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Abstract Background During wound healing, fibroblast to myofibroblast transition is required for wound contraction and remodeling. While hypoxia is an important biophysical factor in wound microenvironment, the exact regulatory mechanism underlying hypoxia and fibroblast-to-myofibroblast transition remains unclear. We previously found that tetraspanin CD9 plays an important role in oxygen sensing and wound healing. Herein, we investigated the effects of physiological hypoxia on fibroblast-to-myofibroblast transition and the biological function and mechanism of CD9 in it. Methods Human skin fibroblasts (HSF) and mouse dermis wounds model were established under physiological hypoxia (2% O2). The cell viability and contractility of HSF under hypoxia were evaluated by CCK8 and collagen gel retraction, respectively. The expression and distribution of fibroblast-to-myofibroblast transition markers and CD9 in HSF were detected by Western blotting and immunofluorescence. CD9 slicing and overexpressing HSFs were constructed to determine the role of CD9 by small interfering RNA and recombinant adenovirus vector. The association of TβR2 and TβR1 was measured by immunoprecipitation to explore the regulatory mechanism. Additionally, further validation was conducted on mouse dermis wounds model through histological analysis. Results Enhanced fibroblast-to-myofibroblast transition and upregulated CD9 expression was observed under hypoxia in vitro and in vivo. Besides, reversal of fibroblast-to-myofibroblast transition under hypoxia was observed when silencing CD9, suggesting that CD9 played a key role in this hypoxia-induced transition. Moreover, hypoxia increased fibroblast-to-myofibroblast transition by activating TGF-β1/Smad2/3 signaling, especially increased interaction of TβR2 and TβR1. Ultimately, CD9 was determined to directly affect TβR1–TβR2 association in hypoxic fibroblast. Conclusion Collectively, these findings suggest that CD9 promotes TβR2–TβR1 association, thus driving the transition of human dermal fibroblasts to myofibroblast under hypoxia.

Turbine blades are mainly subjected to thermal stress and gradient centrifugal stress in operational conditions. The effects of centrifugal stress on the microstructural degradation of single crystal superalloys under a multiaxial stress state remain unclear. Here, the NiAlReRu component was exposed to high-speed rotation at 20,000 rpm and a high temperature of 1000 °C for 100 h. The analysis indicates that the centrifugal stress causes the distortion of dendrite stems and the aggregation of micropores. The centrifugal stress promotes the formation of interfacial dislocation networks, accompanied by more severe rafting of γ′ phases. These findings provide a comprehensive experimental understanding of the microstructural evolution under simulated turbine blade working conditions, which provides a reference for the subsequent tests under near-service conditions.

Abstract Background Racial and ethnic disparities in mortality persist among US cancer survivors, with social determinants of health (SDoH) may have a significant impact on these disparities. Methods A population-based cohort study of a nationally representative sample of adult cancer survivors, who participated in the US National Health and Nutrition Examination Survey from 1999 to 2018 was included. Sociodemographic characteristics and SDoH were self-reported using standardized questionnaires in each survey cycle. The SDoH was examined by race and estimated for associations with primary outcomes, which included all-cause and cancer-specific mortality. Multiple mediation analysis was performed to assess the contribution of each unfavorable SDoH to racial disparities to all-cause and cancer-specific mortality. Results Among 5163 cancer survivors (2724 [57.7%] females and 3580 [69.3%] non-Hispanic White individuals), only 881 (24.9%) did not report an unfavorable SDoH. During the follow-up period of up to 249 months (median 81 months), 1964 deaths were recorded (cancer, 624; cardiovascular, 529; other causes, 811). Disparities in all-cause and cancer-specific mortality were observed between non-Hispanic Black and White cancer survivors. Unemployment, lower economic status, education less than high school, government or no private insurance, renting a home or other arrangements, and social isolation were significantly and independently associated with worse overall survival. Unemployment, lower economic status, and social isolation were significantly associated with cancer-specific mortality. Compared to patients without an unfavorable SDoH, the risk of all-cause mortality was gradually increased in those with a cumulative number of unfavorable SDoHs (1 unfavorable SDoH: hazard ratio [HR] = 1.54, 95% CI 1.25–1.89; 2 unfavorable SDoHs: HR = 1.81, 95% CI 1.46–2.24; 3 unfavorable SDoHs: HR = 2.42, 95% CI 1.97–2.97; 4 unfavorable SDoHs: HR = 3.22, 95% CI 2.48–4.19; 5 unfavorable SDoHs: HR = 3.99, 95% CI 2.99–5.33; 6 unfavorable SDoHs: HR = 6.34 95% CI 4.51–8.90). A similar trend existed for cancer-specific mortality. Conclusions In this cohort study of a nationally representative sample of US cancer survivors, a greater number of unfavorable SDoH was associated with increased risks of mortality from all causes and cancer. Unfavorable SDoH levels were critical risk factors for all-cause and cancer-specific mortality, as well as the underlying cause of racial all-cause mortality disparities among US cancer survivors.

Abstract Superalloy materials exhibit susceptibility to fracture failures stemming from the influence of thermomechanical factors. To comprehensively understand the fracture mechanisms, material properties, root causes of failure, and the subsequent optimization of alloys, a detailed analysis of the internal fracture process and the morphological traits of the fracture surface is imperative. Traditional analysis of fracture surfaces solely relies on 2D images, thus lacking crucial 3D information. Although in situ experiments can capture the fracture process, their effectiveness is confined to the specimen’s surface, precluding insight into internal changes. Here we introduce an integrated framework encompassing the process of 3D reconstruction of fracture surfaces, aiming to enhance the visual information obtained with micron-level accuracy, visual intuitiveness and sense of depth. Additionally, this framework also facilitates the scrutiny and inference of internal fracture processes. These results demonstrate that under specific service conditions, material deformation fracture probably stems from a combination of surface cracking and internal cracking rather than exclusively one or the other. Overall, our description and analysis of internally initiated cracking due to defects within the specimens can be beneficial in guiding future alloy design and optimization efforts.

Abstract Background The displacement and rotation of the Kirschner wire (K-wire) in the traditional tension band wiring (TBW) led to a high rate of postoperative complications. The anti-rotation tension band wiring (ARTBW) could address these issues and achieve satisfactory clinical outcomes. This study aimed to investigate the biomechanical performance of the ARTBW in treating transverse patellar fracture compared to traditional TBW using finite element analysis (FEA) and mechanical testing. Methods We conducted a FEA to evaluate the biomechanical performance of traditional TBW and ARTBW at knee flexion angles of 20°, 45°, and 90°. Furthermore, we compared the mechanical properties under a 45° knee flexion through static tensile tests and dynamic fatigue testing. The K-wire pull-out test was also conducted to evaluate the bonding strength between K-wires and cancellous bone of two surgical approaches. Results The outcome of FEA demonstrated the compression force on the articular surface of ARTBW was 28.11%, 27.32%, and 52.86% higher than traditional TBW at knee flexion angles of 20°, 45°, and 90°, respectively. In mechanical testing, the mechanical properties of ARTBW were similar to the traditional TBW. In the K-wire pull-out test, the pull-out strength of ARTBW was significantly greater than the traditional TBW (111.58 ± 2.38 N vs. 64.71 ± 4.22 N, P

Abstract Photodetectors (PDs) play a crucial role in imaging, sensing, communication systems, etc. Graphene (Gr), a leading two-dimensional material, has demonstrated significant potential for photodetection in recent years. However, its relatively weak interaction with light poses challenges for practical applications. The integration of silicon (Si) and perovskite quantum dots (PQDs) has opened new avenues for Gr in the realm of next-generation optoelectronics. This review provides a comprehensive investigation of Gr/Si Schottky junction PDs and Gr/PQD hybrid PDs as well as their heterostructures. The operating principles, design, fabrication, optimization strategies, and typical applications of these devices are studied and summarized. Through these discussions, we aim to illuminate the current challenges and offer insights into future directions in this rapidly evolving field.

