese medicine (TCM), which comprises various herbs that are abundant in various potential functional components, holds great promise for the prevention and control of ASF. Here, we provide a comprehensive review of the advancements in TCM and the effects of its compound formulas against ASF, including the antiviral abilities, immunoregulatory activities, and practical application of these formulas for the prevention and control of ASF. We specifically examined the potential and constraints of natural product-derived extracts and TCM formulas in combating ASFV. This review aims to offer insights and ideas for the holistic management and containment of ASF.

Abstract Photoluminescence (PL) colour-changing materials in response to photostimulus play an increasingly significant role in intelligent applications for their programmability. Nevertheless, current research mainly focuses on photochemical processes, with less attention to PL transformation through uniform aggregation mode adjustment. Here we show photochromic luminescence in organic crystals (e.g. dimethyl terephthalate) with PL varying from dark blue to purple, then to bright orange-red, and finally to red. This change is attributed to the emergence of clusters with red emission, which is barely achieved in single-benzene-based structures, thanks to the subtle molecular rearrangements prompted by light. Crucial to this process are the through-space electron interactions among molecules and moderate short contacts between ester groups. The irradiated crystals exhibit reversible PL transformation upon sufficient relaxation, showing promising applications in information storage and smart optoelectronic devices. This research contributes to the development of smart photochromic luminescent materials with significant PL colour transformations through molecular rearrangement.

Abstract Alzheimer’s disease (AD) is a serious brain disorder characterized by the presence of beta-amyloid plaques, tau pathology, inflammation, neurodegeneration, and cerebrovascular dysfunction. The presence of chronic neuroinflammation, breaches in the blood-brain barrier (BBB), and increased levels of inflammatory mediators are central to the pathogenesis of AD. These factors promote the penetration of immune cells into the brain, potentially exacerbating clinical symptoms and neuronal death in AD patients. While microglia, the resident immune cells of the central nervous system (CNS), play a crucial role in AD, recent evidence suggests the infiltration of cerebral vessels and parenchyma by peripheral immune cells, including neutrophils, T lymphocytes, B lymphocytes, NK cells, and monocytes in AD. These cells participate in the regulation of immunity and inflammation, which is expected to play a huge role in future immunotherapy. Given the crucial role of peripheral immune cells in AD, this article seeks to offer a comprehensive overview of their contributions to neuroinflammation in the disease. Understanding the role of these cells in the neuroinflammatory response is vital for developing new diagnostic markers and therapeutic targets to enhance the diagnosis and treatment of AD patients.

Abstract Danshen, a prominent herb in traditional Chinese medicine (TCM), is known for its potential to enhance physiological functions such as blood circulation, immune response, and resolve blood stasis. Despite the effectiveness of COVID-19 vaccination efforts, some individuals still face severe complications post-infection, including pulmonary fibrosis, myocarditis arrhythmias and stroke. This study employs a network pharmacology and molecular docking approach to investigate the potential mechanisms underlying the therapeutic effects of candidate components and targets from Danshen in the treatment of complications in COVID-19. Candidate components and targets from Danshen were extracted from the TCMSP Database, while COVID-19-related targets were obtained from Genecards. Venn diagram analysis identified common targets. A Protein–Protein interaction (PPI) network and gene enrichment analysis elucidated potential therapeutic mechanisms. Molecular docking evaluated interactions between core targets and candidate components, followed by molecular dynamics simulations to assess stability. We identified 59 potential candidate components and 123 targets in Danshen for COVID-19 treatment. PPI analysis revealed 12 core targets, and gene enrichment analysis highlighted modulated pathways. Molecular docking showed favorable interactions, with molecular dynamics simulations indicating high stability of key complexes. Receiver operating characteristic (ROC) curves validated the docking protocol. Our study unveils candidate compounds, core targets, and molecular mechanisms of Danshen in COVID-19 treatment. These findings provide a scientific foundation for further research and potential development of therapeutic drugs.

