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IKKε is an IκB kinase participating in the control of NF-κB and type I IFN signal pathways in mammals. However, the function of IKKε in regulating immune response is largely unknown in teleost. Herein, an IKKε homologue named AjIKKε was characterized in Japanese eel (Anguilla japonica). AjIKKε has an N-terminal kinase domain, a ubiquitin-like domain, and a coiled coil-containing domain (CC), which is conserved and similar to its counterpart in mammals. Expression analysis showed that AjIKKε could be up-regulated in kidney, spleen, and particularly in liver under the stimulation of poly I:C, LPS, and Aeromonas hydrophila infection. In vitro, the mRNA levels of AjIKKε were significantly provoked in eel liver cells stimulated by LPS and poly I:C, or the different concentrations of A. hydrophila. The overexpression of AjIKKε could not only induce a significantly higher level of promoter activity of human NF-κB, AP-1, and IFN-β in a dose-dependent manner but also up-regulate the activation of promoters of Japanese eel cRel, AP1, IL6, IFN4, IRF3, and IRF7 in HEK293 cells. RNAi studies showed that after AjIKKε was knocked down, the expression levels of IL1, IL6, TNFα, c-Jun, IFN2, IFN3, MX1, MX2, and IRF3 genes were significantly down-regulated in liver, spleen, and kidney of Japanese eels. In addition, the mutants of AjIKKε-K39A, AjIKKε-S174A, and AjIKKε-ΔCC failed to activate Japanese eel IFN4, IRF3 and human IFN-β promoters in HEK293 cells. Collectively, these results suggest that AjIKKε may function as a positive regulator of NF-κB, MAPK, and IRF3-mediated type I IFN signaling pathways related to immune response evoked by bacterial and viral infection.

Former food products (FFPs) are alternative feed ingredients used in livestock diets. Although the processes of transforming FFPs into animal feed often include mechanical unpacking and grinding, the final products may still be prone to packaging contamination. Common materials of packaging remnants found in FFPs are aluminium, cellulose, and plastic. Therefore, it is important to investigate and to provide information regarding the size and shape attributes of these materials to improve processing techniques in the feed industry. A total of 441 packaging remnants from 17 sources of FFPs were included in this study. Fourier transform infra-red spectroscopy coupled with an optical microscope was used to identify the material of the packaging remnants, which resulted in a categorisation of remnants consisting of 44 aluminium, 308 cellulose, and 89 plastic remnants. The categorised remnants were observed with a stereomicroscope and were subsequently measured by a digital camera and image analysis software. Each measurement contains 21 size attributes and 9 shape attributes, some of which were derived from calculations. The distribution of values for both size and shape attributes overlapped between the three materials though aluminium remnants were on average smaller (p < .05) in size and more regular (p < .05) in shape compared to cellulose and plastic ones. Also, aluminium remnants showed a narrower range in most of the size and shape attributes. Through the information provided by the image analysis and the measurements, it was concluded that the obtained values in size and shape attributes had broadly spread distributions that overlapped for different materials.

Our previous studies have shown that activating α7nAChRs suppresses systemic inflammation and immunity through the cholinergic anti-inflammatory pathway (CAP) in early sepsis. Now that the medullary visceral zone (MVZ) is the center of CAP and responsible for regulating systemic inflammation, what changes will occur in MVZ’s pathology and function in sepsis, especially when interfering with α7nAChRs? Does activation of MVZ’s α7nAChRs contribute to the inhibition of systemic inflammation? To clarify these issues, we explored the systemic inflammation and immunity state by detecting serum levels of TNF-α, IL-6, HMGB1, sCD14, and CD4+CD25+Treg and TH17 lymphocytes percentage, meanwhile, we analyzed the apoptosis of cholinergic and catecholaminergic neurons and the expressions of tyrosine hydroxylase (TH) and choline acetyltransferase (CHAT) in MVZ in sepsis and the interfering effects on α7nAChRs. In this study, we found that in sepsis, serum TNF-α, IL-6, HMGB1, sCD14, CD4+CD25+Treg, and TH17 lymphocytes significantly increased and the ratio of Treg/TH17 significantly decreased, cholinergic and catecholaminergic neurons underwent apoptosis with low expressions of TH and CHAT in MVZ; activation of α7nAChRs not only significantly decreased the levels of septic serum TNF-α, IL-6, HMGB1, sCD14, and TH17 lymphocytes (P ＜ 0.05), but also significantly reduced cholinergic and catecholaminergic neurons’ apoptosis, and promoted expressions of TH/CHAT. Our study reveals that sepsis undermines MVZ through neuroinflammation which contributes to the uncontrolled systemic inflammation. Activating central α7nAChRs is not only helpful to restore MVZ’s structure and function but also beneficial to subside the inflammatory storm in sepsis. Even if MVZ is damaged in sepsis, cholinergic neurons in MVZ still regulate the systemic inflammation stably.

Abstract Background To provide a preoperative prediction model for lymph node metastasis in pancreatic cancer patients and provide molecular information of key radiomic features. Methods Two cohorts comprising 151 and 54 pancreatic cancer patients were included in the analysis. Radiomic features from the tumor region of interests were extracted by using PyRadiomics software. We used a framework that incorporated 10 machine learning algorithms and generated 77 combinations to construct radiomics-based models for lymph node metastasis prediction. Weighted gene coexpression network analysis (WGCNA) was subsequently performed to determine the relationships between gene expression levels and radiomic features. Molecular pathways enrichment analysis was performed to uncover the underlying molecular features. Results Patients in the in-house cohort (mean age, 61.3 years ± 9.6 [SD]; 91 men [60%]) were separated into training (n = 105, 70%) and validation (n = 46, 30%) cohorts. A total of 1,239 features were extracted and subjected to machine learning algorithms. The 77 radiomic models showed moderate performance for predicting lymph node metastasis, and the combination of the StepGBM and Enet algorithms had the best performance in the training (AUC = 0.84, 95% CI = 0.77–0.91) and validation (AUC = 0.85, 95% CI = 0.73–0.98) cohorts. We determined that 15 features were core variables for lymph node metastasis. Proliferation-related processes may respond to the main molecular alterations underlying these features. Conclusions Machine learning-based radiomics could predict the status of lymph node metastasis in pancreatic cancer, which is associated with proliferation-related alterations.

Abstract Background Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide, characterized by high morbidity, high mortality, and poor prognosis. Collagen triple helix repeat containing 1 (CTHRC1) has been shown to be highly expressed in various cancers. However, its biological functions, potential role as a biomarker, and its relationship with immune infiltrates in HNSCC remain unclear. Our principal objective was to analyze CTHRC1 expression, its prognostic implications, biological functions, and its effects on the immune system in HNSCC patients using bioinformatics analysis. Methods The expression matrix was obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). CTHRC1 expression in HNSCC was analyzed between tumor and adjacent normal tissues, different stages were compared, and its impact on clinical prognosis was assessed using Kaplan-Meier analysis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA) were employed for enrichment analysis. The Search Tool for the Retrieval of Interacting Genes database (STRING) was used to analyze protein-protein interactions. Pearson correlation tests were used to investigate the association between CTHRC1 expression and immune checkpoints. The correlation between CTHRC1 and immune infiltration was investigated using CIBERSORT, TIMER, and ESTIMATE. Results Compared to adjacent normal tissues, CTHRC1 was found to be highly overexpressed in tumors. Increased expression of CTHRC1 was more evident in the advanced stage of HNSCC and predicted a poor prognosis. Most genes related to CTHRC1 in HNSCC were enriched in physiological functions of Extracellular matrix(ECM) and tumor. Furthermore, several immune checkpoints, such as TNFSF4 and CD276 have been shown to be associated with CTHRC1 expression. Notably, the level of CTHRC1 expression correlated significantly with immune infiltration levels, particularly activated macrophages in HNSCC. Conclusions High expression of CTHRC1 predicts poor prognosis and is associated with immune infiltration in HNSCC, confirming its utility as a tumor marker for HNSCC. Trial registration Not applicable. All data are from public databases and do not contain any clinical trials.

