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Abstract Introduction ARDS (acute respiratory distress syndrome) is the most severe form of acute hypoxic respiratory failure. Most studies related to ARDS have excluded patients with hematologic diseases, let alone allogeneic hematopoietic stem cell transplantation (allo-HSCT) recipients. Numerous patients experiencing severe hypoxic respiratory failure do not meet the Berlin definition due to the limitations of diagnosis and treatment. A new definition of ARDS, remove some diagnosis restrictions, was proposed in 2023. Based on the 2023 new definition of ARDS, we investigated the clinical features of ARDS in allo-HSCT recipients and reported risk factors for in-hospital mortality in allo-HSCT recipients defined by the Berlin definition and the new definition of ARDS respectively. Methods From Jan 2016 to Dec 2020, 135 allo-HSCT recipients identified with the new definition and 87 identified with the Berlin definition at three teaching hospitals were retrospectively included in this study. Variables (demographic information, characteristics of hematologic disease and ARDS episode, laboratory tests and SOFA score) with P

Abstract Background Previous studies have demonstrated the role of N6-methyladenosine (m6A) RNA methylation in various biological processes, our research is the first to elucidate its specific impact on LCAT mRNA stability and adipogenesis in poultry. Results The 6 100-day-old female chickens were categorized into high (n = 3) and low-fat chickens (n = 3) based on their abdominal fat ratios, and their abdominal fat tissues were processed for MeRIP-seq and RNA-seq. An integrated analysis of MeRIP-seq and RNA-seq omics data revealed 16 differentially expressed genes associated with to differential m6A modifications. Among them, ELOVL fatty acid elongase 2 (ELOVL2), pyruvate dehydrogenase kinase 4 (PDK4), fatty acid binding protein 9 (PMP2), fatty acid binding protein 1 (FABP1), lysosomal associated membrane protein 3 (LAMP3), lecithin-cholesterol acyltransferase (LCAT) and solute carrier family 2 member 1 (SLC2A1) have ever been reported to be associated with adipogenesis. Interestingly, LCAT was down-regulated and expressed along with decreased levels of mRNA methylation methylation in the low-fat group. Mechanistically, the highly expressed ALKBH5 gene regulates LCAT RNA demethylation and affects LCAT mRNA stability. In addition, LCAT inhibits preadipocyte proliferation and promotes preadipocyte differentiation, and plays a key role in adipogenesis. Conclusions In conclusion, ALKBH5 mediates RNA stability of LCAT through demethylation and affects chicken adipogenesis. This study provides a theoretical basis for further understanding of RNA methylation regulation in chicken adipogenesis.

Abstract Background Endometriosis, characterized by the presence of active endometrial-like tissues outside the uterus, causes symptoms like dysmenorrhea and infertility due to the fibrosis of endometrial cells, which involves excessive deposition of extracellular matrix (ECM) proteins. Ubiquitination, an important post-transcriptional modification, regulates various biological processes in human diseases. However, its role in the fibrosis process in endometriosis remains unclear. Methods We employed multi-omics approaches on two cohorts of endometriosis patients with 39 samples. GO terms and KEGG pathways enrichment analyses were used to investigate the functional changes involved in endometriosis. Pearson’s correlation coefficient analysis was conducted to explore the relationship between global proteome and ubiquitylome in endometriosis. The protein expression levels of ubiquitin-, fibrosis-related proteins, and E3 ubiquitin-protein ligase TRIM33 were validated via Western blot. Transfecting human endometrial stroma cells (hESCs) with TRIM33 small interfering RNA (siRNA) in vitro to explore how TRIM33 affects fibrosis-related proteins. Results Integration of proteomics and transcriptomics showed genes with concurrent change of both mRNA and protein level which involved in ECM production in ectopic endometria. Ubiquitylomics distinguished 1647 and 1698 ubiquitinated lysine sites in the ectopic (EC) group compared to the normal (NC) and eutopic (EU) groups, respectively. Further multi-omics integration highlighted the essential role of ubiquitination in key fibrosis regulators in endometriosis. Correlation analysis between proteome and ubiquitylome showed correlation coefficients of 0.32 and 0.36 for ubiquitinated fibrosis proteins in EC/NC and EC/EU groups, respectively, indicating positive regulation of fibrosis-related protein expression by ubiquitination in ectopic lesions. We identified ubiquitination in 41 pivotal proteins within the fibrosis-related pathway of endometriosis. Finally, the elevated expression of TGFBR1/α-SMA/FAP/FN1/Collagen1 proteins in EC tissues were validated across independent samples. More importantly, we demonstrated that both the mRNA and protein levels of TRIM33 were reduced in endometriotic tissues. Knockdown of TRIM33 promoted TGFBR1/p-SMAD2/α-SMA/FN1 protein expressions in hESCs but did not significantly affect Collagen1/FAP levels, suggesting its inhibitory effect on fibrosis in vitro. Conclusions This study, employing multi-omics approaches, provides novel insights into endometriosis ubiquitination profiles and reveals aberrant expression of the E3 ubiquitin ligase TRIM33 in endometriotic tissues, emphasizing their critical involvement in fibrosis pathogenesis and potential therapeutic targets.

This paper investigates the knowledge system in Building Information Modeling (BIM) technology in Medical Construction Projects (MCPs). Specifically, it proposes knowledge graph analysis utilizing scient metric techniques and employs VOSviewer and CiteSpace software to analyze the consequence of relevant statistics from the literature concerning BIM medical construction projects (BIM-MCPs). In order to analyze the collaboration network, the analysis of the co-occurrence network, and the analysis of the co-citation network, 83 journal articles were gathered. The results obtained from the research indicate that (a) the intellectual framework of BIM-MCPs can be categorized into four distinct clusters. (b) The research focus in BIM-MCPs has evolved, including model development, frameworks, implementation, system integration, application throughout the entire lifecycle, emergency medical engineering project applications, and intelligent management of medical facilities and smart buildings. (c) Prospective directions for research are delineated in three distinct areas: enhancement of use for BIM Medical Facility Management (BIM-MFM), integration of BIM technology in sustainability assessment, and application of BIM technology in emergency medical engineering and smart medical construction.

Polycystic ovary syndrome (PCOS) is the most common and multifactorial endocrine disease among women of reproductive age. Aberrant folliculogenesis is a common pathological characteristic of PCOS, but the underlying molecular mechanism remains unclear. Emerging evidence indicated that aberrant expression of long noncoding RNAs (lncRNAs) may contribute to the pathogenesis of PCOS. In this study, we found that lncRNA PKD1P6 expression was remarkably down-regulated in ovarian granulosa cells (GCs) of hyperandrogenic PCOS (HA-PCOS) patients and negatively correlated with serum testosterone (T) levels. We further showed that overexpression of PKD1P6 markedly reduced cell viability, attenuated DNA synthesis capacity, arrested the cell cycle at G0/G1 phase and promoted apoptosis of KGN cells. Exosomes derived from PKD1P6 overexpression cells exerted similar effects to PKD1P6 overexpression on the function of KGN cells. Mechanistically, PKD1P6 could act as a competing endogenous RNA (ceRNA) by directly binding with miR-135b-5p. Overexpression of PKD1P6 significantly suppressed ERK1/2 activation, whereas up-regulation of miR-135b-5p exerted an opposing effect. Additionally, excessive androgen was showed to diminish PKD1P6 expression while promote miR-135b-5p expression of PCOS models in vitro and vivo. Collectively, our findings delineate the clinical significance of PKD1P6 in HA-PCOS and the new regulatory mechanisms involved in abnormal folliculogenesis, providing a promising therapeutic target for HA-PCOS.

Abstract Infection is the most common complication after orthopedic surgery and can result in prolonged ailments such as chronic wounds, enlarged bone defects, and osteomyelitis. Iron, which is essential for bacterial metabolism and immune cell functions, is extremely important. Bacteria harness iron from nearby cells to promote biofilm formation, ensuring their survival. Iron deficiency within the infection microenvironment (IME) consequently hampers macrophage function, enabling further dissemination of the infection and hindering macrophage polarization to the M2 phenotype. Therefore, a novel approach is proposed to regulate macrophage polarization, aiming to restore the inflammatory immune environment. A composite hydrogel derived from natural polymers is developed to address infections and manage iron metabolism in macrophages. This IME‐responsive hydrogel, named FCL‐ECMH, is synthesized by encapsulating vermiculite functional core layers within a decellularized extracellular matrix hydrogel. It is noteworthy that FCL‐ECMH can produce reactive oxygen species within the IME. Supplementary photothermal treatment enhances bacterial iron uptake, leading to ferroptosis‐like death. This process also rejuvenates the iron‐enriched macrophages around the IME, thereby enhancing their antibacterial and tissue repair functions. In vivo experiments confirmed the antibacterial and repair‐promoting capabilities of FCL‐ECMH, indicating its potential for clinical applications.

