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Abstract In order to address challenges such as small target sizes, low contrast, significant intra-class variations, and indistinct inter-class differences in surface defect detection, this paper proposes the Enhanced Context-aware Parallel Fusion Network (EC-PFN). The network employs a Featur Weave Network architecture to enhance contextal awareess and parallel fusion capabilities. It utilizes a Feature Fusion Module (UniFusionLayer) for effective multiscale and multisemantic feature learning, offering new perspectives on feature fusion. Additionally, a Receptive Field Block (RFB) module is introduced to expand the receptive field, enhancing feature extraction in scenarios with low contrast and subtle defects. The Loss Ranking Module (LRM) is incorporated to optimize the target-oriented loss, improving performance by omitting low-confidence bounding boxes. Extensive experiments on a light guide plate defect dataset demonstrate that EC-PFN achieves a detection accuracy (mAP) of 98.9%, a detection speed of 92 FPS, and a computational cost of 14.5 GFLOPs, outperforming mainstream surface defect detection models.

Abstract Introduction The Enhanced Recovery After Surgery (ERAS) protocol, a comprehensive multimodal approach, aims to mitigate surgical stress, expedite recovery, and improve postoperative outcomes. Its implementation has notably advanced perioperative care in colorectal cancer surgeries. Integrating ERAS with multidisciplinary collaboration, involving surgery, anesthesia, nursing, and nutrition, may further enhance patient outcomes, making it a significant focus in clinical practice. Methods This study assessed the effectiveness of integrating the ERAS model with multidisciplinary collaboration during the perioperative period in colorectal cancer patients. A total of 117 patients scheduled for elective surgery at Haiyan People's Hospital between August 2023 and April 2024 were randomly assigned to either a control group (n = 59), receiving traditional care, or an experimental group (n = 58), receiving ERAS-based multidisciplinary care. Key outcomes related to postoperative rehabilitation were evaluated. Results Patients in the ERAS group demonstrated significantly shorter hospital stays, quicker catheter removal, and earlier mobilization compared to the control group (P

Abstract X-linked retinitis pigmentosa (XLRP) is a severe hereditary retinal disorder marked by progressive vision loss due to photoreceptor dysfunction. The retinitis pigmentosa GTPase regulator (RPGR) gene, responsible for most XLRP cases, encodes a protein crucial for the transport of visual signal proteins between the photoreceptor inner and outer segments. However, the mechanism of RPGR mutation causing photoreceptor disorder is not clear and effective treatments remain elusive. This study utilized retinal organoids (ROs) derived from normal and RPGR-mutant human induced pluripotent stem cells (hiPSC) at four developmental stages (40, 90, 150, and 200 days). Single-cell RNA sequencing (scRNA-seq) was conducted on 71,096 cells, including 33,839 cells from the control group and 37,257 cells from the RPGR group. Key retinal cell types were identified and the obtained scRNAseq dataset was validated reliable and high -quality. This study has provided data resources and references for exploring the mechanism of RPGR-related retinal degeneration and support the development of targeted therapies.

Abstract DNBSEQ employs a patterned array to facilitate massively parallel sequencing of DNA nanoballs (DNBs), leading to a considerable boost in throughput. By employing the ultra-high-density (UHD) array with an increased density of DNB binding sites, the throughput of DNBSEQ can be further expanded. However, the typical imaging system of the DNBSEQ sequencer is unable to resolve adjacent DNBs spaced smaller than the resolution limit, resulting in poor base-calling performance of the UHD array and hindering its practical application. In this study, we propose a deep-learning-based DNB image super-resolution network named DNBSRN to address this problem. DNBSRN has a specifically designed structure for DNB images and employs a histogram-matching-based preprocessing approach. For the eight DNB image datasets generated from the DNBSEQ sequencer using UHD arrays with 360 nm pitch, the base-calling performances are significantly improved after super-resolution reconstruction by DNBSRN and reached a comparable level to those of the regular density array. In terms of reconstruction speed, DNBSRN takes only 7.61 ms for an input image with 500 × 500 pixels, which minimizes its influence on throughput. Furthermore, compared with state-of-the-art super-resolution networks, DNBSRN demonstrates superior performance in terms of both the quality and speed of DNB image reconstruction. DNBSRN successfully addresses the DNB image super-resolution task. Integrating DNBSRN into the image analysis workflow of DNBSEQ will allow for the application of UHD array, hence enabling a considerable improvement in throughput as well as tremendous savings in unit reagent cost.

Abstract The relationship between the platelet to albumin ratio (PAR) and the disease activity of early rheumatoid arthritis (ERA) remains to be elucidated. This cross-sectional study included 372 patients with ERA who were hospitalized at Xingtai People’s Hospital between January 2022 and January 2024. Multiple linear regression analysis was employed to assess the correlation of PAR with Disease Activity Score 28 (DAS28) using erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), and then generalized additive models were used to explore their relationship. After accounting for confounding variables, a positive correlation was observed between the PAR and DAS28-ESR and DAS28-CRP in ERA patients (β (95% CI): 0.20 (0.08, 0.32) and 0.20 (0.08, 0.33), respectively), and the general trends were consistent from the lowest tertile group (T1) to the highest tertile group (T3). Furthermore, a nonlinear relationship was identified between PAR and both DAS28-ESR and DAS28-CRP. Threshold effect analysis indicated that a value of 13.73 was a significant inflection point. In brief, PAR has a threshold effect on the disease activity of ERA. Increased PAR levels were independently positive associated with DAS28-ESR and DAS28-CRP among ERA patients with PAR

Abstract This work explores the extraction of the spatial distribution and chemical composition information of pigments in colored relics through visible spectral images. An adaptive superpixel segmentation method is proposed to extract the spatial distribution information of pigments. Quadtree decomposition is applied to generate nonuniform initial seed points based on the image homogeneity. These seed points are used as the initial cluster centers in an extended simple linear iterative clustering (SLIC) algorithm for visible spectral images to create superpixels of varying sizes that reflect the homogeneity. Each superpixel is subsequently treated as an individual area in the colored relics, and a pigment identification method based on the visible spectral reflectance is proposed to identify the pigments in these areas. A standard reference database is constructed using samples that simulate the painting process of ancient wall paintings in the Mogao Grottoes. Geometric features, which are characterized by the linear combination of the normalized visible spectral reflectance, its slope and its curvature, are designed to represent the chemical composition of pigments. The geometric features of the superpixels are compared with those of the pigments in the database using the Euclidean distance to determine the pigments in each area of the colored relics. This work is expected to provide scientific guidance for pigment selection in the color restoration of colored relics.

Abstract This study aims to investigate the interplay between tumor-associated endothelial cells (TECs) and immune cells within the tumor microenvironment (TME) and its impact on tumor prognosis. We conducted single-cell RNA sequencing (scRNA-seq) of tumor, normal, and lymph node tissues obtained from intrahepatic cholangiocarcinoma (ICC) patients to reveal the role of TECs in tumor angiogenesis and their significant heterogeneity. Meanwhile, we identified genes highly expressed in TECs and constructed TEC signatures (TEC.Sig). Next, we calculated TEC scores of samples based on TEC.Sig. Patients with higher TEC scores exhibited a higher frequency of KRAS mutations, which was associated with increased infiltration of neutrophils and immature dendritic cells (iDCs), and decreased numbers of natural killer (NK), CD4 + T, and CD8 + T effector memory (Tem) cells, indicating an inflammation-dominated immunosuppressive phenotype. In contrast, BAP1 mutations and CXCL12 overexpression showed a contrasting trend. Spatial transcriptomics analysis and histological experiments further confirmed that TECs interacted with various tumor-killing immune cells through the CXCL12/CXCR4 axis. Multiple tumor immunotherapy datasets confirmed that the TEC.Sig could predict patient responses to immunotherapy. The TEC score is a promising and reliable biomarker for predicting genetic mutations and prognosis in ICC patients. Enhancing the regulation of the CXCL12/CXCR4 signaling pathway may represent a potential novel therapeutic target for ICC treatment.

