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Fluid intelligence is an individual's innate ability to cope with complex situations and is gradually reduced across adults aging. The realization of fluid intelligence requires the simultaneous activity of multiple brain regions and depends on the structural connection of distributed brain regions. Uncovering the structural features of brain connections associated with fluid intelligence decline will provide reference for the development of intervention and treatment programs for cognitive decline. Using structural magnetic resonance imaging data of 454 healthy participants (18–87 years) from the Cam-CAN dataset, we constructed structural similarity network for each participant and calculated the node degree. Spearman correlation analysis showed that age was positively correlated with degree centrality in the cingulate cortex, left insula and subcortical regions, while negatively correlated with that in the orbito-frontal cortex, right middle temporal and precentral regions. Partial least squares (PLS) regression showed that the first PLS components explained 32 % (second PLS component: 20 %, pperm < 0.001) of the variance in fluid intelligence. Additionally, the degree centralities of anterior insula, supplementary motor area, prefrontal, orbito-frontal and anterior cingulate cortices, which are critical nodes of the multiple-demand network (MDN), were linked to fluid intelligence. Increased degree centrality in anterior cingulate cortex and left insula partially mediated age-related decline in fluid intelligence. Collectively, these findings suggest that the structural stability of MDN might contribute to the maintenance of fluid intelligence.

Study region: The Loess Plateau, China Study focus: The trade-off between carbon uptake and water loss, characterized by ecosystem water use efficiency (WUE), deeply influences ecosystem sustainability. In this study, WUE was estimated based on GPP from the Moderate Resolution Imaging Spectroradiometer (MODIS) product and ET simulated by the Priestley Taylor Jet Propulsion Laboratory (PT-JPL) model. The emerging hot spot analysis (EHSA) was used to comprehensively examine the spatio-temporal heterogeneity of WUE. Additionally, the Geodetector model was employed to identify the main drivers of WUE and quantify the interactive effects of drivers on WUE, focusing on different vegetation types. New hydrological insights for the region: Results indicated obvious spatial heterogeneity of WUE under different vegetation and hydro-climatic conditions. Significant increases in WUE occurred in sub-humid cropland and grassland areas that have experienced large-scale ecological restoration and agricultural intensification. By contrast, slight decreases in WUE were observed in semi-arid grassland areas, some of which were accompanied by intensifying cold spots. It was noteworthy that WUE in some hot spots with excessive vegetation coverage also showed a slight downward trend. Further, the WUE pattern in cropland/forests/grassland was dominated by the interaction of vegetation coverage (characterized by the Normalized Difference Vegetation Index, NDVI) and precipitation/air temperature/vapor pressure deficit, with NDVI playing a leading role and hydro-climatic drivers playing a supporting role.

Summary: Background: The tumour stroma is associated with unfavourable prognosis in diverse solid tumours, but its prognostic and predictive value in bladder cancer (BCa) is unclear. Methods: In this multicentre, retrospective study, we included 830 patients with BCa from six independent cohorts. Differences in overall survival (OS) and cancer-specific survival (CSS) were investigated between high-tumour stroma ratio (TSR) and low-TSR groups. Multi-omics analyses, including RNA sequencing, immunohistochemistry, and single-cell RNA sequencing, were performed to study stroma-immune interactions. TSR prediction models were developed based on pelvic CT scans, and the best performing model was selected based on receiver operator characteristic analysis. Findings: Compared to low-TSR tumours, high-TSR tumours were significantly associated with worse OS (HR = 1.193, 95% CI: 1.046–1.361, P = 0.008) and CSS (HR = 1.337, 95% CI: 1.139–1.569, P

Summary: Background: The pathological examination of lymph node metastasis (LNM) is crucial for treating prostate cancer (PCa). However, the limitations with naked-eye detection and pathologist workload contribute to a high missed-diagnosis rate for nodal micrometastasis. We aimed to develop an artificial intelligence (AI)-based, time-efficient, and high-precision PCa LNM detector (ProCaLNMD) and evaluate its clinical application value. Methods: In this multicentre, retrospective, diagnostic study, consecutive patients with PCa who underwent radical prostatectomy and pelvic lymph node dissection at five centres between Sep 2, 2013 and Apr 28, 2023 were included, and histopathological slides of resected lymph nodes were collected and digitised as whole-slide images for model development and validation. ProCaLNMD was trained at a dataset from a single centre (the Sun Yat-sen Memorial Hospital of Sun Yat-sen University [SYSMH]), and externally validated in the other four centres. A bladder cancer dataset from SYSMH was used to further validate ProCaLNMD, and an additional validation (human-AI comparison and collaboration study) containing consecutive patients with PCa from SYSMH was implemented to evaluate the application value of integrating ProCaLNMD into the clinical workflow. The primary endpoint was the area under the receiver operating characteristic curve (AUROC) of ProCaLNMD. In addition, the performance measures for pathologists with ProCaLNMD assistance was also assessed. Findings: In total, 8225 slides from 1297 patients with PCa were collected and digitised. Overall, 8158 slides (18,761 lymph nodes) from 1297 patients with PCa (median age 68 years [interquartile range 64–73]; 331 [26%] with LNM) were used to train and validate ProCaLNMD. The AUROC of ProCaLNMD ranged from 0.975 (95% confidence interval 0.953–0.998) to 0.992 (0.982–1.000) in the training and validation datasets, with sensitivities > 0.955 and specificities > 0.921. ProCaLNMD also demonstrated an AUROC of 0.979 in the cross-cancer dataset. ProCaLNMD use triggered true reclassification in 43 (4.3%) slides in which micrometastatic tumour regions were initially missed by pathologists, thereby correcting 28 (8.5%) missed-diagnosed cases of previous routine pathological reports. In the human-AI comparison and collaboration study, the sensitivity of ProCaLNMD (0.983 [0.908–1.000]) surpassed that of two junior pathologists (0.862 [0.746–0.939], P = 0.023; 0.879 [0.767–0.950], P = 0.041) by 10–12% and showed no difference to that of two senior pathologists (both 0.983 [0.908–1.000], both P > 0.99). Furthermore, ProCaLNMD significantly boosted the diagnostic sensitivity of two junior pathologists (both P = 0.041) to the level of senior pathologists (both P > 0.99), and substantially reduced the four pathologists’ slide reviewing time (−31%, P

