In this paper, a novel approach of voxelization modelling-based Finite Element (FE) simulation and process parameter optimization for Fused Filament Fabrication (FFF) is presented. In the approach, ...firstly, a general meshing method based on voxelization modelling and automatic voxel element sorting is developed. Then, FE based simulation for the FFF process is conducted by combining the ANSYS Parametric Design Language (APDL) with the element birth and death technique. During the simulation, the influence of key process parameters on the temperature field, including scanning speed, molding chamber temperature and nozzle temperature, is analyzed in detail. Furthermore, an experimental platform with an adjustable molding chamber temperature for the FFF parts is established. Case studies for making Acrylonitrile Butadiene Styrene (ABS)-based parts were carried out to validate the approach. Results showed that among the process parameters, the molding chamber temperature had the most significant effect on the warping deformation of the FFF parts. The optimal parameters for the FFF process with ABS under the analyzed conditions were 50 mm/s for the scanning speed, 80 °C for the molding chamber temperature of, and 180 °C for the nozzle temperature, respectively.
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•A voxelization modelling based Finite Element method for the Fused Filament Fabrication process is developed.•The impacts of key process parameters on the Fused Filament Fabrication process are simulated and optimized.•The effects of the key process parameters were verified by experiments.
Cross-domain fault diagnosis methods have been widely developed to solve domain-shift diagnostic tasks with data distribution discrepancies. However, the quality of the domain-invariant features, ...which was extracted through a single channel, seriously limits the cross-domain diagnostic performance in the existing method. A novel multi-branch domain adaptation network (MBDAN), which incorporate multi-scale average processing into end-to-end deep learning model, is established in this paper. The universal feature extractor with three lightweight branches can capture the fault features from different domains. The domain adaptation strategy, which combines adversarial learning and MK-MMD-based distribution alignment, is built to learn high-quality domain-invariant features. Thus, the well-trained model can implement fault diagnosis on both the labeled source and unlabeled target domain. We conducted a comparison experiment using data from two experimental setups. The results show that MBDAN has a more remarkably cross-domain diagnostic performance than the state-of-the-art ones.
Radiotherapy is a treatment choice for local control of breast cancer. However, intrinsic radioresistance of cancer cells limits therapeutic efficacy. We have recently validated that SCF (SKP1, ...Cullins, and F-box protein) E3 ubiquitin ligase is an attractive radiosensitizing target. Here we tested our hypothesis that MLN4924, a newly discovered investigational small molecule inhibitor of NAE (NEDD8 Activating Enzyme) that inactivates SCF E3 ligase, could act as a novel radiosensitizing agent in breast cancer cells. Indeed, we found that MLN4924 effectively inhibited cullin neddylation, and sensitized breast cancer cells to radiation with a sensitivity enhancement ratio (SER) of 1.75 for SK-BR-3 cells and 1.32 for MCF7 cells, respectively. Mechanistically, MLN4924 significantly enhanced radiation-induced G2/M arrest in SK-BR-3 cells, but not in MCF7 cells at early time point, and enhanced radiation-induced apoptosis in both lines at later time point. However, blockage of apoptosis by Z-VAD failed to abrogate MLN4924 radiosensitization, suggesting that apoptosis was not causally related. We further showed that MLN4924 failed to enhance radiation-induced DNA damage response, but did cause minor delay in DNA damage repair. Among a number of tested SCF E3 substrates known to regulate growth arrest, apoptosis and DNA damage response, p21 was the only one showing an enhanced accumulation in MLN4924-radiation combination group, as compared to the single treatment groups. Importantly, p21 knockdown via siRNA partialy inhibited MLN4924-induced G2/M arrest and radiosensitization, indicating a causal role played by p21. Our study suggested that MLN4924 could be further developed as a novel class of radiosensitizer for the treatment of breast cancer.
Bladder cancer (BLCA) is a malignant tumor with a dismay outcome. Increasing evidence has confirmed that chromatin regulators (CRs) are involved in cancer progression. Therefore, we aimed to explore ...the function and prognostic value of CRs in BLCA patients.
Chromatin regulators (CRs) were acquired from the previous top research. The mRNA expression and clinical information were downloaded from TCGA and GEO datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed to select the prognostic gene and construct the risk model for predicting outcome in BLCA. The Kaplan-Meier analysis was used to assess the prognosis between high- and low-risk groups. We also investigated the drug sensitivity difference between high- and low-risk groups. CMAP dataset was performed to screen the small molecule drugs for treatment.
We successfully constructed and validated an 11 CRs-based model for predicting the prognosis of patients with BLCA. Moreover, we also found 11 CRs-based model was an independent prognostic factor. Functional analysis suggested that CRs were mainly enriched in cancer-related signaling pathways. The CR-based model was also correlated with immune cells infiltration and immune checkpoint. Patients in the high-risk group were more sensitive to several drugs, such as mitomycin C, gemcitabine, cisplatin. Eight small molecule drugs could be beneficial to treatment for BLCA patients.
In conclusion, our study provided novel insights into the function of CRs in BLCA. We identified a reliable prognostic biomarker for the survival of patients with BLCA.
IGF2BP3 expression is associated with poor prognosis in cancers of multiple tissue origins. However, the precise mechanism of its co-carcinogenic action in bladder cancer is unknown.
We aimed to ...demonstrate the relationship between IGF2BP3 expression and pan-cancer using The Cancer Genome Atlas (TCGA) database. We next validated IGF2BP3 expression in the Gene Expression Omnibus (GEO) database (GSE3167). Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic values of IGF2BP3. Cox and logistic regression were used to explore the factors affecting the prognosis. Protein-protein interactions (PPIs) network was constructed by STRING. Enrichment analyses were performed to infer involved pathways and functional categories of IGF2BP3 using the cluster Profiler package. We applied single-sample gene set enrichment analysis (ssGSEA) algorithm and TIMER database to evaluate the expression level of immune genes.