Abstract Background Sepsis-associated acute kidney injury (AKI) is a serious complication of systemic infection with high morbidity and mortality in patients. However, no effective drugs are available for AKI treatment. Dexmedetomidine (DEX) is an alpha 2 adrenal receptor agonist with antioxidant and anti-apoptotic effects. This study aimed to investigate the therapeutic effects of DEX on sepsis-associated AKI and to elucidate the role of mitochondrial dynamics during this process. Methods A lipopolysaccharide (LPS)-induced AKI rat model and an NRK-52E cell model were used in the study. This study investigated the effects of DEX on sepsis-associated AKI and the molecular mechanisms using histologic assessment, biochemical analyses, ultrastructural observation, western blotting, immunofluorescence, immunohistochemistry, qRT-PCR, flow cytometry, and si-mRNA transfection. Results In rats, the results showed that administration of DEX protected kidney structure and function from LPS-induced septic AKI. In addition, we found that DEX upregulated the α2-AR/SIRT1/PGC-1α pathway, protected mitochondrial structure and function, and decreased oxidative stress and apoptosis compared to the LPS group. In NRK-52E cells, DEX regulated the mitochondrial dynamic balance by preventing intracellular Ca2+ overloading and activating CaMKII. Conclusions DEX ameliorated septic AKI by reducing oxidative stress and apoptosis in addition to modulating mitochondrial dynamics via upregulation of the α2-AR/SIRT1/PGC-1α pathway. This is a confirmatory study about DEX pre-treatment to ameliorate septic AKI. Our research reveals a novel mechanistic molecular pathway by which DEX provides nephroprotection.

Nasopharyngeal carcinoma (NPC) is a malignant tumor predominantly influenced by Epstein-Barr virus infection and genetic factors. The transforming growth factor-beta (TGF-β) superfamily is implicated in various cellular processes, including tumorigenesis. This study aimed to detect the role of one TGF-β superfamily member activin receptor type IIB (ACTRIIB) in NPC. This study analyzed NPC datasets, including GSE12452, GSE102349, and GSE53819. ACTRIIB expression and N-glycosylation levels were assessed by western blot, real-time PCR, immunofluorescence, and immunohistochemistry in NPC cells and tissues. As indicated by the datasets, ACTRIIB was significantly upregulated in NPC tissues, and the up-regulation was associated with poor prognosis. This study confirmed the N-glycosylation of ACTRIIB primarily at the forty-second amino acid, an asparagine. The N-glycosylation of ACTRIIB promoted the localization of ACTRIIB to the cell membrane and prevented the degradation of the protein by lysosomes, through which ACTRIIB activated the downstream Smard1/2 to promote tumor cell proliferation and invasion. Inhibition of N-glycosylation or knockdown of ACTRIIB resulted in reduced cell proliferation and invasion and increased the cell sensitivity to docetaxel. In conclusion, N-glycosylation of ACTRIIB was a critical post-translational modification that enhanced protein stability and induced membrane localization, which facilitates the functions of ACTRIIB in cell proliferation and invasion in NPC. Inhibition of ACTRIIB N-glycosylation could potentially serve as a therapeutic strategy to improve the efficacy of chemotherapy in NPC.

ABSTRACT Background and Aims Adolescent major depressive disorder (MDD) is prevalent globally but often goes unnoticed due to differences in symptoms compared to adult criteria. Analyzing the brain from a network perspective provides new insights into higher‐level brain functions and its pathophysiology. This study aimed to investigate changes in the topological organization of functional networks in adolescents with first‐episode, treatment‐naïve MDD. Method The study included 23 adolescents with depression and 27 matched healthy controls (HCs). Resting‐state functional MRI (rs‐fMRI) was conducted, and whole‐brain functional networks were constructed. Graph theory analysis was used to evaluate network topological properties. A machine‐learning multivariate diagnostic model was developed using network metrics associated with depression severity. Results Both the MDD and HC groups displayed small‐world topology, with male MDD patients showing reduced global clustering efficiency (Cp). The nodal Cp (NCp) and local efficiency (NLE) in the bilateral pallidum were significantly positively correlated with depression severity. In contrast, nodal efficiency (NE) in the left medial orbital superior frontal gyri (ORBsupmed) showed a negative correlation with disease severity. A machine‐learning multivariate model using regional network topological features produced an AUROC of 0.71 (95% CI: 0.54–0.92) and an F1 score of 0.65, successfully differentiating adolescent MDD from HCs. Conclusion Our findings suggest disruptions in small‐world topology in both global and local brain networks in adolescent depression. These abnormal nodal properties may serve as novel neural markers of the disorder.

IntroductionThe brain’s spontaneous neural activity can be recorded during rest using resting state functional magnetic resonance imaging (rs-fMRI), and intricate brain functional networks and interaction patterns can be discovered through correlation analysis. As a crucial component of rs-fMRI analysis, effective connectivity analysis (EC) may provide a detailed description of the causal relationship and information flow between different brain areas. It has been very helpful in identifying anomalies in the brain activity of depressed teenagers.MethodsThis study explored connectivity abnormalities in brain networks and their impact on clinical symptoms in patients with depression through resting state functional magnetic resonance imaging (rs-fMRI) and effective connectivity (EC) analysis. We first introduce some common EC analysis methods, discuss their application background and specific characteristics.ResultsEC analysis reveals information flow problems between different brain regions, such as the default mode network, the central executive network, and the salience network, which are closely related to symptoms of depression, such as low mood and cognitive impairment. This review discusses the limitations of existing studies while summarizing the current applications of EC analysis methods. Most of the early studies focused on the static connection mode, ignoring the causal relationship between brain regions. However, effective connection can reflect the upper and lower relationship of brain region interaction, and provide help for us to explore the mechanism of neurological diseases. Existing studies focus on the analysis of a single brain network, but rarely explore the interaction between multiple key networks.DiscussionTo do so, we can address these issues by integrating multiple technologies. The discussion of these issues is reflected in the text. Through reviewing various methods and applications of EC analysis, this paper aims to explore the abnormal connectivity patterns of brain networks in patients with depression, and further analyze the relationship between these abnormalities and clinical symptoms, so as to provide more accurate theoretical support for early diagnosis and personalized treatment of depression.

Organized scientific research is vital to implementing organized technological innovation in modern industrial colleges. Young teachers’ deep participation determines success. Based on the theory of planned behavior and normative activation, a survey of 309 young teachers from modern industrial colleges participating in organized scientific research found that personal norms, subjective norms, behavioral attitudes, and perceived behavioral control significantly impact their intention to participate. Awareness of consequence, ascribed responsibility, and subjective norms indirectly positively impact their participation intention, and the participation intention and perceived behavioral control positively affect their participation behavior. Policy support has a positive mediating effect between participation intention and behavior. There are group differences in gender, education level, professional title, and subject type in demographic characteristics in five aspects: subjective norms, personal norms, perceived behavioral control, participation intention, and participation behavior. This article proposes the following measures: improving the internal interest coordination mechanism of scientific research organizations to enhance young teachers’ sense of scientific research achievement; reconstructing the organized scientific research evaluation model to enhance young teachers’ teamwork awareness; improving the support system for organized research policies to stimulate young teachers’ self-efficacy; concerning young teachers’ group differences and creating a research environment that tolerates failure.

