Full-Matrix Imaging in Fourier Domain towards Ultrasonic Inspection with Wide-Angle Oblique Incidence for Welded Structures.

The total focusing method (TFM) has been increasingly applied to weld inspection given its high image quality and defect sensitivity. Oblique incidence is widely used to steer the beam effectively, considering the defect orientation and structural complexity of welded structures. However, the conventional TFM based on the delay-and-sum (DAS) principle is time-consuming, especially for oblique incidence. In this paper, a fast full-matrix imaging algorithm in the Fourier domain is proposed to accelerate the TFM under the condition of oblique incidence. The algorithm adopts the Chebyshev polynomials of the second kind to directly expand the Fourier extrapolator with lateral sound velocity variation. The direct expansion maintains image accuracy and resolution in wide-angle situations, covering both small and large angles, making it highly suitable for weld inspection. Simulations prove that the third-order Chebyshev expansion is required to achieve image accuracy equivalent to the TFM with wide-angle incidence. Experiments verify the algorithm's performance for weld flaws using the proposed method with the transverse wave and the full-skip mode. The depth deviation is within 0.53 mm, and the sizing error is below 15%. The imaging efficiency is improved by a factor of up to 8 compared to conventional TFM. We conclude that the proposed method is applicable to high-speed weld inspection with various oblique incidence angles.

RAD54B promotes gastric cancer cell migration and angiogenesis via the Wnt/ß-catenin pathway.

BACKGROUND: Gastric cancer is an epidemic malignancy that is commonly diagnosed at the late stage. Evidence has elucidated that RAD54B exerts a crucial role in the progress of various tumors, but its specific role and mechanism in gastric cancer remain gloomy. MATERIALS AND METHODS: The level of RAD54B was detected by western blot. RAD54B expression was downregulated or upregulated in both MKN45 and AGS cells by the transfection of shRAD54B or overexpression plasmid, respectively. The role of RAD54B in the growth, migration, invasion and tube formation of gastric cancer was evaluated by Edu, colony formation, transwell and tube formation assays. In addition, the molecular mechanism of RAD54B in gastric cancer was also determined by western blot. Moreover, in vivo experiment was conducted in xenografted mice. RESULTS: The expression of RAD54B was discovered to be upregulated in gastric cancer based on the ATGC and GEPIA databases, which was also confirmed in gastric cancer cell lines. Moreover, overexpression of RAD54B enhanced the growth, migration, invasion, tube formation and Wnt/ß-catenin signaling axis in AGS and MKN45 cells. As expected, knockdown of RAD54B in AGS and MKN45 cells reversed these promotions. More importantly, in vivo assay also verified that RAD54B accelerated the growth of gastric cancer and Wnt/ß-catenin signaling pathway. CONCLUSIONS: Both loss-of-function and gain-of-function assays demonstrated that RAD54B facilitated gastric cancer cell progress and angiogenesis through the Wnt/ß-catenin axis.

Prediction of arsenic adsorption onto metal organic frameworks and adsorption mechanisms interpretation by machine learning.

Metal-organic frameworks (MOFs) are promising adsorbents for the removal of arsenic (As) from wastewater. The As removal efficiency is influenced by several factors, such as the textural properties of MOFs, adsorption conditions, and As species. Examining all of the relevant factors through traditional experiments is challenging. To predict the As adsorption capacities of MOFs toward organic, inorganic, and total As and reveal the adsorption mechanisms, four machine learning-based models were developed, with the adsorption conditions, MOF properties, and characteristics of different As species as inputs. The results demonstrated that the extreme gradient boosting (XGBoost) model exhibited the best predictive performance (test R2 = 0.93-0.96). The validation experiments demonstrated the high accuracy of the inorganic As-based XGBoost model. The feature importance analysis showed that the concentration of As, the surface area of MOFs, and the pH of the solution were the three key factors governing inorganic-As adsorption, while those governing organic-As adsorption were the concentration of As, the pHpzc value of MOFs, and the oxidation state of the metal clusters. The formation of coordination complexes between As and MOFs is possibly the major adsorption mechanism for both inorganic and organic As. However, electrostatic interaction may have a greater effect on organic-As adsorption than on inorganic-As adsorption. Overall, this study provides a new strategy for evaluating As adsorption on MOFs and discovering the underlying decisive factors and adsorption mechanisms, thereby facilitating the investigation of As wastewater treatment.

Transformer Based Binocular Disparity Prediction with Occlusion Predict and Novel Full Connection Layers.