BackgroundThis study aims to prioritize genes potentially involved in multifactorial or causal relationships with gout.MethodsUsing the Summary Data-based Mendelian Randomization (SMR) approach, this research analyzed expression quantitative trait loci (eQTL) data from blood and renal tissues and genome-wide association study (GWAS) data related to gout. It sought to identify genetic loci potentially involved in gout. Heterogeneity testing was conducted with the HEIDI test, and results were adjusted for the False Discovery Rate (FDR). Blood cis-eQTL data were sourced from the eQTLGen Consortium’s summary-level data, and renal tissue data came from the V8 release of the GTEx eQTL summary data. Gout GWAS data was sourced from the FinnGen Documentation of the R10 release.ResultSMR analysis identified 14 gene probes in the eQTLGen blood summary-level data significantly associated with gout. The top five ranked genes are: ENSG00000169231 (labeled THBS3, PSMR = 4.16 × 10−13), ENSG00000231064 (labeled THBS3-AS1, PSMR = 1.88 × 10−8), ENSG00000163463 (labeled KRTCAP2, PSMR = 3.88 × 10−6), ENSG00000172977 (labeled KAT5, PSMR = 1.70 × 10−5), and ENSG00000161395 (labeled PGAP3, PSMR = 3.24 × 10−5). Notably, increased expression of KRTCAP2 and PGAP3 is associated with an increased risk of gout, whereas increased expression of THBS3, THBS3-AS1, and KAT5 is associated with a reduced gout risk. No significant gene associations with gout were observed in renal tissue, likely due to the limited sample size of kidney tissue.ConclusionOur findings have highlighted several genes potentially involved in the pathogenesis of gout. These results offer valuable insights into the mechanisms of gout and identify potential therapeutic targets for its treatment.

Abstract Lung adenocarcinoma (LUAD) continues to pose a significant mortality risk with a lack of dependable biomarkers for early noninvasive cancer detection. Here, we find that aberrant lipid metabolism is significantly enriched in lung cancer cells. Further, we identified four signature lipids highly associated with LUAD and developed a lipid signature-based scoring model (LSRscore). Evaluation of LSRscore in a discovery cohort reveals a robust predictive capability for LUAD (AUC: 0.972), a result further validated in an independent cohort (AUC: 0.92). We highlight one lipid signature biomarker, PE(18:0/18:1), consistently exhibiting altered levels both in cancer tissue and in plasma of LUAD patients, demonstrating significant predictive power for early-stage LUAD. Transcriptome analysis reveals an association between increased PE(18:0/18:1) levels and dysregulated glycerophospholipid metabolism, which consistently displays strong prognostic value across two LUAD cohorts. The combined utility of LSRscore and PE(18:0/18:1) holds promise for early-stage diagnosis and prognosis of LUAD.

[Introduction] The research aims to explore the integrated calculation method of a 10 MW semi-submersible floating wind turbine, and analyze its coupled dynamic response characteristics in 40～50 m offshore deep water areas. [Method] A 10 MW concrete semi-submersible floating wind turbine was taken as an example, and then numerical calculation was carried out by the integrated calculation method, and its coupled dynamic response under rated and survival conditions was statistically analyzed. [Result] The horizontal motion of the platform is mainly affected by the wave force, wind loading and mooring stiffness. The maximum value of motion and mooring tension occur in the survival condition, and the heave motion is mainly affected by the wave, but the mean value of the pitch/roll motion is mainly affected by the wind loading, all of which meet the design specification. [Conclusion] The integrated calculation method better considers the coupled dynamic behavior of floating wind turbines. Due to the limitation of water depth, the optimization of horizontal motion and mooring nonlinearity of offshore floating wind turbines is more important in offshore deep water areas, and the extreme response mainly occurs in survival conditions. The above conclusions provide an important reference for the research and design of the floating offshore wind turbines in offshore deep water area.

Abstract Cancer testis antigen (CTA) Melanoma Antigen Gene A3 (MAGEA3) were overexpressed in multiple tumor types, but the expression pattern of MAGEA3 in the serum of lung adenocarcinoma (LUAD) remains unclear. Clinically derived serum and serum exosome samples were used to assess the mRNA expression of MAGEA3 and MAGEA4 by qRT-PCR, and serum MAGEA3 and MAGEA4 protein expression were evaluated by ELISA in total 133 healthy volunteers’ and 289 LUAD patients’ serum samples. An analysis of the relationship of the mRNA and protein expression of MAGEA3 and MAGEA4 with clinicopathologic parameters was performed and the diagnostic value of MAGEA3 and MAGEA4 was plotted on an ROC curve. In addition, the correlation of MAGEA3 mRNA with infiltrating immune cells was investigated through TIMER, the CIBERSORT algorithm and the TISIDB database. Expression of serum and serum exosome MAGEA3 and MAGEA4 mRNA were significantly higher in LUAD patients than in healthy donors. MAGEA3 mRNA associated with tumor diameter, TMN stage, and NSE in LUAD serum samples, and MAGEA3 mRNA correlated with N stage in serum-derived exosomes, possessing areas under the curve (AUC) of 0.721 and 0.832, respectively. Besides, serum MAGEA3 protein levels were elevated in LUAD patients, and were closely related to stage and NSE levels, possessing AUC of 0.781. Further analysis signified that the expression of MAGEA3 mRNA was positive correlation with neutrophil, macrophages M2, dendritic cells resting, and eosinophilic, but negatively correlated with B cells, plasma cells, CD8 + T cells, CD4 + T cells, Th17 cells, macrophages and dendritic cells. Collectively, our results suggested that the MAGEA3 expression in mRNA and protein were upregulated in LUAD, and MAGEA3 could be used as a diagnostic biomarker and immunotherapy target for LUAD patients.

Abstract Understanding intracellular phase separation is crucial for deciphering transcriptional control, cell fate transitions, and disease mechanisms. However, the key residues, which impact phase separation the most for protein phase separation function have remained elusive. We develop PSPHunter, which can precisely predict these key residues based on machine learning scheme. In vivo and in vitro validations demonstrate that truncating just 6 key residues in GATA3 disrupts phase separation, enhancing tumor cell migration and inhibiting growth. Glycine and its motifs are enriched in spacer and key residues, as revealed by our comprehensive analysis. PSPHunter identifies nearly 80% of disease-associated phase-separating proteins, with frequent mutated pathological residues like glycine and proline often residing in these key residues. PSPHunter thus emerges as a crucial tool to uncover key residues, facilitating insights into phase separation mechanisms governing transcriptional control, cell fate transitions, and disease development.

Background: It is well known that cognitive function is associated with gender differences. However, the effect of sex differences on the relationship between lipids fractions and cognitive function in older adults has been contentious. Methods: 2,170 participants from the U.S. National Health and Nutrition Examination Surveys (1999-2002 and 2011-2014) were included. Total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) were measured. Cognitive function was assessed using the Digit Symbol Substitution Test (DSST). Multiple linear regression models and restricted cubic spline curve fitting were used. Results: Overall, low HDL-C levels were negatively associated with DSST scores in every group. The levels of TC, TG, and LDL-C were not significantly associated with DSST scores, not only in the total population but also in the males. In the females, after adjustment for potential confounding factors, high TC levels were negatively related to DSST scores (OR = −3.590, 95% CI: – 6.343 to – 0.837), and high TG levels were found positively associated with DSST scores (OR = 2.323, 95% CI: 0.159–4.488). Conclusion: Low plasma HDL is associated with cognitive dysfunction in older adults. In older women, high TC levels are positively associated with cognitive decline while high TG levels may protect cognitive function.

