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Abstract This single-arm phase 2 trial (ChiCTR2100046715) examined previously untreated patients with advanced esophageal squamous cell carcinoma (ESCC) who received four cycles of paclitaxel with carboplatin every 3 weeks. Toripalimab was infused intravenously every 3 weeks for 12 months, or until disease progression or intolerable toxicity. Radiotherapy that encompassed the primary lesions and metastases commenced in the third cycle. The median progression-free survival time was 9.8 months (95% confidence interval [CI]: 6.8–not estimable) in the intent-to-treat population, failing to meet the pre-specified primary endpoints. Secondary endpoints included an objective response rate of 45.5%, a disease control rate of 57.6%, and a median duration of response of 11.5 months (interquartile range, 6.4–15.0). The 1-year progression-free survival and overall survival rates were 41.9% (95% CI: 27.7–63.5) and 69.7% (95% CI: 55.7–87.3), respectively. Lymphopenia was the most frequent grade ≥3 adverse event (82%), and an esophageal fistula developed in three patients (9.1%). No treatment-related deaths occurred. In prespecified exploratory biomarker analysis, higher densities of CD8 + T cells, CD11c+ dendritic cells, and CD68+ macrophages correlated with improved tumor response and prognosis. Radiotherapy supplementation to first-line chemo-immunotherapy for treatment-naive advanced ESCC demonstrated some antitumor activity and manageable safety profiles, warranting further randomized controlled trials.

The process of “high temperature carbonization and low temperature chlorination”, considered as one of the most promising technologies, can extract over 60% titanium from blast furnace slag containing TiO2 at a concentration of 20%–25%. The primary equipment for high temperature carbonization is an electric furnace lined with MgO–C bricks. In this study, post-mortem analysis was conducted to investigate the corrosion and failure mechanisms of MgO–C refractory bricks taken from different areas of a circular electric furnace after 1200 cycles of service. Through XRD, SEM-EDS, and thermodynamic calculation analysis, the reaction and corrosion mechanism between MgO–C bricks and the slag were analyzed. The results demonstrated that the gradual corrosion of the brick was primarily attributed to the alternating effects of carbon oxidation and MgO dissolution. Moreover, an increase in temperature led to a higher corrosion rate of the refractories. At temperatures exceeding 1560 °C, the pre-formed barrier layer composed of MgAl2O4 and MgTi2O4 melted into the slag. And as TiO2 content decreased and TiC content increased, there was a gradual decline in the erosive impact of the slag. Furthermore, the failure mechanisms of refractory bricks in different areas varied due to the distinctive structure of the electric furnace. The bricks at the slag line area were mainly destroyed by slag corrosion, oxidation and thermal shock spalling, which had the shortest service life.

In this study, the effect of heat treatment on the microstructure and mechanical properties of 2209 duplex stainless steel fabricated by wire arc additive was analyzed. It was found that solution treatment at 1100°C for 2 hours and tempering at 300°C for 2 hours can effectively improve the grain inhomogeneity of 2209 duplex stainless steel, eliminate γ2 and harmful brittle phases, and take into account the mechanical properties and corrosion resistance. Compared with the original deposition state, the hardness and yield strength increased by 10% and 31.8% to 245.6 HV and 499.7 MPa, which meet the requirements of engineering propellers. Electron back-scattered diffraction studies showed that the grains became refined and austenite maintained //Z orientation after solution heat treatment. Many small-angle grain boundaries were present in both the original sample and the solid solution, but further tempering transformed the small-angle grain boundaries into large-angle grain boundaries.

Abstract Background Anthracnose, mainly caused by Colletotrichum fructicola, leads to severe losses in pear production. However, there is limited information available regarding the molecular response to anthracnose in pears. Results In this study, the anthracnose-resistant variety ‘Seli’ and susceptible pear cultivar ‘Cuiguan’ were subjected to transcriptome analysis following C. fructicola inoculation at 6 and 24 h using RNA sequencing. A total of 3186 differentially expressed genes were detected in ‘Seli’ and ‘Cuiguan’ using Illumina sequencing technology. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses indicated that the transcriptional response of pears to C. fructicola infection included responses to reactive oxygen species, phytohormone signaling, phenylpropanoid biosynthesis, and secondary metabolite biosynthetic processes. Moreover, the mitogen-activated protein kinase (MAPK) signaling pathway and phenylpropanoid biosynthesis were involved in the defense of ‘Seli’. Furthermore, the gene coexpression network data showed that genes related to plant–pathogen interactions were associated with C. fructicola resistance in ‘Seli’ at the early stage. Conclusion Our results showed that the activation of specific genes in MAPK, calcium signaling pathways and phenylpropanoid biosynthesis was highly related to C. fructicola resistance in ‘Seli’ and providing several potential candidate genes for breeding anthracnose-resistant pear varieties.