Abstract Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) poses a major threat to the global swine industry, yet effective prevention and control measures remain elusive. This study unveils Nitazoxanide (NTZ) as a potent inhibitor of PRRSV both in vitro and in vivo. Through High-Throughput Screening techniques, 16 potential anti-PRRSV compounds are identified from a library comprising FDA-approved and pharmacopeial drugs. We show that NTZ displays strong efficacy in reducing PRRSV proliferation and transmission in a swine model, alleviating viremia and lung damage. Additionally, Tizoxanide (TIZ), the primary metabolite of NTZ, has been identified as a facilitator of NMRAL1 dimerization. This finding potentially sheds light on the underlying mechanism contributing to TIZ’s role in augmenting the sensitivity of the IFN-β pathway. These results indicate the promising potential of NTZ as a repurposed therapeutic agent for Porcine Reproductive and Respiratory Syndrome (PRRS). Additionally, they provide valuable insights into the antiviral mechanisms underlying NTZ’s effectiveness.

Abstract Background We aimed to explore the association and potential causality between polyunsaturated fatty acids concentrations and the risk of periodontal disease. Materials and methods Data were collected from the 2011–2014 National Health and Nutrition Examination Survey (NHANES). Weighted logistic regression analysis and restricted cubic spline (RCS) analysis were used to analyse the associations of the concentrations of omega-3 and omega-6 fatty acids and the omega-6/omega-3 fatty acids ratio with the risk of periodontitis. E-value and propensity score matching (PSM) analyses were used for sensitivity analyses. In addition, two-sample Mendelian randomisation (MR) analyses were performed to assess the potential causal impact of the concentrations of those fatty acids on periodontitis risk. Results A total of 2462 participants from the NHANES were included. Logistic regression analysis revealed that high omega-3 fatty acids levels were negatively associated with the risk of developing periodontitis (P 0.05). Conclusion Although omega-3 fatty acids and the omega-6/omega-3 fatty acids ratio were associated with the risk of periodontitis in cross-sectional studies, the MR results did not support a causal relationship between them. Therefore, there is no indication that an increase in the omega-3 fatty acids concentration or a decrease in the omega-6/omega-3 fatty acids ratio may be beneficial for preventing periodontitis.

Abstract Saline-sodic stress can limit the absorption of available zinc in rice, subsequently impacting the normal photosynthesis and carbohydrate metabolism of rice plants. To investigate the impact of exogenous zinc application on photosynthesis and carbohydrate metabolism in rice grown in saline-sodic soil, this study simulated saline-sodic stress conditions using two rice varieties, 'Changbai 9' and 'Tonghe 899', as experimental materials. Rice seedlings at 4 weeks of age underwent various treatments including control (CT), 2 μmol·L−1 zinc treatment alone (Z), 50 mmol·L−1 saline-sodic treatment (S), and 50 mmol·L−1 saline-sodic treatment with 2 μmol·L−1 zinc (Z + S). We utilized JIP-test to analyze the variations in excitation fluorescence and MR820 signal in rice leaves resulting from zinc supplementation under saline-sodic stress, and examined the impact of zinc supplementation on carbohydrate metabolism in both rice leaves and roots under saline-sodic stress. Research shows that zinc increased the chloroplast pigment content, specific energy flow, quantum yield, and performance of active PSII reaction centers (PI ABS), as well as the oxidation (V OX) and reduction rate (V red) of PSI in rice leaves under saline-sodic stress. Additionally, it decreased the relative variable fluorescence (W K and V J) and quantum energy dissipation yield (φ DO) of the rice. Meanwhile, zinc application can reduce the content of soluble sugars and starch in rice leaves and increasing the starch content in the roots. Therefore, the addition of zinc promotes electron and energy transfer in the rice photosystem under saline-sodic stress. It enhances rice carbohydrate metabolism, improving the rice plants’ ability to withstand saline-sodic stress and ultimately promoting rice growth and development.

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Abstract Predicting magnetic ordering in kagome compounds offers the possibility of harnessing topological or flat-band physical properties through tuning of the magnetism. Here, we examine the magnetic interactions and phases of ErMn6Sn6 which belongs to a family of RMn6Sn6, R = Sc, Y, Gd–Lu, compounds with magnetic kagome Mn layers, triangular R layers, and signatures of topological properties. Using results from single-crystal neutron diffraction and mean-field analysis, we find that ErMn6Sn6 sits close to the critical boundary separating the spiral-magnetic and ferrimagnetic ordered states typical for non-magnetic versus magnetic R layers, respectively. Finding interlayer magnetic interactions and easy-plane Mn magnetic anisotropy consistent with other members of the family, we predict the existence of a number of temperature and field dependent collinear, noncollinear, and noncoplanar magnetic phases. We show that thermal fluctuations of the Er magnetic moment, which act to weaken the Mn-Er interlayer magnetic interaction and quench the Er magnetic anisotropy, dictate magnetic phase stability. Our results provide a starting point and outline a multitude of possibilities for studying the behavior of Dirac fermions in RMn6Sn6 compounds with control of the Mn spin orientation and real-space spin chirality.