BackgroundThis systematic review and meta-analysis aim to systematically assess the diagnostic accuracy of lung ultrasound in predicting weaning failure from mechanical ventilation in critically ill patients.MethodsWe searched the relevant literature up to January 2024 in the databases Web of Science, Cochrane Library, Embase, and PubMed. Two researchers independently screened eligible studies and extracted data; disagreements, if any, were resolved through discussion or consultation with a third-party expert. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Statistical analyses were performed using Review Manager version 5.3 and Stata version 18.0, applying bivariate random-effects models to estimate sensitivity, specificity, diagnostic odds ratios, and their 95% confidence intervals, as well as to summarize receiver operating characteristic curves. Inter-study heterogeneity was assessed using the I-squared statistic, and potential sources of heterogeneity were explored by meta-regression analysis. The study follows the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses in reporting.ResultsFourteen studies were included in the systematic review, of which 13 studies (totaling 988 patients) were included in the meta-analysis. The meta-analysis revealed an overall sensitivity of 0.86 (95% confidence interval: 0.77–0.91) and a specificity of 0.75 (95% confidence interval: 0.66–0.83) for lung ultrasound in predicting extubation failure. The area under the receiver operating characteristic curve was 0.87 (95% confidence interval: 0.84–0.89). Meta-regression analysis identified lung ultrasound thresholds, reference standards (extubation outcomes), and study flow and time bias as significant factors influencing diagnostic accuracy.ConclusionThis systematic review and meta-analysis demonstrated that lung ultrasound has high diagnostic accuracy in predicting extubation failure in mechanically ventilated critically ill patients. Despite some study heterogeneity, lung ultrasound proved to be a reliable predictive tool for extubation failure. Future research should focus on standardizing the definition of extubation failure, exploring the impact of different thresholds on the predictive ability of lung ultrasound, and validating its application in various clinical settings to enhance its utility and accuracy in clinical practice.Systematic review registrationThis systematic review and meta-analysis was registered with PROSPERO (registration number: CRD42024555909). The study adhered to the guidelines set by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Details of the PROSPERO protocol can be found in Supplementary Table 1.

BackgroundPeriodontal disease is widespread among pregnant women, and it is possible that taking action to improve oral health conditions can make improvements in adverse pregnancy outcomes. Herein, we summarize the recent evidence using a network meta-analysis to assess the effects of different periodontal treatment intervention strategies on the risk of adverse pregnancy outcomes in pregnant women.Materials and methodsRandomized controlled trials were retrieved from PubMed, Web of Science, Embase, and Cochrane Library databases. After literature screening, data extraction, and quality evaluation of the included literature were performed, the R studio 4.2.2 “netmeta” package was used for the network meta-analysis.ResultsA total of 20 studies were included, and 5 adverse pregnancy outcomes (preterm birth, low birth weight, preterm birth and/or low birth weight infants, small for gestational age, and pre-eclampsia) were considered to examine the effects of different periodontal treatment interventions strategies on the risk of the abovementioned outcome indicators. The results of the network meta-analysis demonstrated that the three periodontal treatment intervention strategies of sub- and/or supra-gingival scaling and root planing + chlorhexidine rinsing (SRP + CR), sub- and/or supra-gingival scaling and root planing+chlorhexidine rinsing + tooth polishing and plaque control (SRP + CR + TP), and sub- and/or supra-gingival scaling and root planing +sonic toothbrush + tooth polishing and plaque control (SRP + ST + TP) reduced the risk of preterm birth [odds ratio (OR) = 0.29, 95% confidence interval (CI) (0.10–0.88), OR = 0.25, 95CI% (0.10–0.63), OR = 0.28, 95CI% (0.11–0.69), respectively]. In addition, two periodontal treatment intervention strategies, SRP + CR and SRP + CR + TP, were effective methods in terms of the risk of preterm birth and/or low birth weight [OR = 0.18, 95CI% (0.06–0.52), OR = 0.31, 95CI% (0.12–0.79)].ConclusionThe available evidence suggests that the risk of preterm birth and preterm birth and/or low birth weight can be reduced with certain periodontal treatment intervention strategies. Future studies should focus on optimizing intervention strategies and the optimal timing for different periods of pregnancy, in order to provide a reference for pregnant women’s healthcare.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=407901, CRD42023407901.

BackgroundPauwels type III femoral neck fractures, as a serious type of femoral neck fractures, have brought about a heavy economic burden on families and society for the high disability rate. Through bibliometric research and visualized analysis, this study aimed at elucidating the global research status of Pauwels type III femoral neck fractures to date, and predicting the future research trends in this field.MethodsPublications and associated information on Pauwels type III femoral neck fractures to date were retrieved from Web of Science Core Collection, and by VoSviewer and R package “bibliometrix”, bibliometric analysis and visual presentation was conducted.ResultsBy retrieval, a total of 98 studies were refinedly extracted, and the volume of publications in this field increased year-over-year. China ranked first in terms of total publication volume and H-Index, with its total citation records second only to the United States. The country with the highest average citation frequency was Switzerland. SHANGHAI JIAO TONG UNIVERSITY was the most productive research institution. Among the authors in this field, Li, Jiantao had published the most researches. INJURY INTERNATIONAL JOURNAL OF THE CARE OF THE INJURED and JOURNAL OF ORTHOPAEDIC TRAUMA were the two magazines with the highest publication volume, total citation records, and H-index. According to keywords co-occurrence analysis, the research content in the past 24 years is mainly divided into four different dimensions. Finite element analysis, femoral neck system, medial buttress plate, cannulated screws, hip screw, open reduction, complications are hot topics for future research.ConclusionsAccording to the global trends analysis of publications production, Pauwels type III femoral neck fractures are receiving increasing attention and input from scholars. China has made the greatest scientific research contribution among countries, but its academic quality should be improved further. The modified therapeutic methods designed for addressing the complications of traditional internal fixation for Pauwels III femoral neck fractures will be the future research hotspot.