BackgroundSmall cell carcinoma of the esophagus (SCCE) is a rare form of esophageal cancer, which also belongs to the category of neuroendocrine malignant tumors, with a low incidence but high aggressiveness, and a very poor prognosis for patients. Currently, there is a lack of unique staging and treatment guidelines for SCCE. Therefore, a deeper understanding of the therapeutic targets and the mechanisms underlying its occurrence and development is of great importance for early diagnosis, identification of potential therapeutic agents and improvement of the prognosis for patients.MethodsFirstly, the dataset of SCCE was downloaded from the GEO database and GEO2R tool was employed for the analysis of differentially expressed genes (DEGs). GO and KEGG analysis of DEGs were carried out by using the Bioinformatics and OmicStudio tools. Then, up- and down-regulated genes were intersected with the oncogenes and the tumor suppressor genes respectively, to obtain the differentially expressed onco/tumor suppressor genes in SCCE. The STRING database was employed to conduct protein-protein interaction (PPI) analysis of differentially expressed onco/tumor suppressor genes, the network was further constructed in Cytoscape, and hub genes of the network were obtained through the Cytohubba plugin. In addition, miRDB, miRwalk, Targetscan, OncomiR, starbase, Lncbase were used to predict miRNAs and lncRNAs that regulate hub genes, the ceRNA network was built based on this. Transcription factor-miRNA co-regulatory network was analyzed in the NetworkAnalyst database and embellished in Cytoscape. Finally, drugs that may target hub genes were searched through the DGIdb and ConnectivityMAP, and docking verification was performed using Schrodinger.ResultsA total of 820 genes were upregulated and 716 were downregulated, of these, 54 were identified as oncogenes and 85 as tumor suppressor genes. Seven hub genes were identified from the PPI network, which were AURKA, BIRC5, CDK1, EZH2, FOXM1, KLF4 and UBE2C. Furthermore, a total of 38 drugs were searched and filtered in DGIdb and ConnectivityMAP, in the molecular docking results of drugs with hub genes, the docking score of AURKA, CDK1, and EZH2 with multiple drugs were low (

PurposeTo evaluate the clinical performance of expanded non-invasive prenatal testing (NIPT-plus) and compare its effectiveness in screening for chromosomal aneuploidies with that of NIPT.MethodsScreening results, confirmatory invasive testing results, and follow-up data from pregnant women who underwent either NIPT (6792 cases) or NIPT-Plus (5237 cases) testing at Luohe Central Hospital, China, from January 2019 to June 2023 were collected. The positive predictive value (PPV), sensitivity, specificity, and other indicators for different types of chromosomal abnormalities in NIPT/NIPT-plus screening were calculated. The willingness of pregnant women with various types of abnormalities to undergo confirmatory invasive testing and the proportion of pregnancy terminations were investigated.ResultsThe average number of unique reads in NIPT-plus samples was 5.26 times greater than that in NIPT samples. There was no significant difference in the PPV or positive rate between NIPT-plus and NIPT for screening chromosomal aneuploidies. Compared with the low-risk group, the high-risk group had a greater PPV; however, in the NIPT-plus group, there was no significant disparity in the PPV between the low-risk and high-risk groups. Compared with rare autosomal aneuploidies (RAAs), pregnant women had a higher rate of confirmatory invasive testing for common trisomies, sex chromosomal abnormalities (SCAs), and copy number variations (CNVs). However, the rate of pregnancy termination for common trisomies, RAAs, and CNVs was higher than that for SCAs.ConclusionBy enhancing sequencing data, NIPT-plus can effectively screen for CNVs as well as chromosomal aneuploidies. However, NIPT-plus does not have an advantage over standard NIPT in screening for chromosomal aneuploidies.

BackgroundEnhancing motor skills is crucial for the functional development of children with cerebral palsy. Virtual reality has emerged as a promising technology for rehabilitating these children.ObjectiveThe objective of this study was to systematically evaluate the effects of virtual reality motor games on the gross and fine motor skills of children with cerebral palsy.MethodsA comprehensive search was conducted in databases including PubMed, ProQuest, EBSCOhost, Web of Science, and Wanfang Data, covering publications from their inception to June 1, 2024, to identify randomized controlled trials (RCTs) investigating the effects of virtual reality motor games on the gross and fine motor skills of children with cerebral palsy. The quality of the included studies was assessed using the PEDro scale, and data were analyzed with RevMan software (version 5.4).ResultsNineteen studies involving 850 children with cerebral palsy were included. The results indicated that virtual reality motor games significantly improved gross motor skills [mean difference (MD) 1.67, 95% confidence interval (CI): 0.75–2.56; P < 0.001] and fine motor skills [standardized mean difference (SMD) 0.73, 95% CI: 0.30–1.16; P = 0.00008] in children with cerebral palsy.ConclusionsVirtual reality motor games have the potential to significantly enhance both gross and fine motor skills in children with cerebral palsy.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, PROSPERO [CRD42024558713].

The mechanical properties of multi-lithologic reservoir rock masses are complex, and the failure mechanism is not clear. This research belongs to the field of oil and gas exploration and development. Brazilian splitting, and digital image correlation (DIC) tests were performed to study the mechanical properties and failure mechanism of assemblages containing sandstone, shale, and limestone. In the Brazilian splitting test, the tensile strength of the rock mass first increased and then decreased with an increasing angle. As the interface angle increased, The morphology of the splits is also changing. In the DIC test, according to the deformation characteristics at different angles, the Brazilian splitting test process was summarized into three stages: interface deformation, elastoplastic, and failure, the interface has a great influence on the fracture propagation, and the fracture tends to expand along the interface. Interface characteristics were studied based on 3D scanning technology and fractal method, with an increase in the angle, the fractal dimension D showed first decreased, then increased, and further decreased, which was identical to the variation trend of the maximum load in the Brazilian splitting test and the maximum principal strain in the DIC test with the angle. This study provides reference for predicting fracture propagation trend in field hydraulic fracturing work.

Abstract Identifying pigments from colored relics is essential for their color restoration and for facsimile creation. A workflow for identifying pigment information is constructed based on visible spectral imaging technology, aligned with the drawing process of colored relics. This workflow includes three steps: boundary extraction, material identification and prediction of mixture proportions. The methods for segmenting visible spectral images, identifying chemical compositions, and predicting mixture proportions of pigments are extensively reviewed. Future research trends of these methods are also analyzed. The influence of the pigment particle size is currently underexplored but can be accomplished by multidisciplinary research.

The Rainfall thresholds are among the most commonly used criteria for predicting rainfall-induced landslides. However, existing empirical rainfall thresholds mainly focused on regional landslide warnings, lacking consideration for the spatial variability of rainfall thresholds for individual landslides within the region. This study uses historical rainfall-induced landslide data and hourly rainfall data from Bazhong City (2014 – 2021) to employ Kriging interpolation methods. It extracts four types of short-term rainfall (1 hour, 12 hours, 24 hours, 72 hours) and their corresponding long-term rainfall (7 days before the landslide occurrence). In these four threshold models, the distribution of long-term and short-term rainfall thresholds in each group is calculated and then validated using landslide disaster data from 2021. The research results indicate that the prediction accuracy of the four threshold models ranges from 40% to 65%, suggesting good potential for practical application. Additionally, the prediction accuracy improves with the increase in the duration of short-term rainfall. The prediction accuracy for rainfall thresholds calculated from the 72-hour-7-day model is highest, reaching 62%, while the 1-hour-7-day model achieves 46%. Based on the highest prediction accuracy of these models, the study calculates the optimal ratios for short-term and long-term disaster-causing rainfall for four types of models. This leads to a quantitative division between short-term rainfall-induced landslides and long-term rainfall-induced landslides. By calculating the spatial distribution of disaster-causing rainfall, the study extracts rainfall thresholds at potential landslide locations, achieving the goal of one threshold per site in the region and enhancing existing models for calculating rainfall thresholds.