Summary: Background: Urine cytology is an important non-invasive examination for urothelial carcinoma (UC) diagnosis and follow-up. We aimed to explore whether artificial intelligence (AI) can enhance the sensitivity of urine cytology and help avoid unnecessary endoscopy. Methods: In this multicentre diagnostic study, consecutive patients who underwent liquid-based urine cytology examinations at four hospitals in China were included for model development and validation. Patients who declined surgery and lacked associated histopathology results, those diagnosed with rare subtype tumours of the urinary tract, or had low-quality images were excluded from the study. All liquid-based cytology slides were scanned into whole-slide images (WSIs) at 40 × magnification and the WSI-labels were derived from the corresponding histopathology results. The Precision Urine Cytology AI Solution (PUCAS) was composed of three distinct stages (patch extraction, features extraction, and classification diagnosis) and was trained to identify important WSI features associated with UC diagnosis. The diagnostic sensitivity was mainly used to validate the performance of PUCAS in retrospective and prospective validation cohorts. This study is registered with the ChiCTR, ChiCTR2300073192. Findings: Between January 1, 2018 and October 31, 2022, 2641 patients were retrospectively recruited in the training cohort, and 2335 in retrospective validation cohorts; 400 eligible patients were enrolled in the prospective validation cohort between July 7, 2023 and September 15, 2023. The sensitivity of PUCAS ranged from 0.922 (95% CI: 0.811–0.978) to 1.000 (0.782–1.000) in retrospective validation cohorts, and was 0.896 (0.837–0.939) in prospective validation cohort. The PUCAS model also exhibited a good performance in detecting malignancy within atypical urothelial cells cases, with a sensitivity of over 0.84. In the recurrence detection scenario, PUCAS could reduce 57.5% of endoscopy use with a negative predictive value of 96.4%. Interpretation: PUCAS may help to improve the sensitivity of urine cytology, reduce misdiagnoses of UC, avoid unnecessary endoscopy, and reduce the clinical burden in resource-limited areas. The further validation in other countries is needed. Funding: National Natural Science Foundation of China; Key Program of the National Natural Science Foundation of China; the National Science Foundation for Distinguished Young Scholars; the Science and Technology Planning Project of Guangdong Province; the National Key Research and Development Programme of China; Guangdong Provincial Clinical Research Centre for Urological Diseases.

A pH-responsive colorimetric method based on dual-enzyme catalysis for rapid and facile detection and quantification of nanoPET at environment-dependent concentration is proposed. The nanoPET was hydrolyzed by the synergistic catalysis of cutinase and lipase to terephthalic acid which can be sensitive detected using bromocresol purple as the indicator. The color changed from purple to bright yellow as the nanoPET detection concentration increased from 0 mg/mL to 2 mg/mL which can be detected by UV–Vis. This naked-eye method has a high sensitivity for nanoPET detection with the visual detection cutoff of 31.00 μg/mL, and has a good linearity in the range of 0 ∼ 1 mg/mL with LOD of 22.84 μg/mL. The reliability of this method is verified in the detection of nanoPET in lake water and beer samples, with an average recovery of 87.1 %. The as-developed dual-enzyme colorimetric chemosensor holds promising potential as a robust and effective platform for the sensitive detection of nanoPET.

Summary: Kinesin 1 (KIF5) is one major type of motor protein in neurons, but its members’ function in the intact brain remains less studied. Using in vivo two-photon imaging, we find that conditional knockout of Kif5b (KIF5B cKO) in CaMKIIα-Cre-expressing neurons shows heightened turnover and lower stability of dendritic spines in layer 2/3 pyramidal neurons with reduced spine postsynaptic density protein 95 acquisition in the mouse cortex. Furthermore, the RNA-binding protein fragile X mental retardation protein (FMRP) is translocated to the proximity of newly formed spines several hours before the spine formation events in vivo in control mice, but this preceding transport of FMRP is abolished in KIF5B cKO mice. We further find that FMRP is localized closer to newly formed spines after fear extinction, but this learning-dependent localization is disrupted in KIF5B cKO mice. Our findings provide the crucial in vivo evidence that KIF5B is involved in the dendritic targeting of synaptic proteins that underlies dendritic spine plasticity.

Background: This investigation studied the clinical features and outcomes of synovial sarcoma (SS) patients from a single institution. Methods: A retrospective clinicopathologic study was conducted on 129 postoperative SS patients during 2003–2018. Kaplan-Meier curves and Cox proportional hazards regression (Cox) models were performed to determine the parameters associated with recurrence-free survival (RFS), metastasis-free survival (MFS), and cancer-specific survival (CSS) via univariate and multivariate analysis. The impact of unplanned excision (UE) and residual tumor in re-excision specimens was evaluated. Results: The 3-year RFS, MFS and 5-year CSS were 72 %, 70 %, and 76 %, respectively. Independent factors associated with significantly inferior survival included older age, UE without re-excision, UE with residual tumors, high grade, and deep tumor for RFS, trunk-related tumor, UE without re-excision, UE with residual tumors, and deep tumor for MFS, UE with residual tumors, high grade, and deep tumor for CSS. Re-excision after UE was significantly associated with better RFS (P

Thick cloud and cloud shadow often exist in optical remote sensing (RS) images, which limit the application range of RS data. To solve this challenging problem, we propose a multitemporal-based method of cloud removal to reconstruct the large cloud-cover region in RS images. The method can combine the advantages of high accuracy of deep-learning method and low computation cost of traditional method, and reduce the difficulty of establishing multi-temporal dataset to a certain extent. In the proposed method, the complex problem of thick cloud removal is decomposed into two simple processes: coarse stage and refined stage. In the stage of coarse cloud removal, it is found that the cloud-cover images are distributed more regularly in frequency domain than in the space domain. Therefore, the frequency domain residual module is introduced, and the frequency domain attention module is designed to achieve more focused fusion of three-temporal features. In the stage of refined cloud removal, the refined network is designed to retain the effective features of the coarse stage and further refine the reconstruction result based on the image self-correlation. We carried out sufficient experiments to demonstrate the superiority of the proposed method compared with four classical methods, which include WLR, STSCNN, RFRnet and PSTCR. In qualitative experiments, the proposed method has obvious advantages in information accuracy, visual naturalness and semantic rationality. In quantitative experiments, compared with the best of four comparison methods, average values of Peak Signal to Noise Ratio (PSNR) on three-temporal images are improved by 5.1934 dB, 2.7729 dB and 7.2364 dB respectively, and average values of Correlation Coefficient (CC) on three-temporal images are increased by 0.1048, 0.0110 and 0.0966 respectively. Moreover, the proposed method has an obvious advantage in computation cost because it can reconstruct multiple cloud-cover images synchronously.

Methylmercury (MeHg) is a ubiquitous environmental pollutant, which can cross the placenta and blood brain barrier, thus affecting fetal growth and development. Although previous studies have demonstrated that MeHg induces endoplasmic reticulum (ER) stress in rat cerebral cortex and primary neurons, the role of ER stress in MeHg-induced neurodevelopmental toxicity remains unclear. Here, we used ICR pregnant mice and hippocampal neurons cells (HT22 cells) to investigate the molecular mechanism by which MeHg exposure during pregnancy affects neurodevelopment. We found that prenatal MeHg exposure caused developmental delay in offspring, accompanied with ER stress, cell apoptosis, cell cycle arrest and abnormal DNA methylation. Then, we used ER stress specific inhibitor 4-PBA and CHOP siRNA to investigate the role of ER stress on HT22 cells damage caused by MeHg. The results showed that 4-PBA pretreatment restored MeHg-induced axonal shortening and alleviated apoptosis, cell cycle arrest and DNA methylation. At the same time, the activation of CHOP/c-Jun/GADD45A signaling pathway was inhibited, and the interaction between CHOP and c-Jun was weakened. In addition, CHOP siRNA reduced the expression of c-Jun and GADD45A, and relieved DNA methylation levels to some extent. In summary, our study suggested that ER stress induced by MeHg mediated cell apoptosis and cell cycle arrest, and may affect DNA methylation through activation of CHOP/c-Jun/GADD45A signaling pathway, thus leading to neuronal damage.