IntroductionThis study investigates the dynamic shifts in soil bacterial communities within a Salix matsudana afforested ecosystem transitioning from agricultural land. Understanding the temporal variability in bacterial diversity and community structures is crucial for informing forest management and conservation strategies, particularly in regions undergoing afforestation.MethodsWe employed high-throughput sequencing across three distinct months (August, September, and October) to analyze the temporal variability in bacterial community composition and diversity. Network analysis was utilized to identify keystone species and assess community stability under varying environmental conditions, including fluctuations in temperature and precipitation.ResultsWe uncover significant temporal variability in bacterial diversity and community structures, which are closely tied to fluctuations in temperature and precipitation. Our findings reveal the abundance of the dominant bacterial phyla, such as Actinobacteria and Proteobacteria, which did not change overall, highlighting the stability and resilience of the microbial community across seasonal transitions. Notably, the increasing similarity in community composition from August to October indicates a reduction in species turnover, likely driven by more homogeneous environmental conditions. Through comprehensive network analysis, we identify the pivotal role of keystone species, particularly the human pathogen Nocardia, in maintaining community stability under reduced soil moisture. The observed variations in community connectivity underscore the microbial community’s resilience and adaptability to seasonal shifts, with higher stability in August and October contrasting with the instability observed in September.DiscussionThese results underscore the complex interplay between stochastic and deterministic processes in bacterial community assembly, significantly shaped by prevailing environmental conditions. The insights gained from this research have far-reaching implications for forestry management and conservation strategies, particularly in regions undergoing similar afforestation efforts.

In Northeast China, permafrost is controlled by a combination of biotic, climatic, physiographic, and anthropogenic factors. Due to the complexity of these governing or influencing factors, it is challenging to exactly describe the features of the Xing’an permafrost in Northeast China. By integrating remote sensing (RS) and geographic information system (GIS) technologies, we have quantified these influencing factors of permafrost changes as an important approach to understanding the nature of latitudinal and mountain permafrost in Northeast China at the mid-latitudes in the Northern Hemisphere. In this study, we combine Geographical Detector (Geodetector) model, trend analysis, and multi-source RS data to quantify the controlling or influencing factors of permafrost thermal state and of permafrost changes, and explain the interactions among permafrost, environment, and climate. The results indicate that, at the regional scale, changes in the thermal state of permafrost are primarily governed or influenced by mean annual land surface temperature (MALST), precipitation, and snow cover duration (SCD). Topographic factors also affect the spatial patterns of permafrost development. Additionally, in the context of climate warming, the insulation effect of snow cover on the permafrost is weakened, or has been weakening. Moreover, the interactive effects among various factors significantly enhance their explanatory power for changes in the thermal state of permafrost. The study emphasizes the complexity of the interactions among permafrost, climate, and the environment, and highlights the significance of understanding these interactions for regional socio-economic development, ecological management, carbon pool stabilization, and research on future climate change in Northeast China.

This work investigates the effect of Ru on the precipitation of the topologically close-packed (TCP) phase from the γ-γʹ microstructural degradation analysis. The addition of Ru is found to delay the precipitation time of the TCP phase from 100 h to 1000 h after thermal exposure at 950 °C. The effect of Ru on the γ-γʹ microstructural degradation is also investigated. Adding Ru slightly decreases the coarsening rate of the γʹ size and promotes the reduction of the volume fraction of γʹ phase. The reduction of the volume fraction of γʹ phase causes the ''inverse partitioning'' effect and increases the solution limit of the TCP forming element in the matrix. These help inhibit the precipitation of the TCP phases in Ni-based single crystal superalloys during thermal exposure.

Abstract Extracellular matrix (ECM) stiffening is a typical characteristic of cartilage aging, which is a quintessential feature of knee osteoarthritis (KOA). However, little is known about how ECM stiffening affects chondrocytes and other molecules downstream. This study mimicked the physiological and pathological stiffness of human cartilage using polydimethylsiloxane (PDMS) substrates. It demonstrated that epigenetic Parkin regulation by histone deacetylase 3 (HDAC3) represents a new mechanosensitive mechanism by which the stiffness matrix affected chondrocyte physiology. We found that ECM stiffening accelerated cultured chondrocyte senescence in vitro, while the stiffness ECM downregulated HDAC3, prompting Parkin acetylation to activate excessive mitophagy and accelerating chondrocyte senescence and osteoarthritis (OA) in mice. Contrarily, intra-articular injection with an HDAC3-expressing adeno-associated virus restored the young phenotype of the aged chondrocytes stimulated by ECM stiffening and alleviated OA in mice. The findings indicated that changes in the mechanical ECM properties initiated pathogenic mechanotransduction signals, promoted the Parkin acetylation and hyperactivated mitophagy, and damaged chondrocyte health. These results may provide new insights into chondrocyte regulation by the mechanical properties of ECM, suggesting that the modification of the physical ECM properties may be a potential OA treatment strategy.

This work conducted tensile experiments from room temperature to 1100 °C on a new fourth-generation single crystal superalloys along [001]. The effect of temperature on the tensile properties and deformation mechanism of the alloy were investigated. Results showed that the yield strength of the alloy reached its peak value (1017 MPa) at 850 °C. From room temperature to 760 °C, the occurrence of stacking faults (SFs) was observed in the γ matrix channel. From the room temperature to 850 °C, the occurrence of stacking faults was observed in the γʹ phase. The interface dislocation networks appeared at 980 °C and above. At room temperature, stacking faults sheared γʹ phase, in which three types of stacking faults occurred, which controlled deformation process. The main deformation mechanism from 650 °C to 850 °C was the shearing of γʹ phase by stacking faults and super dislocations, and at 850 °C, stacking faults in different directions of propagation corresponded to the formation of Lomer-Cottrell locks (L-C locks), which greatly increased the yield strength. At 980 °C and 1100 °C, dislocation climbing and bypassing and anti-phase boundary (APB) coupled dislocation pair shear γʹ phase became the main deformation mechanism.

Low cost stainless steels (SS) with high performance, especially with good mechanical properties and oxidation resistance at high temperature, are continuous pursuit for manufacture industry. To achieve good combination of mechanical properties, oxidation resistance and cost, nanoprecipitate strengthened alumina forming austenitic (NSAFA) SS have been developed by removing expensive elements such as Nb and Ta, lowering Ni concentration in Fe–26Ni–15Cr-1.5Mo-(2.8–3.5)Al-(1–2.35)Ti (wt.%) alloys. Microstructure investigation demonstrates that NSAFA SS are strengthened by L12 structured (Ni, Fe)3(Al, Ti) γ′ nanoprecipitates with area fraction around 25%–40%. Oxidation experiments (750 °C/480 h) in air demonstrates that all the NSAFA steels have alumina forming ability. In addition, it is found that Al and Ti are all necessary for γ′ nanoprecipitates, but additional Al and Ti facilitate formation of the Ni2AlTi phase. To ensure high volume fraction of γ′ nanoprecipitates and reduce Ni2AlTi phase, 2.8–3.0 wt% Al and 1.8–2.35 wt% Ti are preferred. Benefit from the high volume fraction of γ′ nanoprecipitates, yield strength of NSAFA steels is much higher than that of Fe-base superalloy (e.g., A286) and can compare to some Ni-base superalloys (e.g., Hayners 282 & Inconel 740H).

Abstract Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors, we utilize a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identify a preponderance differential Cytosine-phosphate-Guanine site hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like histone deacetylase 4 and insulin-like growth factor 1 receptor, are associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric central nervous system tumors.