Abstract Background Deciphering the assembly rules of microbial communities is vital for a mechanistic understanding of the general principles driving microbiome structures and functions. In this study, a null modeling-based framework was implemented to infer the assembly rules of bacterial community in oat silages harvested in southern China starting from the grain-filling stage through to full ripening. Results Most silages displayed “inferior” or “very inferior” fermentation quality. The fermentation qualities of silages tended to further decrease with the delay of harvest. Lactobacillus, Pediococcus, unclassified_f_Enterobacteriaceae, and Hafnia–Obesumbacterium constituted the predominated genera in silages. Delaying harvest increased the proportions of Hafnia–Obesumbacterium. Null model analysis revealed that stochastic processes were the primary contributor to the assembly of rare subcommunity during silage fermentation. The succession of abundant subcommunity was controlled both by stochastic and deterministic processes. Deterministic processes, more specifically, heterogeneous selection, were more prominent in the assembly of abundant bacteria in silages with the delay of harvest. Linear regression analysis indicated the important roles of DM, WSC and pH in the assembly of abundant subcommunity. Conclusion This study, from the ecological perspectives, revealed the ecological processes controlling the bacterial community assembly in silage, providing new insights into the mechanisms underlying the construction of silage bacterial community. Graphical Abstract

Abstract Background Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. The large variation of anisotropic spatial resolution limits the ability of 3D convolutional neural networks (CNNs) to learn features from different views. The background texture of gastric tumor is complex, and its size, shape and intensity distribution are highly variable, which makes it more difficult for deep learning methods to capture the boundary. In particular, while multi-center datasets increase sample size and representation ability, they suffer from inter-center heterogeneity. Methods In this study, we propose a new cross-center 3D tumor segmentation method named Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale context features from the CT images with anisotropic resolution, and a hierarchical class-aware domain alignment (HCADA) module for adaptively aligning multi-scale context features across two domains by integrating a class attention map with class-specific information. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers and validate its segmentation performance in both in-center and cross-center test scenarios. Results Our baseline segmentation network (i.e., AsTr) achieves best results compared to other 3D segmentation models, with a mean dice similarity coefficient (DSC) of 59.26%, 55.97%, 48.83% and 67.28% in four in-center test tasks, and with a DSC of 56.42%, 55.94%, 46.54% and 60.62% in four cross-center test tasks. In addition, the proposed cross-center segmentation network (i.e., HCA-DAN) obtains excellent results compared to other unsupervised domain adaptation methods, with a DSC of 58.36%, 56.72%, 49.25%, and 62.20% in four cross-center test tasks. Conclusions Comprehensive experimental results demonstrate that the proposed method outperforms compared methods on this multi-center database and is promising for routine clinical workflows.

The advent of immune checkpoint inhibitors (ICIs) has greatly improved the prognosis of advanced lung cancer patients, but can lead to pseudoprogression (PsP), which complicates clinical evaluation and management. PsP is manifested as temporary enlargement of the tumour or the appearance of new lesions, etc., and improvement in imaging occurs with continued treatment, mostly without worsening of clinical symptoms. Currently, there are still difficulties in the early diagnosis of PsP, and its occurrence mechanism is not yet clear, lacking good predictive factors and related biomarkers. This article reviews the current research status of PsP of ICIs in non-small cell lung cancer in order to provide helpful clinical strategies for oncologists using these drugs.

This experiment aimed to evaluate the effects of exogenous L. plantarum, fibrolytic enzymes, and their combination on fermentation quality, structural carbohydrate components, and in vitro dry matter digestibility (IVDMD). The treatments included (i) no additives (control), (ii) L. plantarum (L), (iii) fibrolytic enzymes (E), and (iv) a combination of fibrolytic enzymes and L. plantarum (EL). After being fermented for 1, 3, 7, and 30 days, the silos were opened for subsequent analysis. L and EL increased the lactic acid content and decreased the pH value and NH3-N content compared to silages without the addition of L. plantarum (p < 0.05). Compared to the control, enzymes alone or combined with L. plantarum improved enzymatic hydrolysis with higher water-soluble carbohydrates being retained at the early stage of ensiling; lower contents of NDF, ADF, hemicellulose, and cellulose were observed at the end of ensiling (p < 0.05). The IVDMD was improved in E and EL silage, and the highest IVDMD was observed in E. The L silage showed no significant difference in terms of the structural carbohydrate components or IVDMD compared to the control (p > 0.05). A principal component analysis showed that L. plantarum addition did not contribute to an increase in IVDMD, whereas LA fermentation was further enhanced when EL was synergistically involved.

Immunotherapy has profoundly changed the treatment of gastric cancer, but only a minority of patients benefit from immunotherapy. Therefore, numerous studies have been devoted to clarifying the mechanisms underlying resistance to immunotherapy or developing biomarkers for patient stratification. However, previous studies have focused mainly on the tumor microenvironment. Systemic immune perturbations have long been observed in patients with gastric cancer, and the involvement of the peripheral immune system in effective anticancer responses has attracted much attention in recent years. Therefore, understanding the distinct types of systemic immune organization in gastric cancer will aid personalized treatment designed to pair with traditional therapies to alleviate their detrimental effects on systemic immunity or to directly activate the anticancer response of systemic immunity. Herein, this review aims to comprehensively summarize systemic immunity in gastric cancer, including perturbations in systemic immunity induced by cancer and traditional therapies, and the potential clinical applications of systemic immunity in the detection, prediction, prognosis and therapy of gastric cancer.

Background: Microvascular invasion (MVI) is associated with an unfavorable prognosis and early recurrence of hepatocellular carcinoma (HCC), which is the crucial pathological hallmark of immunotherapy. While microvascular invasion (MVI) in hepatocellular carcinoma (HCC) currently lacks a detailed single-cell analysis of the tumor microenvironment (TME), it holds significant promise for immunotherapy using immune checkpoint inhibitors (ICI). Methods: We performed single-cell RNA sequencing (scRNA-seq) on 3 MVI positive (MVIP) and 14 MVI-negative (MVIN) tumor tissues, as well as their paired adjacent non-tumoral tissues. Results: We identified SPP1+ macrophages and CD4+ proliferative T cells as intertumoral populations critical for the formation of cold tumors and immunosuppressive environments in MVI-positive patients and verified their prognostic value in correlation with MVIP HCC patients. Additionally, we identified SPP1+ dominated interactions between SPP1+ macrophages and the immunosuppressive T population as contributors to MVI destruction and tumorigenesis. Conclusions: We provide a comprehensive single-cell atlas of HCC patients with MVI, shedding light on the immunosuppressive ecosystem and upregulated signaling associated with MVI. These findings demonstrate that intercellular mechanisms drive MVI and provide a potential immunotherapeutic target for HCC patients with HCC and underlying MVI.

The contamination of arsenic (As) in aqueous environments has drawn widespread attention, and iron compounds may largely alter the migration ability of As. However, the stability of As(III) in Fe-As system with the intervention of organic matter (OM) remains unclear. Herein, we had explored the co-precipitation and co-oxidation processes of As-Fe system by using batch experiments combined with Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Photoelectron Spectroscopy (XPS) in this research. The precipitation quantity of As(III) increased (28.85–92.41 %) when the As/Fe ratio decreased, and increased (24.20–64.20 %) with pH increased. The main active substance for oxidizing As(III) was H2O2, which was produced in the As-Fe system. FTIR and XPS revealed that As(III) was first oxidized in neutral, and then absorbed and enteredthe interior of Fe(OH)3 colloids. But under alkaline conditions, As(III) was adsorbed by Fe (Oxyhydr) oxides firstly, and then oxidized. The intervention of OM would inhibit the redistribution process of As(III) in aqueous environments. Functional groups and unsaturation of the carbon chain were the dominant factors that affected the precipitation and oxidation processes of As(III), respectively. Co-existing ions (especially PO43-) also signally affected the precipitation quantity of As(Ⅲ) in the system and, when coexisting with OM, could exacerbate this process. The influence of co-existing ions on the redistributive process of As(III) in the As-Fe system with/without OM were as follows: PO43- > SO42- > mixed ions > SiO32-. Moreover, high concentration of OM and PO43- might lead to morphological alterations of As, acting as a threat to aqueous environments. In summary, the present findings were to further understand and appreciate the changes of As toxicity in the aqueous environments. Particularly, the coexistence of OM and As can potentially increase the risk to drinking water safety.