IntroductionHemorrhagic transformation (HT) is a serious complication that can occur spontaneously after an acute ischemic stroke (AIS) or after a thrombolytic/mechanical thrombectomy. Our study aims to explore the potential correlations between fibrinogen levels and the occurrence of spontaneous HT (sHT) and HT after mechanical thrombectomy (tHT).MethodsA total of 423 consecutive AIS patients diagnosed HT who did not undergone thrombolysis and 423 age- and sex-matched patients without HT (non-HT) were enrolled. Fibrinogen levels were measured within 24 h of admission after stroke. The cohorts were trisected according to fibrinogen levels. The HT were further categorized into hemorrhagic infarction (HI) or parenchymal hematoma (PH) based on their imaging characteristics.ResultsIn sHT cohort, fibrinogen levels were higher in HT patients than non-HT patients (p

BackgroundWhile some studies have suggested a link between gut microbiota (GM) and abortion, the causal relationship remains unclear.MethodsTo explore the causal relationship between GM and abortion, including spontaneous abortion (SA) and habitual abortion (HA), we performed a two-sample Mendelian randomization (MR) analysis. We used summary statistics data from MiBioGen and FinnGen for genome-wide association studies (GWAS), with GM data as the exposure variable and abortion data as the outcome variable.ResultsIn the absence of heterogeneity and horizontal pleiotropy, the inverse-variance weighted (IVW) method identified five genetically predicted GM genera linked to the risk of abortions. Lactococcus was negatively correlated with the risk of SA, whereas the Eubacterium fissicatena group was positively correlated with the risk of SA. Genetic predictions of Coprococcus3 and Odoribacter were linked to a reduced risk of HA, while the Eubacterium ruminantium group was associated with an increased risk of HA.ConclusionOur study suggests a genetic causal relationship between specific GM and two types of abortions, improving our understanding of the pathological relationship between GM and abortion.

Objective1. To assess the Inter-rater reliability and test-retest reliability of FPI-6 total score and individual scores in static foot posture evaluation among elderly female patients with knee osteoarthritis (KOA), aiming to establish the reliability of the FPI-6 scale. 2. To investigate the disparity between dominant and non-dominant quadriceps characteristics in elderly female KOA patients, as well as explore the correlation between quadriceps characteristics and abnormal foot posture, thereby offering novel insights for the prevention and treatment of KOA.MethodsThe study enrolled a total of 80 lower legs of 40 participants (all female) with unilateral or bilateral KOA, who were assessed by two raters at three different time points. The inter-rater and test-retest reliability of the FPI-6 was evaluated using the intra-class correlation coefficient (ICC), while the absolute reliability of FPI-6 was examined using the standard error of measurement (SEM), minimum detectable change (MDC), and Bland-Altman analysis. The internal consistency of FPI-6 was assessed using Spearman’s correlation coefficient. Additionally, MyotonPRO was employed to assess quadriceps muscle tone and stiffness in all participants, and the association between quadriceps muscle tone/stiffness and the total score of FPI-6 was analyzed.ResultOur study found excellent inter-rater and test-retest reliability (ICC values of 0.923 and 0.931, respectively) for the FPI-6 total score, as well as good to excellent reliability (ICC values ranging from 0.680 to 0.863 and 0.739–0.883) for individual items. The SEM and MDC values for the total score of FPI-6 among our study inter-rater were 0.78 and 2.15, respectively. and the SEM and MDC values for the test-retest total score of FPI-6 were found to be 0.76 and 2.11, respectively. Furthermore, the SEM and MDC values between inter-rater and test-retest across six individual items ranged from 0.30 to 0.56 and from 0.84 to 1.56. The Bland-Altman plots and respective 95% LOA showed no evidence of systematic bias. In terms of the mechanical properties of the quadriceps on both sides, the muscle tone and stiffness of rectus femoris (RF), vastus medialis (VM), and vastus lateralis (VL) were significantly higher in the non-dominant leg compared to the dominant leg. Additionally, in the non-dominant leg, there was a significant positive correlation between the muscle tone and stiffness of VM, VL, RF and the total score of FPI-6. However, in the dominant leg, only VM’s muscle tone and stiffness showed a significant positive correlation with the total score of FPI-6.ConclusionThe reliability of the FPI-6 total score and its six individual items was good to excellent. Our findings offer a straightforward and dependable approach for researchers to assess foot posture in elderly female patients with KOA. Furthermore, we observed significantly greater quadriceps tension and stiffness in the non-dominant leg compared to the dominant leg. The FPI-6 total score exhibited a significant correlation with changes in quadriceps muscle performance among KOA patients. These observations regarding the relationship between changes in quadriceps muscle performance and foot posture in elderly female KOA patients may provide novel insights for disease prevention, treatment, and rehabilitation.

BackgroundGut microbiome (GM) and type 2 diabetes mellitus (T2DM) have two-way effects. Improving T2DM by modulating GM in various ways, such as diet, exercise, and medication, is gradually becoming popular, and related studies have yielded positive results. However, there is still a lack of high-quality bibliometric analyses of research in this area. This study aims to systematize and comprehensively summarize the knowledge structure, research tropics, and research trends of GM and T2DM through bibliometric analysis.MethodsPublications related to GM and T2DM before January 9, 2024, in the Web of Science Core Collection (WOSCC) were searched in this study. Microsoft Excel 2019 was used to analyze publishing trends and CiteSpace (v.6.1.R6 Advanced) was used to analyze institutions, cited journals, references, and keywords.SCImago Graphica (v.1.0.39) was used to analyze countries/regions, institutions’ collaborations, cited authors, and published journals.ResultsWe finally included 1004 articles published from 2008 to 2023. The number of published articles showed an upward trend and reached its peak in 2022. China is the country with the largest number of articles, Univ Copenhagen is the institution with the largest number of articles, Fukui, Michiaki, Hamaguchi, Masahide are the scholars with the largest number of articles, and Cani and Patrice D. are the scholars with the largest number of citations. NUTRIENTS(Q1/5.9) published the most publications, while Nature (Q1/64.8; Cited 804 times) is the most frequently cited journal. Gut microbiota, Obesity, and insulin resistance are the most frequently used keywords. This study found that current researches focus on the effects of diet, exercise, and pharmacological modification of GM to improve T2DM and explores specific mechanisms. Future researches will focus on three areas: complications of T2DM and specific physiological processes, methods and measures to regulate GM, and new experimental techniques and assays.ConclusionThe current researches confirmed the effects and specific mechanisms of modulating GM to improve T2DM. Further exploration of the effects of modulating GM on T2DM complications and specific physiologic processes is a future trend of research. Exploring specific methods for regulating GM and developing new experimental techniques and assays are important for future research.

The emergence of maize tassels is the turning of vegetative stage to reproductive stage of maize (Zea mays L.), which is critical for estimating maize grain yields. Recent advances in unmanned aerial vehicles (UAVs) remote sensing and deep learning-based object detection technique have provided a new approach for detecting maize tassels. Meanwhile, there still exists challenges for accurate detection due to the uncertainties in the complex field environment. The existing object detection networks fall in accurately detecting overlapping or small-scale maize tassels, as well as exhibiting insufficient detection capability in strong lighting conditions. Furthermore, the current dataset exhibits a limited temporal scope, unable to encompass the whole tasseling progress. In this study, we proposed FMTS dataset, designed a novel approach called RESAM-YOLOv8n (Residual Spatial Attention Module-You Only Look Once v8n), introducing the RESAM module and training the network with larger input image sizes. These enabled RESAM-YOLOv8n to focus on important tassel features and neglect irrelevant information, thereby enhancing its detection capability. The RESAM-YOLOv8n network was trained and evaluated using FMTS dataset, the mAP0.5, mAP0.75, Recall, Precision, and F1 of the network were 95.74 %, 66.70 %, 89.28 %, 95.59 %, and 92.00 %, respectively. Furthermore, in counting the number of maize tassels, the R2 value between the network’s detection and the ground truth reached 0.976, with a low RMSE of 1.56 tassels. The results showed the better performance of the RESAM-YOLOv8n network, providing an effective method for accurately identifying the maize tassels.

The launch of intelligent projectiles via electromagnetic railguns is a highly dynamic process characterized by complex multi-physical field coupling phenomena. This process encompasses the interplay of multiple physical domains, including the electric, magnetic, thermal, and mechanical fields. Accurate monitoring of the coupling relationships among various physical quantities is constrained by experimental methodologies and presents significant challenges. To address these challenges, we employ the Finite Element Method (FEM) to conduct an in-depth investigation into the launch dynamics, bore environment and the intricate coupling mechanisms of the multi-physical fields within the electromagnetic railgun launch system. The results indicate that during the launch process, sliding friction is the predominant factor when the armature moves at low speeds, whereas air resistance becomes the primary influence at high speeds. Moreover, the velocity skin effect is present throughout the entire launch process, with the heat generated being predominantly concentrated in the armature’s throat and the area of contact with the track. These results may offer fundamental data in understanding the coupling mechanism of multiple physical fields of the electromagnetic orbit launch system.