African countries continue to import the same agricultural products that they produce locally. Africa's dependence on food aid or importation is often a challenge to understand, considering the immense potential of its land and favorable climate for agriculture. This paper investigates whether China's agricultural support generates positive effects on Africa despite controversial narratives. China's agricultural assistance in Africa has aroused hope and mistrust in African peoples. Most projects that were initiated in Africa flourished at the beginning, only to collapse after the withdrawal of Chinese partners. In addition, China's agricultural aid in Africa is perceived differently inside and outside of China. Western donors accuse China of having hidden motives behind financial aid and donations. African countries consider China's agricultural assistance in Africa to be mutually beneficial. The international collaboration of African agriculture should prioritize sustainable impacts.

Coal is a vital resource in China. With limited coal and the deteriorating eco-environment, it is consensus to take the sustainable development road in coal power exploitation regions. Taking Xilingol League, Inner Mongolia as an example, based on the cascade framework constituted by the “Driver - Pressure - State - Impact - Response” (DPSIR) model, this paper aimed to analyze the ecological carrying capacity at the league (city) scale and at the banner (county) scale contrastively. It found that the overall change trend was a wide “V” type at the league (city) scale. Moreover, the ecological carrying capacity at the banner (county) scale showed that the north was superior to the central and south in 2005, 2011 and 2018. The principal component analysis (PCA) approach had been adopted to analyze the driving factors. Driving factors were economic development and coal and electricity development at the league (city) scale, as well as resource supply and urbanization development at the banner (county) scale. At the league (city) scale, the economic benefits of industrial development had improved people’s living standards, and part of the funds could be used in the investment in ecological restoration, thus improving the ecological carrying capacity; at the banner (county) scale, most counties were still coal-dependent areas, and the number of resources had a more significant impact on the ecological carrying capacity. This study provided ideas for sustainable development in energy exploitation regions at different scales.

Synovial sarcoma is typical aggressive malignant without satisfactory treatment outcome in adult series. Cyclin-dependent kinases (CDKs) in transcription have been considered promising molecular targets in cancer. Among these, CDK7 has been shown to play important roles in the pathogenesis of malignancies. However, the modulation mechanism of CDK7-regulated transcription in synovial sarcoma is unknown. In the present study, we aim to determine the expression and function of CDK7 in the transcription cycle of RNA polymerase II (RNAP II), and evaluate its prognostic and therapeutic significance in synovial sarcoma. Results showed that overexpression of CDK7 correlates with higher clinical stage and grade, and worse outcomes in clinic. High CDK7 expression was confirmed in all tested human synovial sarcoma cell lines and CDK7 was largely localized to the cell nucleus. Downregulation through siRNA or inhibition with the CDK7-targeting agent BS-181 exhibited dose-dependent cytotoxicity and prevented cell colony formation. Western blots demonstrated that inhibition of CDK7 paused transcription by a reduction of RNAP II phosphorylation. Blocking CDK7-dependent transcriptional addiction was accompanied by promotion of apoptosis. Furthermore, the CDK7-specific inhibitor reduced 3D spheroid formation and migration of synovial sarcoma. Collectively, our findings highlight the role of CDK7-dependent transcriptional addiction in human synovial sarcoma. CDK7-specific cytotoxic agents are therefore promising novel treatment options for synovial sarcoma.

Urban agglomeration is an important geospatial unit for achieving SDGs. At the same time, air environment is the key to ensure the healthy life and safe production of mankind. Despite growing concern over the sustainable development in urban agglomeration, there is still little discussion on the coordination between air environment and urbanization within urban agglomeration. Here, the coupling evaluation index system was constructed from four urbanization aspects and three air environment aspects. Based on the real nonlinear internal relationship, the CG-GSO model was used to quantify the urbanization development level in the Beijing(Jing)-Tianjin(Jin)-Hebei(Ji) urban agglomeration from 2003 to 2017. It showed that social urbanization and economic urbanization were mainly the driving factors in single regions, and the urbanization development levels were rising steadily in single regions and urban agglomeration. In the air environment subsystem, the driving factors mainly existed in the air environment pressure and control, while the dynamic trends were fluctuating upward. On urbanization, urban agglomeration was conductive to the realization of regional sustainable development; but on air environment, the result was the opposite. In addition, the dynamic trend of the coupling coordination degree was far worse in urban agglomeration than in single regions. The scenarios were predicted and analyzed on barely and superiorly balanced development during different periods, and strong national pollution control policy was the key to promote the coordinated development between urbanization and air environment in the Beijing(Jing)-Tianjin(Jin)-Hebei(Ji) urban agglomeration. This study provides reference for sustainable development in urban agglomerations.

Summary: Enzyme-metal hybrid catalysts (EMHCs), combining the merits of both enzyme and metal catalysts with high selectivity, efficiency, and broad substrate spectrum, are potentially ideal catalysts for organic synthesis. In recent years, significant progress has been made in the design and application of EMHCs in driving reactions that cannot be easily catalyzed by native enzymes or metals. This review summarizes recent advances in the application of EMHCs in organic synthesis. Examples of enzymatic reactions, selective chemical transformations, and chemoenzymatic cascade reactions catalyzed by biohybrid catalysts are provided, and the structure-function relationships of EMHCs are discussed. Finally, perspectives and limitations in this field are highlighted, and research directions on this exciting theme are proposed.

Lightweight and high-efficiency electromagnetic wave (EMW) absorption sulfur doped graphene (S-GS) was fabricated by reducing and doping graphene oxide (GO) using chemical method. The obtained S-GS exhibits an extremely low reflection loss (RL), wide effective absorption frequency bandwidth at a thin coating thickness (d), light weight and low cost. The minimum RL reaches −52.3 dB at 17.5 GHz with a matching thickness of 1.22 mm. More excitedly, the EM absorption properties could be double-adjusted. By changing the amount of dopant and the thickness of S-GS, The minimum RL touches −52.2 dB at 9.4 GHz and −49.3 dB at 8.5 GHz, respectively, even much lower than most kinds of GS based composites which combined with polymers, magnetic nanoparticles and so on. In a word, S-GS can be promised to be an ideal candidate for constructing novel MW absorber with lightweight, strong absorption characteristics, and thin matching thickness. Keywords: Graphene, Sulfur doped, Microwave absorption, Lightweight

The Beijing(Jing)-Tianjin(Jin)-Hebei(Ji) (JJJ) urban agglomeration is one of the three major urban agglomeration in China, which plays an important strategic role in China’s regional development pattern. Based on the concern of inter-relationship within urban agglomeration, this paper creatively proposed a comprehensive gravity-Gram Schmidt Orthogonalization coupling model (CG-GSO) to quantitatively measure the urbanization development level at different regional scales. Compared with the information entropy method, the CG-GSO model was more reliable and fairer in evaluating the urbanization development of an urban agglomeration in the inter-annual dimension. We used the obstacle factor model to find out important ways to realize sustainable development at different regional scales. According to the trends from 2003 to 2017, the urbanization development levels were predicted by using the gray forecast model at different regional scales during 2018–2030. There was an upward trend in urbanization development at different regional scales. Compared with the internal single regions, a high starting and slow developing trend was in the urban agglomeration. In Beijing, the obstacle factors in urbanization development were in the demographic, social and eco-environmental subsystems. However, the obstacle factors were in the economic, eco-environmental and social subsystems in Tianjin, Hebei and JJJ urban agglomeration. The forecast results showed that urbanization development was going strong in internal regions from 2018 to 2030, while the overall urbanization development was slow in urban agglomeration. Urbanization development should focus on labor redistribution, weakening the boundary on social welfare, improving the efficiency of economic development and paying attention to protecting eco-environment for taking the sustainable development road in the JJJ urban agglomeration.