[Objective] When tunnel boring machine advancing in the water-rich sand layer, problems such as high cutter torque, serious wear and spout of the screw conveyer can be easily generated. In order to ensure the safety of construction, it is necessary to improve the muck. [Method] Based on a shield tunnel section project of Xi’an Metro Line 16, the muck modifier is selected according to the characteristics of poor self-stability, high friction and high permeability of sandy soil stratum. By combining slump test, permeability test and on-site test, two schemes of foam alone conditioning and mixed foam-bentonite slurry conditioning are used to carry out the experimental research on muck conditioning in water-rich sand layer. [Result & Conclusion] In the actual shield tunneling construction, on the premise of safety, efficiency and economy and based on the data obtained in laboratory tests, the conditioning scheme of mixed foam-bentonite slurry is adopted. The performance of the modifier meets the requirements of shield tunneling construction when the ratio of bentonite to water is 1/8 and the foam mass fraction is 3%. The muck conditioning by foam alone is greatly affected by water content. The higher the water content is, the smaller the optimal foam injection rate (FIR) becomes. The best muck conditioning option for medium sand is 10% of the bentonite slurry injection ratio (BIR) and 20%~25% of FIR. The best muck conditioning option for gravel sand is 10% of BIR and 35%~40% of FIR. Engineering practice shows that the muck conditioning has good effect and the parameters of shield tunneling are stable.

Abstract This study combined applied PICRUSt2 and BugBase tools to evaluate the effects of these two strains on the fermentation characteristics, microbial community, potential microbial risk and hygienic quality of whole-crop corn (WCC) silage. Fresh WCC harvested at the dough stage was ensiled with distilled water (CON), Leuconostoc lactis (LS) and Weissella confusa (WA) for 2, 4, 8, 15 and 30 days. After ensiling, all WCC silages presented desirable fermentation with high lactic acid and Lactobacillus proportions, low pH and ammonia nitrogen levels and absent butyric acid. Ensiling decreased the complexity of bacterial co-occurrence networks, and the Lc. lactis and W. confusa inoculation further decreased the complexity. The inoculation of W. confusa suppressed the most pathogenic pathways and related modules associated with zoonosis. In bacterial phenotype predicted analysis, although CON had lower proportions of ‘Potentially pathogenic’ than fresh material, this undesirable phenotype declined to negligible levels via LS and WA inoculation. Even for well-fermented WCC silages, the risk of pathogens remained after 30 days of ensiling. Therefore, WA could be developed as a promising fast start-up inoculant for reducing pathogenic contamination and improving hygienic quality of silage. Graphical Abstract

Spaceborne bistatic synthetic aperture radar (BiSAR) systems utilize an intersatellite link to achieve phase synchronization. However, radio frequency interference (RFI) from communication satellites and ground-based radars often contaminates the synchronization signal, leading to inaccuracies in the inverted digital elevation model (DEM). Therefore, this article puts forward an advanced phase-preserving synchronization RFI suppression method and validates it using data from an $L$-band BiSAR system, LuTan-1 (LT-1). The method involves detecting and locating RFI within a monopulse synchronization signal, and the signal at the estimated RFI position is removed to obtain a preprocessed signal. Then, based on the preprocessed signal and the RFI model, RFI is estimated using a gradient-based approach. Finally, the estimated RFI is subtracted from the monopulse signal to obtain the desired signal. In addition, synchronization RFI suppression and DEM generation experiments are performed on the LT-1 data to verify the proposed method. Experimental results demonstrate that the method effectively suppresses synchronization RFI and improves DEM accuracy, and it has extensive application prospects in future low-band distributed interferometric synthetic aperture radar missions.

Abstract Nowadays a unit quaternion is widely employed to represent the three-dimensional (3D) rotation matrix and then applied to the 3D similarity coordinate transformation. A unit dual quaternion can describe not only the 3D rotation matrix but also the translation vector meanwhile. Thus it is of great potentiality to the 3D coordinate transformation. The paper constructs the 3D similarity coordinate transformation model based on the unit dual quaternion in the sense of errors-in-variables (EIV). By means of linearization by Taylor's formula, Lagrangian extremum principle with constraints, and iterative numerical technique, the Dual Quaternion Algorithm (DQA) of 3D coordinate transformation in weighted total least squares (WTLS) is proposed. The algorithm is capable to not only compute the transformation parameters but also estimate the full precision information of computed parameters. Two numerical experiments involving an actual geodetic datum transformation case and a simulated case from surface fitting are demonstrated. The results indicate that DQA is not sensitive to the initial values of parameters, and obtains the consistent values of transformation parameters with the quaternion algorithm (QA), regardless of the size of the rotation angles and no matter whether the relative errors of coordinates (pseudo-observations) are small or large. Moreover, the DQA is advantageous to the QA. The key advantage is the improvement of estimated precisions of transformation parameters, i.e. the average decrease percent of standard deviations is 18.28%, and biggest decrease percent is 99.36% for the scaled quaternion and translations in the geodetic datum transformation case. Another advantage is the DQA implements the computation and precision estimation of traditional seven transformation parameters (which still are frequent used yet) from dual quaternion, and even could perform the computation and precision estimation of the scaled quaternion. Graphical Abstract

Abstract The scale of propagation of hydraulic fractures in deep shale is closely related to the effect of stimulation. In general, the most common means of revealing hydraulic fracture propagation rules are laboratory hydraulic fracture physical simulation experiments and numerical simulation. However, the former is difficult to meet the real shale reservoir environment, and the latter research focuses mostly on fracturing technology and the interaction mechanism between hydraulic fractures and natural fractures, both of which do not consider the influence of temperature effect on hydraulic fracture propagation. In this paper, the hydraulic fracturing process is divided into two stages (thermal shock and hydraulic fracture propagation). Based on the cohesive zone method, a coupled simulation method for sequential fracturing of deep shale is proposed. The effects of different temperatures, thermal shock rates, and times on the scale of thermal fractures are analyzed. As well as the effects of horizontal stress difference and pumping displacement on the propagation rule of hydraulic fractures. The results show that the temperature difference and the thermal shock times determine the size and density of thermal fractures in the surrounding rock of the borehole, and the number of thermal fractures increases by 96.5% with the increase of temperature difference. Thermal fractures dominate the initiation direction and propagation path of hydraulic fractures. The main hydraulic fracture width can be increased by 150% and the length can be increased by 46.3% by increasing the displacement; the secondary fracture length can be increased by 148.7% by increasing the thermal shock times. This study can provide some inspiration for the development of deep shale by improving the complexity of hydraulic fractures.

Spaceborne bistatic synthetic aperture radar (BiSAR) employs single-pass cross-track interferometry (XTI) to invert digital elevation models (DEMs) of target areas. However, the accuracy of the inverted DEM is affected by various factors, especially interferometric baseline estimation. To this end, this article proposes an advanced interferometric baseline estimation method (IBEM) for spaceborne BiSAR. First, the IBEM compensates for the time synchronization deviation of BiSAR systems using the pulse exchange phase synchronization method. Subsequently, the time, position, and velocity vectors of satellites are input into a high-precision orbit propagator to derive the state vectors at the imaging time. Leveraging these state vectors, the interferometric baseline is calculated with the main satellite that transmits radar signals as the origin of the coordinate system. Eventually, interferometric baseline estimation and DEM generation are performed based on two sets of data from the LuTan-1 (LT-1) BiSAR system, and the generated DEMs are compared with the external reference DEM to evaluate the performance of the IBEM and the LT-1 system. Results indicate that the proposed IBEM can accurately estimate the interferometric baseline and has extensive application prospects in future spaceborne multibaseline Interferometric SAR missions.