The depth estimation algorithm based on the convolutional neural network has many limitations and defects by constructing matching cost volume to calculate the disparity: using a limited disparity range, the authentic disparity beyond the predetermined range can not be acquired; Besides, the matching process lacks constraints on occlusion and matching uniqueness; Also, as a local feature extractor, a convolutional neural network lacks the ability of global context information perception. Aiming at the problems in the matching method of constructing matching cost volume, we propose a disparity prediction algorithm based on Transformer, which specifically comprises the Swin-SPP module for feature extraction based on Swin Transformer, Transformer disparity matching network based on self-attention and cross-attention mechanism, and occlusion prediction sub-network. In addition, we propose a double skip connection fully connected layer to solve the problems of gradient vanishing and explosion during the training process for the Transformer model, thus further enhancing inference accuracy. The proposed model in this paper achieved an EPE (Absolute error) of 0.57 and 0.61, and a 3PE (Percentage error greater than 3 px) of 1.74% and 1.56% on KITTI 2012 and KITTI 2015 datasets, respectively, with an inference time of 0.46 s and parameters as low as only 2.6 M, showing great advantages compared with other algorithms in various evaluation metrics.

A Cooperative Lightweight Translation Algorithm Combined with Sparse-ReLU.

In the field of natural language processing (NLP), machine translation algorithm based on Transformer is challenging to deploy on hardware due to a large number of parameters and low parametric sparsity of the network weights. Meanwhile, the accuracy of lightweight machine translation networks also needs to be improved. To solve this problem, we first design a new activation function, Sparse-ReLU, to improve the parametric sparsity of weights and feature maps, which facilitates hardware deployment. Secondly, we design a novel cooperative processing scheme with CNN and Transformer and use Sparse-ReLU to improve the accuracy of the translation algorithm. Experimental results show that our method, which combines Transformer and CNN with the Sparse-ReLU, achieves a 2.32% BLEU improvement in prediction accuracy and reduces the number of parameters of the model by 23%, and the sparsity of the inference model increases by more than 50%.

FGA inhibits metastases and induces autophagic cell death in gastric cancer via inhibiting ITGA5 to regulate the FAK/ERK pathway.

This study aims to investigate the expression levels of fibrinogen α chain (FGA) in human gastric cancer (GC) tissues and cell lines, clarify its role in gastric cancer progression, and explore its underlying mechanism. Bioinformatics analysis, Immunoblot, Immunohistochemical (IHC), and quantitative PCR assays were performed to assess the expression of FGA in human gastric cancer tissues and cell lines. CCK-8 and colony formation assays were performed to detect its role in the proliferation of gastric cancer cells. Wound healing, transwell, and Immunofluorescence were performed to detect its effects on gastric cancer cell motility and epithelial-mesenchymal transition (EMT) processes. Luciferase and CHIP assays were performed to confirm the transcriptional regulation of FGA on ITGA5. Immunoblot assays and double-label RFP-GFP-LC3 immunofluorescence analysis were conducted to detect its effects on gastric cancer cell autophagy and FAK/ERK pathway, and in vivo tumor growth assays were further performed. We found the low expression of FGA in human gastric cancer tissues and cell lines. FGA suppressed gastric cancer cell proliferation, motility, and EMT process, and stimulated cell autophagy. We further found that FGA suppressed the expression of Integrin-α5 (ITGA5) and inhibited the FAK/ERK pathway, therefore suppressing the progression of gastric cancer. The in vivo assays further confirmed that FGA suppressed tumor growth of gastric cancer cells in the BALB/c nude mice (18-22 g, female, 8-week-old) through suppressing ITGA5-mediated FAK/ERK pathway in mice. We demonstrated the mechanism underlying FGA suppressing gastric cancer progression, and therefore we thought FGA could serve as a tumor suppressor protein in gastric cancer.

HOXB13 suppresses proliferation, migration and invasion, and promotes apoptosis of gastric cancer cells through transcriptional activation of VGLL4 to inhibit the involvement of TEAD4 in the Hippo signaling pathway.