Summary: Integration of stem cell-derived neurons into the central nervous system (CNS) remains a challenge. A co-culture system is developed to understand whether mouse embryonic stem cell (ESC)-derived spiral ganglion neuron (SGN)-like cells (ESNs) synapse with native mouse cochlear nucleus (CN) neurons. A Cre system is used to generate Cop-GFP ESCs, which are induced into ESNs expressing features similar to auditory SGNs. Neural connections are observed between ESNs and CN neurons 4–6 days after co-culturing, which is stimulated by thrombospondin-1 (TSP1). Antagonist and loss-of-function small hairpin RNA studies indicate that the α2δ1 receptor is critical for TSP1-induced synaptogenesis effects. Newly generated synapse-like structures express pre- and post-synaptic proteins. Synaptic vesicle recycling, pair recording, and blocker electrophysiology suggest functional synaptic vesicles, transsynaptic activities, and formation of glutamatergic synapses. These results demonstrate the synaptogenesis capability of ESNs, which is important for pluripotent ESC-derived neurons to form functional synaptic connections to CNS neurons. : Integration of stem cell-derived neurons (ESNs) into native cellular environment remains a major challenge. This research develops methods to study ESNs and brain nerve cells in the culture dish. ESNs form functional nerve connections with brain nerve cells in morphology, protein expression, and electrophysiology studies. The results are critical for stem cell-based neural pathway regeneration. Keywords: α2δ1 receptor, auditory, cochlear nucleus, embryonic stem cell, regeneration, spiral ganglion neuron, synapse, thrombospondin-1

Ginsenoside Rg1 (Rg1), the major effective component of ginseng, has been shown to have multiple bioactivities, but low oral bioavailability. The aim of this study was to develop a simple, sensitive and rapid high performance liquid chromatography–tandem mass spectrometry (LC–MS/MS) method, which could be used to validate and quantify the concentrations of Rg1 and its metabolites in Sprague-Dawley rat bile, urine, and feces after oral administration (25 mg/kg). Calibration curves offered satisfactory linearity (r>0.995) within the determined ranges. Both intra-day and inter-day variances were less than 15%, and the accuracy was within 80–120%. The excretion recoveries of Rg1, ginsenoside Rh1 (Rh1), and protopanaxatriol (Ppt) in bile, urine, and feces combined were all greater than 70%. The fecal excretion recoveries of Rg1, Rh1, and Ppt were 40.11%, 22.19%, and 22.88%, respectively, whereas 6.88% of Rg1 and 0.09% of Rh1 were excreted in bile. Urinary excretion accounted for only 0.04% of Rg1. In conclusion, the observed excretion profiles for Rg1 and its metabolites after oral administration are helpful for understanding the poor oral bioavailability of Rg1 and will aid further investigations of Rg1 as a pharmacologically active component.

Summary: Agrobacterium tumefaciens transfers oncogenic DNA (T-DNA) and effector proteins into various host plants. T-DNA is generated inside the bacteria and subsequently delivered into plant cells along with the companion effectors VirD2, VirE2, and VirE3. However, it is not clear how the T-complex consisting of VirD2 and VirE2 is assembled inside plant cells. Here, we report that the effector protein VirE3 localized to plant plasma membranes as an anchorage through a conserved α-helical-bundle domain. VirE3 interacted with itself and enabled VirE2 accumulation at host entry sites through direct interactions. VirE3 was critical for VirE2 function in T-DNA protection. Our data indicate that VirE3 functions as a previously unrecognized anchorage protein consisting of membrane-binding, self-interacting, and VirE2-interacting domains. Both VirE2 and VirE3 are conserved among Agrobacterium and rhizobia species but not other organisms, suggesting that a group of anchorage proteins have been generated through evolution to facilitate the nucleoprotein assembly at plant membranes. : Agrobacterium transfers T-DNA and virulence proteins into host cells. Li et al. show that a virulence protein, VirE3, anchors its companion protein VirE2 on host membranes at the entrance to facilitate T-DNA protection. VirE3 represents a class of proteins, anchorage proteins, which are present only in agrobacteria and rhizobia. Keywords: anchorage protein, Agrobacterium, VirE3, VirE2, T-complex, T-DNA, plasma membrane

Alcoholic liver disease is one of the main causes of global liver disease-related deaths, which has emerged as a major health concern for people. In this study, a peptide was prepared from Pinctada Martensii meat (named HPM-3), and its protective effect against alcohol-induced liver damage in mice was investigated. The results indicated that intervention of HPM-3 significantly decreased the levels of transaminase and lactate dehydrogenase in serum, and improved the antioxidant enzyme activities in the liver. Besides, HPM-3 could alleviate alcohol-induced liver injury by mediating Keap1/Nrf2 signaling pathway. Moreover, HPM-3 could preserve the diversity of the gut microbiota, and restore the relative abundance of some microbial communities that were impacted by alcohol intervention. HPM-3 intervention also affected the secretion of L-glutamine and other associated metabolites and mediated the related metabolic pathways. The results would help to clarify the mechanisms of protective effects of marine peptides against alcohol-induced liver injury.

The effective integration of ecosystem services (ESs) and urban growth simulation is crucial for spatial planning decisions that support sustainable and high-quality development. Interactions (i.e., trade-offs, synergies, and irrelevance) generally exist among ESs, however, few studies have been done on the methodology for simulating urban growth that optimizes the synergistic effect of ESs. In this study, the Wuhan metropolitan area was taken as the research area. Four key ESs in 2000, 2010, and 2020: water yield (WY), carbon storage (CS), habitat quality (HQ), and soil retention (SR) were evaluated by using the InVEST model. Then the ordered weighted averaging (OWA) method was applied to optimally allocate ESs trade-offs and identify ecological priority protection areas (EPPAs). Finally, the PLUS model was used to simulate urban growth scenarios under different levels of ESs protection constraints and propose spatial planning strategies. The results revealed the following. (1) From 2000 to 2020, the three ESs (i.e., WY, CS, and HQ) exhibited the spatial distribution pattern with high in the east and low in the west, and higher in the woodland than in built-up areas, except SR, which remained at a weak level throughout the region. (2) During the 20 years, SR and the other three ESs, HQ and CS showed a synergistic relationship; WY exhibited a very weak trade-off with HQ; and WY and CS changed from a very weak trade-off to a weak synergy. (3) Seven ecosystem service patterns (ESPs) were generated, and ESP 5 was compared as the ideal option. The weights of SR, WY, CS, and HQ in this pattern were 0.063, 0.187, 0.313, and 0.437, respectively. (4) By 2030, the built-up areas will exhibit edge expansion and interconnection development trends, with the high-constraint ecological protection scenario as the most compact mode, and the spatial structure of “two districts, one axis, four cities as one” is consistent with the development tendency. Our findings can help to enrich the method of urban growth management oriented by ecological protection, and provide a scientific basis for ecological environment restoration and land use spatial planning practices.