Abstract Matching abbreviated names with their full names (full-abbr matching) plays a key role in data integration, address matching, information retrieval, and other fields. Traditional full-abbr matching technology often encounters issues related to near homophones and near homoglyphs. First, a near-homophone full-abbr matching model based on Simbert and VGG was first proposed, which integrates character and speech features, leveraging a speech recognition model and combining a brain-like cognitive learning dual-process mechanism which involves linguistic knowledge and neural network together. Second, to address the problem of near-homoglyph full-abbr matching in Chinese, a DenseNet-based model that fuses glyph structure and image features was proposed, in which statistical feature extractors are employed to extract feature vectors for glyphic features including stroke, Wubi and structural features separately. Lastly, the near-homophone model and the near-homoglyph model are coupled to work together in the full-abbr matching task, in which expert knowledge is used as a component of the feature optimizer. Experimental results showed that the integrated model significantly increased the matching accuracy to 87.5%, demonstrating a 12.3% improvement.

Abstract Background This study aimed to explore the mechanism of Ge-Gen-Qin-Lian decoction (GGQLD) in the alleviation of symptoms of type 2 diabetes mellitus (T2DM) with inflammatory bowel disease (IBD) by network pharmacology and experimental validation. Methods The active components and targets of GGQLD were identified from the TCMSP database. The potential therapeutic targets of T2DM and IBD were identified from the GEO database and 4 online disease target databases. The PPI network and KEGG/GO analyses were performed with the common targets among GGQLD, T2DM and IBD. Molecular docking was carried out between the core compounds and hub targets. To verify the above results, UHPLC-MS technology was used to identify the chemical compounds in GGQLD, and a T2DM with IBD rat model was used to explore the mechanism by which GGQLD treats T2DM with IBD. Results Totally, 70 potential therapeutic targets were identified among GGQLD, T2DM and IBD. Ten hub genes were selected from the PPI network. KEGG analysis revealed that GGQLD is tightly involved in the AGE-RAGE signaling pathway. Berberine, baicalein, wogonin, and quercitrin are the main active compounds of GGQLD. Animal experiments showed that GGQLD could decrease blood glucose and alleviate intestinal inflammation. Notably, the concentrations of AGEs, the expression of RAGE, c-JUN and NF-κB and the expression of inflammatory cytokines were decreased by GGQLD. Conclusions Our study initially demonstrated that GGQLD has favorable anti-hyperglycemic and anti-intestinal inflammation effects in a T2DM with IBD rat model, and the AGE-RAGE pathway plays a vital role in this process.

Abstract Background The flower colour of H. syriacus ‘Qiansiban’ transitions from fuchsia to pink–purple and finally to pale purple, thereby enhancing the ornamental value of the cultivars. However, the molecular mechanism underlying this change in flower colour in H. syriacus has not been elucidated. In this study, the transcriptomic data of H. syriacus ‘Qiansiban’ at five developmental stages were analysed to investigate the impact of flavonoid components on flower colour variation. Additionally, five cDNA libraries were constructed from H. syriacus ‘Qiansiban’ during critical blooming stages, and the transcriptomes were sequenced to investigate the molecular mechanisms underlying changes in flower colouration. Results High-performance liquid chromatography‒mass spectrometry detected five anthocyanins in H. syriacus ‘Qiansiban’, with malvaccin-3-O-glucoside being the predominant compound in the flowers of H. syriacus at different stages, followed by petunigenin-3-O-glucoside. The levels of these five anthocyanins exhibited gradual declines throughout the flowering process. In terms of the composition and profile of flavonoids and flavonols, a total of seven flavonoids were identified: quercetin-3-glucoside, luteolin-7-O-glucoside, Santianol-7-O-glucoside, kaempferol-O-hexosyl-C-hexarbonoside, apigenin-C-diglucoside, luteolin-3,7-diglucoside, and apigenin-7-O-rutinoside. A total of 2,702 DEGs were identified based on the selected reference genome. Based on the enrichment analysis of differentially expressed genes, we identified 9 structural genes (PAL, CHS, FLS, DRF, ANS, CHI, F3H, F3’5’H, and UFGT) and 7 transcription factors (3 MYB, 4 bHLH) associated with flavonoid biosynthesis. The qRT‒PCR results were in good agreement with the high-throughput sequencing data. Conclusion This study will establish a fundamental basis for elucidating the mechanisms underlying alterations in the flower pigmentation of H. syriacus.