Gastric cancer (GC) is one of the most common types of malignancy worldwide and is accompanied by both high mortality and morbidity rates. Homeobox B13 (HOXB13) has been reported to act as a tumor suppressor gene in multiple types of human cancer. The present study aimed to investigate the effects and potential underlying molecular mechanisms of HOXB13 in the progression of GC. The expression of HOXB13 in GC cells was first examined using the Cancer Cell Line Encyclopedia database and subsequently validated in a number of GC cell lines. Following HOXB13 overexpression (Ov­HOXB13), HGC­27 cell proliferation was evaluated by colony formation and Cell Counting Kit­8 assays. Wound healing and Matrigel assays were used to determine the migratory and invasive abilities, respectively. Additionally, cell apoptosis was assessed using TUNEL staining, and the expression of apoptosis­related proteins was detected by western blot analysis. Subsequently, TEA domain transcription factor 4 (TEAD4) was overexpressed to evaluate the effects on HGC­27 cell proliferation, migration, invasion and apoptosis following co­transfection with Ov­HOXB13. The potential binding sites of HOXB13 on the vestigial­like family member 4 (VGLL4) promoter were verified using chromatin immunoprecipitation and dual luciferase reporter assays. Moreover, the expression levels of proteins involved in the Hippo signaling pathway were analyzed using western blotting. The results revealed that the expression of HOXB13 was notably lower in GC cells compared with normal gastric cells. The overexpression of HOXB13 significantly inhibited the proliferation, migration and invasion, but promoted the apoptosis of HGC­27 cells. Moreover, Ov­HOXB13 downregulated TEAD4 expression. Notably, Ov­TEAD4 transfection partially reversed the effects of Ov­HOXB13 on the cellular behaviors of HGC­27 cells. HOXB13 was also confirmed to bind with the VGLL4 promoter. The knockdown of VGLL4 restored the inhibitory effects of Ov­HOXB13 on the expression levels of VGLL4 and Hippo pathway signaling proteins. In conclusion, the findings of the present study suggested that Ov­HOXB13 may suppress the proliferation, migration and invasion, and promote the apoptosis of GC cells through the transcriptional activation of VGLL4 to inhibit the involvement of TEAD4 in the Hippo signaling pathway. These results may provide novel and potent targets for the treatment of GC.

Identification of Molecular Subtypes and a Prognostic Signature Based on Inflammation-Related Genes in Colon Adenocarcinoma.

Both tumour-infiltrating immune cells and inflammation-related genes that can mediate immune infiltration contribute to the initiation and prognosis of patients with colon cancer. In this study, we developed a method to predict the survival outcomes among colon cancer patients and direct immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and captured inflammation-related genes from the GeneCards database. The package "ConsensusClusterPlus" was used to generate molecular subtypes based on inflammation-related genes obtained by differential expression analysis and univariate Cox analysis. A prognostic signature including four genes (PLCG2, TIMP1, BDNF and IL13) was also constructed and was an independent prognostic factor. Cluster 2 and higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints. Immune cell infiltration calculated by the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumour immune dysfunction and exclusion (TIDE), and tumour stemness indices (TSIs) were also compared on the basis of inflammation-related molecular subtypes and the risk signature. In addition, analyses of stratification, somatic mutation, nomogram construction, chemotherapeutic response prediction and small-molecule drug prediction were performed based on the risk signature. We finally used qRT-PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction. Our findings demonstrated a four-gene prognostic signature that could be useful for prognostication in colon cancer patients and designing personalized treatments, which could provide new versions of personalized management for these patients.

Effects of short interfering RNA-mediated gene silencing of SKA1 on proliferation of hepatocellular carcinoma cells.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the common causes of cancer resulting in death in China. Here, we found that spindle- and kinetochore-associated complex subunit 1 (SKA1) is a critical factor that plays an essential role in the regulation of HCC cell proliferation and apoptosis. METHODS: Using immunohistochemistry in 38 HCC tissues, we showed that the expression of SKA1 was upregulated in HCC tissues compared with the normal tissues. Then, we investigated the effects of SKA1-targeted small interfering RNA (siRNA) on the HCC cell proliferation and apoptosis as of HCC cells. SKA1-targeted siRNA was delivered to HCC SMMC-7721 and Bel-7404 cells to evaluate its antiproliferation effects using lentivirus. RESULTS: The lentivirus-mediated siRNA-targeting SKA1 treatment leads to a significant (p < 0.01) downregulation of SKA1 expression at mitochondrial RNA (mRNA) level. Knockdown of SKA1 inhibited HCC cell proliferation by inducing cell cycle arrest in the G0/G1 phase. Furthermore, lentivirus-mediated siRNA efficiently inhibited cell proliferation and colony formation while promoting the apoptosis. CONCLUSIONS: Of note, our data suggest that SKA1 might play an important role in the proliferation of HCC cells, and the absence of this gene in HCC may open promising therapeutic approaches for HCC.