In recent years, with the rapid development of UAV technology, drones have been widely applied in various fields. Obtaining accurate location information is crucial for UAVs when performing tasks in challenging environments. In previous research, a pure vision-based self-positioning method was employed, which determined the UAV’s position by matching the vertical view image of the UAV with offline satellite remote sensing images. However, in certain urban areas where architectural features are similar, and due to variations in image viewing angles, shooting positions, and sensor parameters, non-rigid deformations exist between UAV images and remote sensing images, posing significant challenges for UAV visual positioning. To address these challenges, we propose a novel approach called Deformable Convolutional Transformer-based UAV Positioning (DCD-FPI). Our approach combines the Transformer-based model with deformable convolution, enhancing the model’s capability to handle non-rigid image deformations and capture detailed information. We also employ adaptive spatial feature fusion through multi-scale fusion to preserve critical distinguishing features. Experimental comparisons on the UL14 dataset demonstrate that our model achieves improved performance, with an increase from 76.25 to 77.15 in terms of the evaluation index RDS. Moreover, our model reduces computational complexity from 14.28 GFLOPS to 11.54 GFLOPS and parameter quantity from 14.76 M to 13.96 M.

The performance of an optoelectronic reservoir computing (RC) system for recognizing apple leaf diseases is numerically investigated. The disease spot feature extraction and selection methods suitable for time-delay RC are investigated. Two masking methods of sequence extension masking and matrix transformation masking are examined, with appropriate parameters, the optoelectronic RC achieves minimum errors of 0.01 and 0.03 under sequence extension masking and matrix masking methods, respectively. The nonlinear dynamics of the optoelectronic reservoir in the absence of data injection are compared with its recognition performance to establish the relationship between recognition capabilities and the system’s nonlinear dynamics. Additionally, we compared the recognition errors of optoelectronic RC, linear RC, and k-nearest neighbor algorithms. It illustrates that optoelectronic RC, under the nonlinear transformation of the Mach-Zehnder Modulator (MZM) and delayed feedback, can map input information nonlinearly to a high-dimensional state space, enabling effective classification along linear hyperplanes in this high-dimensional space.

Abstract Background The TRIANGLE operation benefits patients with pancreatic cancer; however, the Heidelberg triangle, where the operation occurs, contains vessels that can impact safety, especially in laparoscopic pancreaticoduodenectomy (LPD) with the TRIANGLE operation. This study aimed to identify Heidelberg triangle vessel types and their implications in pancreaticoduodenectomy (PD). Methods Retrospective collection of radiographic data was performed from January 2017 to April 2023. Three-dimensional (3D) CT reconstructions were performed on patients. Vascular types in the Heidelberg triangle were classified based on named vessels crossing its interior. The impact of these types on surgical outcomes and complications in PD with the TRIANGLE operation was assessed. Results Preoperative CT reconstruction was conducted on 184 pancreatic surgery patients. The findings revealed 99 patients (53.8%) with the type I Heidelberg triangle, lacking named vessels crossing the interior. Type II (n = 85, 46.2%), with named vessels crossing the interior, was identified. Among reconstructed patients who underwent PD with the TRIANGLE operation (n = 103), they were categorized as type I (n = 57) or type II (n = 46). The results showed that LPD patients with type II had significantly higher median intraoperative blood loss (300 mL vs. 200 mL, P = 0.030) and mean examined lymph nodes (17.2 ± 7.6 vs. 13.4 ± 5.2, P = 0.019) compared to those with type I. No significant differences were found in operative time or postoperative complications. Conclusion The presence of named vessels crossing the interior of the Heidelberg triangle was associated with increased intraoperative bleeding during LPD combined with the TRIANGLE operation. Therefore, targeted preoperative planning is required before the operation, thus improving the safety of the TRIANGLE operation in minimally invasive surgery.

In recent years, time series forecasting has been widely used in various fields, especially in financial markets. Stock trend forecasting has become one of the most common and complex challenges faced by investors and researchers. However, much of the current research relies primarily on single-granularity stock data for forecasting, with relatively few studies on multi-granularity data and fewer studies on spatial correlation of multi-granularity data. This inherent limitation restricts the comprehensive extraction of valuable information. To address this challenge, we propose the Multi-Granularity Deep Spatio-Temporal Correlation Framework (MDSTCF). Our approach combines the strengths of a multi-granularity residual learning, gated recurrent units, and graph attention networks to extract spatio-temporal information specific to each granularity. Subsequently, predictions at each granularity are generated through the prediction layer. Finally, a soft attention mechanism is employed to assign weights to the predictions at each granularity to obtain the final result. Comprehensive experiments conducted on tow stock datasets show that the proposed forecasting model improves the F1 score by about 7.88% and 11.2%, and the cumulative relative returns are close to 80% and 40%, respectively, compared to the previously studied time series forecasting models. The results clearly indicates that fusing multi-granularity information can significantly improve the performance of time series forecasting.

The geolocalization and navigation technology of unmanned aerial vehicles (UAVs) in denied environments is currently a prominent research area. Prior approaches mainly employed a two-stream network with nonshared weights to extract features from UAV and satellite images separately, followed by related modeling to obtain the response map. However, the two-stream network extracts UAV and satellite features independently. This approach significantly affects the efficiency of feature extraction and increases the computational load. To address these issues, we propose a novel coarse-to-fine one-stream network. Our approach allows information exchange between UAV and satellite features during early image feature extraction. To improve the model's performance, the framework retains feature maps generated at different stages of the feature extraction process for the feature fusion network and establishes additional connections between UAV and satellite feature maps in the feature fusion network. In addition, the framework introduces offset prediction to further refine and optimize the model's prediction results based on the classification tasks. Our proposed model boasts a similar inference speed to FPI while significantly reducing the number of parameters. It can achieve better performance with fewer parameters under the same conditions. Moreover, it achieves state-of-the-art performance on the UL14 dataset. Compared with previous models, our model achieved a significant 10.92-point improvement on the RDS metric, reaching 76.25. Furthermore, its performance in meter-level localization accuracy is impressive, with 82.62% improvement in 3-m accuracy, 64.17% improvement in 5-m accuracy, and 37.43% improvement in 10-m accuracy.

Excessive N-acetyl-p-benzoquinone imine (NAPQI) formation is a starting event that triggers oxidative stress and subsequent hepatocyte necrosis in acetaminophen (APAP) overdose caused acute liver failure (ALF). S-glutathionylation is a reversible redox post-translational modification and a prospective mechanism of APAP hepatotoxicity. Glutaredoxin-1 (Glrx1), a glutathione-specific thioltransferase, is a primary enzyme to catalyze deglutathionylation. The objective of this study was to explored whether and how Glrx1 is associated with the development of ALF induced by APAP. The Glrx1 knockout mice (Glrx1−/−) and liver-specific overexpression of Glrx1 (AAV8-Glrx1) mice were produced and underwent APAP-induced ALF. Pirfenidone (PFD), a potential inducer of Glrx1, was administrated preceding APAP to assess its protective effects. Our results revealed that the hepatic total protein S-glutathionylation (PSSG) increased and the Glrx1 level reduced in mice after APAP toxicity. Glrx1−/− mice were more sensitive to APAP overdose, with higher oxidative stress and more toxic metabolites of APAP. This was attributed to Glrx1 deficiency increasing the total hepatic PSSG and the S-glutathionylation of cytochrome p450 3a11 (Cyp3a11), which likely increased the activity of Cyp3a11. Conversely, AAV8-Glrx1 mice were defended against liver damage caused by APAP overdose by inhibiting the S-glutathionylation and activity of Cyp3a11, which reduced the toxic metabolites of APAP and oxidative stress. PFD precede administration upregulated Glrx1 expression and alleviated APAP-induced ALF by decreasing oxidative stress. We have identified the function of Glrx1 mediated PSSG in liver injury caused by APAP overdose. Increasing Glrx1 expression may be investigated for the medical treatment of APAP-caused hepatic injury.