Current knowledge graph-based recommendation methods heavily rely on high-quality knowledge graphs, often falling short in effectively addressing issues such as the cold start problem and heterogeneous noise in user interactions. This leads to biases in user interest and popularity. To overcome these challenges, this paper introduces a novel recommendation approach termed Knowledge-enhanced Perceptive Graph Attention with Graph Contrastive Learning (KPA-GCL), which leverages relational graph convolutional neural networks. The proposed method optimizes the triplet embedding representation of entity-item interactions based on relationships between adjacent entities in a heterogeneous graph. Subsequently, a graph convolutional neural network is employed for enhanced aggregation. Similarity scores from a contrastive view serve as the selection criterion for high-quality embedded representations, facilitating the extraction of refined knowledge subgraphs. Multiple adaptive contrast-loss optimization functions are introduced by combining Bayesian Personalized Ranking (BPR) and hard negative sampling techniques. Comparative experiments are conducted with ten popular existing methods using real public datasets. Results indicate that the KPA-GCL method outperforms compared methods in all datasets based on Recall, NDCG, Precision, and Hit-ratio measures. Furthermore, in terms of mitigating cold start and noise, the KPA-GCL method surpasses other ten methods. This validates the reasonability and effectiveness of KPA-GCL in real-world datasets.

Antarctic krill oil (KO) is known for its poor oxidative stability, especially in emulsion systems. In this experiment, a complex of scallop water-soluble protein-resveratrol (SWPs-RES) was mixed with KO to create high internal phase emulsions (HIPEs) with varying RES ratios. The addition of RES led to noticeable conformational changes in SWPs, including fluorescence bursts, alterations in secondary structure, and modifications in binding motifs. The SWPs-RES complex (1:0.2) demonstrated the most effective free radical scavenging activities (HO: 38.61%, DPPH: 72.49%, ABTS: 85.66%), while the SWPs-RES complex (1:0.025) exhibited the highest emulsifying capacity. Furthermore, HIPEs containing the SWPs-RES complex (1:0.2) displayed improved rheological properties, physical stability, and enhanced oxidative stability against lipid oxidation during storage and simulated in vitro digestion. This study lays a scientific foundation for the utilization of scallop protein and Antarctic krill oil in the food industry.

Precooked treatments are essential in food processing, extending beyond mere sterilization to include the enhancement of nutritional value, flavor profile, and digestibility. This research scrutinizes the effects of water boiling and microwaving on red swamp crayfish, two distinct precooked methodologies. A comparative analytical framework has been employed to assess the efficacy of two precooked methods across a spectrum of quality indicators, including aerobic plate counts, texture, nutrient composition, volatile compound characterization, protein oxidation, and digestive properties. The findings revealed that both water boiling and microwaving effectively reduced bacterial counts to a safe level of 500 CFU/g. Microwave precooking facilitated a moderate oxidation of lipids in crayfish, preferentially liberating flavor compounds, thereby enhancing their sensory attributes. The boiling process imparted a pronounced denaturation to proteins, consequently augmenting the hardness of the crayfish. Notably, the enhanced digestibility of boiled crayfish proteins results from the denaturing action of boiling, promoting efficient protein digestion.

Backgroud: Iron overload is a prevalent condition in the elderly, often associated with various degenerative diseases, including intervertebral disc degeneration (IDD). Nevertheless, the mechanisms responsible for iron ion accumulation in tissues and the mechanism that regulate iron homeostasis remain unclear. Transferrin receptor-1 (TFR1) serves as the primary cellular iron gate, playing a pivotal role in controlling intracellular iron levels, however its involvement in IDD pathogenesis and the underlying mechanism remains obscure. Methods: Firstly, IDD mice model was established to determine the iron metabolism associated proteins changes during IDD progression. Then CEP chondrocytes were isolated and treated with TBHP or pro-inflammatory cytokines to mimic pathological environment, western blotting, immunofluorescence assay and tissue staining were employed to explore the underlying mechanisms. Lastly, TfR1 siRNA and Feristatin II were employed and the degeneration of IDD was examined using micro-CT and immunohistochemical analysis. Results: We found that the IDD pathological environment, characterized by oxidative stress and pro-inflammatory cytokines, could enhance iron influx by upregulating TFR1 expression in a HIF-2α dependent manner. Excessive iron accumulation not only induces chondrocytes ferroptosis and exacerbates oxidative stress, but also triggers the innate immune response mediated by c-GAS/STING, by promoting mitochondrial damage and the release of mtDNA. The inhibition of STING through siRNA or the reduction of mtDNA replication using ethidium bromide alleviated the degeneration of CEP chondrocytes induced by iron overload. Conclusion: Our study systemically explored the role of TFR1 mediated iron homeostasis in IDD and its underlying mechanisms, implying that targeting TFR1 to maintain balanced iron homeostasis could offer a promising therapeutic approach for IDD management. The translational potential of this article: Our study demonstrated the close link between iron metabolism dysfunction and IDD, indicated that targeting TfR1 may be a novel therapeutic strategy for IDD.

Di-(2-ethylhexyl) phthalate (DEHP) might led to chronic and long-term effects on human organs due to its widespread use and bioaccumulation. Despite some cohorts reporting an association between DEHP exposure and BPH, its underlying mechanisms have not been investigated. Our findings indicate that exposure to DEHP or MEHP (main metabolites of DEHP in the human body) leads to increased prostate weights, elevated prostate index, and notable epithelial thickening in rats. It has been observed to promote BPH-1 cell proliferation with effects ranging from low to high concentrations. Transcriptome sequencing analysis of rat prostate tissues identified KIF11 as the key hub gene. KIF11 is highly expressed after DEHP/MEHP exposure, and knocking down of KIF11 inhibits the MEHP-induced promotion of cell proliferation. Exposure to MEHP has been observed to increase the expression of p-GSK-3β and elevate the levels of β-catenin, thereby activating the Wnt/β-catenin signaling pathway. Knocking down of KIF11 significantly inhibits these effects. Histone H3 at Lysine 27 acetylation (H3K27ac) is implicated in the upregulation of KIF11 expression, as evidenced by the addition of the acetylation inhibitor C646. In summary, our findings established that DEHP exposure could promote BPH through H3K27ac regulated KIF11/Wnt/β-catenin signaling pathway.

As traditional recommendation systems ignore the hidden information among different user behaviors (such as clicks, add-to-favorites, add-to-cart, and purchases), this often leads to low accuracy in recommendation results. We propose a meta-graph network recommendation system via multi-behavior encoding (MBGR). Firstly, the graph convolutional neural network is used to extract features from various interactive behavior heterogeneous graphs of user-items for behavior heterogeneous modeling. Secondly, matrix decomposition algorithm and meta-knowledge learner are used respectively to process the semantic information of user behavior, and then attention mechanism is used to learn and distinguish the importance of different types of user item interaction behaviors. Finally, meta-knowledge transfer network is used to combine meta-learning paradigm and neural network framework to establish user target behavior recommendation. We conducted comparative experiments comparing MBGR with 7 different baseline models such as NCF and DMF. Extensive experiments on three real datasets (Tmall, Yelp, ML10M) demonstrate that the proposed MBGR method outperforms the baselines. The performance of MBGR is improved by 10.97 % on average with the metric of HR@10 and 10.96 % with the metric of NDCG@10. Under different top-N value evaluation conditions (HR@10, HR@7, NDCG@10, NDCG@7, etc.), the proposed model's performance can also be improved by more than 10 %, which proves the rationality and effectiveness of the proposed MBGR method.