The isothermal oxidation behavior of fourth-generation nickel-based single crystal superalloys with 2-4 wt% Mo content were studied at 900 °C and 1100 °C.At 1100 °C, the oxide layer can be divided into three layers, the outermost layer is Ni–Co oxide, the intermediate layer is spinel and heavy metal element oxide, and the innermost layer is Al2O3 layer. With the increase of Mo content, the oxide layer accumulates more growth stress, resulting in more severe spallation. At 900 °C, the oxide layer can be divided into three layers, the outermost layer is Ni–Co oxide, the intermediate layer is Cr–Al oxide, and the innermost layer has obvious selective oxidation of Al element. The volatilization of oxide promotes the diffusion of O into the matrix, thus promoting the oxidation of heavy metal elements in the matrix. With the increase of Mo content, this phenomenon is more obvious. The results show that the increase of Mo content of alloy promotes the oxidation of Mo, W and Re elements, which let the volatilization of the oxide and the spalling of the oxide layer more severe, thus decreasing the oxidation resistance of the alloy.

ABSTRACT: Mesenchymal stem cells can differentiate into adipocyte precursor cells, and the balance of stem cell differentiation determines the quantity of adipocytes. Early-stage adipose tissue expression profiling revealed abnormal expression of DKK3 in the high-fat group. Moreover, DKK3 is enriched in the Wnt/β-catenin signaling pathway, and studies have shown that DKK3 can serve as a gene involved in early regulation of adipogenesis. Therefore, this study focuses on exploring how DKK3 regulates the molecular mechanism of adipocyte differentiation through the Wnt/β-catenin signaling pathway. In this experiment, the role of DKK3 in the differentiation of bone marrow mesenchymal stem cells into adipocyte precursors was validated using in vitro cultured chicken bone marrow mesenchymal stem cells. The results showed that overexpression of DKK3 led to a significant downregulation of Wnt/β-catenin signaling pathway-related marker gene expression (P < 0.05), a significant upregulation of adipogenic differentiation-related genes (P < 0.05), an increase in lipid droplet content, a significant increase in OD value (P < 0.05), a significant upregulation of mitochondrial oxidative respiratory-related marker gene expression (P < 0.05), and a significant downregulation of mitochondrial autophagy-related marker genes (P < 0.05). Conversely, the results were opposite after interfering with DKK3 gene expression. In this study, 4 SNP sites, including g.8419139, g.8419556, g.8419560, and g.8419598, were detected in the 7th exon of DKK3, among which the g.8419598 (C > T) site was significantly correlated with abdominal fat weight and abdominal fat rate in 100-day-old Ma Huang chickens (P < 0.001).

With the increasing of aging population and the consumption of high-fat diets (HFD), the incidence of Alzheimer’s disease (AD) has skyrocketed. Natural antioxidants show promising potential in the prevention of AD, as oxidative stress and neuroinflammation are two hallmarks of AD pathogenesis. Here, we showed that quinic acid (QA), a polyphenol derived from millet, significantly decreased HFD-induced brain oxidative stress and neuroinflammation and the levels of Aβ and p-Tau. Examination of gut microbiota suggested the improvement of the composition of gut microbiota in HFD mice after QA treatment. Metabolomic analysis showed significant increase of gut microbial tryptophan metabolites indole-3-acetic acid (IAA) and kynurenic acid (KYNA) by QA. In addition, IAA and KYNA showed negative correlation with pro-inflammatory factors and AD indicators. Further experiments on HFD mice proved that IAA and KYNA could reproduce the effects of QA that suppress brain oxidative stress and inflammation and decrease the levels of of Aβ and p-Tau. Transcriptomics analysis of brain after IAA administration revealed the inhibition of DR3/IKK/NF-κB signaling pathway by IAA. In conclusion, this study demonstrated that QA could counteract HFD-induced brain oxidative stress and neuroinflammation by regulating inflammatory DR3/IKK/NF-κB signaling pathway via gut microbial tryptophan metabolites.

ABSTRACTIt is crucial to mangrove management by understanding its conservation efforts. This study proposed a spatiotemporal analysis model to evaluate mangrove changes in the Guangxi Beibu Gulf (GBG) and the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), focusing on past, present, and future dynamics. The analysis covered mangrove loss, stability, and gain from 1990 to 2020 and provided projections for 2020 to 2035 across multiple scenarios. Then further assessed the efficacy of past mangrove conservation with the Sustainable Development Goals (SDGs) and the prospective conservation potential with the Global Mangrove Alliance Goal (GMAG) indicators. Findings indicate that by 2020, the GBG and GBA had 81.65 km² and 40.92 km² of mangroves, respectively, with increases of 31.51% and 164.09% since 1990. Projected expansions are anticipated under all three scenarios during 2020- 2035, with the highest growth projected under the trend continuation scenario, followed by ecological restoration and protection, and economic development. While mangrove protection in these regions met SDG targets 6.6.1 and 14.5.1 from 2015 to 2020, the 80% conservation milestone set by GMAG for 2030 was not achieved, necessitating the establishment of nature reserves. The study offers novel insights for sustainable governance of mangrove forests at local, regional, and global levels.

IntroductionIndividual leaves in the image are partly veiled by other leaves, which create shadows on another leaf. To eliminate the interference of soil and leaf shadows on cotton spectra and create reliable monitoring of cotton nitrogen content, one classification method to unmanned aerial vehicle (UAV) image pixels is proposed.MethodsIn this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the performance of the model.Results(i) after the spectrum were preprocessed by gaussian filter (GF), SG smooth (SG), and combination of GF and SG (GF&SG), the significant relationship between VI and LNC was greatly improved, so the Standard deviation of datasets was also decreased greatly; (ii) the image pixels were classified twice sequentially. Following the first classification, the influence of soil on vegetation index (VI) decreased. Following secondary classification, the influence of soil and LS to VI can be minimized. The relationship between the VI and LNC had improved significantly; (iii) After classifying the image pixels, the VI of 2-3, 2-4, and 2-5 have a stronger relationship with LNC accordingly. Correlation coefficients (r) can reach to 0.5. That optimizes monitoring performance when combined with GF&SG to predict LNC, support vector machine regression (SVMR) has the better performance, R2, RMSE, and MAE up to 0.86, 1.01, and 0.71, respectively. The UAV image classification technique in this study can minimize the negative effects of soil and LS on cotton spectrum, allowing for efficient and timely predict LNC.

Abstract Grasping the physical interactions when two tropical cyclones (TCs) (TC) are in proximity is essential for boosting the accuracy of TC forecasts. This study dissects an uncommon scenario wherein the merging with Tropical Depression 13 W significantly hastened the rapid weakening of Super Typhoon Hinnamnor (2022), utilizing comparative experiments with and without 13 W in simulation's initial field. The findings reveal strong correlations between the merger, amplified environmental vertical wind shear (VWS), and Hinnamnor's consecutive weakening, unfolding in two stages— “top‐down” (Stage 1) and “bottom‐up” weakening (Stage 2) stage. In Stage 1, 13 W led to downdrafts from upper level, hindering the eyewall updrafts and weakening the warm core. In Stage 2, 13 W merged into Hinnamnor's outer rainband, introduced low‐entropy air into the boundary layer and also vied with the eyewall for energy. This research emphasizes that even minor, less‐intense vortices can have profound impacts on the rapid intensity change in TCs.