Smart distribution networks (SDNs) can integrate the flexible resources from source-network-load-storage (SNLS) to cope with the fluctuation due to a high proportion of distributed generation (DG). However, such SNLS resources are characterized by complex coupling relationships; their control authority may belong to different stakeholders. Challenged by the above, the laminar flow structure from the communication field is introduced for distributed optimal dispatching in SDNs. A day-ahead laminar dispatching method considering the effective interaction of SNLS resources is proposed. First, the applicability of the laminar flow structure is analyzed. An upper-layer dispatching model for the SDN and a lower-layer dispatching model for users with DG are established. Then, by introducing intermediate variables, the lower dispatching model is transformed into a quadratic programming problem and the upper dispatching model is transformed into a second-order cone relaxation programming problem. Selecting the tie-line power flow as the exchanged information in the boundary, the upper- and lower-layer models are alternately solved until the convergence criterion is met. Finally, an improved IEEE 33-bus system is experimentally analyzed. We find that the SNLS flexible resource dispatching scheme can be obtained with only a few iterations, and the DG consumption can be significantly improved.

BFT is closely related to meat quality and lean meat percentage in pigs. The BFT traits of European LW pigs significantly differ from those of Chinese indigenous fatty MZ pigs. CNV is a prevalent genetic variation that plays an important role in economically important traits in pigs. However, the potential contribution of CNV to BFT in LW and MZ pigs remains unclear. In this study, whole-genome CNV detection was performed using next-generation sequencing data from LW and MZ pigs, and transcriptome data from back fat tissue of 180-day-old LW and MZ pigs were integrated for expression quantitative trait loci (eQTL) analysis. We identified a copy number variation in the TGFBR3 gene associated with BFT, showing a dose effect between the genome and transcriptome levels of the TGFBR3 gene. In porcine preadipocytes, TGFBR3 expression continuously increased during differentiation. Knockdown of TGFBR3 using specific siRNA inhibited preadipocyte differentiation and proliferation. Our study provides insights into the genetic regulation of pork quality and offers a theoretical basis for improving carcass quality by modulating BFT in pigs.

Background: Recent studies have established associations between the gut microbiota (GM) and thyroid diseases (TDs). However, their causal relationships remain elusive. Methods: To investigate this causality, we conducted a two-sample Mendelian randomization (MR) analysis using genome-wide association study (GWAS) data from MiBioGen and FinnGen, with GM as the exposure and six TDs as outcomes. Results: We identified 32 microbial taxa linked to the risk of six TDs. The Clostridium innocuum group, Ruminiclostridium5, and Lachnoclostridium exhibited protective effects against nontoxic diffuse goiter (NDG). Conversely, an increased risk of NDG was associated with Ruminococcaceae UCG002, Alistipes, Methanobrevibacter, Marvinbryantia, and Ruminococcaceae UCG014. Bifidobacterium and Sutterella were protective against nontoxic multinodular goiter (NMG), while the Ruminococcus gauvreauii group and Rikenellaceae RC9 gut group heightened NMG risk. Protective effects against nontoxic single thyroid nodule (NSTN) were observed with Defluviitaleaceae UCG011, Ruminococcus1, and Ruminococcaceae UCG010, whereas increased risk was linked to Alistipes, the Ruminococcus gauvreauii group, and Lachnospiraceae UCG010. Ruminiclostridium9, Victivallis, and Butyricimonas offered protection against thyrotoxicosis with Graves’ Disease (GD), while the Eubacterium rectale group, Desulfovibrio, Bifidobacterium, Collinsella, Oscillospira, and Catenibacterium were risk factors. For thyrotoxicosis with Plummer Disease (PD), protective taxa included Butyricimonas and Lachnospira, whereas Dorea, Eggerthella, Odoribacter, Lactobacillus, Intestinimonas, and Phascolarctobacterium increased risk. Lastly, Parasutterella was protective against thyrotoxicosis with toxic single thyroid nodule (TSTN), while increased risk was associated with Sutterella, Oscillibacter, and Clostridium sensu stricto1. Conclusions: Our findings support a causal relationship between specific GM and TDs at the genetic level, laying the foundation for future research into potential mechanisms and the identification of novel therapeutic targets.

The Paleogene sandstone reservoir of Huizhou Sag is an important target for deep exploration in the Pearl River Mouth Basin, South China Sea. Because of the intense volcanic activity, it had a significant impact on the development of reservoirs, making it hard to predict. The diagenetic process of volcanogenic sediment and their influence of the reservoir have been studied by petrographic analysis, X-ray diffraction and scanning electron microscopy (SEM). Four types of volcanogenic sediment were identified: volcanic dust (

Sooty mould (SM) disease affects the growth, development and metabolism of plants and reduces the commodity and economic value of crops. SM disease is one of the important leaf diseases in tea plants. Nonetheless, studies on the effect of SM disease in tea plants are rare. Herein, we found that SM disease disrupted the cell morphology and structure and reduced the contents of caffeine, theanine, and catechins in the mature leaves of tea plants. Transcriptome analysis revealed that SM disease inhibited the biosynthesis of lignin, chlorophyll, catechin, caffeine, and theanine and affected the plant-pathogen interactions in the mature leaves of tea plants by downregulating gene expression. In addition, two fungal isolates, MTzyqA and MTzyqB, were obtained from the mature leaves of diseased tea plants. These strains were identified as Cladosporium pseudocladosporioides by mulitgene phylogenetic analysis, and they grew epiphytically on the leaves of tea plants. The biocontrol bacteria JT68, ZGT5, and BX1 had obvious inhibitory effect on MTzyqA and MTzyqB. These results provide a basis for understanding the effect of SM disease in tea plants.

In recent years, the use of BIM software in clinical building tasks has been paid greater and extra attention. Taking Chengdu's scientific development mission as an example, this paper discusses the utility of BIM in scientific building projects and researches the impediment elements and answer strategies. This paper makes use of semi-structured interviews and literature evaluations to evaluate every influencing aspect earlier than by conducting quantitative research. After amassing facts via qualitative methods, an online questionnaire survey was once built in accordance with the interview consequences and previous lookup papers, and a number of factors blanketed in the online questionnaire had been comprehensively explained, and the software state of affairs and present issues of Building Information Modeling (BIM) in scientific development tasks have been summarized. The consequences of the lookup exhibit that the predominant boundaries to the positive adoption of BIM technological know-how in Medical Building Construction Projects (MBCPs) departments in Chengdu are multifaceted. First, issues from collaboration and verbal exchange stand out. These challenges are compounded with the aid of doubtful roles and obligations inside the mission team. This ambiguity now not solely hinders fantastic teamwork but also prevents BIM from seamlessly integrating with present workflows. Second, there are full-size boundaries to interoperability and compatibility problems between exclusive BIM software program applications. This incompatibility creates limitations to the clean alternate of records and data, leading to inefficiencies and plausible blunders in challenge execution. The large range of software programs used at one-of-a-kind degrees of MBCPs exacerbates this problem. Finally, the inherent complexity of BIM software programs and tools, coupled with the time-consuming and cumbersome BIM process, poses a big challenge. This complexity frequently leads to a reluctance amongst stakeholders to embody BIM technology. Given these findings, this study learns about the stage for future lookup and sensible interventions aimed at advertising wider acceptance and the tremendous use of BIM in the Medical Building Industry (MBI) and Construction Industry (CI).

Objective: This study was aimed at exploring the causal relationships of four sarcopenia-related traits (appendicular lean mass, usual walking pace, right hand grip strength, and levels of moderate to vigorous physical activity) with body mass index (BMI) and ovarian cancer risk, by using univariable and multivariable Mendelian randomization (MR) methods. Materials and Methods: Univariable and multivariable MR was performed to estimate causal relationships among sarcopenia-related traits, BMI, and ovarian cancer risk, in aggregated genome-wide association study (GWAS) data from the UK Biobank. Genetic variants associated with each variable (P < 5 × 10−8) were identified as instrumental variables. Three methods—inverse variance weighted (IVW) analysis, weighted median analysis, and MR-Egger regression—were used. Results: Univariable MR analyses revealed positive causal effects of high appendicular lean mass (P = 0.02) and high BMI (P = 0.001) on ovarian cancer occurrence. In contrast, a genetically predicted faster usual walking pace was associated with lower risk of ovarian cancer (P = 0.03). No evidence was found supporting roles of right hand grip strength and levels of moderate to vigorous physical activity in ovarian cancer development (P = 0.56 and P = 0.22, respectively). In multivariable MR analyses, the association between a genetically predicted faster usual walking pace and lower ovarian cancer risk remained significant (P = 0.047). Conclusions: Our study highlights a role of slower usual walking pace in the development of ovarian cancer. Further studies are required to validate our findings and understand the underlying mechanisms.