Abstract Pyroptosis possesses potent antitumor immune activity, making pyroptosis inducer development a promising direction for tumor immunotherapy. Persistent luminescence nanoparticles (PLNPs) are highly sensitive optical probes extensively employed in tumor diagnosis and therapy. However, a pyroptosis inducer based on PLNPs has not been reported yet. Herein, polyethylene glycol–poly lactic acid‐co‐glycolic acid (PEG–PLGA: PP) modified biodegradable CaS:Eu2+ (CSE@PP) PLNPs are synthesized as a pyroptosis inducer for tumor immunotherapy for the first time. The synthesized CSE@PP possesses biowindow persistent luminescence (PersL) and pH‐responsive degradation properties, allowing it to remain stable under neutral pH but degrade when exposed to weak acid (pH < 6.5). During degradation within the tumor, CSE@PP constantly releases H2S and Ca2+ while its PersL gradually fades away. Thus, the PersL signal can self‐monitor H2S and Ca2+ release. Furthermore, the released H2S and Ca2+ result in mitochondrial dysfunction and the inactivation of reactive oxygen species scavenging enzymes, synergistic facilitating intracellular oxidative stress, which induces caspase‐1/GSDM‐D dependent pyroptosis and subsequent antitumor immune responses. In a word, it is confirmed that CSE@PP can self‐monitor H2S and Ca2+ release and pyroptosis‐mediated tumor Immunotherapy. This work will facilitate biomedical applications of PLNPs and inspire pyroptosis‐induced tumor immunotherapy.

This study enhances the molecular analysis of bitumen by transitioning from traditional chemical descriptors, such as SARA (Saturates, Aromatics, Resins, and Asphaltenes) fractions and elemental compositions, to specific force field atom types in Molecular Dynamics (MD) models. This shift improves the precision in predicting material properties critical for bituminous material characterization. Machine Learning Models (MLMs) were developed to use these atom types as input features, inherently reflecting fundamental chemical characteristics. Trained on data from over 1,770 LAMMPS simulations of diverse bitumen types and conditions, these MLMs enable the prediction of properties like density, heat capacity, solubility parameters, and thermal expansion coefficients without the need for additional MD simulations. The models utilize 30 chemical descriptors corresponding to specific atom types in the PCFF force field, which collectively account for over 95% of the influence on these properties. By accurately predicting fundamental, thermodynamic, and kinetic properties, the use of MLMs and force field atom types allows researchers to efficiently tweak the chemical nature of organic molecules and mixtures to achieve desired properties. With near-instantaneous prediction times, these MLMs offer valuable insights for advancing bitumen research in the construction and petroleum industries, reducing the need for more intensive simulation techniques.

ObjectiveThe purpose of this study was to observe the relationship between objectively measured levels of physical activity and cognitive impairment (CI) in the presence or absence of diabetes in middle-aged and elderly hemodialysis patients.MethodsIn this multicenter cross-sectional study, 339 clinically stable hemodialysis patients (210 males; mean age: 67.38 ± 8.07 years) aged ≥55 years were included from 7 dialysis units in Shanghai, China. The Chinese version of the Modified Mini-Mental State Examination (MMSE) was used to assess the CI. The duration of physical activity at different intensities, including moderate to vigorous physical activity (MVPA) as well as light physical activity (LPA), was measured using a triaxial accelerometer (ActiGraph GT3X+, Pensacola, FL, USA). Logistic regression and multiple linear regression were used for analyses.ResultsThe prevalence of CI was higher in hemodialysis patients with comorbid diabetes (24.3%). In diabetic patients, MVPA (increase per 10 min/day) was negatively associated with CI after adjusting for covariates [(OR = 0.89, 95%CI = 0.79–0.99), p = 0.042]. However, no significant association between physical activity and CI was found in non-diabetic hemodialysis patients. Further analyses revealed that MVPA was positively associated with temporal orientation, attention and calculation and recall in diabetic hemodialysis patients.ConclusionPhysical activity was associated with CI in diabetic hemodialysis patients rather than the non-diabetes group. This study is important for early differential diagnosis of CI and improvement of cognitive status in hemodialysis patients.

Fetomaternal hemorrhage (FMH) is a perplexing obstetric condition that predominantly occurs during the third trimester or at the time of delivery. Its insidious and non-specific onset often leads to diagnostic challenges. The underlying pathophysiology of FMH remains incompletely understood, though it is primarily attributed to compromise of the placental barrier. The severity of the condition is intrinsically associated with the volumn of blood loss, the hemorrhage rate, and the presence of alloimmunity. Upon the occurrence of severe FMH, it can rapidly lead to intrauterine distress, fetal anemia, and the possibility of fetal demise, presenting a considerable threat to both maternal and neonatal well-being. In this article, I present a substantial case of FMH and conduct a systematic review of the current scientific literature regarding the etiology, clinical manifestations, diagnostic approaches, treatment highlights, and prognosis of this condition. The objective of this work is to improve clinicians’ comprehension and diagnostic proficiency concerning FMH.

The global spread of viral respiratory infections continues to pose a substantial threat to human health, exacerbating the societal burden. Timely and precise detection of viruses is pivotal in mitigating pandemic transmission. Currently, the prevalent diagnostic techniques for viruses include real-time quantitative polymerase chain reaction (RT-qPCR), enzyme-linked immunosorbent assay (ELISA), and colloidal gold assays. However, intricate workflows and protracted processing times of RT-qPCR and ELISA preclude real-time diagnostics, despite their high accuracy. Colloidal gold assays offer rapid turnaround. However, their accuracy and sensitivity are limited, particularly in the context of emerging variants like SARS-CoV-2, which renders them suboptimal test tools. Mounting evidence suggests that surface-enhanced Raman spectroscopy (SERS), with its streamlined operation, rapid analysis, high specificity and sensitivity, holds significant potential as a superior alternative test tool. This review consolidates various SERS-based approaches for detecting respiratory infection virus (RIV) and delineates their characteristics. The unique strengths of SERS technology, including its exceptional sensitivity, robust specificity, and expedited turnaround times, earmark it as particularly well-suited for large-scale instant screening of viral infections within populations.

Ferroptosis is a type of programmed cell death driven by iron-dependent lipid peroxidation. The TNF-mediated biosynthesis of glutathione has been shown to protect synovial fibroblasts from ferroptosis in the hyperplastic synovium. Ferroptosis induction provides a novel therapeutic approach for rheumatoid arthritis (RA) by reducing the population of synovial fibroblasts. The beginning and maintenance of synovitis in RA are significantly influenced by macrophages, as they generate cytokines that promote inflammation and contribute to the destruction of cartilage and bone. However, the vulnerability of macrophages to ferroptosis in RA remains unclear. In this study, we found that M2 macrophages are more vulnerable to ferroptosis than M1 macrophages in the environment of the arthritis synovium with a high level of iron, leading to an imbalance in the M1/M2 ratio. During ferroptosis, HMGB1 released by M2 macrophages interacts with TLR4 on M1 macrophages, which in turn triggers the activation of STAT3 signaling in M1 macrophages and contributes to the inflammatory response. Knockdown of TLR4 decreased the level of cytokines induced by HMGB1 in M1 macrophages. The ferroptosis inhibitor liproxstatin-1 (Lip-1) started at the presymptomatic stage in collagen-induced arthritis (CIA) model mice, and GPX4 overexpression in M2 macrophages at the onset of collagen antibody-induced arthritis (CAIA) protected M2 macrophages from ferroptotic cell death and significantly prevented the development of joint inflammation and destruction. Thus, our study demonstrated that M2 macrophages are vulnerable to ferroptosis in the microenvironment of the hyperplastic synovium and revealed that the HMGB1/TLR4/STAT3 axis is critical for the ability of ferroptotic M2 macrophages to contribute to the exacerbation of synovial inflammation in RA. Our findings provide novel insight into the progression and treatment of RA.

