Mixing with native broadleaf trees modified soil microbial communities of Cunninghamia lanceolata monocultures in South China.

Mixing with different broadleaf trees into the monocultures of Cunninghamia lanceolata is widely adopted as an efficient transformation of the pure C. lanceolata forest. However, it is unclear how native broad-leaved trees influence the belowground ecological environment of the pure C. lanceolata culture plantation in nutrient-poor soil of South China. Herein, we aimed to investigate how a long-time mixing with native broadleaf trees shape soil microbial community of the pure C. lanceolata forest across different soil depth (0-20 cm and 20-40 cm) and to clarify relationships between the modified soil microbial community and those affected soil chemical properties. Using high-throughput sequencing technology, microbial compositions from the mixed C. lanceolata-broadleaf forest and the pure C. lanceolata forest were analyzed. Network analysis was utilized to investigate correlations among microorganisms, and network robustness was assessed by calculating network natural connectivity. Results demonstrated that the content of soil microbial biomass carbon and nitrogen, total phosphorus and pH in mixed forest stand were significantly higher than those in pure forest stand, except for available phosphorus in topsoil (0-20 cm). Simultaneously, the mixed C. lanceolata-broadleaf forest has a more homogeneous bacterial and fungal communities across different soil depth compared with the pure C. lanceolata forest, wherein the mixed forest recruited more diverse bacterial community in subsoil (20-40 cm) and reduced the diversity of fungal community in topsoil. Meanwhile, the mixed forest showed higher bacterial community stability while the pure forest showed higher fungal community stability. Moreover, bacterial communities showed significant correlations with various soil chemical indicators, whereas fungal communities exhibited correlations with only TP and pH. Therefore, the mixed C. lanceolata-broadleaf forest rely on their recruiting bacterial community to enhance and maintain the higher nutrient status of soil while the pure C. lanceolata forest rely on some specific fungi to satisfy their phosphorus requirement for survive strategy.

Exploring pollution control in transboundary watersheds under the perspective of ecological compensation based on aggregate game.

Pollution control in inter-provincial river basins involves many complex subjects, so it is difficult to effectively implement ecological compensation policies. To clarify the interest relationship among pollution control subjects and stimulate their willingness to cooperate in collaborative governance, this paper builds a multi-agent coordinated pollution control model based on the theory of aggregate game; explores the change of equilibrium action of a single pollution control agent and multi-actors, symmetric, and asymmetric situations under the influence of synergistic benefits; and studies the incentive mechanism design to maintain the cooperation of various agents. The research results show that an increase in the number of upstream firms would lead to a reduction in the incentive effects of downstream government compensation and an increase in the likelihood of "free-riding" and "coordination failure." Synergy benefits vary positively with the degree of cooperation between government and enterprises, and higher synergy benefits can effectively compensate for the high transaction costs caused by multiple entities, alleviate the financial pressure on downstream governments, and increase the willingness of upstream and downstream entities to cooperate. In addition, focusing on wastewater reduction from core enterprises, such as heavy polluters can help improve the efficiency of regional emissions reduction, while having a catalytic effect on small enterprises.

The fatty acid-related gene signature stratifies poor prognosis patients and characterizes TIME in cutaneous melanoma.