We propose an enhanced floor field model (FFM) to analyze the behavioral characteristics of crowds with varying attributes proportions during evacuation. This model governs pedestrian movement through the Dynamic Floor Field (DFF) and the Static Floor Field (SFF). The DFF takes into account individual factors such as the gender, familiarity with the environment, and social relationships of evacuees, which influence safe evacuation. Concurrently, the SFF encapsulates the impact of environmental factors like obstacles, exits, and guidance effects. Subsequently, this refined FFM was applied and validated using a sports center evacuation scenario. The results demonstrated that the enhanced FFM accurately replicated evacuees' asymmetric behavior and queuing, and aligned well with other models when the number of evacuees fluctuated over time. In the absence of guidance, both environmental familiarity and gender emerged as primary factors influencing partial evacuation. Additionally, the gender of pedestrians significantly affected the overall evacuation. Notably, compared to pre-existing environmental information available to evacuees, the implementation of guidance to augment pedestrians' environmental familiarity resulted in a more efficient evacuation. The FFM model and these findings could be instrumental in simulating personnel evacuation and formulating emergency management strategies in crowded areas.

Land cover classification is of great value and can be widely used in many fields. Earlier land cover classification methods used traditional image segmentation techniques, which cannot fully and comprehensively extract the ground information in remote sensing images. Therefore, it is necessary to integrate the advanced techniques of deep learning into the study of semantic segmentation of remote sensing images. However, most of current high-resolution image segmentation networks have disadvantages such as large parameters and high network training cost. In view of the problems above, a lightweight land cover classification model via semantic segmentation, DeepGDLE, is proposed in this paper. The model DeepGDLE is designed on the basis of DeeplabV3+ network and utilizes the GhostNet network instead of the backbone feature extraction network in the encoder. Using Depthwise Separable Convolution (DSC) instead of dilation convolution. This reduces the number of parameters and increases the computational speed of the model. By optimizing the dilation rate of parallel convolution in the ASPP module, the “grid effect” is avoided. ECANet lightweight channel attention mechanism is added after the feature extraction module and the pyramid pooling module to focus on the important weights of the model. Finally, the loss function Focal Loss is utilized to solve the problem of category imbalance in the dataset. As a result, the model DeepGDLE effectively reduces the parameters of the network model and the network training cost. And extensive experiments compared with several existing semantic segmentation algorithms such as DeeplabV3+, UNet, SegNet, etc. show that DeepGDLE improves the quality and efficiency of image segmentation. Therefore, compared to other networks, the DeepGDLE network model can be more effectively applied to land cover classification. In addition, in order to investigate the effects of different factors on the semantic segmentation performance of remote sensing images and to verify the robustness of the DeepGDLE model, a new remote sensing image dataset, FRSID, is constructed in this paper. This dataset takes into account more influences than the public dataset. The experimental results show that on the WHDLD dataset, the experimental metrics mIoU, mPA, and mRecall of the proposed model, DeepGDLE, are 62.29%, 72.85%, and 72.46%, respectively. On the FRSID dataset, the metrics mIoU, mPA, and mRecall are 65.89%, 74.43%, and 74.08%, respectively. For the future scope of research in this field, it may focus on the fusion of multi-source remote sensing data and the intelligent interpretation of remote sensing images.

Abstract Aims We aimed to investigate the independent associations between growth differentiation factor 15 (GDF‐15) level at admission and cardiovascular (CV) death, thrombotic events, heart failure (HF), and bleeding outcomes in patients with coronary artery disease (CAD). Methods and results We measured the plasma concentrations of GDF‐15 centrally in patients from the BIomarker‐based Prognostic Assessment for patients with Stable angina and acute coronary Syndrome (BIPass) registry, which consecutively enrolled patients with CAD from November 2017 to September 2019 at five tertiary hospitals in China. The outcomes included CV death, thrombotic events [myocardial infarction (MI) and ischaemic stroke], HF events [acute HF during hospitalization and hospitalization for HF post‐discharge (A/H HF) and cardiogenic shock], and bleeding outcomes [non‐coronary artery bypass grafting‐related major bleeding and clinically significant bleeding (CSB)] during the 12 month follow‐up period after hospitalization. Among 6322 patients with CAD {65.4% male, median age 63.7 [inter‐quartile range (IQR)] 56.0–70.1 years}, the median concentration of plasma GDF‐15 at admission was 1091 (IQR 790.5–1635.0) ng/L. Higher concentrations of GDF‐15 were associated with an increased risk of CV death [hazard ratio (HR) 1.98, 95% confidence interval (CI) 1.35–2.88, P

Cellulose nanocrystals (CNCs) with a high content of polar groups were prepared via the oxidation process by controlling the amount of mixed acid, incorporating additional additive of citric acid and vitamin C as active agents, and applying ultrasonic crosslinking. Subsequently, cellulose nanocrystal–silver (CNC–Ag) nanohybrid materials were synthesized via an oxidation hydrolysis reaction, which displayed good dispersibility and high interaction, leading to the hydrogen bonding between polar groups (-OH and -COOH) on the surface of CAC–Ag nanohybrids. The positive effects of hydrogen bonding on the surface of CAC–Ag nanohybrids were confirmed by the high carboxyl group content (2.69 mmol/g) and low contact angle (53.7°) tested. In addition, CAC–Ag nanohybrids showed significant antibacterial activity against both Gram-negative E. coli and Gram-positive S. aureus. These results showed that the high-performance CNC–Ag nanohybrids prepared in this study may be highly suitable as nano-fillers for polyester materials used in antibacterial food packaging.

Abstract The shape and internal structure of an atomic nucleus can change significantly with increasing excitation energy, angular momentum, or isospin asymmetry. As an example of this structural evolution, linear-chain configurations in carbon or heavier isotopes have been predicted for decades. Recent studies have found non-stability of this structure in 12C while evidenced its appearance in 16C. It is then necessary to investigate the linear-chain molecular structures in 14C to clarify the exact location on the nuclear chart where this structure begins to emerge, and thus to benchmark theoretical models. Here we show a cluster-decay experiment for 14C with all final particles coincidentally detected, allowing a high Q-value resolution, and thus a clear decay-path selection. Unambiguous spin-parity analyses are conducted, strongly evidencing the emergence of the π-bond linear-chain molecular rotational band in 14C. The present results encourage further studies on even longer chain configurations in heavier neutron-rich nuclei.

Abstract Background New vertebral compression fractures (NVCFs) are common adverse events in percutaneous kyphoplasty (PKP). The present study aimed to investigate the risk factors for NVCFs in patients after PKP and to construct a nomogram for the prediction of the risk of re-fracture. Methods We retrospectively analyzed the medical records of patients after PKP surgery between January 2017 and December 2020. Patients were divided into an NVCF group (n = 225) and a control group (n = 94) based on the presence or absence of NVCFs, respectively, at follow-up within 2 years after surgery. Lasso regression was used to screen for risk factors for re-fracture. Based on the results, a Lasso-logistic regression model was developed, and its prediction performance was evaluated using receiver operating characteristic curves, calibration, and decision curve analysis. The model was visualized, and a nomogram was constructed. Results A total of eight potential predictors were obtained from Lasso screening. Advanced age, low body mass index, low bone mineral density, lack of anti-osteoporosis treatment, low preoperative vertebral body height, vertebral body height recovery ≥ 2, cement leakage, and shape D (lack of simultaneous contact of bone cement with the upper and lower plates) were included in the logistic regression model. Conclusions A nomogram for predicting postoperative NVCF in PKP was developed and validated. This model can be used for rational assessment of the magnitude of the risk of developing NVCFs after PKP, and can help orthopedic surgeons make clinical decisions aimed at reducing the occurrence of NVCFs.