Liver fibrosis is a reversible pathological process caused by chronic liver damage and a major risk factor for hepatocellular carcinoma (HCC). Hepatic stellate cell (HSC) activation is considered the main target for liver fibrosis therapy. However, the efficiency of this strategy is limited due to the complex microenvironment of liver fibrosis, including excessive extracellular matrix (ECM) deposition and hypoxia-induced imbalanced ECM metabolism. Herein, nilotinib (NIL)-loaded hyaluronic acid (HA)-coated Ag@Pt nanotriangular nanozymes (APNH NTs) were developed to inhibit HSCs activation and remodel the microenvironment of liver fibrosis. APNH NTs efficiently eliminated intrahepatic reactive oxygen species (ROS) due to their inherent superoxide dismutase (SOD) and catalase (CAT) activities, thereby downregulating the expression of NADPH oxidase-4 (NOX-4) and inhibiting HSCs activation. Simultaneously, the oxygen produced by the APNH NTs further alleviated the hypoxic microenvironment. Importantly, the released NIL promoted collagen depletion by suppressing the expression of tissue inhibitor of metalloproteinase-1 (TIMP-1), thus synergistically remodeling the microenvironment of liver fibrosis. Notably, an in vivo study in CCl4-induced mice revealed that APNH NTs exhibited significant antifibrogenic effects without obvious long-term toxicity. Taken together, the data from this work suggest that treatment with the synthesized APNH NTs provides an enlightening strategy for remodeling the microenvironment of liver fibrosis with boosted antifibrogenic activity.

The increasingly improved transportation infrastructure network is the prerequisite and foundation for driving regional development. Scientifically investigating the mechanisms by which the transport superiority degree (TSD) in arid regions affects the health of ecosystems holds significant practical value and importance for ecosystem protection and regional sustainable development. This study investigates the impact of the TSD on the Ecosystem Health Levels (EHL) in Southern Xinjiang, China, an area characterized by its arid climate and ecological vulnerability. Arid regions, such as Southern Xinjiang, often face unique ecological challenges, including water scarcity, soil erosion, and biodiversity loss, which can be exacerbated by human activities including transportation infrastructure development. By leveraging multi-source remote sensing data, this research aims to uncover the intricate dynamics between TSD and EHL in such a fragile ecological context. The findings of this study are critical for understanding the balance between infrastructural development and environmental conservation in arid regions and provide valuable insights for sustainable planning and policy-making in similar ecological settings. The research results show the following: (1) Over the study period, county-scale TSD in southern Xinjiang generally increased, while the conditions of EHL deteriorated, with higher EHL in the eastern counties than in the western counties. (2) Bivariate spatial autocorrelation analysis reveals a significant spatial dependency between county-scale TSD and EHL, with primary relationship types being LH (Low TSD − High EHL) and HL (High TSD − Low EHL). (3) Spatial regression model results indicate that an increase in TSD leads to the deterioration of county-scale EHL, and during the study period, it was observed that the interference of TSD on EHL gradually weakened.

Biogas slurry (BS), waste water of energy production, holds potential as both an irrigation water resource and a liquid fertilizer source. Typically combined with water and mineral fertilizer at specific ratios, BS is applied in fields with drip irrigation systems to enhance crop growth. However, the soil water infiltration process with BS drip irrigation remains poorly understood, mainly owing to the BS's differing characteristics from conventional water sources. This study investigated the morphological characteristics, transport and distribution of water in three ratios of BS-water using a soil column experiment, with the post-irrigation surface soil pores and elements analyzed using electron microscopy and energy spectrum scanning techniques. The findings reveal that BS drip irrigation significantly alters the water morphological characteristics, transport process and distribution compared to conventional water sources. The morphology of the wetting-front changed from nearly ''hemispherical'' to a ''half-pear'' shape with time in BS drip irrigation. The soil-wetting front's vertical distance was notably smaller, approximately 50% of the vertical depth seen with traditional water source drip irrigation, even after redistribution of soil moisture, it was still difficult to reach the depth of the main root zone of most crops. Moreover, The carbon content on the soil surface was increased, ranging between 19.05–47.62% in the BS irrigation scenario, which led to soil pore blockage and a decrease in porosity ranging between 11.99–40.5%. The dynamic viscosity of BS is approximately 50% higher than that of CF.Theses indicate that the combined effect of soil porosity and dynamic viscosity affects the BS infiltration.In conclusion, this paper proposes a BS drip irrigation model with integrated agronomic measures to mitigate the potential adverse effects of BS drip irrigation caused by changes in soil water transportation and distribution.

Deposition of potentially toxic elements (PTEs) in soils due to different types of mining activities has been an increasingly important concern worldwide. Quantitative differences of soil PTEs contamination and related health risk among typical mines remain unclear. Herein, data from 110 coal mines and 168 metal mines across China were analyzed based on 265 published literatures to evaluate pollution characteristics, spatial distribution, and probabilistic health risks of soil PTEs. The results showed that PTE levels in soil from both mine types significantly exceeded background values. The geoaccumulation index (Igeo) revealed metal-mine soil pollution levels exceeded those of coal mines, with average Igeo values for Cd, Hg, As, Pb, Cu, and Zn being 3.02–15.60 times higher. Spearman correlation and redundancy analysis identified natural and anthropogenic factors affecting soil PTE contamination in both mine types. Mining activities posed a significant carcinogenic risk, with metal-mine soils showing a total carcinogenic risk an order of magnitude higher than in coal-mine soils. This study provides policymakers a quantitative foundation for developing differentiated strategies for sustainable remediation and risk-based management of PTEs in typical mining soils.

Community detection in complex networks often suffers from insufficient data and limited utilization of prior knowledge. In this paper we propose “Semi-supervised Generative Adversarial Network” (GANSE), a novel algorithm that integrates Generative Adversarial Networks (GANs) and semi-supervised learning to address these challenges. This method addresses the issues above through a multi-step process. Initially, the network is rewired using vertex similarity metrics, thereby enhancing its structural integrity. Subsequently, a novel generative adversarial network model is designed, and our model facilitates the reconstruction of the network, thereby yielding partitions. Which form the basis for identifying core communities. Additionally, the local clustering coefficient is incorporated as a reward signal and injected into the node selection process. Moreover, isolated nodes are reallocated, ultimately culminating in the derivation of the final community structure. Experimental results on four large real-life datasets demonstrate the clear superiority of the proposed algorithm in terms of F1 and Jaccard metrics when compared to existing algorithms. Notably, our GANSE method outperforms the traditional algorithms in networks with “missing data”. Thus showing its robustness and effectiveness in real-world incomplete datasets. Our findings highlight the potential of GANs and semi-supervised learning for enhancing community detection accuracy in complex networks.