Convolutional Neural Network (CNN) is widely used for image target classifications in Synthetic Aperture Radar (SAR), but the lack of mechanism transparency prevents it from meeting the practical application requirements, such as high reliability and trustworthiness. The Class Activation Mapping (CAM) method is often used to visualize the decision region of the CNN model. However, existing methods are primarily based on either channel-level or space-level class activation weights, and their research progress is still in its infancy regarding more complex SAR image datasets. Based on this, this paper proposes a CNN model visualization method for SAR images, considering the feature extraction ability of neurons and their current network decisions. Initially, neuronal activation values are used to visualize the capability of neurons to learn a target structure in its corresponding receptive field. Further, a novel CAM-based method combined with channel-wise and spatial-wise weights is proposed, which can provide the foundation for the decision-making process of the trained CNN models by detecting the crucial areas in SAR images. Experimental results showed that this method provides interpretability analysis of the model under different settings and effectively expands the application of CNNs for SAR image visualization.

Biomedical magnesium (Mg) alloys have garnered significant attention because of their unique biodegradability, favorable biocompatibility, and suitable mechanical properties. The incorporation of rare earth (RE) elements, with their distinct physical and chemical properties, has greatly contributed to enhancing the mechanical performance, degradation behavior, and biological performance of biomedical Mg alloys. Currently, a series of RE-Mg alloys are being designed and investigated for orthopedic implants and cardiovascular stents, achieving substantial and encouraging research progress. In this work, a comprehensive summary of the state-of-the-art in biomedical RE-Mg alloys is provided. The physiological effects and design standards of RE elements in biomedical Mg alloys are discussed. Particularly, the degradation behavior and mechanical properties, including their underlying action are studied in-depth. Furthermore, the preparation techniques and current application status of RE-Mg alloys are reviewed. Finally, we address the ongoing challenges and propose future prospects to guide the development of high-performance biomedical Mg-RE alloys.

Abstract Objectives This study aimed to evaluate the prognostic significance of postoperative Creatine Kinase type M and B (CK-MB) to total Creatine Kinase (CK) ratio (CK-MB/CK) in colorectal cancer (CRC) patients after radical resection. Methods This was a single-center retrospective cohort analysis. Subjects were stage I-III CRC patients hospitalized in Sichuan Cancer Hospital from January 2017 to May 2021. Patients were divided into abnormal group and normal group according to whether the CK-MB/CK ratio was abnormal after surgery. Through a comparative analysis of clinical data, laboratory test results, and prognosis differences between the two groups, we aimed to uncover the potential relationship between abnormal CK-MB > CK results and CRC patients. To gauge the impact of CK-MB/CK on overall survival (OS) and disease-free survival (DFS), we employed the multivariable COX regression and LASSO regression analysis. Additionally, Spearman correlation analysis, logistic regression, and receiver-operating characteristic (ROC) curve analysis were conducted to assess the predictive value of the CK-MB/CK ratio for postoperative liver metastasis. Results Cox regression analysis revealed that the CK-MB/CK ratio was a stable risk factors for OS (HR = 3.82, p