BACKGROUND: The aim of this study is to build a prognostic model for cutaneous melanoma (CM) using fatty acid-related genes and evaluate its capacity for predicting prognosis, identifying the tumor immune microenvironment (TIME) composition, and assessing drug sensitivity. METHODS: Through the analysis of transcriptional data from TCGA-SKCM and GTEx datasets, we screened for differentially expressed fatty acids-related genes (DEFAGs). Additionally, we employed clinical data from TCGA-SKCM and GSE65904 to identify genes associated with prognosis. Subsequently, utilizing all the identified prognosis-related fatty acid genes, we performed unsupervised clustering analysis using the ConsensusClusterPlus R package. We further validated the significant differences between subtypes through survival analysis and pathway analysis. To predict prognosis, we developed a LASSO-Cox prognostic signature. This signature's predictive ability was rigorously examined through multivariant Cox regression, survival analysis, and ROC curve analysis. Following this, we constructed a nomogram based on the aforementioned signature and evaluated its accuracy and clinical utility using calibration curves, cumulative hazard rates, and decision curve analysis. Using this signature, we stratified all cases into high- and low-risk groups and compared the differences in immune characteristics and drug treatment responsiveness between these two subgroups. Additionally, in this study, we provided preliminary confirmation of the pivotal role of CD1D in the TIME of CM. We analyzed its expression across various immune cell types and its correlation with intercellular communication using single-cell data from the GSE139249 dataset. RESULTS: In this study, a total of 84 DEFAGs were identified, among which 18 were associated with prognosis. Utilizing these 18 prognosis-related genes, all cases were categorized into three subtypes. Significant differences were observed between subtypes in terms of survival outcomes, the expression of the 18 DEFAGs, immune cell proportions, and enriched pathways. A LASSO-Cox regression analysis was performed on these 18 genes, leading to the development of a signature comprising 6 DEFAGs. Risk scores were calculated for all cases, dividing them into high-risk and low-risk groups. High-risk patients exhibited significantly poorer prognosis than low-risk patients, both in the training group (p < 0.001) and the test group (p = 0.002). Multivariate Cox regression analysis indicated that this signature could independently predict outcomes [HR = 2.03 (1.69-2.45), p < 0.001]. The area under the ROC curve for the training and test groups was 0.715 and 0.661, respectively. Combining risk scores with clinical factors including metastatic status and patient age, a nomogram was constructed, which demonstrated significant predictive power for 3  and 5 years patient outcomes. Furthermore, the high and low-risk subgroups displayed differences in the composition of various immune cells, including M1 macrophages, M0 macrophages, and CD8+ T cells. The low-risk subgroup exhibited higher StromalScore, ImmuneScore, and ESTIMATEScore (p < 0.001) and demonstrated better responsiveness to immune therapy for patients with PD1-positive and CTLA4-negative or positive expressions (p < 0.001). The signature gene CD1D was found to be mainly expressed in monocytes/macrophages and dendritic cells within the TIME. Through intercellular communication analysis, it was observed that cases with high CD1D expression exhibited significantly enhanced signal transductions from other immune cells to monocytes/macrophages, particularly the (HLA-A/B/C/E/F)-CD8A signaling from natural killer (NK) cells to monocytes/macrophages (p < 0.01). CONCLUSIONS: The prognostic signature constructed in this study, based on six fatty acid-related genes, exhibits strong capabilities in predicting patient outcomes, identifying the TIME, and assessing drug sensitivity. This signature can aid in patient risk stratification and provide guidance for clinical treatment strategies. Additionally, our research highlights the crucial role of CD1D in the CM's TIME, laying a theoretical foundation for future related studies.

Excess Deaths of Gastrointestinal, Liver, and Pancreatic Diseases During the COVID-19 Pandemic in the United States.

Objectives: To evaluate excess deaths of gastrointestinal, liver, and pancreatic diseases in the United States during the COVID-19 pandemic. Methods: We retrieved weekly death counts from National Vital Statistics System and fitted them with a quasi-Poisson regression model. Cause-specific excess deaths were calculated by the difference between observed and expected deaths with adjustment for temporal trend and seasonality. Demographic disparities and temporal-spatial patterns were evaluated for different diseases. Results: From March 2020 to September 2022, the increased mortality (measured by excess risks) for Clostridium difficile colitis, gastrointestinal hemorrhage, and acute pancreatitis were 35.9%; 24.8%; and 20.6% higher than the expected. For alcoholic liver disease, fibrosis/cirrhosis, and hepatic failure, the excess risks were 1.4-2.8 times higher among younger inhabitants than older inhabitants. The excess deaths of selected diseases were persistently observed across multiple epidemic waves with fluctuating trends for gastrointestinal hemorrhage and fibrosis/cirrhosis and an increasing trend for C. difficile colitis. Conclusion: The persistently observed excess deaths of digestive diseases highlights the importance for healthcare authorities to develop sustainable strategies in response to the long-term circulating of SARS-CoV-2 in the community.

Convenient and Controllable Synthesis of Poly(2-oxazoline)-Conjugated Doxorubicin for Regulating Anti-Tumor Selectivity.

Polyethylene glycol (PEG)-doxorubicin (DOX) conjugation is an important strategy to improve toxicity and enhance clinically therapeutic efficacy. However, with the frequent use of PEG-modified drugs, the accumulation of anti-PEG antibodies has become a tough issue, which limits the application of PEG-drug conjugation. As an alternative solution, poly(2-oxazoline) (POX)-DOX conjugation has shown great potential in the anti-tumor field, but the reported conjugation process of POX with DOX has drawbacks such as complex synthetic steps and purification. Herein, we propose a convenient and controllable strategy for the synthesis of POX-DOX conjugation with different chain lengths and narrow dispersity by N-boc-2-bromoacetohydrazide-initiated 2-ethyl-oxazoline polymerization and the subsequent deprotection of the N-Boc group and direct reaction with DOX. The DOX-PEtOx conjugates were firstly purified, and the successful conjugations were confirmed through various characterization methods. The synthetic DOX-PEtOxn conjugates reduce the toxicity of DOX and increase the selectivity to tumor cells, reflecting the promising application of this POX-DOX conjugation strategy in drug modification and development.