Pork belly, prized for its unique flavor and texture, is often overlooked in breeding programs that prioritize lean meat production. The quality of pork belly is determined by the number and distribution of muscle and fat layers. This study aimed to assess the number of pork belly layers using deep learning techniques. Initially, semantic segmentation was considered, but the intersection over union (IoU) scores for the segmented parts were below 70%, which is insufficient for practical application. Consequently, the focus shifted to image classification methods. Based on the number of fat and muscle layers, a dataset was categorized into three groups: three layers (n = 1811), five layers (n = 1294), and seven layers (n = 879). Drawing upon established model architectures, the initial model was refined for the task of learning and predicting layer traits from B-ultrasound images of pork belly. After a thorough evaluation of various performance metrics, the ResNet18 model emerged as the most effective, achieving a remarkable training set accuracy of 99.99% and a validation set accuracy of 96.22%, with corresponding loss values of 0.1478 and 0.1976. The robustness of the model was confirmed through three interpretable analysis methods, including grad-CAM, ensuring its reliability. Furthermore, the model was successfully deployed in a local setting to process B-ultrasound video frames in real time, consistently identifying the pork belly layer count with a confidence level exceeding 70%. By employing a scoring system with 100 points as the threshold, the number of pork belly layers in vivo was categorized into superior and inferior grades. This innovative system offers immediate decision-making support for breeding determinations and presents a highly efficient and precise method for assessment of pork belly layers.

With the continuous development and popularization of remote sensing technology, remote sensing images have been widely used in the field of land cover classification. Since remote sensing images have complex spatial structure and texture features, it is becoming a challenging problem to accurately categorize them. Land cover classification has practical application value in various fields, such as environmental monitoring and protection, urban and rural planning and management, and climate change research. In recent years, remote sensing image classification methods based on deep learning have been rapidly developed, in which semantic segmentation technology has become one of the mainstream methods for land cover classification using remote sensing image. Traditional semantic segmentation algorithms tend to ignore the edge information, resulting in poor classification of the edge part in land cover classification, and there are numerous attention mechanisms to make improvements for these problems. In this paper, a triple attention mechanism, BETAM (Border-Enhanced Triple Attention Mechanism), for edge feature enhancement of high-resolution remote sensing images is proposed. Furthermore, a new model on the basis of the semantic segmentation network model DeeplabV3+ is also introduced, which is called DeepBETAM. The triple attention mechanism BETAM is able to capture feature dependencies in three dimensions: position, space, and channel, respectively. Through feature importance weighting, modeling of spatial relationships, and adaptive learning capabilities, the model BETAM pays more attention to edge features, thus improving the accuracy of edge detection. A remote sensing image dataset SMCD (Subject Meticulous Categorization Dataset) is constructed to verify the robustness of the attention mechanism BETAM and the model DeepBETAM. Extensive experiments were conducted on the two self-built datasets FRSID and SMCD. Experimental results showed that the mean Intersection over Union (mIoU), mean Pixel Accuracy (mPA), and mean Recall (mRecall) of DeepBETAM are 63.64%, 71.27%, and 71.31%, respectively. These metrics are superior to DeeplabV3+, DeeplabV3+(SENet), DeeplabV3+(CBAM), DeeplabV3+(SAM), DeeplabV3+(ECANet), and DeeplabV3+(CAM), which are network models that incorporate different attention mechanisms. The reason is that BETAM has better edge segmentation results and segmentation accuracy. Meanwhile, on the basis of the self-built dataset, the four main classifications of buildings, cultivated land, water bodies and vegetation were subdivided and detected, and good experimental results were obtained, which verified the robustness of the attention mechanism BETAM and the model DeepBETAM. The method has broad application prospects and can provide favorable support for research and application in the field of surface classification.

Reasonable application of nitrogen fertilizer can improve the yield and quality of tea. This study used Jin Xuan as the tested variety and applied nitrogen fertilizer at rates of 0 kg/ha (N0), 150 kg/ha (N150), 300 kg/ha (N300), and 450 kg/ha (N450) in the summer and autumn seasons to analyze the effects of nitrogen application on the quality components and gene expression of tea leaves. The results showed that the N150 treatment significantly increased total polyphenols (TP), total catechins (TC), and caffeine contents, with the most significant increase observed in the content of six monomers of catechins (EGCG, ECG, EGC, GCG, GC, and EC) in the summer. The N300 treatment significantly increased TP and AA contents in the autumn while decreasing TC content. Additionally, the N300 treatment significantly increased caffeine and theanine contents in the autumn. Notably, the N300 treatment significantly increased both summer and autumn tea yields. Multivariate statistical analysis showed that TPs, AAs, TCs, EGC, and caffeine were key factors affecting the quality of Jin Xuan. Furthermore, the N150 treatment upregulated the expression of the phenylalanine ammonia-lyase (PAL) gene, which may increase the accumulation of catechins. In conclusion, it is recommended to apply 150 kg/ha of nitrogen fertilizer in the summer and 300 kg/ha of nitrogen fertilizer in the autumn. This recommendation provides a theoretical basis for improving the quality and yield of tea leaves in summer and autumn.

Nitrogen fertilization level and harvesting season significantly impact tea aroma quality. In this study, we analyzed the volatile organic compounds of fresh Jin Xuan (JX) tea leaves under different nitrogen application levels (N0, N150, N300, N450) during summer and autumn. A total of 49 volatile components were identified by gas chromatography–mass spectrometry (GC-MS). Notably, (E)-2-hexenal, linalool, and geraniol were the main contributors to the aroma of fresh JX leaves. The no-nitrogen treatment (N0) presented the greatest quantity and variety of volatiles in both seasons. A greater difference in volatile compounds was observed between nitrogen treatments in summer vs. autumn. The N0 treatment had a greater total volatile concentration in summer, while the opposite was observed in the nitrogen application treatments (N150, N300, N450). Summer treatments appeared best suited to black tea production. The concentration of herbaceous aroma-type volatiles was higher in summer, while the concentration of floral volatiles was higher in autumn. Volatile concentrations were highest in the N0 and N450 treatments in autumn and appeared suitable for making black tea and oolong tea. Overall, this research provides valuable insights into how variations in N application rates across different harvesting seasons impact the aroma characteristics of tea leaves.

Sorting out the requirements for intelligent functions is the prerequisite and foundation of the top-level design for the development of intelligent ships. In light of the development of inland intelligent ships for 2030, 2035, and 2050, based on the analysis of the division of intelligent ship functional modules by international representative classification societies and relevant research institutions, eight necessary functional modules have been proposed: intelligent navigation, intelligent hull, intelligent engine room, intelligent energy efficiency management, intelligent cargo management, intelligent integration platform, remote control, and autonomous operation. Taking the technical realization of each functional module as the goal, this paper analyzes the status quo and development trend of related intelligent technologies and their feasibility and applicability when applied to each functional module. At the same time, it clarifies the composition of specific functional elements of each functional module, puts forward the stage goals of China’s inland intelligent ship development and the specific functional requirements of different modules under each stage, and provides reference for the Chinese government to subsequently formulate the top-level design development planning and implementation path of inland waterway intelligent ships.

An indoor accelerated freezing and thawing test of polypropylene fibre-reinforced concrete in chloride and sulphate environments was conducted using the “fast-freezing method” with the objective of investigating the damage law of the post-freezing mechanical properties of hydraulic concrete structures and studying the effects of different mixing amounts of polypropylene fibres on the mechanical properties of concrete. Furthermore, in order to reduce the cost of concrete tests and shorten the time required for conducting concrete tests, a backpropagation neural network based on a Beetle Antenna Search algorithm (BAS-BPNN) was established to simulate and predict the mechanical properties of polypropylene fibre-reinforced concrete. The accuracy of the model was verified. The results indicate that the order of improvement in the macro-physical properties of concrete due to fibre doping is as follows: PPF1.2 exhibited the greatest improvement in macro-physical properties of concrete, followed by PPF0.9, PPF1.5, PPF0.6, and PC. When the freezing and thawing medium and the number of cycles are identical, all four assessment indexes (R2, RMSE, SI, MAPE) demonstrate that the four groups of polypropylene fibre concrete exhibit superior performance to the control group of ordinary concrete. This indicates that polypropylene fibre can enhance the mechanical properties and freezing resistance of the concrete matrix, delay the process of freezing and thawing damage to the matrix, and extend the lifespan of the matrix, yet cannot prevent the ultimate failure of the matrix. The application of intelligent algorithms to optimise the parameters of an artificial neural network model can enhance its capacity to generalise and predict the mechanical properties of concrete. In terms of the coefficient of determination (R2), the Beetle Antenna Search algorithm (0.9782) outperforms the Particle Swarm Optimization (PSO; 0.9676), the Genetic Algorithm (GA; 0.9645), and the backpropagation neural network (BPNN; 0.9460). The improved backpropagation neural network based on the Beetle Antenna Search algorithm not only avoids the trap of local optimality but also improves the model accuracy while further accelerating the convergence speed. This approach can address the complexity, non-linearity, and modelling difficulties encountered during the freezing process of concrete. Moreover, it offers relatively accurate prediction outcomes at a reduced cost in comparison to traditional experimental methodologies.