Urban agglomerations are important spatial carriers for regional economic development, but sustained urban expansion has triggered a series of issues such as conflicts between “people and land” and “built-up areas and green spaces”. However, many studies have rarely incorporated spatiotemporal patterns and driving mechanism into scenario simulation of urban growth, and has also overlooked subsequent discussions on the specific integration of simulation results with spatial planning. Considering the case of Min Delta Region, this study analyzes the spatiotemporal evolution of urban expansion based on multi-source data and explore the influential driving mechanism using Random forest algorithm. A combination of Markov and FLUS models was used to dynamically simulate future urban growth patterns. The results showed that 1) From 1995 to 2015, the urban expansion increased by 2.26 times, showing a trend of first accelerating and then slowing down. It was also discovered that there is an anisotropy toward expansion in the trajectory of urban growth. 2) With marginal increase and enclave increase, general urban expansion was observed to be changing from diffusion to agglomeration, and the urban expansion hotspots mainly located in the southeast coastal area. 3) Humanistic links, geographic position, social and economic considerations all played a role in the development of the Min Delta region, and can be summarized as the “four forces” driving mechanism model. 4) By 2035, the trend of continuous development with the Min Delta built-up area as the main body will be basically formed, and incorporating ecological restrictions can effectively restrain urban growth's encroachment into ecological spaces. Based on the simulation results, Min Delta region gradually showing a spatial structure evolution trend from the single core to the dual core mode, and then to the multi-center and networked mode. This study would serve as a multi-angle decision-making reference for regional spatial and ecological protection planning and urban growth management, and as a scientific foundation for high-quality development planning in the region.

Social media platforms have rich text data, which can be used in data mining and analysis. However, given the fact, the evolution speed of natural languages is rapid in social media, and data on social media is very noisy. This is a great challenge to the accuracy of data analysis. To overcome this problem, we propose a topic-relevant content extraction (TRCE) based on deep multiple instance classification, leveraging existing information and hierarchical relationships among texts under a thread on social media as weak supervision to extract topic-strong-relevant data and filter out noise accurately without manually labeling data. The proposed method introduces latent variables, Bernoulli distribution, and variational inference into multiple-instance learning (MIL) to generate pseudo labels. Then we employ a dual-stream neural network with a 3-stage training process to achieve training MIL end-to-end. Experimental results show TRCE has a significant improvement compared with other MIL methods. Meanwhile, it only has a little decrease compared with supervised text classification on accuracy and F1 score. Given the fact TRCE does not need manually labeled data at all, while supervised classification relies heavily on labeled data, TRCE is a competitive method to extracting topic-relevant data and filtering out noise on social media.

Information cascade prediction is a crucial task in social network analysis. However, previous research has only focused on the impact of social relationships on cascade information diffusion, while ignoring the differences caused by the characteristics of cascade information itself, which limits the performance of prediction results. We propose a novel cascade information diffusion prediction model (Topic-HGAT). Firstly, we extract features from different topic features to enhance the learned cascade information representation. To better implement this method, we use hypergraphs to better characterize cascade information and dynamically learn multiple diffusion sub-hypergraphs according to the time process; secondly, we introduce cross-attention mechanisms to learn each other's feature representations from the perspectives of both user representation and cascade representation, thereby achieving deep fusion of the two features. This solves the problem of poor feature fusion effect caused by simply calculating self-attention on learned user representation and cascade representation in previous studies; finally, we conduct comparative experiments on four real datasets, including Twitter and Douban. Experimental results show that the proposed Topic-HGAT model achieves the highest improvements of 2.91% and 1.59% on Hits@100 and MAP@100 indicators, respectively, compared to other 8 baseline models, verifying the rationality and effectiveness of the proposed Topic-HGAT model.

Background: Due to its widespread prevalence, migraine is a common neurovascular condition that has a major impact on people's health and quality of life. Rutaecarpine (RUT) is one of the main effective components of Evodia rutaecarpa, which has a wide range of biological activities. However, the exact mechanism by which RUT improves migraine remain unknown. Purpose: The purpose of this study was to investigate whether RUT improves migraine by inhibiting oxidative stress via activating the Nrf2 antioxidant system through the PTEN/PGK1 signaling pathway. Methods: In vivo, a mouse model of chronic migraine (CM) was established by repeated intraperitoneal injection of nitroglycerin (NTG). After treatment with RUT and Sumatriptan, behavioral tests were performed, followed by measurements of oxidative stress-related indicators in the trigeminal nucleus caudalis, expression of proteins associated with the Nrf2 antioxidant system, and the PTEN/PGK1 pathway. In vitro, PC12 cells were stimulated by 100 μM H2O2 for 24 h to induce oxidative stress, which was then treated with RUT. Furthermore, the role of PTEN in antioxidant stress of RUT was elucidated by knockout of the PTEN gene. Results: The results showed that RUT treatment improved NTG-induced migraine in mice by inhibiting oxidative stress. Importantly, RUT inhibited oxidative stress in NTG-induced mice or H2O2-induced PC12 cells via activating the Nrf2 antioxidant system by inhibiting PGK1 activity through PTEN. These results provide evidence that RUT improves migraine by activation of the Nrf2 antioxidant system through the PTEN/PGK1 pathway and provide new insights into the potential use of RUT as an effective drug development candidate for migraine.

Ecological security pattern (ESP) is an effective way to integrate landscape patterns and ecological processes from the perspective of ecological connectivity. Its scientific construction can promote the coordinated development of social system and ecosystem. However, the construction methods of ESP still need to be explored, most studies assume that different ecological processes do not interfere with each other, and then spatial overlay different types of ecosystem services to determine the ecological source. Metropolitan area is an important space carrier for the high-quality development of Chinese urban agglomeration. Taking Shenzhen metropolitan area as the target area, we identified ecological sources according to the ecosystem services trade-offs and ecological sensitivity assessment system and OWA algorithm, revised the basic resistance surface by using the night light data, and simulated and extracted ecological corridors by combining the minimum cumulative resistance (MCR) model and potential model. We identified 13 important ecological sources with a total area of 9938.23.23 km2. The spatial distribution of them was concentrated in Huizhou and Shanwei, as well as the junctions of mountain woodlands in Huizhou, Heyuan, and Shanwei. Notably, 66 ecological corridors were simulated, including 26 important and 40 general corridors. The ecological corridors generally exhibited a spider web radial distribution with substantial spatial heterogeneity and were densely distributed in the suburbs and northern hilly regions far from urban areas. Aggregated ecological elements were used to build a comprehensive ESP for the Shenzhen metropolitan area, which comprises 13 ecological sources, 26 important ecological corridors, 40 general ecological corridors, 4 security levels of resistance surface, and 24 ecological strategic nodes. This study comprehensively considered the ecosystem services interactions and the environmental sensitivity of ecological processes, provided an effective quantitative framework for constructing the ESP, and the results can provide decision-making reference for territorial ecological protection and restoration planning of Shenzhen metropolitan area.

Text classification is an emerging topic in the field of text data mining, but the current methods of deducing sentence polarity have two major shortcomings: on the one hand, there is currently a lack of a large and well-curated corpus; on the other hand, current solutions based on deep learning are particularly vulnerable to attacks from adversarial samples. To overcome the limitations above, we propose an adversarial training method HNN-GRAT (Hierarchical Neural Network and Gradient Reversal) for text classification. Firstly, A Robustly Optimized BERT Pretraining Approach (RoBERTa) pretraining model is used to extract text features and feature gradient information; secondly, the original gradient information is passed through the gradient reversal layer designed to obtain the inverted gradient information; finally, the original gradient information and the inverted gradient information are fused to obtain the new gradient of the model. HNN-GRAT method are tested on three real datasets and five attack methods, compared with RoBERTa pretraining model, HNN-GRAT improves the robustness accuracy and reduces the probability of the model being attacked. In addition, using six text defense methods, HNN-GRAT achieves the best Boa and Succ (Such as, DeepWordBug attack, for AGNEWS, IMDB, SST-2 datasets, with an improvement of Boa up to 41.50%, 67.50%, 28.15% and Succ drop to 55.90%, 27.45%, 69.89%, respectively).