Abstract In the production process, the presence of surface defects seriously affects the quality of industrial products. Existing defect detectors are not suitable for surface with scattered distribution and complex texture of defects. In this study, a dual-branch information extraction and local attention anchor-free network for defect detection (DLA-FCOS), which is based on the fully convolutional one-stage network, is proposed to accurately locate and detect surface defects of industrial products. Firstly, a dual-branch feature extraction network (DFENeT) is proposed and used to improve the extraction ability of complex defects. Then, a local feature enhancement module is proposed, and a residual connection is established to enrich local semantic information. Meanwhile, the self-attention mechanism is introduced to form local attentional residual feature pyramid networks (LA-RFPN) to eliminate the influences of feature misalignments. The mean average accuracy (mAP) and frames per second (FPS) of the proposed DLA-FCOS on the cut layer of the tobacco packet defect dataset (CLTP-DD) are 96.8% and 20.7, respectively, which meets the requirements for accurate and real-time defect detection. Meanwhile, the average accuracy of the proposed DLA-FCOS on the NEU-DET and GC10-DET datasets is 78.4% and 67.7%, respectively. The results demonstrate that the DLA-FCOS has good feasibility and high generalization capability to perform defect detection tasks of industrial products.

Abstract The WRKY transcription factors play essential roles in a variety of plant signaling pathways associated with biotic and abiotic stress response. The transcriptional activity of many WRKY members are regulated by a class of intrinsically disordered VQ proteins. While it is known that VQ proteins interact with the WRKY DNA-binding domains (DBDs), also termed as the WRKY domains, structural information regarding VQ-WRKY interaction is lacking and the regulation mechanism remains unknown. Herein we report a solution NMR study of the interaction between Arabidopsis WRKY33 and its regulatory VQ protein partner SIB1. We uncover a SIB1 minimal sequence neccessary for forming a stable complex with WRKY33 DBD, which comprises not only the consensus “FxxhVQxhTG” VQ motif but also its preceding region. We demonstrate that the βN-strand and the extended βN-β1 loop of WRKY33 DBD form the SIB1 docking site, and build a structural model of the complex based on the NMR paramagnetic relaxation enhancement and mutagenesis data. Based on this model, we further identify a cluster of positively-charged residues in the N-terminal region of SIB1 to be essential for the formation of a SIB1-WRKY33-DNA ternary complex. These results provide a framework for the mechanism of SIB1-enhanced WRKY33 transcriptional activity.

Abstract The need for tumor postoperative treatments aimed at recurrence prevention and tissue regeneration have raised wide considerations in the context of the design and functionalization of implants. Herein, an injectable hydrogel system encapsulated with anti-tumor, anti-oxidant dual functional nanoparticles has been developed in order to prevent tumor relapse after surgery and promote wound repair. The utilization of biocompatible gelatin methacryloyl (GelMA) was geared towards localized therapeutic intervention. Zeolitic imidazolate framework-8@ceric oxide (ZIF-8@CeO2, ZC) nanoparticles (NPs) were purposefully devised for their proficiency as reactive oxygen species (ROS) scavengers. Furthermore, injectable GelMA hydrogels loaded with ZC NPs carrying doxorubicin (ZC-DOX@GEL) were tailored as multifunctional postoperative implants, ensuring the efficacious eradication of residual tumor cells and alleviation of oxidative stress. In vitro and in vivo experiments were conducted to substantiate the efficacy in cancer cell elimination and the prevention of tumor recurrence through the synergistic chemotherapy approach employed with ZC-DOX@GEL. The acceleration of tissue regeneration and in vitro ROS scavenging attributes of ZC@GEL were corroborated using rat models of wound healing. The results underscore the potential of the multifaceted hydrogels presented herein for their promising application in tumor postoperative treatments.