Ionic liquids (ILs) have many beneficial properties that are extensively used in various fields. Despite their utility, the phytotoxic aspects of ILs are poorly known. This is especially true at the transcriptomic level and the role of nitric oxide (NO) in this process. Herein, we studied the mechanism by which endogenous NO reduces the toxicity of ILs in Arabidopsis. We examined the effects of two imidazolium-based ILs (IILs) on three Arabidopsis lines, each characterized by distinct endogenous NO levels, using a combination of physiological and transcriptomics methods. IILs impaired seed germination, seedling development, chlorophyll content, and redox homeostasis in Arabidopsis. Notably, 1,3-dibutyl imidazole bromide had greater toxicity than 1-butyl-3-methylimidazolium chloride. Nox1, a mutant with an elevated NO level, had enhanced resistance, while nia1nia2, a mutant with a diminished NO level, had increased susceptibility compared to the wild type. RNA sequencing results suggested that NO mitigates IILs-induced phytotoxicity by modulating the metabolism of chlorophyll and secondary metabolites, and by bolstering the antioxidant defense system. These findings illustrate the complex molecular networks that respond to IIL stress and reveal the potential of endogenous NO as a mitigating factor in plant stress physiology.

The elusive factors determining peroxymonosulfate (PMS) activation lead to the “trial-and-error” method when developing efficient catalysts. Herein, we demonstrate that the suitable bonding pattern between PMS and exposed facets of CuO is the key parameter in catalysis. The kobs of ibuprofen removal by CuO with (110) facet (p-CuO) was 1.67 times higher than that of CuO with (001) facet (c-CuO). Simultaneously, Cu(III) was confirmed to be the primary intermediate oxidant among multiple reactive species (Cu(III), •OH, and 1O2 in both CuO/PMS systems, whereas O2•− only in c-CuO/PMS system). Furthermore, density functional theory calculations unveiled that compared to chaise longue connection between p-CuO and PMS, the higher adsorption energy (Eads) of bridge-like connection between c-CuO and PMS leads to a more stable intermediate (−5.56 eV vs −3.76 eV), thus hindering the formation of Cu(III). This work provides new opportunities for fabricating diverse metal oxides with new interfaces as PMS catalysts.

The virtual synchronous generator (VSG) has been widely used to improve the system inertia and damping in the renewable energy generation system. However, the in-depth understanding of VSG’s stability under disturbances on different control parameters is lacked. In order to solve the problem, the small-signal model of single-VSG is established at first. The influences of key control parameters on the stability of system are analyzed by using the eigenvalue analysis method in detail. On this basis, a novel optimization strategy for control parameters is proposed based on the Particle Swarm Optimization (PSO) algorithm. The control parameters are optimized to realize excellent damping and stability of VSG system. Finally, the simulation and experimental results verify the effectiveness of stability analysis and parameter optimization strategy.

A three-stage displacement amplification microgripper system based on compliant mechanisms was designed for microassembly and microoperation tasks. The static mechanical models of the microgripper system were established, and the mechanical characteristics of the output displacements, stresses, and strains of the microgripper system were comprehensively simulated and analyzed. It was demonstrated that the microgripper system demonstrated excellent leveling performance, with the stresses and strains being concentrated at the flexible hinges. Subsequently, the tracking control experiments of the finger displacement and gripping force of the microgripper system were conducted using sinusoidal signals, variable amplitude signals, and frequency signals. It was clearly illustrated that the dynamic performances of the microgripper system are remarkable, and the standard deviation of the tracking error of the finger displacement is no more than 0.2 µm, while that of the gripping force is 0.35 mN under closed-loop conditions. The designed microgripper system achieves accurate control of displacements and gripping forces, effectively fulfilling the requirements of high-precision microassembly tasks.

Abstract The DNA‐guided (gDNA) Argonaute from Thermus thermophilus (TtAgo) has little potential for nucleic acid detection and gene editing due to its poor dsDNA cleavage activity at relatively low temperature. Herein, the dsDNA cleavage activity of TtAgo is enhanced by using 2′‐fluorine (2′F)‐modified gDNA and developes a novel nucleic acid testing strategy. This study finds that the gDNA with 2′F‐nucleotides at the 3′‐end (2′F‐gDNA) can promote the assembly of the TtAgo‐guide‐target ternary complex significantly by increasing its intermolecular force to target DNA and TtAgo, thereby providing ≈40‐fold activity enhancement and decreasing minimum reaction temperature from 65 to 60 °C. Based on this outstanding advance, a novel nucleic acid testing strategy is proposed, termed FAST, which is performed by using the 2′F‐gDNA/TtAgo for target recognition and combining it with Bst DNA polymerase for nucleic acid amplification. By integrating G‐quadruplex and Thioflavin T, the FAST assay achieves one‐pot real‐time fluorescence analysis with ultra‐sensitivity, providing a limit of detection up to 5 copies (20 µL reaction mixture) for miR‐21 detection. In summary, an atom‐modification‐based strategy has been developed for enhancing the cleavage activity of TtAgo efficiently, thereby improving its practicability and establishing a TtAgo‐based nucleic acid testing technology with ultra‐sensitivity and high‐specificity.