Diagnosing faults in wheelset bearings is critical for train safety. The main challenge is that only a limited amount of fault sample data can be obtained during high-speed train operations. This scarcity of samples impacts the training and accuracy of deep learning models for wheelset bearing fault diagnosis. Studies show that the Auxiliary Classifier Generative Adversarial Network (ACGAN) demonstrates promising performance in addressing this issue. However, existing ACGAN models have drawbacks such as complexity, high computational expenses, mode collapse, and vanishing gradients. Aiming to address these issues, this paper presents the Transformer and Auxiliary Classifier Generative Adversarial Network (TACGAN), which increases the diversity, complexity and entropy of generated samples, and maximizes the entropy of the generated samples. The transformer network replaces traditional convolutional neural networks (CNNs), avoiding iterative and convolutional structures, thereby reducing computational expenses. Moreover, an independent classifier is integrated to prevent the coupling problem, where the discriminator is simultaneously identified and classified in the ACGAN. Finally, the Wasserstein distance is employed in the loss function to mitigate mode collapse and vanishing gradients. Experimental results using the train wheelset bearing datasets demonstrate the accuracy and effectiveness of the TACGAN.

The Dongzhongla deposit is a skarn-type lead–zinc ore deposit located in the eastern segment of the Gangdese metallogenic belt, situated in the Xizang province, China. The high-altitude mountainous terrain of the region poses significant challenges to ground-based exploration. To facilitate more accurate mineral exploration in the deposit and its surrounding area, a high-resolution airborne magnetic survey was conducted over the mining area and its periphery. The airborne magnetic data were processed using derivative and Euler deconvolution methods, yielding results that reflect the geological structural features of the study area. By integrating the geological characteristics of the ore deposit, we inferred that the areas of magnetic anomaly extensions and the peripheries of other magnetic anomalies are favorable zones for mineralization, providing positive leads for further mineral exploration.

Landslides are the most serious geological disaster in our country, causing economic losses. Because they go undetected, a large number of landslides that have caused disasters are not in the catalogue. At present, Interferometric Synthetic Aperture Radar (InSAR) has been widely used in the identification of landslides. However, it is time-consuming, inefficient, etc., to survey landslides throughout our large country. In the context of massive SAR data, this problem is more obvious. Therefore, based on the current technique of using differential interferogram phase gradient stacking to avoid phase unwrapping errors, a landslide phase gradient dataset has been constructed. To validate the dataset’s effectiveness and applicability, deep learning methods were introduced, applying the dataset to four networks: U-Net, Attention-Unet, Bisenet v2, and Deeplab v3. The results indicate that the phase gradient dataset performs well across different models, with the Attention-Unet network demonstrating the best performance. Specifically, the precision, recall, and accuracy on the test dataset were 0.8771, 0.8712, and 0.9834, respectively, and the accuracy on the validation dataset was 0.8523. Finally, in this paper, the model is applied to landslide identification in Gansu Province, China, during 2022-2023, and a total of 1882 landslides are found. These landslides are mainly concentrated in the south of Gansu Province, where the terrain is relatively undulating. The results show that this method can quickly and accurately realize landslide automatic identification in a wide area and provide technical support for large-scale landslide disaster surveys.

The objective was to evaluate the effectiveness of lactic acid bacteria (LAB) inoculants and chemical additives on the fermentation quality, aerobic stability and in vitro gas production kinetics and digestibility of total mixed ration (TMR) silage. Total mixed ration (568 g/kg dry matter (DM)) was ensiled with six experimental treatments: (1) no additives (control); (2) Lactobacillus buchneri (LB; applied at 1 × 106 cfu/g fresh weight (FW)); (3) Lactobacillus casei (LC; applied at 1 × 106 cfu/g FW); (4) calcium propionate (CAP; applied at 0.5% FW); (5) sodium diacetate (SD; applied at 0.5% FW); (6) potassium sorbate (PS; applied at 0.1% FW). All silos (18 L) were opened for fermentation quality, in vitro gas production kinetics and digestibility analysis after 90 days of ensiling, and then subjected to aerobic stability test for 14 days. All the TMR silage was well-preserved with low pH (4.36 ∼ 4.66) and acceptable levels of butyric acid (1.02 ∼ 2.51 g/kg DM) and ammonia nitrogen (86.3 ∼ 107 g/kg total nitrogen). All the groups were steady during 14 days of aerobic exposure, while SD group was more stable with lower (p

Since 2019, the Ministry of Natural Resources has comprehensively promoted the research and development of universal equipment and the automatic monitoring and forewarning of geological hazard. As of June 2023, it has been applied to 55 000 potential points of geological hazard in 17 key geological hazard prevention provinces, and the national geological hazard monitoring and forewarning system has entered a rapid development stage. At present, the system receives more than 10 million monitoring data every day, and has made positive progress in massive data storage management, parallel processing of time series data, intelligent analysis of big data and multi-parameter risk early warning. However, there are many challenges in data security of the system, especially in data consistency, data tamper resistance and system reliability. On the basis of systematic research on the characteristics of the blockchain technology and its typical application in related fields, this paper proposes a “trusted data flow” technology based on distributed ledger and consensus mechanism, according to which the overall framework of “blockchain + NRWSL” is formed, and a prototype system is established. The test data show that the improved system can solve the problem of data inconsistency between provinces and Ministry of Natural Resources, identify and prevent illegal data tampering, ensure the stable operation of the system under multi-node failure conditions, and the overall performance loss of the system is less than 20%. The research results provide technical methods for the application of blockchain technology to geological hazard risk early warning systems at all levels in China, and also provide top-level design ideas for improving the security of geological hazard monitoring data and the early warning system.

Abstract Objectives Primary blast lung injury (PBLI) is the main cause of death in blast injury patients, and is often ignored due to the absence of a specific diagnosis. Circular RNAs (circRNAs) are becoming recognized as new regulators of various diseases, but the role of circRNAs in PBLI remain largely unknown. This study aimed to investigate PBLI-related circRNAs and their probable roles as new regulators in PBLI in order to provide new ideas for PBLI diagnosis and treatment. Methods The differentially expressed (DE) circRNA and mRNA profiles were screened by transcriptome high-throughput sequencing and validated by quantitative real-time PCR (qRT-PCR). The GO and KEGG pathway enrichment was used to investigate the potential function of DE mRNAs. The interactions between proteins were analyzed using the STRING database and hub genes were identified using the MCODE plugin. Then, Cytoscape software was used to illustrate the circRNA-miRNA-hub gene network. Results A total of 117 circRNAs and 681 mRNAs were aberrantly expressed in PBLI, including 64 up-regulated and 53 down-regulated circRNAs, and 315 up-regulated and 366 down-regulated mRNAs. GO and KEGG analysis revealed that the DE mRNAs might be involved in the TNF signaling pathway and Fanconi anemia pathway. Hub genes, including Cenpf, Ndc80, Cdk1, Aurkb, Ttk, Aspm, Ccnb1, Kif11, Bub1 and Top2a, were obtained using the MCODE plugin. The network consist of 6 circRNAs (chr18:21008725–21020999 + , chr4:44893533–44895989 + , chr4:56899026–56910247-, chr5:123709382–123719528-, chr9:108528589–108544977 + and chr15:93452117–93465245 +), 7 miRNAs (mmu-miR-3058-5p, mmu-miR-3063-5p, mmu-miR-668-5p, mmu-miR-7038-3p, mmu-miR-761, mmu-miR-7673-5p and mmu-miR-9-5p) and 6 mRNAs (Aspm, Aurkb, Bub1, Cdk1, Cenpf and Top2a). Conclusions This study examined a circRNA-miRNA-hub gene regulatory network associated with PBLI and explored the potential functions of circRNAs in the network for the first time. Six circRNAs in the circRNA-miRNA-hub gene regulatory network, including chr18:21008725–21020999 + , chr4:44893533–44895989 + , chr4:56899026–56910247-, chr5:123709382–123719528-, chr9:108528589–108544977 + and chr15:93452117–93465245 + may play an essential role in PBLI.