Abstract Orthopedic conditions have emerged as global health concerns, impacting approximately 1.7 billion individuals worldwide. However, the limited understanding of the underlying pathological processes at the cellular and molecular level has hindered the development of comprehensive treatment options for these disorders. The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized biomedical research by enabling detailed examination of cellular and molecular diversity. Nevertheless, investigating mechanisms at the single-cell level in highly mineralized skeletal tissue poses technical challenges. In this comprehensive review, we present a streamlined approach to obtaining high-quality single cells from skeletal tissue and provide an overview of existing scRNA-seq technologies employed in skeletal studies along with practical bioinformatic analysis pipelines. By utilizing these methodologies, crucial insights into the developmental dynamics, maintenance of homeostasis, and pathological processes involved in spine, joint, bone, muscle, and tendon disorders have been uncovered. Specifically focusing on the joint diseases of degenerative disc disease, osteoarthritis, and rheumatoid arthritis using scRNA-seq has provided novel insights and a more nuanced comprehension. These findings have paved the way for discovering novel therapeutic targets that offer potential benefits to patients suffering from diverse skeletal disorders.

Abstract Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

Nanocrystalline NiTi shape memory alloys (SMAs) generally possess outstanding biocompatibility, good corrosion resistance, high cyclic stability and superior fatigue resistance. Nanocrystalline NiTi SMAs are frequently made into the components by experiencing plastic deformation at moderate temperatures in order to prevent nanocrystalline grains from growing up at high temperatures. Therefore, it is of great significance to investigate plastic deformation of nanocrystalline NiTi SMAs at moderate temperature by combining crystal plasticity finite element method (CPFEM) with experiment. In the present work, a CPFEM is employed for studying strengthening mechanism of nanocrystalline Ni50·3Ti49.7 SMA during hot deformation at 400 °C, where statistically stored dislocation (SSD) and geometrically necessary dislocation (GND) densities are integrated into the crystal plasticity constitutive model to account for strain gradients based on dislocation slip. The results show that nanocrystalline Ni50·3Ti49.7 SMA exhibits a strong size effect during plastic deformation at 400 °C, where yield stress along with SSD and GND densities increases with the decrease of grain size. It is evident that grain boundary strengthening and dislocation strengthening play an important role in size effect of nanocrystalline Ni50·3Ti49.7 SMA. The occurrence of grain rotation is induced by significant plastic strain gradients and high dislocation densities. With the increase of plastic strain, grain rotation results in the decrease of average grain boundary misorientation and activates additional available slip systems to+ accommodate the deformation, thereby leading to the decrease of the GND density and the increase of SSD density.

The unique combination of lightweight, strength, corrosion resistance, high-temperature resistance, and biocompatibility make Ti6Al4V alloy an important material in metal contact of solar cells, and selective laser melting (SLM) enables the production of Ti6Al4V parts complex geometries that would be difficult or impossible to produce using traditional manufacturing methods. The build orientation of SLM process presents an essential effect on the quality of the produced parts, and thus, in this study it investigates the impact of multi-build orientation (0°, 30°, 45°, 60°, 90°) on the microstructural evolution and mechanical behavior of truly as-built Ti6Al4V alloys produced by SLM. The microstructure analysis revealed the presence of ordered subsurface pores, located at a distance of about 50 μm–150 μm from the sample surface. The microstructure consisted primarily of α′ martensite with a small amount of β phase. Massive transformation was observed in the 30° and 45° samples, with corresponding grain sizes of 5 μm and 10 μm, respectively. To be specific, the content of β phase and ultimate tensile strength (UTS) varied with the sample orientation, and the 45° build orientation exhibited the highest content of β phase with value of approximately 2.8%, but also the lowest UTS and elongation, which were 1161 MPa and 3.99%, respectively.

High silicon steel has excellent soft magnetic properties, but a poor plasticity limits its practical application seriously. In this research, a novel FeSiCrNi alloy short for Fe-6.5Si–2Cr–12Ni (wt%) was designed and deformed via the local canning compression process at room temperature. It was shown that the FeSiCrNi alloy presents a relatively high plasticity in a triaxial compressive stress state. Geometrically necessary dislocations ensure the compatibility of plastic deformation for FeSiCrNi alloy. There are a small number of η and γ textures inside the FeSiCrNi sample. The intensity of η texture nearly keeps steady, while the intensity of γ texture increases slightly with the level of plastic deformation increasing. Additionally, the intensity of {112} copper texture increases with the increase of plastic deformation. Lath structures dominate the inhomogeneous plastic deformation of FeSiCrNi alloy and then integrate with the phase α-Fe. The main deformation mechanism of FeSiCrNi alloy is dislocation slip rather than twinning which has not been observed yet. The order degree of FeSiCrNi alloy continuously decreases, and deformation bands, dislocation walls and dislocation cells are successively formed, as the compression proceeds. It is feasible to produce FeSiCrNi components in cold forming process.

Abstract To explore the impact of the Mediterranean diet on cardiovascular risk factors, glycemic control and weight loss in patients with type 2 diabetes(T2D) by a meta-analysis of randomized controlled trials (RCTs). We systematically searched PubMed, Cochrance Library, EMBASE and four Chinese databases to identify RCTs that compared the Mediterranean diet with control diets in patients with T2D up to December 2021. The Risk of Bias of the included studies was assessed using the version 2 of the Cochrane risk-of-bias tools for randomized trials (ROB 2). Seven RCTs with 1371 patients met the eligibility criteria and entered into the meta-analysis. Compared to control diets, the beneficial effects of Mediterranean diet were not statistically significant in high-density lipoprotein (MD = 2.33; 95% CI: -0.27 to 4.92), low-density lipoprotein (MD = -2.34; 95% CI -5.67 to 0.99) and total cholesterol (MD = 2.60; 95% CI: -0.95 to 6.15). But Mediterranean diet led to reduce the level of diastolic blood pressure (MD = -1.20; 95% CI: -2.21 to -0.19) and systolic blood pressure (MD = -4.17; 95% CI: -7.12 to -1.22). Meanwhile, Mediterranean diet showed beneficial effects in glycemic control (HbA1[%]: MD = -0.39, 95% CI: -0.58 to -0.20; fasting plasma glucose: MD = -15.12, 95% CI: -24.69 to -5.55) and weight loss (BMI: MD = -0.71, 95% CI: -1.30 to -0.78; WC: MD = -1.69; 95% CI: -3.35 to -0.02) compared to the control diets. The meta-analysis presented evidence supporting the beneficial effects of the Mediterranean diet on blood pressure, glycemic control, and weight loss. However, the impact of the Mediterranean diet on the lipid profile was not found to be significant, warranting further verification. This Meta-analysis was registered on the INPLASY website (Registration number: INPLASY 202160096).