Deep learning has emerged as a powerful tool for investigating intricate biological processes in plants by harnessing the potential of large-scale data. Gene regulation is a complex process that transcription factors (TFs), cooperating with their target genes, participate in through various aspects of biological processes. Despite its significance, the study of gene regulation has primarily focused on a limited number of notable instances, leaving numerous aspects and interactions yet to be explored comprehensively. Here, we developed DEGRN (Deep learning on Expression for Gene Regulatory Network), an innovative deep learning model designed to decipher gene interactions by leveraging high-dimensional expression data obtained from bulk RNA-Seq and scRNA-Seq data in the model plant Arabidopsis. DEGRN exhibited a compared level of predictive power when applied to various datasets. Through the utilization of DEGRN, we successfully identified an extensive set of 3,053,363 high-quality interactions, encompassing 1430 TFs and 13,739 non-TF genes. Notably, DEGRN’s predictive capabilities allowed us to uncover novel regulators involved in a range of complex biological processes, including development, metabolism, and stress responses. Using leaf senescence as an example, we revealed a complex network underpinning this process composed of diverse TF families, including bHLH, ERF, and MYB. We also identified a novel TF, named MAF5, whose expression showed a strong linear regression relation during the progression of senescence. The mutant maf5 showed early leaf decay compared to the wild type, indicating a potential role in the regulation of leaf senescence. This hypothesis was further supported by the expression patterns observed across four stages of leaf development, as well as transcriptomics analysis. Overall, the comprehensive coverage provided by DEGRN expands our understanding of gene regulatory networks and paves the way for further investigations into their functional implications.

Abstract Background The main objective of this study was to investigate whether the use of bone cement in total knee arthroplasty (TKA) has an effect on postoperative coagulation status and bleeding. Methods 153 patients who underwent unilateral TKA between September 2019 and February 2023 were collected and divided into Bone and Cement&Bone groups according to whether bone cement was used to seal the bone medullary canal intraoperatively. Routine blood and thromboelastography (TEG) examinations were performed on the day before, the first day and the seventh day after surgery; postoperative bleeding, drainage, transfusion rate and the number of people suffering from deep venous thrombosis (DVT) were recorded. Results There were no significant differences between the two groups in terms of baseline clinical characteristics before surgery (P > 0.05). In terms of TEG indicators, the coagulation index (CI) of the Bone&Cement group was lower than that of the Bone group on the first postoperative day and on the seventh postoperative day (P

Arid and humid ecosystems are undergoing significant changes over the past decades due to the warming temperature and frequent occurrence of climate extremes. Quantifying climatic vegetation sensitivity in regions with different climate classifications is crucial for understanding the interaction mechanisms between vegetation and ongoing climate change. However, such knowledge about the spatiotemporal variations of vegetation sensitivity and its climate driving factors in arid and humid regions is still unclear. Based on this consideration, a moving-window-based Vegetation Sensitivity Index (VSI) scheme was applied to Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Solar-induced Chlorophyll Fluorescence (GOSIF) data to evaluate the spatiotemporal variations of terrestrial ecosystem in response to climate changes and explore its driving climatic factors from 2003 to 2020. Results indicated that high VSI (>60) was observed for tropical rainforests in humid regions and central Eurasia in semi-arid regions. By contrast, low VSI (

Coronavirus disease 2019 (COVID-19) is associated with immune dysregulation and cytokine storm. It is essential to explore the immune response characteristics of peripheral circulation in COVID-19 patients to reveal pathogenesis and predict disease progression. In this study, the levels of total immunoglobulins (IgG, IgM, IgA), complement (C3, C4)，lymphocyte subsets (CD3+ cell，CD4+ cell，CD8+ cell, NK cell, CD19+ cell and CD45+ cell) and cytokines (IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-17, IL-12p, IL-1β, TNF-α, IFN-α and IFN-γ) were retrospectively analyzed in COVID-19 patients. A total of 513 patients were enrolled in this study, cases were distributed according to clinical status as mild or moderate (n = 212), severe survivors (n = 197) and severe non-survivors (n = 104). IL-6, IL-8, IL-10 and IFN-γ were increased in severe patients compared with non-severe patients, despite decreased CD45+ cell, CD3+ cell, CD4+ cell, CD8+ cell, CD19+ cell, and NK cell. Compared with severe survivors, the levels of L-6, IL-8 and IL-10 in non-survivors increased significantly, and levels of C3, CD45+ cell, CD3+ cell，CD4+ cell，CD8+ cell, and NK cell decreased. Moreover, age, IL-8, IL-10, CD8+cells and NK cell were independent risk factors for the severity of COVID-19. Multivariable regression showed increasing odds ratio of in-hospital death associated with tumor, older age, higher IL-8 level, and decreasing odds ratio of in-hospital death associated with increased levels of CD8+cell and NK cell. Finally, patients with tumor, or high IL-6 or high IL-10 expression and lower CD8+ or lower NK levels exhibited a significantly shorter survival time. In conclusion, our study provides findings of the immunological characteristics associated with disease severity to predict the progression of COVID-19. The immune inflammation factors, such as IL-6, IL-8, IL-10, CD8+ cell and NK cell, could serve as excellent biomarkers for monitoring or predicting COVID-19 progression therapeutic to COVID-19 patients.

Major Sn deposits are commonly linked to crust-derived and highly evolved granites, with magma generation aided by mantle heating. However, whether and how the mantle components contribute to Sn polymetallic mineralization remains unclear. In this study, in combination with a compilation of equivalent data in the region, we provide new constraints on this issue based on detailed investigations on the petrogenesis and metallogenic significance of granitoids including the causative batholith and later granodiorite porphyry dike in the giant Dachang Sn deposit from South China. The former has zircon U-Pb ages of 93–91 Ma and belongs to highly evolved S-type biotite granite, which experienced fractionation of massive feldspar. The latter shows zircon U-Pb ages of 90 Ma and displays I-type granite features. The batholith was mainly derived from the dehydration melting of biotite in the metasedimentary sources, as revealed by the relatively low whole-rock Pb contents (850 °C), whole-rock Mg# (52 to 58), apatite εNd(t) (−9.2 to −6.5) and zircon εHf(t) (−7.6 to 2.5) values but lower zircon δ18O values (6.33 to 8.30 ‰) of the later granodiorite porphyry dike than those of the batholith also suggest that mantle material was involved in the generation of the dikes, which is evident by the variational features of zircon and apatite trace elements. In addition, at the zircon Hf 0.05, the higher zircon ΔFMQ values (mostly from −1.8 to 2.0) and H2O contents (100–1100 ppm) of the Dachang granitoids than the pure crust-derived S-type granites (ΔFMQ = mostly from −3.7 to −1.5; H2O

Background It remains controversial whether changes of lipids over menopause transition (MT) are more age‐related or more menopause‐related. We aimed to classify women into different trajectory groups based on pattern and level of total cholesterol, low‐density lipoprotein cholesterol, apolipoprotein B (ApoB), high‐density lipoprotein cholesterol (HDL‐C), triglyceride, and apolipoprotein A‐I over the MT, as well as examine the effect of MT‐related factors on lipid trajectory groups and levels. Methods and Results The cohort included 2582 subjects from the Study of Women's Health Across the Nation. Different trajectory patterns of lipids during the MT were determined using the latent class growth mixture model. The predictors of distinct blood lipids trajectory groups were determined by multiple linear regression models and multinomial logistic regression models. Women were categorized into either inverse U‐shape or progressing trajectory group in each blood lipids measurement. The inverse U‐shape total cholesterol, low‐density lipoprotein cholesterol, apolipoprotein B, high‐density lipoprotein cholesterol, log(TG), and apolipoprotein A‐I trajectories showed an increasing trend before menopause but a decreasing trend after menopause. The U‐shape total cholesterol, low‐density lipoprotein cholesterol, and apolipoprotein B trajectories started to rise 5 years before menopause. Age at menopause, follicle‐stimulating hormone, vasomotor symptoms, and estradiol predicted the shape and level of the women's lipids over the MT. Conclusions Distinct lipid trajectories were identified during the MT, and the existence of at least 1 trajectory in each lipid parameters suggested a contribution of menopause. Our study highlights the need for earlier and continuous surveillance of lipids during the MT.