Abstract Background The PI3K/AKT pathway transduces the majority of the metabolic actions of insulin. In addition to cytosolic targets, insulin-stimulated phospho-AKT also translocates to mitochondria in the myocardium. Mouse models of diabetes exhibit impaired mitochondrial AKT signaling but the implications of this on cardiac structure and function is unknown. We hypothesized that loss of mitochondrial AKT signaling is a critical step in cardiomyopathy and reduces cardiac oxidative phosphorylation. Methods To focus our investigation on the pathophysiological consequences of this mitochondrial signaling pathway, we generated transgenic mouse models of cardiac-specific, mitochondria-targeting, dominant negative AKT1 (CAMDAKT) and constitutively active AKT1 expression (CAMCAKT). Myocardial structure and function were examined using echocardiography, histology, and biochemical assays. We further investigated the underlying effects of mitochondrial AKT1 on mitochondrial structure and function, its interaction with ATP synthase, and explored in vivo metabolism beyond the heart. Results Upon induction of dominant negative mitochondrial AKT1, CAMDAKT mice developed cardiac fibrosis accompanied by left ventricular hypertrophy and dysfunction. Cardiac mitochondrial oxidative phosphorylation efficiency and ATP content were reduced, mitochondrial cristae structure was lost, and ATP synthase structure was compromised. Conversely, CAMCAKT mice were protected against development of diabetic cardiomyopathy when challenged with a high calorie diet. Activation of mitochondrial AKT1 protected cardiac function and increased fatty acid uptake in myocardium. In addition, total energy expenditure was increased in CAMCAKT mice, accompanied by reduced adiposity and reduced development of fatty liver. Conclusion CAMDAKT mice modeled the effects of impaired mitochondrial signaling which occurs in the diabetic myocardium. Disruption of this pathway is a key step in the development of cardiomyopathy. Activation of mitochondrial AKT1 in CAMCAKT had a protective role against diabetic cardiomyopathy as well as improved metabolism beyond the heart.

Abstract Light guide plate (LGP) is a key component of liquid crystal display (LCD) display systems, so its quality directly affects the display effect of LCD. However, LGPs have complex background texture, low contrast, varying defect size and numerous defect types, which makes realizing efficient and accuracy-satisfactory surface defect automatic detection of LGPS still a big challenge. Therefore, combining its optical properties, dot distribution, defect imaging characteristics and detection requirements, a surface defect detection algorithm based on LGP-YOLO for practical industrial applications is proposed in this paper. To enhance the feature extraction ability of the network without dimensionality reduction, expand the effective receptive field and reduce the interference of invalid targets, we built the receptive field module (RFM) by combining the effective channel attention network (ECA-Net) and reviewing large kernel design in CNNs (RepLKNet). For the purpose of optimizing the performance of the network in downstream tasks, enhance the network's expression ability and improve the network’s ability of detecting multi-scale targets, we construct the small detection module (SDM) by combining space-to-depth non-strided convolution (SPDConv) and omini-dimensional dynamic convolution (ODConv). Finally, an LGP defect dataset is constructed using a set of images collected from industrial sites, and a multi-round experiment is carried out to test the proposed method on the LGP detect dataset. The experimental results show that the proposed LGP-YOLO network can achieve high performance, with mAP and F1-score reaching 99.08% and 97.45% respectively, and inference speed reaching 81.15 FPS. This demonstrates that LGP-YOLO can strike a good balance between detection accuracy and inference speed, capable of meeting the requirements of high-precision and high-efficiency LGP defect detection in LGP manufacturing factories.

Rare diseases have various types, low incidence rates, complex conditions, and are often difficult to diagnose. Due to China’s large population, there is a significant number of rare disease patients, but there is a shortage of orphan drugs. Consequently, these patients often find themselves in a situation where necessary medications are either unavailable or unaffordable. To address this urgent clinical need, China has implemented a series of orphan drug policies aimed at improving drug accessibility and affordability. In terms of drug accessibility, companies are encouraged to expedite drug development through the implementation of tax incentives, guidance for clinical research on rare diseases, and the provision of data protection periods of 6 years, along with market exclusivity periods limited to a maximum of 7 years. Moreover, exemptions for clinical trials, acceptance of overseas clinical trial data, and the creation of a list prioritizing clinically urgent new drugs from overseas have been introduced to expedite the drug registration application, review, inspection, and approval processes. In terms of drug affordability, the import value-added tax on rare disease drugs has been reduced by 3%, and various provinces and cities have established a representative rare disease protection model, which includes special funds, medical assistance programs, and serious disease insurance. The national medical insurance catalog has been adjusted to reduce the financial burden on rare disease patients, resulting in an increase in the number of orphan drugs covered by the catalog to 95 as of March 2024. By comparing orphan drug policies in the United States, the European Union, Japan, Australia, and other countries (or regions), we will provide relevant suggestions to further improve orphan drug policies in China, thus bringing more treatment options and hope to patients with rare diseases.

ObjectiveAerobic exercise (AE) interventions are beginning to be used as an emerging adjunctive treatment modality in the treatment of children with Attention Deficit Hyperactivity Disorder (ADHD). However, to date, there is no substantial evidence to support the improved effects of aerobic exercise intervention in children with ADHD aged 6–12 years. This study aims to investigate the effect of aerobic exercise therapy on executive function in children with attention deficit hyperactivity disorder aged 6–12 years.MethodWe conducted a systematic review and meta-analysis using PubMed and Web of Science. The cut-off date was June 1, 2023. The aim was to assess the impact of aerobic exercise interventions on children with ADHD and all randomized controlled trials eligible for aerobic exercise interventions for children with ADHD were included. Nine randomized controlled trials were screened for eligibility for systematic evaluation, and the nine studies were assessed for risk of bias using the PEDro score and the GRADE Quality of Evidence Evaluation System for quality grading of outcome indicators. After testing for heterogeneity, a random-effects model was selected for analysis. Finally, meta-analyses and regression analyses were performed on the core functions (inhibitory control, cognitive flexibility, and working memory) and subgroups of the nine studies on executive function using Revman 5.4 and Stata 16.0.ResultsThe risk of bias evaluation showed a mean PEDro score of 7.78, and of the nine studies, two were rated as having excellent methodological quality, while the remaining seven had a good level of evidence, and the GRADE evidence evaluation showed that the outcome indicators were all of moderate quality. Inhibitory control [SMD = 0.83,95% CI (0.37–1.29), Z = 3.51, p = 0.0005], cognitive flexibility [SMD = 0.65,95% CI (0.37–0.93), Z = 4.58, p