Electrocardiogram (ECG) is a powerful tool to detect cardiovascular diseases (CVDs) and health conditions. We proposed a new method for evaluating ECG for efficient medical diagnosis in daily life. By splitting the signal according to the cardiac activity cycle, the periodic split attractor reconstruction (PSAR) method is proposed with time embedding, including three types of splitting methods to show its chaotic domain characteristics. We merged the CVDs dataset and the obstructive sleep apnea syndrome (OSAS) first-lead ECG signal dataset to validate the performance of PSAR for diagnosis and health monitoring using PSAR density maps as SE-ResNet input features. PSAR under 3 split methods showed different sensitivities for different CVDs. While in OSAS monitoring, PSAR showed good ability to recognize sleep abnormalities.

The energy consumption of a large-scale industrial hydrogen liquefaction system is primarily determined by the hydrogen turbo-expanders. To estimate the performance of the expander quickly, a one-dimensional preliminary design is conducted. This paper aims to identify dependable one-dimensional design codes for hydrogen turbo-expanders by employing three mean-line design approaches. The precision of the loss models is validated through hydrogen experiments, revealing a maximum deviation of less than 10 %. This study presents a summary of the optimal design parameter ranges for achieving high-efficiency hydrogen turbo-expanders. One common factor among the three methods is the characteristic ratio, which exhibits an optimal value range of 0.67–0.71. Furthermore, the three methods are applied to design hydrogen turbo-expanders of three different sizes, and the compositions of losses are thoroughly analyzed. The PR method exhibits the highest losses related to geometry sizes, while the VC method shows the greatest losses associated with velocities. The SS-CR method with moderate parameters is recommended for hydrogen turbo-expander design due to its superior maximum efficiency design limit and convenience. The two initial design parameters (specific speed and characteristic ratio) of this method play crucial roles in determining the scalability and efficiency of the hydrogen turbo-expander design.

IntroductionRapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status.MethodsIn this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multi-vegetation index fusion in the estimation of cotton LAI.ResultsThe RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively. DiscussionAppropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications.

The turbo-expander is the crucial equipment for obtaining refrigeration capacity and power in the system, and the aerodynamic performance is vital for the system. Spontaneous condensation of moist air in a high-speed turbo expander has a significant effect on the efficiency and output power. Based on the model of non-equilibrium spontaneous condensation, the flow of moist air in a high-speed turbo expander was studied, which is verified by the experiments. The meridional surface shape affects the variation of the flow passage cross-sectional area. The impeller with a uniform variation in cross-sectional area has the highest efficiency. Based on the optimal meridional surface shape, the condensation flow of moist air with different impeller blade diversion curvature was compared. The optimal curvature can increase the efficiency of the moist air turbo-expander by 2.83 %, and the optimized impeller has 3.53 % higher output power. The largest curvature of the blade leads to an efficiency reduction by 3.06 %, but it has little droplet condensation in the impeller, which can help to avoid damage of high-speed blades.

Re-epithelialization is an important step in skin wound healing, referring to the migration, proliferation, and differentiation of keratinocytes around the wound. During this process, the edges of the wound begin to form new epithelial cells, which migrate from the periphery of the wound towards the center, gradually covering the entire wound area. These newly formed epithelial cells proliferate and differentiate, ultimately forming a protective layer over the exposed dermal surface. Wound endogenous electric fields (EFs) are known as the dominant factor to facilitate the epidermal migration to wound center. However, the precise mechanisms by which EFs promote epidermal migration remains elusive. Here, we found that in a model of cultured keratinocyte monolayer in vitro, EFs application reversed the differentiation of cells, as indicated by the reduction of the early differentiation markers K1 and K10. Genetic manipulation confirmed that EFs reversed keratinocyte differentiation through down-regulating the E-cadherin-mediated adhesion. By RNA-sequencing analysis, we screened out Snail as the transcription suppressor of E-cadherin. Snail knockdown abolished the down-regulation of E-cadherin and the reversal of differentiation induced by EFs. KEGG analysis identified PI3K/AKT signaling for Snail induction under EFs. Inhibition of PI3K by LY294002 diminished the EFs-induced AKT activation and Snail augmentation, largely restoring the level of E-cadherin reduced by EFs. Finally, in model of full-thickness skin wounds in pigs, we found that weakening of the wound endogenous EFs by the direction-reversed exogenous EFs resulted in an up-regulation of E-cadherin and earlier differentiation in newly formed epidermis in vivo. Our research suggests that electric fields (EFs) decrease E-cadherin expression by suppressing the PI3K/AKT/Snail pathway, thereby reversing the differentiation of keratinocytes. This discovery provides us with new insights into the role of electric fields in wound healing. EFs intervene in intracellular signaling pathways, inhibiting the expression of E-cadherin, which results in a lower differentiation state of keratinocytes. In this state, keratinocytes exhibit increased migratory capacity, facilitating the migration of epidermal cells and wound reepithelialization.

The expansion applications of semiconducting polymer dots (Pdots) among optical nanomaterial field have long posed a challenge for researchers, promoting their intelligent application in multifunctional nano-imaging systems and integrated nanomedicine carriers for diagnosis and treatment. Despite notable progress, several inadequacies still persist in the field of Pdots, including the development of simplified near-infrared (NIR) optical nanoprobes, elucidation of their inherent biological behavior, and integration of information processing and nanotechnology into biomedical applications. This review aims to comprehensively elucidate the current status of Pdots as a classical nanophotonic material by discussing its advantages and limitations in terms of biocompatibility, adaptability to microenvironments in vivo, etc. Multifunctional integration and surface chemistry play crucial roles in realizing the intelligent application of Pdots. Information visualization based on their optical and physicochemical properties is pivotal for achieving detection, sensing, and labeling probes. Therefore, we have refined the underlying mechanisms and constructed multiple comprehensive original mechanism summaries to establish a benchmark. Additionally, we have explored the cross-linking interactions between Pdots and nanomedicine, potential yet complete biological metabolic pathways, future research directions, and innovative solutions for integrating diagnosis and treatment strategies. This review presents the possible expectations and valuable insights for advancing Pdots, specifically from chemical, medical, and photophysical practitioners’ standpoints.

Rapid urbanization has led to changes in the urban land surface thermal environment. However, there are still much unknown about the urban land surface thermal conditions in permafrost regions. Permafrost is a unique geological environment, changes in the urban land surface thermal environment may trigger geological disasters caused by permafrost degradation. This study utilized remote sensing data and geographic detectors to identify the dynamic changes in land surface temperature (LST) and land use/land cover (LU/LC) in Yakutsk, as well as the potential factors contributing to LST variations. Between 1992 and 2020, the built-up area in Yakutsk increased by 36%, and the annual average LST in Yakutsk has risen by 6.67 °C, accompanied by an expansion of high-temperature areas. Despite ongoing greening efforts, rapid urbanization poses a threat to these green spaces. Changes in the normalized difference built-up index (NDBI) and land use transfer (LDT) were identified as the primary drivers of urban LST changes. By integrating geographic detector technology and artificial neural network models, we optimized the selection of input factors in the prediction model and used it to explore the future changes in LST in Yakutsk. The average LST in Yakutsk is expected to reach 23.4 °C and 25.1 °C in 2030 and 2040, respectively, with a further increase in high-temperature areas. This study provides a reference for ecological, hydrological, and geological assessments of cities in permafrost regions.