Background: We created and validated a computed tomography (CT)-based radiomic model using both clinical factors and the radiomic signature for assessing the strangulation risk of acute intestinal obstruction. This would assist surgeons in accurately predicting intestinal ischemia and strangulation in patients with intestinal obstruction. Methods: We recruited 289 patients with acute intestinal obstruction admitted in the Affiliated Hospital of Qingdao University from January 2019 to February 2022. The patients were allocated to a training (n = 226) and validation cohort (n = 63). Radiomic features were collected from CT images, and the radiomic signature was extracted and used to calculate a radiomic score (Rad-score). A nomogram was constructed using the clinical features and the Rad-score, and the performance of the clinical, radiomics, and nomogram models was assessed in the two cohorts. Results: Six robust features were used to construct the radiomic signature. The nomogram incorporating hemoglobin levels, C-reactive protein levels, American Society of Anesthesiologists score, time of obstruction, CT image of mesenteric fluid (P < 0.05), and the signature demonstrated good predictive ability for intestinal ischemia in patients with acute intestinal obstruction, with areas under the curve of 0.892 (95% confidence interval, 0.837–0.947) and 0.781 (95% confidence interval, 0.619–0.944) for the training and validation sets, respectively. The decision curve analysis showed that this model outperformed the clinical and radiomic signature models. Conclusion: The radiomic nomogram may effectively predict intestinal ischemia in patients with acute intestinal disease and may assist clinical decision-making.

Foundation models have reshaped the landscape of remote sensing (RS) by enhancing various image interpretation tasks. Pretraining is an active research topic, encompassing supervised and self-supervised learning methods to initialize model weights effectively. However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks. In this study, we explore the multitask pretraining (MTP) paradigm for RS foundation models to address this issue. Using a shared encoder and task-specific decoder architecture, we conduct multitask supervised pretraining on the segment anything model annotated remote sensing segmentation dataset, encompassing semantic segmentation, instance segmentation, and rotated object detection. MTP supports both convolutional neural networks and vision transformer foundation models with over 300 million parameters. The pretrained models are finetuned on various RS downstream tasks, such as scene classification, horizontal, and rotated object detection, semantic segmentation, and change detection. Extensive experiments across 14 datasets demonstrate the superiority of our models over existing ones of similar size and their competitive performance compared to larger state-of-the-art models, thus validating the effectiveness of MTP.

The fabrication of zinc (Zn) parts devoid of inherent defects through Powder Bed Fusion-Laser Beam (PBF-LB) remains a formidable challenge. This study focuses on the optimization of process parameters and the implementation of tailored heat treatment to achieve high-performance PBF-LB fabricated (PBF-LBed) pure Zn bulk and scaffolds. Microstructural analysis, residual stress assessment, mechanical property testing, electrochemical experiments, immersion tests, in vitro and in vivo evaluations were conducted. The research results revealed that, under optimized process parameters and subsequent annealing, the yield strength, ultimate tensile strength, and ductility of PBF-LBed parts exhibit remarkable enhancements. Concurrently, the grain size was refined to 3 μm, accompanied by a notable improvement in biocompatibility. It could be roughly concluded that the microstructure refinement obtained through the heat treatment could significantly enhance their mechanical and electrochemical performances. Meanwhile, an appropriate Zn ion release rate achieved through grain refinement might created a more conducive microenvironment for new bone formation. Consequently, heat-treated PBF-LBed pure Zn scaffolds exhibits superior performance and application potential compared to untreated scaffolds.