Pan-cancer analyses reveal that IGF2BP3 was higher in most cancer types, including bladder cancer, and the same results were found in GSE3167. The area under the ROC curve of IGF2BP3 was 0.736, which indicated that IGF2BP3 may be a potential diagnostic biomarker. High IGF2BP3 expression was associated with poorer overall survival (OS) (P = 0.015). For validation, we collected 95 bladder cancer samples and found that IGF2BP3 expression was higher in bladder cancer tissues than that in non-tumor bladder tissues by immunohistochemistry staining. We found a positive correlation between the expression level of IGF2BP3 and the clinical stage of bladder cancer. Immunocyte infiltration analysis showed that high IGF2BP3 expression was correlated with regulating the infiltration level of immune cell, including neutrophil cells and macrophages. IGF2BP3 promotes migration and invasion of bladder cancer cells, while IGF2BP3 inhibition had the opposite effects. Higher IGF2BP3 expression was closely associated with advanced TNM stage.
IGF2BP3 overexpression was related to disease progression and poor prognosis, as well as infiltration of immune cells in bladder cancer. IGF2BP3 can be a promising independent prognostic biomarker and potential treatment target for bladder cancer.
Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of ...tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer.
Lipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics.
An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset.
In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients.
Belt conveyors are one type of the most important transportation devices for continuous transportation of bulk materials in bulk cargo piers, whose operating environments are complicated. The belt ...damages change in a wide scale range and multiple scales coexist. Thus, the reliability of damage detection methods is challenging. A Yolov5-based improved EMA-YOLO belt multi-scale damage detection method is put forward here. First, a high-efficiency multi-scale attention module EMA is introduced in view of there being complex background interferences in belt damage detection samples so that important areas shall be more concerned in our model. The multi-scale features can be extracted at different levels and the resistance ability of background interferences may be enhanced. Additionally, the feature fusion network is improved. The weight of learning is introduced by means of a simple and efficient weighted bidirectional feature pyramid network (BiFPN) to learn the weights of different input feature layers and modular repeated application is performed so that our model can simply and quickly fuse multi-scale features. Thus, the training performance of our model can be improved. Our measurements indicate that the accuracy of our detection method is up to 98.51%, and its detection speed is up to 82.26 FPS. Compared with the existing methods, our method can be more reliable and real time.
Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in ...prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-related lncRNA signature in prostate cancer and explore its latent molecular function.
RNA-seq data and clinical information were downloaded from the TCGA datasets. Then, cuproptosis-related gene was identified from the previous literature and further applied to screen the cuproptosis-related differentially expressed lncRNAs. Patients were randomly assigned to the training cohort or the validation cohort with a 1:1 ratio. Subsequently, the machine learning algorithms (Lasso and stepwise Cox (direction = both)) were used to construct a novel prognostic signature in the training cohorts, which was validated by the validation and the entire TCGA cohorts. The nomogram base on the lncRNA signature and several clinicopathological traits were constructed to predict the prognosis. Functional enrichment and immune analysis were performed to evaluate its potential mechanism. Furthermore, differences in the landscape of gene mutation, tumour mutational burden (TMB), microsatellite instability (MSI), drug sensitivity between both risk groups were also assessed to explicit their relationships.
The cuproptosis-related lncRNA signature was constructed based on the differentially expressed cuproptosis-related lncRNAs, including AC005790.1, AC011472.4, AC099791.2, AC144450.1, LIPE-AS1, and STPG3-AS1. Kaplan-Meier survival and ROC curves demonstrate that the prognosis signature as an independent risk indicator had excellent potential to predict the prognosis in prostate cancer. The signature was closely associated with age, T stage, N stage, and the Gleason score. Immune analysis shows that the high-risk group was in an immunosuppressive microenvironment. Additionally, the significant difference in landscape of gene mutation, tumour mutational burden, microsatellite instability, and drug sensitivity between both risk groups was observed.
A novel cuproptosis-related lncRNA signature was constructed using machine learning algorithms to predict the prognosis of prostate cancer. It was closely with associated with several common clinical traits, immune cell infiltration, immune-related functions, immune checkpoints, gene mutation, TMB, MSI, and the drug sensitivity, which may be useful to improve the clinical outcome.
MLN4924, also known as pevonedistat, is the first-in-class inhibitor of NEDD8-activating enzyme, which blocks the entire neddylation modification of proteins. Previous preclinical studies and current ...clinical trials have been exclusively focused on its anticancer property. Unexpectedly, we show here, to our knowledge for the first time, that MLN4924, when applied at nanomolar concentrations, significantly stimulates in vitro tumor sphere formation and in vivo tumorigenesis and differentiation of human cancer cells and mouse embryonic stem cells. These stimulatory effects are attributable to (i) c-MYC accumulation via blocking its degradation and (ii) continued activation of EGFR (epidermal growth factor receptor) and its downstream pathways, including PI3K/AKT/mammalian target of rapamycin and RAS/RAF/MEK/ERK, via inducing EGFR dimerization. Finally, MLN4924 accelerates EGF-mediated skin wound healing in mouse and stimulates cell migration in an in vitro culture setting. Taking these data together, our study reveals that neddylation modification could regulate stem cell proliferation and differentiation and that a low dose of MLN4924 might have a therapeutic value for stem cell therapy and tissue regeneration.
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