Fabricating SiC matrix with controllable Si/C ratio for C/C–SiC composites via precursor infiltration and pyrolysis is difficult to realize due to the absence of suitable precursors. Here, SiC precursors with Si-rich (Si/C = 1.23), near-stoichiometric (Si/C = 1.01), and C-rich (Si/C = 0.88) pyrolyzed ceramics at 1200 ℃ were successfully synthesized by the novel modification of polymethysilane. The structure and ceramization of synthesized SiC precursors were studied, and C/C–SiC composites with similar Si/C ratio in SiC matrix were also fabricated. The results revealed that the defects and Young’s modulus of SiC matrix and the thermal residual stress on pyrolytic carbon coated carbon fiber were determined by the Si/C ratio. As a result, the Si/C ratio exerted significant effects on the crack deflection behavior and the mechanical properties of the composites. The composites with near-stoichiometric SiC matrix showed excellent mechanical properties because of the effective crack deflection and the moderate Young’s modulus of their SiC matrix. The flexural strength, flexural modulus, and compressive strength were 423±27 MPa, 33.41±4.52 GPa, and 393±8 MPa, respectively. Meanwhile, after a heat treatment at 1600 ℃, the role of carbon fiber toughening was improved and accompanied by a decrease in mechanical properties for all composites, which was attributed to the increased defects in SiC matrix and the damaged interfaces by the thermal residual stress.

Abstract The presence of ice-layers in the subgrade soils makes the hydrothermal state of road subgrade built in island permafrost regions more susceptible to external environmental influences. In order to deepen the study of the ice-layers subgrade, a hydrothermal study of subgrade under constant temperature and dynamic loading was carried out. It was found that dynamic loading can change the temperature, moisture and pore water pressure distribution. Under dynamic loading, the hydrothermal and pore water pressure state of the soil in the upper part of the ice layer changed significantly at the beginning of the test. The application of dynamic loads alters the spatial distribution of pore water pressure in the soil, resulting in pressure differences between different areas, which affects the migration of moisture and ultimately leads to the formation of areas with higher moisture content in the area below the load. However, the reduction in soil temperature will weaken the effect of the load, therefore, the temperature of the soil should be controlled for frozen subgrade with ice-layers to prevent the accumulation of moisture in the soil.

Intelligent inspection has become an important trend in the development of substation operation and maintenance technology, and fast and accurate point cloud segmentation of the large amount of point cloud data collected in the process of intelligent inspection is an important basis for effective fault localization and risk early warning of electricity equipment. However, the existing point cloud segmentation methods have problems such as messy spatial information, low classification accuracy and unstable model. Therefore, a new point cloud component segmentation method is proposed by combining the local and global information characteristics of the point cloud and applied to the diagnosis of electricity equipment. Firstly, apply dynamic-k neighbor retrieval for each point to facilitate subsequent local feature extraction. Then, the input point cloud data is processed by graph convolution to capture the local characteristics of the data; then, the global information is extracted by cyclic use of a set abstraction layer, and the two information features are fused and the model is trained to obtain a more powerful and efficient point cloud component segmentation model. Finally, experimental validation was carried out using point cloud data of lightning arrester devices in electricity equipment. The experimental results show that the accuracy and average mIoU of the part segmentation results of this method reaches more than 90%. With 2%-4% improvement and 4-10 times stability improvement compared to traditional point cloud processing methods, this method has better stability and accuracy in point cloud part segmentation tasks. This method not only provides an effective method for the segmentation of 3D point cloud data, but also provides strong technical support for the subsequent fault diagnosis and detection work.

Semisupervised change detection (CD) methods have garnered increasing attention due to their capacity to alleviate the dependency of fully-supervised methods on a large number of pixel-level labels. These methods predominantly leverage generative adversarial network architecture and consistency regularization technology. However, they encounter challenges associated with background noise from cross-temporal images. In this article, we propose a novel multilevel consistency-regularization-based semisupervised CD approach that incorporates Fourier-based frequency transformation and a reliable pseudolabel selection scheme. Specifically, we replace the low-frequency spectrum of one temporal image with a frequency domain transformation derived from the corresponding image in the same bitemporal remote sensing image pair, enhancing the model's capability to discern meaningful changes amidst background noise, thereby contributing to more robust CD. Furthermore, excessively high pseudolabel thresholds in consistency regularization methods may result in the underutilization of valuable unlabeled data. To address this issue, we design a straightforward sigmoid-like function to dynamically adjust the selection threshold for the reliable pseudolabel selection scheme. This strategy takes into consideration the learning status throughout the entire training process, ensuring more effective utilization of unlabeled information. We demonstrate significant performance improvements across three widely-used public datasets, namely, LEVIR-CD, WHU-CD, and CDD. Notably, on the three datasets with only 1% labeled data, our method achieved an $\text{IoU}^{c}$ of 71.29%, 63.90%, and 51.00%, outperforming existing state-of-the-art methods by 2.84%, 1.21%, and 0.98%, respectively. These results robustly substantiate the effectiveness of our approach, showcasing its potential in scenarios where labeled data is limited.

Abstract The green regeneration of urban inventory is now much more viewed as a strategy to create “green finance”, in contrast to the urban renewal movement that was started in the 1950s and focused on the physical renovation of the built environment. This study draws on evidence-based theory to examine the spatial forms of residential communities in 36 urban districts of China. From a morphological perspective, it summarizes the spatial forms of residential environments at the level of buildings and microdistricts. The results indicate that in urban areas, the aspect ratio and shape coefficient of residential buildings have a significant impact on household energy consumption, and an appropriate increase in building density will help reduce carbon emissions. In conclusion, this study emphasizes the role of carbon reduction as a kind of “green finance” and provides a reference for subsequent urban renewal decisions in combination with China’s actual cases.

Abstract Background Among men, prostate cancer (PCa) is the second most common cancer and the second leading cause of cancer death. Etiologic factors associated with both prostate carcinogenesis and somatic alterations in tumors are incompletely understood. While genetic variants associated with PCa have been identified, epigenetic alterations in PCa are relatively understudied. To date, DNA methylation (DNAm) and gene expression (GE) in PCa have been investigated; however, these studies did not correct for cell-type proportions of the tumor microenvironment (TME), which could confound results. Methods The data (GSE183040) consisted of DNAm and GE data from both tumor and adjacent non-tumor prostate tissue of 56 patients who underwent radical prostatectomies prior to any treatment. This study builds upon previous studies that examined methylation patterns and GE in PCa patients by using a novel tumor deconvolution approach to identify and correct for cell-type proportions of the TME in its epigenome-wide association study (EWAS) and differential expression analysis (DEA). Results The inclusion of cell-type proportions in EWASs and DEAs reduced the scope of significant alterations associated with PCa. We identified 2,093 significantly differentially methylated CpGs (DMC), and 51 genes associated with PCa, including PCA3, SPINK1, and AMACR. Conclusions This work illustrates the importance of correcting for cell types of the TME when performing EWASs and DEAs on PCa samples, and establishes a more confounding-adverse methodology. We identified a more tumor-cell-specific set of altered genes and epigenetic marks that can be further investigated as potential biomarkers of disease or potential therapeutic targets.