With the rapid extensive development of the Internet, users not only enjoy great convenience but also face numerous serious security problems. The increasing frequency of data breaches has made it clear that the network security situation is becoming increasingly urgent. In the realm of cybersecurity, intrusion detection plays a pivotal role in monitoring network attacks. However, the efficacy of existing solutions in detecting such intrusions remains suboptimal, perpetuating the security crisis. To address this challenge, we propose a sparse autoencoder-Bayesian optimization-convolutional neural network (SA-BO-CNN) system based on convolutional neural network (CNN). Firstly, to tackle the issue of data imbalance, we employ the SMOTE resampling function during system construction. Secondly, we enhance the system’s feature extraction capabilities by incorporating SA. Finally, we leverage BO in conjunction with CNN to enhance system accuracy. Additionally, a multi-round iteration approach is adopted to further refine detection accuracy. Experimental findings demonstrate an impressive system accuracy of 98.36%. Comparative analyses underscore the superior detection rate of the SA-BO-CNN system.

Abstract Aim To develop a comprehensive training course for training ICU nurses in prone positioning. Design A mixed study combining semi‐structured interviews and two rounds of Delphi surveys. Methods We constructed a questionnaire after collecting data through a literature review and semi‐structured interviews. We used the Delphi expert correspondence method to conduct two rounds of research among 17 experts in the field of critical illness. Data collection took place between May and August 2022. Results The effective questionnaire recovery rate was 88.2%. The expert authority coefficient was 0.876; the Kendall coordination coefficient was 0.402; the average importance score for each index ranged from 4.00 to 4.93; and the coefficient of variation for each index ranged from 0.05 to 0.19. We established 13 second‐level indicators and 41 third‐level indicators on prone position ventilation training according to three aspects: training contents, training methods and training assessment. The training system of prone mechanical ventilation for ICU nurses established in this study will provide an effective framework for training and evaluating the practical ability of prone mechanical ventilation for ICU nurses.

Fruit skin color is an important trait of the hawthorn tree, which has an important influence on fruit quality. Crataegus pinnatifida Bge. var. Major N.E.Br. Is one of the most widely cultivated varieties in China and has a long history of medicinal use. In recent years, it has attracted the attention of the world due to its nutritional and medicinal values. Skin color is the focus of breeders and food processors. At present, skin color-related genes have still not been mapped. In this study, “Shandong Da Mianqiu” (♀, red skin color), “Da Huang Mianzha” (♂, yellow skin color) and 131 F1 hybrids were used to construct genetic map of hawthorn by RAD-seq, and QTL mapping was performed by combining these features with the hue angle and the observed color. In this study, 13,260 SNP was assigned to 17 linkage groups, with an integrated map covering 2,297.75 cM was constructed. A total of 5 QTLs related to hawthorn skin color were detected on LG1, LG3 and LG15. Whether hue angle or pericarp color acts as phenotype for QTL mapping, the candidate genes include bHLH086, WD repeat regions and Myb-like. bHLH, WD and Myb play an important role in the color regulation of Hawthorn skin color. These results lay a solid foundation for QTL mapping and molecular marker-assisted breeding of hawthorn.

Abstract Background The aim of the study is to explore the role of preoperative folate receptor-positive circulating tumor cell (FR+CTC) levels in predicting disease-free survival (DFS) and overall survival (OS) in patients with esophageal squamous cell carcinomas (ESCC). Methods Three ml blood samples were prospectively drawn from ESCC patients, and ligand-targeted polymerase chain reaction (LT-PCR) was used for the quantification of FR+CTCs. Other serum indicators were measured by traditional methods. Clinicopathological characteristics were obtained from the hospital medical record system, DFS and OS data were obtained by follow-up. The correlation between clinico-pathological characteristics, DFS, and OS and FR+CTCs were analyzed, respectively. Risk factors potentially affecting DFS and OS were explored by Cox regression analysis. Results there were no significant correlations between FR+CTCs and patient age, sex, albumin, pre-albumin, C-reactive protein (CRP), ferritin and CRP/Albumin ratio, tumor size, grade of differentiation, lymph node metastasis, TNM stage, perineural invasion/vessel invasion (all P > 0.05). Nevertheless, preoperative FR+CTCs were an independent prognostic factor for DFS (HR 2.7; 95% CI 1.31-, P = 0.007) and OS (HR 3.37; 95% CI 1.06-, P = 0.04). DFS was significantly shorter for patients with post-operative FR+CTCs ≥ 17.42 FU/3ml compared with patients