Objective: Hepatic insulin resistance, which leads to increased hepatic gluconeogenesis, is a major contributor to fasting hyperglycemia in type 2 diabetes mellitus (T2DM). However, the mechanism of impaired insulin-dependent suppression of hepatic gluconeogenesis remains elusive. Delta/Notch-like epidermal growth factor (EGF)-related receptor (DNER), firstly described as a neuron-specific Notch ligand, has been recently identified as a susceptibility gene for T2DM through genome-wide association studies. We herein investigated whether DNER regulates hepatic gluconeogenesis and whether this is mediated by enhanced insulin signaling. Methods: The association between DNER, tribbles homolog 3 (TRB3) and Akt signaling was evaluated in C57BL/6J, ob/ob and db/db mice by western blot analysis. DNER loss-of-function and gain-of-function in hepatic gluconeogenesis were analyzed by western blot analysis, quantitative real-time PCR, glucose uptake and output assay in AML-12 cells and partially validated in primary mouse hepatocytes. Hepatic DNER knockdown mice were generated by tail vein injection of adenovirus to confirm the effects of DNER in vivo. The interaction between DNER and TRB3 was investigated by rescue experiments, cycloheximide chase analysis, co-immunoprecipitation and immunofluorescence. The potential insulin-stimulated phosphorylation sites of DNER were determined by co-immunoprecipitation, LC-MS/MS analysis and site-specific mutagenesis. Results: Here we show that DNER enhanced hepatic insulin signaling in gluconeogenesis by inhibiting TRB3, an endogenous Akt inhibitor, through the ubiquitin-proteasome degradation pathway. In AML-12 hepatocytes, insulin-stimulated activation of Akt and suppression of gluconeogenesis are attenuated by DNER knockdown, but potentiated by DNER over-expression. In C57BL/6J mice, hepatic DNER knockdown is accompanied by impaired glucose and pyruvate tolerance. Furthermore, the in vitro effects of DNER knockdown or over-expression on both Akt activity and hepatic gluconeogenesis can be rescued by TRB3 knockdown or over-expression, respectively. In response to insulin stimulation, DNER interacted directly with insulin receptor and was phosphorylated at Tyr677. This site-specific phosphorylation is essential for DNER to upregulate Akt activity and then downregulate G6Pase and PEPCK expression, by interacting with TRB3 directly and inducing TRB3 proteasome-dependent degradation. Conclusions: Taken together, the crosstalk between insulin-Akt and DNER-TRB3 pathways represents a previously unrecognized mechanism by which insulin regulates hepatic gluconeogenesis.

An algorithm, named long short-term memory (LSTM)-logistic chaos mapping chicken swarm algorithm (LCCSA), is proposed for initializing the weights and thresholds of LSTM neural networks using the Logistic chaotic mapping chicken swarm algorithm (CSA). This algorithm aims to improve mid- to long-term runoff sequence prediction in river basins. In this model, the logistic chaotic mapping method is used to initialize the chicken swarm, and LCCSA is employed to pre-train the weights and thresholds of each layer of LSTM 50 times, using the training results' initial weights of LSTM to enhance convergence accuracy and speed. Taking the Manas river and Kuitun river, two typical basins in northern Xinjiang, China, as the research objects, LSTM-LCCSA was used to forecast the mean monthly runoff in the mid- to long-term under different lag time series by using the runoff evolution data within a certain period. The example using the basin located in northern Xinjiang demonstrates the effectiveness, stability, and generality of the LSTM-LCCSA method in mid- to long-term prediction of average monthly runoff, and the prediction accuracy and universality of LSTM-LCCSA are better than other data-driven models. HIGHLIGHTS Proposed an LSTM-LCCSA algorithm to address mid- to long-term runoff sequence prediction in river basins.; Initialization of the chicken swarm using a logistic chaotic mapping method improved population diversity and avoided CSA falling into a local extreme value.; The intercomparison of data-driven models (CM-SVM, GA-SAA, MRA, GA-FFNN) with respect to prediction accuracy and stability.;

Reservoir water fluctuation can often trigger deformation and failure of reservoir bank landslides. This study focuses on the Hongyanzi landslide in the Pubugou hydropower Station of the Dadu River to investigate the deformation characteristics and deformation mechanisms of the reservoir bank landslides. Through detailed surface macro deformation surveys, monitoring data analysis, and GeoStudio numerical simulation, the deformation characteristics, seepage field, stability and the influence of reservoir water on landslide were studied in depth. The research revealed significant macro deformation of the Hongyanzi landslide surface, with a cumulative displacement curve exhibiting a “step-like” characteristic. The primary inducing factor of landslide deformation was found to be the decrease in the reservoir water level. When the reservoir water level dropped from 850 m to below 830 m, the displacement step was triggered, and the cumulative deformation of the “step” segment accounted for over 90% of the total annual deformation. Accelerated deformation of the landslide occurred when the rate of the reservoir water level decline was greater than 0.5 m/d. The landslide deformation mode was identified as creep slip-tensile cracking, with the rise and fall of the reservoir water level significantly impacting the internal permeability of the sliding body and causing a large impact on the landslide’s stability, leading to the “step-like” deformation of the landslide.

Abstract Background Amputation is a serious complication of acute compartment syndrome (ACS), and predicting the risk factors associated with amputation remains a challenge for surgeons. The aim of this study was to analyze the risk factors for amputation in patients with ACS and develop a nomogram to predict amputation risk more accurately. Methods The study population consisted of 143 patients (32 in the amputation group and 111 in the limb preservation group) diagnosed with ACS. LASSO and multivariate logistic regression were used to screen predictors and create a nomogram. The model’s accuracy was assessed by receiver operating characteristic (ROC) curves, C-index, calibration curves, and decision curve analysis (DCA). Results The predictors included cause of injury, vascular damage, shock, and fibrinogen in the nomogram. The C-index of the model was 0.872 (95% confidence interval: 0.854–0.962), and the C-index calculated by internal validation was 0.838. The nomogram’s area under the curve (AUC) was 0.849, and the calibration curve demonstrated a high degree of agreement between the nomogram’s predictions and actual observations. Additionally, the DCA indicated good clinical utility for the nomogram. Conclusion The risk of amputation in ACS patients is associated with the cause of injury, vascular damage, shock, and fibrinogen. Our nomogram integrating clinical factors and biochemical blood markers enables doctors to more conveniently predict the risk of amputation in patients with ACS.

Abstract Background Soybean is one of the most important oil crops in the world. The domestication of wild soybean has resulted in significant changes in the seed oil content and seed size of cultivated soybeans. To better understand the molecular mechanisms of seed formation and oil content accumulation, WDD01514 (E1), ZYD00463 (E2), and two extreme progenies (E23 and E171) derived from RILs were used for weighted gene coexpression network analysis (WGCNA) combined with transcriptome analysis. Results In this study, both seed weight and oil content in E1 and E171 were significantly higher than those in E2 and E23, and 20 DAF and 30 DAF may be key stages of soybean seed oil content accumulation and weight increase. Pathways such as “Photosynthesis”, “Carbon metabolism”, and “Fatty acid metabolism”, were involved in oil content accumulation and grain formation between wild and cultivated soybeans at 20 and 30 DAF according to RNA-seq analysis. A total of 121 oil content accumulation and 189 seed formation candidate genes were screened from differentially expressed genes. WGCNA identified six modules related to seed oil content and seed weight, and 76 candidate genes were screened from modules and network. Among them, 16 genes were used for qRT-PCR and tissue specific expression pattern analysis, and their expression-levels in 33-wild and 23-cultivated soybean varieties were subjected to correlation analysis; some key genes were verified as likely to be involved in oil content accumulation and grain formation. Conclusions Overall, these results contribute to an understanding of seed lipid metabolism and seed size during seed development, and identify potential functional genes for improving soybean yield and seed oil quantity.

Abstract Background The purpose of this research is to study the contribution of epiphytic microbiota in fresh oat (OT), Italian ryegrass (IR) and whole-crop maize (MZ) to silage fermentation products and bacterial community structure of MZ. After γ-ray irradiation, the sterile MZ was treated via microbiota transplantation method: (1) sterile deionized water (STMZ); (2) microbiota epiphytic on MZ (MZMZ); (3) microbiota epiphytic on OT (MZOT); (4) microbiota epiphytic on IR (MZIR). Triplicate silos of each treatment were tested after 1, 3, 7, 15, 30 and 60 days of ensiling. Results MZMZ had higher (P