Sun-induced chlorophyll fluorescence (SIF) has been a promising indicator of plants’ physiological status, but its response to physiological changes under the verticillium wilt (VW) stress of cotton plants is complicated by concurrent nonphysiological changes. The relative contributions of the above two components to SIF variations are unclear at different VW stress severities, hindering the accurate diagnosis of VW levels. Therefore, the objective of this study is to investigate the dynamic responses of SIF, physiological and nonphysiological factors, and to evaluate the contributions of the two factors to SIF variations under different VW stress degrees of cotton. We continuously observed the diurnal variation of the top-of-canopy reflectance and SIF on healthy and VW-infected cotton during the peak incidence period of VW disease. To accurately quantify the relative contribution of each component to SIF, we proposed a practical strategy to estimate unmeasurable parameter when using Lindeman, Merenda, and Gold method. The results demonstrated the dominant role of physiological factors in SIF with an arch diurnal change pattern at the early stages of VW development. As the VW severity increased, the contribution of physiological components declined, with the maximum contribution decreasing by 47.7%, and the diurnal pattern was disrupted, with the diurnal variation amplitude of the parameter declining by 63.4%, followed by a shift in the regulatory role toward nonphysiological factors. This study contributes to a further understanding of the roles of physiological and nonphysiological components in SIF variations of cotton under VW stress, thus advancing the accurate monitoring of VW stress severity.

Accurate and reliable mangrove datasets are essential for the protection and management of mangrove ecosystems. Therefore, the evaluation of the current mangrove datasets and understanding the differences among them are critical. This study takes the Guangxi Beibu Gulf (GBG) and Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the study areas and analyzes the agreement and accuracy of eight mangrove datasets from 2000 to 2020 using area comparison, spatial agreement analysis, and absolute accuracy evaluation. The results show that; 1) significant differences exist in mangrove area and spatial distribution among the different mangrove datasets, with the percentage of high agreement areas ranging from 10% to 42%. 2) The overall accuracy of the evaluated mangrove datasets ranges from 56.2% to 95.6%, and the classification accuracy of mangrove datasets in inland areas is lower than the overall level. 3) There are regional differences in the quality of mangrove datasets, with the agreement and accuracy of mangrove datasets in the GBG being greater than those in the GBA. 4) Fine-scale mangrove mapping based on high-resolution remote sensing images, such as Sentinel, and global mangrove mapping based on the Google Earth Engine (GEE) cloud platform should be emphasized in the future. The findings of this study can provide guidance for data users to select appropriate mangrove datasets and a reference for future mangrove mapping research.

Abstract Parkinson’s disease (PD) is the second most common neurodegenerative disease in the United States. Decades before motor symptoms manifest, non-motor symptoms such as hyposmia and rapid eye movement (REM) sleep behavior disorder are highly predictive of PD. Previous immune profiling studies have identified alterations to the proportions of immune cells in the blood of clinically defined PD patients. However, it remains unclear if these phenotypes manifest before the clinical diagnosis of PD. We utilized longitudinal DNA methylation (DNAm) microarray data from the Parkinson’s Progression Marker’s Initiative (PPMI) to perform immune profiling in clinically defined PD and prodromal PD patients (Prod). We identified previously reported changes in neutrophil, monocyte, and T cell numbers in PD patients. Additionally, we noted previously unrecognized decreases in the naive B cell compartment in the defined PD and Prod patient group. Over time, we observed the proportion of innate immune cells in PD blood increased, but the proportion of adaptive immune cells decreased. We identified decreases in T and B cell subsets associated with REM sleep disturbances and early cognitive decline. Lastly, we identified increases in B memory cells associated with both genetic (LRRK2 genotype) and infectious (cytomegalovirus seropositivity) risk factors of PD. Our analysis shows that the peripheral immune system is dynamic as the disease progresses. The study provides a platform to understand how and when peripheral immune alterations occur in PD and whether intervention at particular stages may be therapeutically advantageous.

A high-efficiency laser coherent combination method based on the virtue of the self-transverse acceleration effect of Airy beams is proposed and demonstrated by converting an array of Gaussian beams into Airy beams array to reduce laser beams separation distance. The coherent combination procedures of circular array for both Airy and Gaussian beams are theoretically and experimentally studied. The results show that the central intensity of the laser coherent combination of the circular array of Airy beams is 14.33 times higher than that of the Gaussian beams array, and the combined efficiency of the circular array of Airy beams is 1.64 times higher than that of the Gaussian beams circular array. The method may provide an efficient way to increase combination efficiency of laser coherent combination array and may have a broad application prospect in optical communication, particle capture, material processing.

Abstract Background It is controversial whether patients with hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT) should undergo salvage surgery following the combination therapy of tyrosine kinase inhibitors (TKIs) and programmed cell death protein 1 (PD-1) inhibitors. This study aimed to elucidate the efficiency and safety of salvage surgery following combination therapy, while also summarizing a novel surgical approach for Vp3/4 PVTT. Methods Between April 2019 and December 2022, a consecutive series of unresectable HCC patients with PVTT who received salvage surgery following combination therapy were enrolled. Evaluation included perioperative and long-term follow-up outcomes. The complete removal of Vp3/4 PVTT was achieved using a novel surgical approach characterized by “longitudinal incision and transverse suturing” and “angle-to-straight conversion”. Results Forty patients including 22 patients with Vp3 and 18 patients with Vp4 were included. Long-term follow-up showed similar rates of portal vein patency (Vp3: 95.5%, Vp4:94.4%, p = 0.900), and 3-year portal vein patency rates were 95.0%. There were no significant differences observed in combination therapy-related adverse events (p = 0.253) and perioperative complications (p = 0.613) between the Vp3 and Vp4 groups. The recurrence patterns were similar between the two groups (p = 0.131). There were no significant differences in overall survival (OS) and recurrence-free (RFS) survival between the Vp3 and Vp4 groups (OS p = 0.457, RFS p = 0.985). Patients who achieved a pathological complete response had significantly better RFS (p = 0.011). Conclusion Salvage surgery after combination therapy demonstrated favorable efficacy and safety. The novel surgical approach for PVTT can effectively achieve complete removal of PVTT and ensured long-term portal vein patency.

Underwater optical imaging is essential for exploring the underwater environment to provide information for planning and regulating underwater activities in various underwater applications, such as aquaculture farm observation, underwater topographical survey, and underwater infrastructure monitoring. Thus, there is a need to investigate the underwater imaging process and propose clear and long-range underwater optical imaging methods to fulfill the demands of academia and industry. In this manuscript, we classify the eighteen most commonly used underwater optical imaging methods into two groups regarding the imaging principle, (1) hardware and (2) software-based methods, each with an explanation of the theory, features, and applications. Furthermore, we also discuss the current challenges and future directions for improving the performance of current methods, such as improving the accuracy of underwater image formation model estimation, enlarging the underwater image dataset, proposing comprehensive underwater imaging evaluation metrics, estimating underwater depth and integrating different methods (e.g., hardware- and software-based methods for computational imaging) to promote the imaging performance not only in the laboratory but also in practical underwater scenarios.