Abstract Background Mean platelet volume (MPV), as a marker of platelet activity, has been shown to be an efficient prognostic biomarker in several types of cancer. Using MPV, this study aimed to create and validate a prognostic nomogram to the overall survival in esophageal squamous cell carcinoma (ESCC) patients. Methods The nomogram was constructed and tested using data from a retrospective study of 1893 patients who were randomly assigned to the training and testing cohorts with a 7:3 randomization. In order to screen out the optimal predictors for overall survival (OS), we conducted the LASSO‐cox regression, univariate, and multivariate cox regression analyses. Subsequently, the predictive accuracy of the nomogram was validated in both the training and the testing cohorts. Finally, decision curve analysis (DCA) was used to confirm clinical validity. Results Age, MPV, nerve invasion, T stage, and N stage were found as independent prognostic variables for OS and were further developed into a nomogram. The nomogram's prediction accuracy for 1‐, 3‐, and 5‐year OS was 0.736, 0.749, 0.774, and 0.724, 0.719, 0.704 in the training and testing cohorts, respectively. Furthermore, DCA results indicated that nomograms outperformed the AJCC 8th and conventional T, N staging systems in both the training and testing cohorts. Conclusions The nomogram, in conjunction with MPV and standard clinicopathological markers, could improve the accuracy of prediction of OS in ESCC patients.

Convolutional neural networks (CNNs) have achieved remarkable success in fully supervised medical image segmentation tasks. However, the acquisition of large quantities of homogeneous labeled data is challenging, making semi-supervised training methods that rely on a small amount of labeled data and pseudo-labels increasingly popular in recent years. Most existing semi-supervised learning methods, however, underestimate the importance of the unlabeled regions during training. This paper posits that these regions may contain crucial information for minimizing the model’s uncertainty prediction. To enhance the segmentation performance of the left-atrium database, this paper proposes a triple consistency segmentation network based on the squeeze-and-excitation mechanism (SETC-Net). Specifically, the paper constructs a symmetric architectural unit called SEConv, which adaptively recalibrates the feature responses in the channel direction by modeling the inter-channel correlations. This allows the network to adaptively weigh each channel according to the task’s needs, thereby emphasizing or suppressing different feature channels. Moreover, SETC-Net is composed of an encoder and three slightly different decoders, which convert the prediction discrepancies among the three decoders into unsupervised loss through a constructed iterative pseudo-labeling scheme, thus encouraging consistent and low-entropy predictions. This allows the model to gradually capture generalized features from these challenging unmarked regions. We evaluated the proposed SETC-Net on the public left-atrium (LA) database. The proposed method achieved an excellent Dice score of 91.14% using only 20% of the labeled data. The experiments demonstrate that the proposed SETC-Net outperforms seven current semi-supervised methods in left-atrium segmentation and is one of the best semi-supervised segmentation methods on the LA database.

In high-tech areas such as nuclear fusion, aerospace, and high-performance tools, tungsten and its alloys are indispensable due to their high melting point, low thermal expansion, and excellent mechanical properties. The rise of Additive Manufacturing (AM) technologies, particularly Laser Powder Bed Fusion (L-PBF), has enabled the precise and rapid production of complex tungsten parts. However, cracking and densification remain major challenges in printing tungsten samples, and considerable efforts have been made to study how various processing conditions (such as laser power, scanning strategy, hatch spacing, scan speed, and substrate preheating) affect print quality. In this review, we comprehensively discuss various critical processing parameters and the impact of oxygen content on the control of the additive manufacturing process and the quality of the final parts. Additionally, we introduce additive manufacturing-compatible W materials (pure W, W alloys, and W-based composites), summarize the differences in their mechanical properties, densification, and microstructure, and further provide a clear outlook for developing additive manufactured W materials.

Multimodal knowledge graph completion necessitates the integration of information from multiple modalities (such as images and text) into the structural representation of entities to improve link prediction. However, most existing studies have overlooked the interaction between different modalities and the symmetry in the modal fusion process. To address this issue, this paper proposed a Transformer-based knowledge graph link prediction model (MM-Transformer) that fuses multimodal features. Different modal encoders are employed to extract structural, visual, and textual features, and symmetrical hybrid key-value calculations are performed on features from different modalities based on the Transformer architecture. The similarities of textual tags to structural tags and visual tags are calculated and aggregated, respectively, and multimodal entity representations are modeled and optimized to reduce the heterogeneity of the representations. The experimental results show that compared with the current multimodal SOTA method, MKGformer, MM-Transformer improves the Hits@1 and Hits@10 evaluation indicators by 1.17% and 1.39%, respectively, proving that the proposed method can effectively solve the problem of multimodal feature fusion in the knowledge graph link prediction task.