Abstract Objective This study aimed to construct a model based on 23 enrolled molecules to evaluate prognoses of pT2/3N0M0 esophageal squamous cell carcinoma (ESCC) patients with up to 20 years of follow-up. Methods The lasso-Cox model was used to identify the candidate molecule. A nomogram was conducted to develop the survival model (molecular score, MS) based on the molecular features. Cox regression and Kaplan-Meier analysis were used in this study. The concordance index (C-index) was measured to compare the predicted ability between different models. The primary endpoint was overall survival (OS). Results A total of 226 patients and 23 proteins were enrolled in this study. Patients were classified into high-risk (MS-H) and low-risk (MS-L) groups based on the MS score of 227. The survival curves showed that the MS-L cohort had better 5-year and 10-year survival rates than the MS-H group (5-year OS: 51.0% vs. 8.0%; 10-year OS: 45.0% vs. 5.0%, all p

Abstract Glioblastoma is one of the most lethal malignant cancers, displaying striking intratumor heterogeneity, with glioblastoma stem cells (GSCs) contributing to tumorigenesis and therapeutic resistance. Pharmacologic modulators of ubiquitin ligases and deubiquitinases are under development for cancer and other diseases. Here, we performed parallel in vitro and in vivo CRISPR/Cas9 knockout screens targeting human ubiquitin E3 ligases and deubiquitinases, revealing the E3 ligase RBBP6 as an essential factor for GSC maintenance. Targeting RBBP6 inhibited GSC proliferation and tumor initiation. Mechanistically, RBBP6 mediated K63-linked ubiquitination of Cleavage and Polyadenylation Specific Factor 3 (CPSF3), which stabilized CPSF3 to regulate alternative polyadenylation events. RBBP6 depletion induced shortening of the 3’UTRs of MYC competing-endogenous RNAs to release miR-590-3p from shortened UTRs, thereby decreasing MYC expression. Targeting CPSF3 with a small molecular inhibitor (JTE-607) reduces GSC viability and inhibits in vivo tumor growth. Collectively, RBBP6 maintains high MYC expression in GSCs through regulation of CPSF3-dependent alternative polyadenylation, providing a potential therapeutic paradigm for glioblastoma.

Abstract Objectives To assess the performance of multi-modal ultrasomics model to predict efficacy to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and compare with the clinical model. Materials and methods This study retrospectively included 106 patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 at our hospital, randomly divided into a training set of 74 and a validation set of 32 in a 7: 3 ratios. Ultrasomics features were extracted from the tumors’ region of interest of B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) images based on PyRadiomics. Mann-Whitney U test, spearman, and least absolute shrinkage and selection operator algorithms were utilized to reduce features dimension. Five models were built with ultrasomics and clinical analysis using multilayer perceptron neural network classifier based on python. Including BUS, CEUS, Combined_1, Combined_2 and Clinical models. The diagnostic performance of models was assessed with the area under the curve (AUC) of the receiver operating characteristic. The DeLong testing algorithm was utilized to compare the models’ overall performance. Results The AUC (95% confidence interval [CI]) of the five models in the validation cohort were as follows: BUS 0.675 (95%CI: 0.481–0.868), CEUS 0.821 (95%CI: 0.660–0.983), Combined_1 0.829 (95%CI: 0.673–0.985), Combined_2 0.893 (95%CI: 0.780-1.000), and Clinical 0.690 (95%CI: 0.509–0.872). The Combined_2 model was the best in the overall prediction performance, showed significantly better compared to the Clinical model after DeLong testing (P

AbstractObjective Incomplete thermal ablation (ITA) fosters the malignancy of residual cells in Hepatocellular carcinoma (HCC) with unclear mechanisms now. This study aims to investigate the expression changes of NDST2 following ITA of HCC and its impact on residual cancer cells.Methods An in vitro model of heat stress-induced liver cancer was constructed to measure the expression of NDST2 using Quantitative Real-Time PCR and Western blotting experiments. The sequencing data from nude mice were used for validation. The clinical significance of NDST2 in HCC was evaluated by integrating datasets. Gene ontology and pathway analysis were conducted to explore the potential signaling pathways regulated by NDST2. Additionally, NDST2 was knocked down in heat stress-induced HCC cells, and the effects of NDST2 on these cells were verified using Cell Counting Kit-8 assays, scratch assays, and Transwell assays.Results NDST2 expression levels are elevated in HCC, leading to a decrease in overall survival rates of HCC patients. Upregulation of immune checkpoint levels in high NDST2-expressing HCC may contribute to immune evasion by liver cancer cells. Additionally, the low mutation rate of NDST2 in HCC suggests a relatively stable expression of NDST2 in this disease. Importantly, animal and cell models treated with ITA demonstrate upregulated expression of NDST2. Knockdown of NDST2 in heat stress-induced liver cancer cells results in growth inhibition associated with gene downregulation.Conclusion The upregulation of NDST2 can accelerate the progression of residual HCC after ITA, suggesting a potential role for NDST2 in the therapeutic efficacy and prognosis of residual HCC.

The corrosion resistance of M390 powder metallurgical martensitic stainless steel with different tempering temperatures was investigated by potentiodynamic polarization measurements, salt spray tests, and microstructural analyses utilizing scanning electron microscopy (SEM), X-ray diffraction (XRD), and transmission electron microscopy (TEM). The tempering temperature had no significant effect on the size and volume fraction of carbides. The corrosion resistance of M390 steel gradually deteriorated with increasing tempering temperature, and a loss passivation (LOP) effect was observed when tempered at 450 °C, 500 °C, and 550 °C. Transmission electron microscopy (TEM) analysis showed that the width of the Cr-depleted zones around the undissolved M7C3 carbides increased with increasing tempering temperature, while the Cr content in these zones decreased, which was the main reason for the deterioration of corrosion resistance. This study offers valuable insights into optimizing the tempering process to improve the corrosion resistance of M390 steel for practical applications.

Abstract Immunosuppression is a major hallmark of tumor progression in non‐small cell lung cancer (NSCLC). Cluster of differentiation 147 (CD147), an important pro‐tumorigenic factor, is closely linked to NSCLC immunosuppression. However, the role of CD147 di‐methylation in the immunosuppressive tumor microenvironment (TME) remains unclear. Here, di‐methylation of CD147 at Lys148 (CD147‐K148me2) is identified as a common post‐translational modification (PTM) in NSCLC that is significantly associated with unsatisfying survival outcomes among NSCLC sufferers, especially those in the advanced stages of the disease. The methyltransferase NSD2 catalyzes CD147 to generate CD147‐K148me2. Further analysis demonstrates that CD147‐K148me2 reestablishes the immunosuppressive TME and promotes NSCLC progression. Mechanistically, this modification promotes the interaction between cyclophilin A (CyPA) and CD147, and in turn, increases CCL5 gene transcription by activating p38‐ZBTB32 signaling, leading to increased NSCLC cell‐derived CCL5 secretion. Subsequently, CD147‐K148me2‐mediated CCL5 upregulation facilitates M2‐like tumor‐associated macrophage (TAM) infiltration in NSCLC tissues via CCL5/CCR5 axis‐dependent intercellular crosstalk between tumor cells and macrophages, which is inhibited by blocking CD147‐K148me2 with the targeted antibody 12C8. Overall, this study reveals the role of CD147‐K148me2‐driven intercellular crosstalk in the development of NSCLC immunosuppression, and provides a potential interventional strategy for PTM‐targeted NSCLC therapy.

The initial temperature has a considerable effect on aluminum polycrystals' physical stability and mechanical performance, with the possibility to optimize their mechanical properties for practical applications. Thus, using a molecular dynamics technique, the effect of temperature on the mechanical properties of aluminum polycrystals is studied. Stress-strain curves, ultimate strength, and Young's modulus were all measured at temperatures of 300, 350, 400, and 450 K. The findings from MD simulations show that the initial temperature significantly affects the physical stability and mechanical performance of designed aluminum polycrystals. The aluminum polycrystal experiences a numerical increase in ultimate strength and Young's modulus from 6640 to 74.072 to 7.055 and 79.226 GPa, respectively, when subjected to the optimal initial conditions of 350 K. With further increasing temperature to 450 K, ultimate strength and Young's modulus decrease to 6.461 and 74.413 GPa, respectively. The observed decrease in ultimate strength and Young's modulus of the aluminum polycrystal as the temperature increased from the optimal condition of 350 K–450 K can be attributed to the weakening of interatomic attraction forces at higher temperatures. This reduction in interatomic bonding strength resulted in decreased material stiffness and resistance to deformation, leading to lower ultimate strength and Young's modulus values. This study's novelty lies in its comprehensive assessment of the initial temperature's effects on the mechanical performance of aluminum polycrystals, providing valuable insights for practical applications and advancing beyond previous efforts in the literature.