Background:Asarum heterotropoides and Asarum sieboldii are commonly used in traditional Chinese medicine. However, little is known about how they differ in terms of essential oil (EO) and ethanol extract (EE) content and composition. Moreover, the effect of various geographical locations on the essential oil (EO), ethanol extract (EE), and asarinin content of different Asarum samples remains unknown. We tested four root-drying methods, i.e., soil removal and shade drying (P1), water washing and shade drying (P2), and water washing and drying at 30 °C (P3) and 40 °C (P4). We used LC-MS and GC-MS to investigate these differences. We also investigated the pharmacodynamic effects of EO and EE. Results: Overall, the EO, EE and asarinin contents of the analysed samples were 19.21–51.53 μL.g−1, 20.00–45.00 μL.g−1, and 1.268–2.591 mg.g−1, respectively. P1 treatment yielded the lowest volatile oil content compared to the other three treatments. GC-MS analysis revealed 78 EO components. Among the six major EO components, eucarvone, 3,5-dimethoxytoluene, and methyl eugenol were higher in A. heterotropoides than in A. sieboldii. However, the latter had a higher myristicin content. LC-MS analysis identified 888 EE components in roots and leaves of A. heterotropoides and A. sieboldii; 317 differentially accumulated metabolites were identified. EO and EE showed a dose-dependent reduction in the degree of swelling and an increase in the inhibition rate of drug concentration on acetic acid writhing in mice. Asarum EO proved to be more effective than EE in the pharmacodynamic study. Conclusions: We conclude that Asarum species show inter- and intra-specific differences in EO and EE content and composition, which may influence the pharmacodynamics of Asarum root extracts.

Nitrogen fertilizer levels significantly affect crop growth and development, necessitating precision management. Most studies focus on nitrogen nutrient estimation using vegetation indices and textural features, overlooking the diagnostic potential of color features. Hence, we investigated cotton nitrogen nutrition status using unmanned aerial vehicle (UAV) image features and the nitrogen nutrient index (NNI). Random frog algorithm - and random forest-screened image feature sets significantly correlated with the NNI, which were substituted into four machine learning algorithms for NNI estimation modeling. The composite scores (F) of optimal image feature sets were calculated using the coefficient of variation method for comprehensive cotton nitrogen nutrient diagnosis. Validation of the model for determining the critical nitrogen concentration in cotton yielded a coefficient of determination R2 = 0.89, root mean square error RMSE = 0.50 g (100 g)-1, and mean absolute error MAE = 0.44, demonstrating improved performance. Additionally, our novel NNI estimation model constructed based on the optimal image feature sets exhibited R2c = 0.97, RMSEc = 0.02, MAEc = 0.02, R2v = 0.85, RMSEv = 0.05, and MAEv = 0.04. Polynomial fitting of the composite index with NNI indicated that the model was reliable and yielded the following diagnostic criterion: 0.48 < F2 < 0.67 indicated nitrogen overapplication, whereas F2 < 0.48 or F2 > 0.67 indicated nitrogen deficiency. This study demonstrates the superior effectiveness of using UAV RGB image feature sets for NNI estimation and the quick, accurate diagnosis of cotton nitrogen levels, which will help guide nitrogen fertilizer application.

Diosmin is a flavonoid derived from plants, possessing anti-inflammatory, antioxidant, antidiabetic, neuroprotective and cardiovascular protective properties. However, diosmin has low solubility in water, leading to low bioavailability. In this study, we constructed bilayer nanoparticles with trimethyl chitosan and soy peptides to improve the oral bioaccessibility and bioavailability of diosmin, and determined the characteristics and antioxidant properties of the diosmin-loaded nanoparticles. The results showed that the size of the nanoparticles was around 250 nm with the encapsulation efficiency higher than 97 %, and the nanoparticles were stable under regular conditions. In vitro digestion suggested the nanoparticles could protect diosmin from releasing in gastric digestion but promote the bioaccessibility of diosmin in intestine. Furthermore, the diosmin-loaded nanoparticles presented excellent antioxidant activities in vitro and significantly decreased the Lipopolysaccharides-induced brain Malondialdehyde (MDA) level by oral administration. Therefore, the reported nanoparticles may be an effective platform for improving the oral bioavailability of diosmin.

Abstract This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process. In this paper, we introduce a solution called Lightning Cat which is based on deep learning techniques. We train three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. Experimental results show that, in the Lightning Cat we propose, Optimized-CodeBERT model surpasses other methods, achieving an f1-score of 93.53%. To precisely extract vulnerability features, we acquire segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately. To demonstrate the feasibility of our proposed solution, we evaluate its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types.

Here we investigated the nucleation of the Re, Ru-rich δ phase affected by stacking faults in the γ phase in a 4th generation Ni-based single crystal superalloy during the service at 800°C. The stacking faults in the γ phase were rich in Re and Ru, similar to the observed δ phase. And the stacking fault regions possessed a similar structure to the δ phase, promoting the transformation from γ phase to δ phase. Furthermore, the calculation results indicated that Ru had more significant promoting effects on the δ phase precipitation than Re, providing a reference for the alloy design.

Abstract Cotton maps (10 m) of Xinjiang (XJ_COTTON10), which is the largest cotton production region of China, were produced from 2018 to 2021 through supervised classification. A two-step mapping strategy, i.e., cropland mapping followed by cotton extraction, was employed to improve the accuracy and efficiency of cotton mapping for a large region of about 1.66 million km2 with high heterogeneity. Additionally, the time-series satellite data related to spectral, textural, structural, and phenological features were combined and used in a supervised random forest classifier. The cotton/non-cotton classification model achieved overall accuracies of about 95% and 90% on the test samples of the same and adjacent years, respectively. The proposed two-step cotton mapping strategy proved promising and effective in producing multi-year and consistent cotton maps. XJ_COTTON10 agreed well with the statistical areas of cotton at the county level (R2 = 0.84–0.94). This is the first cotton mapping for the entire Xinjiang at 10-meter resolution, which can provide a basis for high-precision cotton monitoring and policymaking in China.

Functional nucleic acids including aptamers and DNAzymes are a class of valuable molecular tool for biotechnology. However, DNA and RNA aptamers and catalysts suffer from low biological stability and limited chemical diversity. Xeno-nucleic acids (XNAs) refer to nucleic acid analogues containing sugar moieties that are structurally distinct from DNA and RNA and possess advantageous properties. In this article, we first focus on two types of XNAs, threose nucleic acid (TNA) and 2’-fluoroarabinose nucleic acid (FANA), and summarize recent in vitro selections of TNA and FANA aptamers and catalysts. We then review three classes of unnatural base pairs (UBPs) and highlight examples of UBP-containing DNA aptamers and DNAzymes. Lastly, we briefly describe an XNA-modified DNAzyme 10–23 (X10-23) and its application in RNA knockdown and virus detection. Functional XNAs provide important chemical biology tools for biomedical research and future interdisciplinary collaboration will boost XNA basic research and clinical translation.

A L12-structured precipitation (γˊ) strengthened Co-based superalloy is identified based on thermodynamic calculation and experimental validation. An alloy with composition of Co9Al5Mo30Ni2Ta10Cr is fabricated to explore the relationships among processing, microstructures and mechanical properties. Typical γ/γˊ two-phase microstructures have been obtained after heat treatments, where the spherical γˊ phase with diameter of ∼100 nm is uniformly distributed in the face centered cubic (FCC) matrix. The L12 structured γʹ-(Ni, Co)3(Al, Ta) phase can be stabilized, which coherently precipitates in the FCC matrix (γ) during aging treatment. The alloy exhibits promising strength with comparison to traditional Co-based superalloys at a temperature range from room temperature (RT) to 1000 °C. The predominant deformation mechanism transits from shearing of dislocation pairs to dislocations bypassing γʹ phase when deformation temperature increases from RT to 1000 °C.