The issue of agricultural non-point source pollution has attracted global attention. A buffer zone is an effective, eco-friendly, and economically feasible remediation ecosystem to reduce the impact of agricultural non-point source pollution on water bodies. They can effectively remove pollutants in agricultural drainage through physical processes (infiltration, filtration, deposition, etc.), plant absorption and assimilation, and microbial processes, improving the water quality of water bodies. This article provides a comprehensive review of the current studies on using buffer zones to remediate agricultural non-point source pollution, with a focus on the key affecting factors for pollutant removal efficiencies. The main factors included buffer zone width, vegetation type, slope, seasonal variation, soil variation, and vegetation density. The influencing mechanisms of these factors on the pollutant removal efficiencies of buffer zones were also discussed. This review can serve as a reference for a deep understanding of buffer zones and help optimize their design and management in real ecological remediation projects.

Excavation of deep and long pits can cause strata deformation and settlement of adjacent buildings. The excavation of a deep foundation pit of a nearby building in Zhengzhou City, China, is taken as the research object. The foundation of the foundation pit is an independent foundation under the column, the depth of the foundation pit is 24.5∼26.3 m, and the support form of an underground continuous wall and four internal supports is adopted. The stress and deformation characteristics of retaining structures during the construction of deep and long pits and their impact on the deformation of nearby buildings were studied through on-site monitoring and finite element simulation. The analysis focuses on the effects of different excavation stages on adjacent structures, with the main conclusions as follows. After excavation, the underground continuous wall far from the building undergoes a “rotating-kick” displacement, while the underground continuous wall on the side near the building only shows a “kick” displacement. Adjacent buildings have little influence on the distribution of shear force and bending moment of the underground continuous wall of the foundation pit. The first horizontal strut experiences pressure, gradually shifting to tension as excavation continues, with the maximum stress reaching 3.6 × 103 kN. The second, third, and fourth horizontal struts mainly bear compressive forces, increasing with the depth of the struts. The building primarily undergoes settlement in the early stages of deep and long pit excavation. As the excavation progresses, the building points near the pit begin to bulge, but the building corners far from the pit continue to show settlement. During the excavation of the deep and long pit, the differential settlement of the long side of the nearby building first decreases and then increases, and the differential settlement of the short side changes from a slight decrease to a significant increase. The settlement evolution of the building is as follows: overall settlement ⟶ overall uplift ⟶ uplift of the side of the building near the pit ⟶ outward tilting away from the pit. The findings may provide references for designing, constructing, and operating deep and large foundation pits.

Eisenia fetida is recognised as advantageous model species in ecotoxicological and regeneration investigations. The intensive utilization of carbamate pesticides (CARs) imposes heavy residue burdens and grave hazards on edaphic environments as well as soil fauna therein. However, precise mechanisms whereby the specific CAR exerted toxic effects on earthworms remain largely elusive, notably from regenerative perspective. Herein, acute responses and regenerative toxicity of two carbamates (metolcarb, MEB and fenoxycarb, FEB) against E. fetida were dissected using biochemical, histological as well as molecular approaches following OECD guidelines at the cellular, tissue and organismal level. The acute toxicity data implied that MEB/FEB were very toxic/medium to extremely toxic, respectively in filter paper contact test and low to medium toxic/low toxic, respectively in artificial soil test. Chronic exposure to MEB and FEB at sublethal concentrations significantly mitigated the soluble protein content, protein abundance while enhanced the protein carbonylation level. Moreover, severely retarded posterior renewal of amputated earthworms was noticed in MEB and FEB treatments relative to the control group, with pronouncedly compromised morphology, dwindling segments and elevated cell apoptosis of blastema tissues, which were mediated by the rising Sox2 and decreasing TCTP levels. Taken together, these findings not only presented baseline toxicity cues for MEB and FEB exposure against earthworms, but also yielded mechanistic insights into regenerative toxicity upon CAR exposure, further contributing to the environmental risk assessment and benchmark formulation of agrochemical pollution in terrestrial ecosystem.