Objective: This study aimed to delineate the clear cell renal cell carcinoma (ccRCC) intrinsic subtypes through unsupervised clustering of radiomics and transcriptomics data and to evaluate their associations with clinicopathological features, prognosis, and molecular characteristics. Methods: Using a retrospective dual-center approach, we gathered transcriptomic and clinical data from ccRCC patients registered in The Cancer Genome Atlas and contrast-enhanced computed tomography images from The Cancer Imaging Archive and local databases. Following the segmentation of images, radiomics feature extraction, and feature preprocessing, we performed unsupervised clustering based on the “CancerSubtypes” package to identify distinct radiotranscriptomic subtypes, which were then correlated with clinical-pathological, prognostic, immune, and molecular characteristics. Results: Clustering identified three subtypes, C1, C2, and C3, each of which displayed unique clinicopathological, prognostic, immune, and molecular distinctions. Notably, subtypes C1 and C3 were associated with poorer survival outcomes than subtype C2. Pathway analysis highlighted immune pathway activation in C1 and metabolic pathway prominence in C2. Gene mutation analysis identified VHL and PBRM1 as the most commonly mutated genes, with more mutated genes observed in the C3 subtype. Despite similar tumor mutation burdens, microsatellite instability, and RNA interference across subtypes, C1 and C3 demonstrated greater tumor immune dysfunction and rejection. In the validation cohort, the various subtypes showed comparable results in terms of clinicopathological features and prognosis to those observed in the training cohort, thus confirming the efficacy of our algorithm. Conclusion: Unsupervised clustering based on radiotranscriptomics can identify the intrinsic subtypes of ccRCC, and radiotranscriptomic subtypes can characterize the prognosis and molecular features of tumors, enabling noninvasive tumor risk stratification.

Band selection (BS) is a crucial concept within the realm of remote sensing, involving the selection of the most suitable bands to accurately capture features of landforms and surfaces. Despite the promising results achieved by many existing methods, certain limitations remain. First, most methods rely on a single criterion for band evaluation, leading to an incomplete assessment and limited generalizability of bands. Second, there is a lack of emphasis on target detection; thus, some BS techniques commonly used for classification are less effective for detection. Therefore, this article proposes MOBS-TD, a multiobjective optimization (MO) based BS method specifically designed for target detection, which aims to select bands with better target separation and stronger robustness across various application scenes. Initially, we develop an MO model with three objectives and introduce a novel metric to quantify the target–background separability of bands. Subsequently, a weighted similarity to ideal solution strategy is developed to clearly describe the dominance relations and strike a balance among multiple objectives in evolution. In addition, we devise an evaluation mechanism based on the ratio of maximum to submaximum, which is devised for selecting the optimal solution from the Pareto front, which has been empirically validated to be effective in reducing false alarms. Extensive experiments on real-world datasets demonstrate the competitiveness of MOBS-TD in remote sensing applications.

Abstract Background Ras gene mutation and/or overexpression are drivers in the progression of cancers, including colorectal cancer. Blocking the Ras signaling has become a significant strategy for cancer therapy. Previously, we constructed a recombinant scFv, RGD-p21Ras-scFv by linking RGD membrane-penetrating peptide gene with the anti-p21Ras scFv gene. Here, we expressed prokaryotically RGD-p21Ras-scFv on a pilot scale, then investigated the anti-tumor effect and the mechanism of blocking Ras signaling. Methods The E. coli bacteria which could highly express RGD-p21Ras-scFv was screened and grown in 100 L fermentation tank to produce RGD-p21Ras-scFv on optimized induced expression conditions. The scFv was purified from E. coli bacteria using His Ni-NTA column. ELISA was adopted to test the immunoreactivity of RGD-p21Ras-scFv against p21Ras proteins, and the IC50 of RGD-p21Ras-scFv was analyzed by CCK-8. Immunofluorescence colocalization and pull-down assays were used to determine the localization and binding between RGD-p21Ras-scFv and p21Ras. The interaction forces between RGD-p21Ras-scFv and p21Ras after binding were analyzed by molecular docking, and the stability after binding was determined by molecular dynamics simulations. p21Ras-GTP interaction was detected by Ras pull-down. Changes in the MEK-ERK /PI3K-AKT signaling paths downstream of Ras were detected by WB assays. The anti-tumor activity of RGD-p21Ras-scFv was investigated by nude mouse xenograft models. Results The technique of RGD-p21Ras-scFv expression on a pilot scale was established. The wet weight of the harvested bacteria was 31.064 g/L, and 31.6 mg RGD-p21Ras-scFv was obtained from 1 L of bacterial medium. The purity of the recombinant antibody was above 85%, we found that the prepared on a pilot scale RGD-p21Ras-scFv could penetrate the cell membrane of colon cancer cells and bind to p21Ras, then led to reduce of p21Ras-GTP (active p21Ras). The phosphorylation of downstream effectors MEK-ERK /PI3K-AKT was downregulated. In vivo antitumor activity assays showed that the RGD-p21Ras-scFv inhibited the proliferation of colorectal cancer cell lines. Conclusion RGD-p21Ras-scFv prokaryotic expressed on pilot-scale could inhibited Ras-driven colorectal cancer growth by partially blocking p21Ras-GTP and might be able to be a hidden therapeutic antibody for treating RAS-driven tumors.

Abstract Background Many controversies still exist concerning the optimal extent of lymphadenectomy during esophagectomy in esophageal squamous cell carcinoma (ESCC). The objective of this study was to explore the characteristics of 4R metastasis and evaluate the clinical value of 4R node dissection in ESCC. Methods A total of 736 ESCC patients who underwent radical esophagectomy between 2005 and 2013 were retrospectively collected, among which 393 ones underwent 4R dissection. Propensity score matching (PSM) method was applied to reduce the effects of confounding variables between the 4R dissection and non-dissection groups to analyze overall survival. Results Patients showed a low 4R metastasis rate of 5.1% (20/393) (5.2%, 5.8%, and 1.8% for upper, middle, and lower tumors, respectively). Correlation analyses identified that 4R metastasis was significantly associated with station 2R metastasis (p

The lag in quantitative methods and detection techniques for geologic information has resulted in time-consuming and human-experienced geologic analysis in tunnels. Geochemical indicators of rocks can be used to identify adverse geology and to explain the intrinsic causes of damage to normal rocks. This study proposes a method to identify adverse geology by extracting and imaging the indicator elements. The mapping relationship between rock components and geologic bodies is quickly determined by indicator element extraction based on factor analysis, and then the data are gridded for image output. The location and size of the target adverse geology are visually identified through the distribution images of the indicator elements, thus reducing data dimensions and analysis time. A non-destructive, in-situ and fast element detection technique in tunnels was adopted to speed up the process of geology identification. The accuracy of the detection was validated by comparing field and laboratory test results. This study further confirms and refines the previous research, and the results provide references for geological, mining and underground projects.

Following the promotion of animal welfare awareness, modern meat production should be applied ethically and sustainably. Alternative circular feeds such as algae, insects, agro-industrial by-products (AIBPs) and former food products (FFPs) play a key role to re-define the current meat production system. This review highlights the effects of feed ingredients mentioned above on red meat quality, from a blue-bio/circular economy point of view. The results show that when algae are added in adequate amounts, they can improve nutritional and sensory quality of meat. Insects, and AIBPs, can affect meat quality mainly in terms of selected components like fat content and quality, while the effects of FFPs as feed ingredients on meat quality are still limited. These alternative feedingstuffs are regarded as interesting protein/energy sources for animal diets and are expected to be increasingly used globally as a replacement for conventional feedstuffs. The inclusion level of insects, AIBPs and FFPs is often higher than that of algae, because algae are considered more as feed supplements instead of ingredients that mainly provide macronutrients to the animals. However, more research is needed for a comprehensive evaluation of these materials, especially in terms of: (i) feed formulation and processing methods (inclusion level of such materials and technology used for feed production); (ii) their potential impacts on animal growth and health status and on environmental footprint; (iii) carcass quality; and (iv) final meat product quality, safety and wholesomeness.