Ferroptosis (FPT), a novel form of programmed cell death, is characterized by overwhelming iron/reactive oxygen species (ROS)-dependent accumulation of lipid peroxidation (LPO). However, the insufficiency of endogenous iron and ROS level limited the FPT therapeutic efficacy to a large extent. To overcome this obstacle, the bromodomain-containing protein 4 (BRD4)-inhibitor (+)-JQ1 (JQ1) and iron-supplement ferric ammonium citrate (FAC)-loaded gold nanorods (GNRs) are encapsulated into the zeolitic imidazolate framework-8 (ZIF-8) to form matchbox-like GNRs@JF/ZIF-8 for the amplified FPT therapy. The existence of matchbox (ZIF-8) is stable in physiologically neutral conditions but degradable in acidic environment, which could prevent the loaded agents from prematurely reacting. Moreover, GNRs as the drug-carriers induce the photothermal therapy (PTT) effect under the irradiation of near-infrared II (NIR-II) light owing to the absorption by localized surface plasmon resonance (LSPR), while the hyperthermia also boosts the JQ1 and FAC releasing in the tumor microenvironment (TME). On one hand, the FAC-induced Fenton/Fenton-like reactions in TME can simultaneously generate iron (Fe3+/Fe2+) and ROS to initiate the FPT treatment by LPO elevation. On the other hand, JQ1 as a small molecule inhibitor of BRD4 protein can amplify FPT through downregulating the expression of glutathione peroxidase 4 (GPX4), thus inhibiting the ROS elimination and leading to the LPO accumulation. Both in vitro and in vivo studies reveal that this pH-sensitive nano-matchbox achieves obvious suppression of tumor growth with good biosafety and biocompatibility. As a result, our study points out a PTT combined iron-based/BRD4-downregulated strategy for amplified ferrotherapy which also opens the door of future exploitation of ferrotherapy systems.

The problem of detecting key nodes in a network (i.e. nodes with the greatest ability to spread an infection) has been studied extensively in the past. Some approaches to key node detection compute node centrality, but there is no formal proof that central nodes also have the greatest spreading capacity. Other methods use epidemiological models (e.g., the SIR model) to describe the spread of an infection and rely on numerical simulations to find out key nodes; these methods are highly accurate but computationally expensive. To efficiently but accurately detect key nodes, we propose a novel deep learning method called Rank by Graph Convolutional Network, RGCN. Our method constructs a subnetwork around each node to estimate its spreading power; then RGCN applies a graph convolutional network to each subnetwork and the adjacency matrix of the network to learn node embeddings. Finally, a neural network is applied to the node embeddings to detect key nodes. Our RGCN method outperforms state-of-the-art approaches such as RCNN and MRCNN by 11.84% and 13.99%, respectively, when we compare the Kendall’s τ coefficient between the node ranking produced by each method with the true ranking obtained by SIR simulations.

The utilisation of recycled aggregate concrete (RAC) is commonly recognised as one of the most effective means to achieve cleaner and sustainable development in civil engineering. To promote the application of RAC, prestressing technique can be employed to overcome the inherent disadvantages of RAC beams associated with poor cracking resistance and large deflections. However, the research on the prestressed RAC beam is quite limited, and no research has been performed on their long-term behaviour. This paper presents a finite element (FE) analysis for investigating the long-term behaviour of RAC beams prestressed with carbon fibre-reinforced polymer (CFRP) tendons considering the effects of concrete creep, concrete shrinkage, tendon relaxation as well as the cracking and tension stiffening of concrete. Based on the principle of superposition, the variations of the stress and strain with time are considered in the numerical analysis using step-by-step method. The FE model is calibrated with the experimental results obtained from the literature. Based on the validated model, a comprehensive parametric study is performed to investigate the effects of the replacement ratio of recycled coarse aggregate (RCA), concrete strength, reinforcement ratio, prestress level, and sustained load level on the long-term behaviour of prestressed RAC beams. The obtained results demonstrate that increasing the prestress load is an effective way to reduce the long-term deflection of RAC beams, and the use of RAC with low RCA replacement ratio is suggested. Besides, the main causes of the long-term deflection and axial shorting of the CFRP prestressed RAC beams are also studied. This research provides a pioneering and insightful study of the long-term behaviour of CFRP prestressed RAC beams. The obtained results demonstrate that the time-dependent effects should be well considered in the design before the prestressed RAC beam is used in practice.

In view of most current studies on text sentiment classification focus on the deep learning model to obtain the sentimental characteristics of English text. Chinese text sentiment analysis is rarely involved, and only the context information of the statement is considered, but the syntax information of the statement is rarely considered. In this paper, a novel sentiment classification model is proposed (Dependency Tree Graph Convolutional Network, DTGCN) combined Chinese syntactically dependent tree with graph convolution. Firstly, the Bi-GRU (Bi-directional Gated Recurrent Unit) model is used to learn the contextual feature representation of a given text. Secondly, the syntax-dependent tree structure of a given text is constructed, then obtain its adjacency matrix according to the syntax-dependent tree, with the initial features extracted from the bidirectional gate control network, input into the graph convolutional neural network (GCN) to extract the sentimental features of the text; the obtained sentimental characteristics are then input into the classifier SoftMax for text sentimental polarity classification. Finally, the data set is compared with the mainstream neural network model. The experimental results show that the accuracy of the proposed DTGCN model proposed on the data set is 90.51% and the recall rate is 90.34%. Compared with the benchmark models (LSTM, CNN, TextCNN and Bi-GRU), the proposed DTGCN model shows a 4.45% advantage in accuracy. It shows that the proposed DTGCN model can effectively use the grammatical information of Chinese text to mine the hidden relationship in statements, it can improve the accuracy of Chinese text sentiment classification. In addition, the proposed DTGCN model not only improves the performance of sentiment classification in the essay, it also provides a new research method for social network public opinion identification.

Background: In traditional Chinese medicine (TCM) field, the benefits of observational studies was more significant. Whether the evidence from observational studies agreed with RCTs in the field of TCM was still unclear. Methods: A meta-epidemiological study was conducted. Meta-analyses and systematic reviews including cohort studies and case-control studies of TCM were included. Ratio of odds ratio (ROR) of randomized controlled trials and observational studies were calculated individually and intercomparisons were conducted by pool analysis. Results: A total of 11 studies and 30 outcome pairs were included in the pool analysis. Using results from the observational studies as the reference group, the polled ROR comparing randomized controlled trials with observational studies was 1.23 (95% confidence interval 1.05 to 1.44, and 95% prediction interval 0.90 to 1.68). The ROR by subgroup analysis were 1.15 (95% confidence interval 0.96 to 1.38; 95% prediction interval 0.95 to 1.39) and 1.12 (95% confidence interval 0.86 to 1.46; 95% prediction interval 0.51 to 2.47) for cohort studies and case-control studies, respectively. Conclusions: There is difference in pooled results between randomized controlled studies and observational studies on TCM. However, the prediction interval shows the difference is small, which suggests observational studies of TCM can be included in data analysis to provide evidence for TCM. Future studies are needed to verify the above conclusion.