Background: Epidural-related maternal fever (ERMF) is a common phenomenon that appears to be unique to laboring women and presents diagnostic and therapeutic dilemmas for anesthesiologists. It is crucial to identify and predict the occurrence of ERMF at an early stage to improve the outcomes for mothers and infants. This study evaluated the degree of sympathetic blockade induced by epidural labor analgesia (ELA) by quantifying blood flow spectral parameters using Pulsed-wave Doppler (PWD). The aim was to determine the predictive value of these parameters for the onset of ERMF. Methods: A total of 103 women who had vaginal deliveries with ELA were recruited into the study. PWD ultrasound was used to measure peak systolic velocity (PSV, cm/s) and end-diastolic velocity (EDV, cm/s) in the anterior and posterior tibial arteries. Measurements were taken 1 minute before induction of analgesia and at 5-minute intervals for the subsequent 30 minutes. The change of PSV (ΔPSV) and EDV (ΔEDV) at 30 minutes relative to baseline after induction of analgesia was calculated. Participants were categorized into two groups based on their body temperature during labor and delivery: febrile and afebrile. Generalized estimating equations were used to assess differences both between and within groups across multiple time points. The correlation between two variables was analyzed using Spearman's rank correlation coefficient. Receiver operating characteristic (ROC) curves were plotted to ascertain the cut-off values of lower extremity arterial ultrasound flow spectral parameters for predicting ERMF. Results: Of the 103 study participants, 73 were ultimately included for analysis. Thirteen participants (17.8%) in the study developed ERMF. PSV was significantly higher in the febrile group than the non-febrile group at 10 min after ELA (p < 0.05). In contrast, EDV showed a significant difference between the two groups at 15 min after ELA (p < 0.01). Based on linear correlation analysis, there was a positive correlation between PSV and EDV at 30 minutes after analgesia induction and the peak labor temperature (p < 0.05). ROC curve analysis identified a cut-off value of 43.35 and an area under the curve (AUC) of 0.701 for ΔPSV in the anterior tibial artery region (95% confidence interval (CI) 0.525 to 0.878, p = 0.025) and a cut-off value of 29.94 and an AUC of 0.733 for ΔEDV (95% CI 0.590 to 0.877, p = 0.001). The cut-off value for ΔPSV in the region of the posterior tibial artery was 39.96 with an AUC of 0.687 (95% CI 0.514 to 0.860, p = 0.034), and the cut-off value for ΔEDV was 33.10 with an AUC of 0.713 (95% CI 0.558 to 0.869, p = 0.007). Conclusions: Regional blood flow spectral parameters after epidural analgesia induction can predict the occurrence of ERMF by indirectly reflecting the degree of sympathetic activity inhibition. Specifically, the amount of change in peak systolic velocity and end-diastolic velocity relative to baseline parameters 30 min after ELA induction was the most predictive. Clinical Trial Registration: The study has been registered in the Chinese Clinical Trial Registry https://www.chictr.org.cn/ (reference number: ChiCTR2400080507, 31/01/2024).

The identification of drug–target interaction (DTI) is crucial for drug discovery. However, how to reduce the graph neural network’s false positives due to its bias and negative transfer in the original bipartite graph remains to be clarified. Considering that the impact of heterogeneous auxiliary information on DTI varies depending on the drug and target, we established an adaptive enhanced personalized meta-knowledge transfer network named Meta Graph Association-Aware Contrastive Learning (MGACL), which can transfer personalized heterogeneous auxiliary information from different nodes and reduce data bias. Meanwhile, we propose a novel DTI association-aware contrastive learning strategy that aligns high-frequency drug representations with learned auxiliary graph representations to prevent negative transfer. Our study improves the DTI prediction performance by about 3%, evaluated by analyzing the area under the curve (AUC) and area under the precision–recall curve (AUPRC) compared with existing methods, which is more conducive to accurately identifying drug targets for the development of new drugs.

Multi-satellite imaging mission planning (MSIMP) is an important focus in the field of satellite application. MSIMP involves a variety of coupled constraints and optimization objectives, which often require extensive simulation and evaluation when solving, leading to high computational costs and slow response times for traditional algorithms. Surrogate model expensive multi-objective evolutionary algorithms (SM-EMOEAs), which are computationally efficient and converge quickly, are effective methods for the solution of MSIMP. However, the recent advances in this field have not been comprehensively summarized; therefore, this work provides a comprehensive overview of this subject. Firstly, the basic classification of MSIMP and its different fields of application are introduced, and the constraints of MSIMP are comprehensively analyzed. Secondly, the MSIMP problem is described to clarify the application scenarios of traditional optimization algorithms in MSIMP and their properties. Thirdly, the process of MSIMP and the classical expensive multi-objective evolutionary algorithms are reviewed to explore the surrogate model and the expensive multi-objective evolutionary algorithms based on MSIMP. Fourthly, improved SM-EMOEAs for MSIMP are analyzed in depth in terms of improved surrogate models, adaptive strategies, and diversity maintenance and quality assessment of the solutions. Finally, SM-EMOEAs and SM-EMOEA-based MSIMP are analyzed in terms of the existing literature, and future trends and directions are summarized.

With the increase in UAV scale and mission diversity, trajectory planning systems faces more and more complex constraints, which are often conflicting and strongly coupled, placing higher demands on the real-time and response capabilities of the system. At the same time, conflicts and strong coupling pose challenges the autonomous decision-making capability of the system, affecting the accuracy and efficiency of the planning system in complex environments. However, recent research advances addressing these issues have not been fully summarized. An in-depth exploration of constraint handling techniques and autonomous decision-making issues will be of great significance to the development of large-scale UAV systems. Therefore, this paper aims to provide a comprehensive overview of this topic. Firstly, the functions and application scenarios of large-scale UAV trajectory planning are introduced and classified in detail according to the planning method, realization function and the presence or absence of constraints. Then, the constraint handling techniques are described in detail, focusing on the priority ranking of constraints and the principles of their fusion and transformation methods. Then, the importance of autonomous decision-making in large-scale UAV trajectory planning is described in depth, and related dynamic adjustment algorithms are introduced. Finally, the future research directions and challenges of large-scale UAV trajectory planning are outlooked, providing directions and references for future research in the fields of UAV clustering and UAV cooperative flight.

In order to explore the feasibility and efficacy of reed-fiber-modified bitumen (RFMB), three lengths and three dosages of reed fibers were selected to modify bitumen and bituminous mortar, while the physicochemical properties of RFMB and RFMB mortar were analyzed. In this work, FTIR spectroscopy was employed to characterize the chemical impact of fiber on bitumen. The viscidity and rheology of RFMB and the tensile strength of RFMB mortar were evaluated using a Brookfield viscometer, dynamic shear rheometer, and monotonic tensile test. The results showed that adding fibers primarily affects the physical structure rather than the chemical composition of bitumen, confirmed by FTIR spectroscopy. RFMB viscosity increased with higher fiber dosage and fiber length. Rheological evaluations showed an enhanced complex shear modulus for RFMB, suggesting improved performance at higher temperatures but increased stiffness at lower temperatures, with the latter indicating reduced flexibility. RFMB also demonstrated superior fatigue and rutting resistance, albeit with compromised stress sensitivity. Tensile tests on RFMB mortar highlighted significant improvements, especially with longer fibers, while shorter 0.4 mm fibers showed modest reinforcement effects, possibly due to uneven distribution during sample preparation.

Background: Although electromagnetic navigation bronchoscopy (ENB) is highly sensitive in the diagnosis of peripheral pulmonary nodules (PPNs), its diagnostic yield for subgroups of smaller PPNs is under evaluation. Objectives: Diagnostic yield evaluation of biopsy using ENB for PPNs

This paper presents a United Atom (UA) force field for simulating hydrocarbon molecules in bituminous materials, integrating explicit hydrogens into beads with their parent atom. This method simplifies all-atom molecular models, significantly accelerating Molecular Dynamics (MD) simulations of bitumen by 10 to 100 times. Key advantages include halving the particle count, eliminating complex hydrogen interactions, and decreasing the degrees of freedom of the molecules. Developed by mapping forces from an all-atom model to the centers of mass of UA model beads, the force field ensures accurate replication of energies, forces, and molecular conformations, mirroring properties like pressure and density. It features 17 bead types and 287 interaction types, encompassing various hydrocarbon molecules. The UA force field's stability, surpassing all-atom models, is a notable achievement. This stability, stemming from smoother potential energy surfaces, leads to consistent property measurements and improved stress tensor accuracy. It enables the extension of MD simulations to larger spatiotemporal scales, crucial for understanding complex phenomena such as phase separation in bituminous materials. This foundational work sets the stage for future developments, including refining parameters and introducing new bead types, to enhance the modeling capabilities of the force field, thereby advancing the application and understanding of bituminous materials.

Fe-6.5 wt% Si high silicon steel alloy presents excellent soft magnetic performances, but a poor plasticity limits its industrial application seriously. In this work, a novel FeSiCrNi alloy with the chemical composition of Fe-6.5Si-2Cr-12Ni (wt%) is designed and subjected to thermomechanical processing including local canning compression at room temperature and annealing at elevated temperatures. It is observed that microstructures of FeSiCrNi specimens with a compression degree of 70 % are dominated by dislocation cells. The microstructural evolution is transformed from static recovery to static recrystallization when FeSiCrNi specimens are annealed from 400 to 700 °C. Grains with high strain energy are successively nucleated via the integration of subgrain boundaries at 600 °C and a pronounced growth of recrystallized grains occurs at 700 °C. In particular, microtextures of the FeSiCrNi alloy essentially consist of γ-fiber ({111}∥ND) and λ-fiber ({100}∥ND) textures during recovery and recrystallization. The intensity of λ-fiber texture is weakened with increasing annealing temperature, while the intensity of γ-fiber which is composed of {111} 〈112〉 and {111} 〈110〉 textures is strengthened. The intensity of {111} 〈112〉 texture within the recrystallized FeSiCrNi grains reaches the maximum value at 700 °C.

Abstract The accuracy and reliability of circuit board fault detection are significantly influenced by the uncertainty inherent in the VI curve. Here, an ensemble neural network is proposed, which combines the neural network‐based prediction interval and ensemble approaches, to improve the accuracy of fault detection using the VI curve. First, a loss function with multiple objectives is formulated by integrating curve fitting and prediction interval. The aim is to achieve the curve fitting between current and voltage while simultaneously determining the optimal upper and lower bounds of the prediction interval. Second, an ensemble approach is employed to reduce model uncertainty and derive the ultimate current predictions and intervals. These predictions and intervals are then used in a comparative approach to automatically detect faults in circuit boards. Finally, the effectiveness of the proposed algorithm in improving the accuracy of fault detection is verified on experimental circuit boards.

Abstract Glioblastoma (GBM) ranks among the most lethal of human cancers, containing glioma stem cells (GSCs) that display therapeutic resistance. Here, we report that the lncRNA INHEG is highly expressed in GSCs compared to differentiated glioma cells (DGCs) and promotes GSC self-renewal and tumorigenicity through control of rRNA 2’-O-methylation. INHEG induces the interaction between SUMO2 E3 ligase TAF15 and NOP58, a core component of snoRNP that guides rRNA methylation, to regulate NOP58 sumoylation and accelerate the C/D box snoRNP assembly. INHEG activation enhances rRNA 2’-O-methylation, thereby increasing the expression of oncogenic proteins including EGFR, IGF1R, CDK6 and PDGFRB in glioma cells. Taken together, this study identifies a lncRNA that connects snoRNP-guided rRNA 2’-O-methylation to upregulated protein translation in GSCs, supporting an axis for potential therapeutic targeting of gliomas.

Morphine is a frequently used analgesic that activates the mu-opioid receptor (MOR), which has prominent side effects of tolerance. Although the inefficiency of morphine in inducing the endocytosis of MOR underlies the development of morphine tolerance, currently, there is no effective therapy to treat morphine tolerance. In the current study, we aimed to develop a monoclonal antibody (mAb) precisely targeting MOR and to determine its therapeutic efficacy on morphine tolerance and the underlying molecular mechanisms. We successfully prepared a mAb targeting MOR, named 3A5C7, by hybridoma technique using a strategy of deoxyribonucleic acid immunization combined with cell immunization, and identified it as an immunoglobulin G mAb with high specificity and affinity for MOR and binding ability to antigens with spatial conformation. Treatment of two cell lines, HEK293T and SH-SY5Y, with 3A5C7 enhanced morphine-induced MOR endocytosis via a G protein-coupled receptor kinase 2 (GRK2)/β-arrestin2-dependent mechanism, as demonstrated by immunofluorescence staining, flow cytometry, Western blotting, coimmunoprecipitation, and small interfering ribonucleic acid (siRNA)-based knockdown. This mAb also allowed MOR recycling from cytoplasm to plasma membrane and attenuated morphine-induced phosphorylation of MOR. We established an in vitro morphine tolerance model using differentiated SH-SY5Y cells induced by retinoic acid. Western blot, enzyme-linked immunosorbent assays, and siRNA-based knockdown revealed that 3A5C7 mAb diminished hyperactivation of adenylate cyclase, the in vitro biomarker of morphine tolerance, via the GRK2/β-arrestin2 pathway. Furthermore, in vivo hotplate test demonstrated that chronic intrathecal administration of 3A5C7 significantly alleviated morphine tolerance in mice, and withdrawal jumping test revealed that both chronic and acute 3A5C7 intrathecal administration attenuated morphine dependence. Finally, intrathecal electroporation of silencing short hairpin RNA illustrated that the in vivo anti-tolerance and anti-dependence efficacy of 3A5C7 was mediated by enhanced morphine-induced MOR endocytosis via GRK2/β-arrestin2 pathway. Collectively, our study provided a therapeutic mAb, 3A5C7, targeting MOR to treat morphine tolerance, mediated by enhancing morphine-induced MOR endocytosis. The mAb 3A5C7 demonstrates promising translational value to treat clinical morphine tolerance.

Abstract Immunotherapy has good potential to eradicate tumors in the long term. However, due to the low immunogenicity of tumor cells, current cancer immunotherapies are not effective. To address this limitation, we constructed a BSA-FA functionalized iron-containing metal-organic framework (TPL@TFBF) that triggers a potent systemic anti-tumor immune response by inducing ferroptosis and pyroptosis in tumor cells and releasing large quantities of damage-associated molecular patterns (DAMPs) to induce immunogenicity, and showing excellent efficacy against melanoma lung metastases in vivo. This nanoplatform forms a metal-organic framework through the coordination between tannic acid (TA) and Fe3+ and is then loaded with triptolide (TPL), which is coated with FA-modified BSA. The nanoparticles target melanoma cells by FA modification, releasing TPL, Fe3+ and TA. Fe3+ is reduced to Fe2+ by TA, triggering the Fenton reaction and resulting in ROS production. Moreover, TPL increases the production of intracellular ROS by inhibiting the expression of nuclear factor erythroid-2 related factor (Nrf2). Such simultaneous amplification of intracellular ROS induces the cells to undergo ferroptosis and pyroptosis, releasing large amounts of DAMPs, which stimulate antigen presentation of dendritic cells (DCs) and the proliferation of cytotoxic T lymphocytes (CD4+/CD8 + T cells) to inhibit tumor and lung metastasis. In addition, combining nanoparticle treatment with immune checkpoint blockade (ICB) further inhibits melanoma growth. This work provides a new strategy for tumor immunotherapy based on various combinations of cell death mechanisms.

Abstract Emerging memories have been developed as new physical infrastructures for hosting neural networks owing to their low-power analog computing characteristics. However, accurately and efficiently programming devices in an analog-valued array is still largely limited by the intrinsic physical non-idealities of the devices, thus hampering their applications in in-situ training of neural networks. Here, we demonstrate a passive electrochemical memory (ECRAM) array with many important characteristics necessary for accurate analog programming. Different image patterns can be open-loop and serially programmed into our ECRAM array, achieving high programming accuracies without any feedback adjustments. The excellent open-loop analog programmability has led us to in-situ train a bilayer neural network and reached software-like classification accuracy of 99.4% to detect poisonous mushrooms. The training capability is further studied in simulation for large-scale neural networks such as VGG-8. Our results present a new solution for implementing learning functions in an artificial intelligence hardware using emerging memories.

Freeze–thaw damage significantly contributes to the degradation of concrete structures. A critical precondition for concrete to experience frost damage is reaching its critical saturation level. This study conducted freeze–thaw experiments on concrete specimens under both open and sealed moisture conditions to elucidate the mechanisms of freeze–thaw damage and the pivotal role of moisture. The research assessed concrete’s water absorption, ultrasonic pulse velocity, and compressive strength under restricted water conditions to study damage accumulation patterns. The findings indicate that implementing water limitation measures during freeze–thaw cycles can regulate concrete’s water absorption rate, reduce the loss of ultrasonic pulse velocity, and minimize strength degradation, with an observed strength increase of up to 36.22%. Consequently, these measures protect concrete materials from severe frost damage. Furthermore, a predictive model for concrete freeze–thaw deterioration was established based on regression analysis and relative dynamic modulus theory, confirming the critical role of water limitation in extending the service life of concrete structures in cold regions.

Plastics are produced, consumed, and disposed of worldwide, with more than eight million tons of plastic litter entering the ocean each year. Plastic litter accumulates in marine and terrestrial environments through a variety of pathways. Large plastic debris can be broken down into micro- and nano-plastic particles through physical/mechanical mechanisms and biologically or chemically mediated degradation. Their toxicity to aquatic organisms includes the scavenging of pollutant compounds and the production of reactive oxygen species (ROS). Higher levels of ROS cause oxidative damages to microalgae and bacteria; this triggers the release of large amounts of exopolymeric substances (EPSs) with distinct molecular characteristics. This review will address what is known about the molecular mechanisms phytoplankton and bacteria use to regulate the fate and transport of plastic particles and identify the knowledge gaps, which should be considered in future research. In particular, the microbial communities react to plastic pollution through the production of EPSs that can reduce the plastic impacts via marine plastic snow (MPS) formation, allowing plastics to settle into sediments and facilitating their removal from the water column to lessen the plastic burden to ecosystems.

With the increasing complexity of unmanned aerial vehicle (UAV) missions, single-objective optimization for UAV trajectory planning proves inadequate in handling multiple conflicting objectives. There is a notable absence of research on multi-objective optimization for UAV trajectory planning. This study introduces a novel two-stage co-evolutionary multi-objective evolutionary algorithm for UAV trajectory planning (TSCEA). Firstly, two primary optimization objectives were defined: minimizing total UAV flight distance and obstacle threats. Five constraints were defined: safe distances between UAV trajectory and obstacles, maximum flight altitude, speed, flight slope, and flight corner limitations. In order to effectively cope with UAV constraints on object space limitations, the evolution of the TSCEA algorithm is divided into an exploration phase and an exploitation phase. The exploration phase employs a two-population strategy where the main population ignores UAV constraints while an auxiliary population treats them as an additional objective. This approach enhances the algorithm’s ability to explore constrained solutions. In contrast, the exploitation phase aims to converge towards the Pareto frontier by leveraging effective population information, resulting in multiple sets of key UAV trajectory points. Three experimental scenarios were designed to validate the effectiveness of TSCEA. Results demonstrate that the proposed algorithm not only successfully navigates UAVs around obstacles but also generates multiple sets of Pareto-optimal solutions that are well-distributed across objectives. Therefore, compared to single-objective optimization, TSCEA integrates the UAV mathematical model comprehensively and delivers multiple high-quality, non-dominated trajectory planning solutions.

To address the issues of low visibility, poor real-time performance, and weak interaction capabilities in the operation of magnetic levitation circular knitting needles, an online monitoring method based on digital twin technology for magnetic levitation knitting needles is proposed. This method first conducts an in-depth analysis of the digital twin five-dimensional model of the magnetic levitation knitting needle to monitor the operational status of the equipment in real time. Additionally, an online monitoring system based on the Unity3D engine and a digital twin-based architecture for magnetic levitation knitting needles are designed. On this basis, the comprehensive monitoring, operation, and maintenance of the magnetic levitation knitting needles are achieved through the design of virtual models, visual interfaces, and real-time data acquisition and driving technology. Finally, this method takes a 5-inch 32-needle magnetic levitation circular knitting machine as the application research object. By analyzing the movement trajectory and real-time current feedback data of the three working positions of the magnetic levitation knitting needles, the rationality, effectiveness, and accuracy of the proposed method and model are demonstrated, achieving online monitoring of the magnetic levitation knitting needles and reflecting the full lifecycle process of the magnetic levitation knitting needles.

Throughout the nuclear power production process, the disposal of radioactive waste has consistently raised concerns about environmental safety. When the metal tanks used for waste disposal are corroded, radionuclides seep into the groundwater environment and eventually into the biosphere, causing significant damage to the environment. Hence, investigating the adsorption behavior of radionuclides on the corrosion products of metal tanks used for waste disposal is an essential component of safety and evaluation protocols at disposal sites. In order to understand the adsorption behavior of important radionuclides 60Co, 59Ni, 90Sr, 135Cs and 129I on α-FeOOH, the influences of different pH values, contact time, temperature and ion concentration on the adsorption rate were studied. The adsorption mechanism was also discussed. It was revealed that the adsorption of key nuclides onto α-FeOOH is significantly influenced by both pH and temperature. This change in surface charge corresponds to alterations in the morphology of nuclide ions within the system, subsequently impacting the adsorption efficiency. Sodium ions (Na+) and chlorate ions (ClO3−) compete for coordination with nuclide ions, thereby exerting an additional influence on the adsorption process. The XPS analysis results demonstrate the formation of an internal coordination bond (Ni–O bond) between Ni2+ and iron oxide, which is adsorbed onto α-FeOOH.

Traditional ventilation methods consume excessive energy but still fail to meet requirements in underground tunnel group construction. Thus, a closed-loop intelligent control system for ventilation-on-demand (VOD) was developed. To address dynamic changes in ventilation load and reduce energy consumption, firstly, the developed system calculates the real-time ventilation load and establishes a ventilation-network-based control mode to represent the ventilation system structure. The deep deterministic policy gradient (DDPG) method was then employed for the closed-loop control ensuring the required air volume in each branch of tunnel groups while minimizing energy consumption. After that, the developed closed-loop intelligent ventilation control system encompasses comprehensive perception, real analysis, real-time control, and continuous optimization. This system treats decision-making, control, and feedback as subsystems that reflect the adaptability between ventilation efficiency, construction progress, and power consumption. Finally, the end-edge-cloud-based software of the system was developed to enable remote control and display on large screens, personal computers (PCs), and mobile applications (Apps) to ensure precise and timely operation. The system was employed in tunnel group under construction at the Xulong Hydropower Station in Southwestern China, and the obtained results validate its advanced closed-loop control based on reinforcement learning (RL) and confirm its feasibility in engineering practice.