Unsupervised domain adaptation for Covid-19 classification based on balanced slice Wasserstein distance.

Covid-19 has swept the world since 2020, taking millions of lives. In order to seek a rapid diagnosis of Covid-19, deep learning-based Covid-19 classification methods have been extensively developed. However, deep learning relies on many samples with high-quality labels, which is expensive. To this end, we propose a novel unsupervised domain adaptation method to process many different but related Covid-19 X-ray images. Unlike existing unsupervised domain adaptation methods that cannot handle conditional class distributions, we adopt a balanced Slice Wasserstein distance as the metric for unsupervised domain adaptation to solve this problem. Multiple standard datasets for domain adaptation and X-ray datasets of different Covid-19 are adopted to verify the effectiveness of our proposed method. Experimented by cross-adopting multiple datasets as source and target domains, respectively, our proposed method can effectively capture discriminative and domain-invariant representations with better data distribution matching.

Annexin A1 induces oxaliplatin resistance of gastric cancer through autophagy by targeting PI3K/AKT/mTOR.

Resistance to oxaliplatin (OXA) is a major cause of recurrence in gastric cancer (GC) patients. Autophagy is an important factor ensuring the survival of cancer cells under chemotherapeutic stress. We aimed to investigate the role of OXA-related genes in autophagy and chemoresistance of gastric cancer cells. We established OXA-resistant gastric cancer cells and used RNA-seq to profile gene expression within OXA-resistant GC and corresponding parental cells. Immunohistochemistry and RT-qPCR was performed to detect gene expression in tissues of two cohorts of GC patients who received OXA-based chemotherapy. The chemoresistant effects of the gene were assessed by cell viability, apoptosis, and autophagy assays. The effects of the gene on autophagy were assessed with mRFP-GFP-LC3 and Western blotting (WB). Gene set enrichment analysis (GSEA) and WB were performed to detect the activity of PI3K/AKT/mTOR signaling under the regulation of the gene. The OXA-resistant property of GC cells is related to their enhanced autophagic activity. Based on RNA-seq profiling, ANXA1 was selected as a candidate, as it was upregulated significantly in OXA-resistant cells. Furthermore, we found that higher ANXA1 expression before chemotherapy was associated with subsequent development of resistance to oxaliplatin, and overexpression of ANXA1 promoted the resistance of gastric cancer cells to oxaliplatin. So, it may serve as a key regulator in GC chemo-resistance knockdown of ANXA1, via inhibiting autophagy, enhancing the sensitivity of OXA-resistant GC cells to OXA in vitro and in vivo. Mechanically, we identified that PI3K/AKT/mTOR signaling pathway was activated in the ANXA1 stable knockdown AGS/OXA cells, which leads to the suppression of autophagy. ANXA1 functions as a chemoresistant gene in GC cells by targeting the PI3K/AKT/mTOR signaling pathway and might be a prognostic predictor for GC patients who receive OXA-based chemotherapy.

Tuning pyridinic-N and graphitic-N doping with 4,4'-bipyridine in honeycomb-like porous carbon and distinct electrochemical roles in aqueous and ionic liquid gel electrolytes for symmetric supercapacitors.

Doping engineering in nanostructured carbon materials is an effective approach to modify heteroatom species and surface electronic structures. Herein, an advanced electrode material based on a honeycomb-like porous carbon matrix with tunable N-doped configurations is prepared via 4,4'-bipyridine (4,4'-bpy)-assisted pyrolysis of SiO2@ZIF-8 templates and subsequent etching treatment. Interestingly, the amounts of pyridinic-N and graphitic-N can be controlled by rationally varying the content of 4,4'-bpy which acts as the N source in the pyrolysis process. Both experimental results and density functional theory calculations have revealed that synergistically with 3D interconnected porous architecture, pyridinic-N and graphitic-N have different effects on the electrochemical performances in aqueous and ionic liquid gel electrolytes for symmetric supercapacitors. Highly exposed pyridinic-N endows the carbon electrode with a strengthened pseudocapacitance contribution manifested as a high specific capacitance of 436.1 F g-1 and exceptional stability of almost 100% capacitance retention after 5000 cycles at 10 A g-1 in the KOH/polyvinyl alcohol (PVA) electrolyte. By contrast, graphitic-N is propitious for reinforced electrical double-layer capacitance contribution, reflected by a maximum energy density of 125.4 Wh kg-1 in the 1-ethyl-3-methylimidazolium tetrafluoroborate/poly(vinylidene fluoride-co-hexafluoropropylene) (EMIMBF4/PVDF-HFP) electrolyte. This work offers an in-depth insight into the understanding of the energy storage mechanism of N-rich carbon electrodes in different electrolyte media.

Switching from membrane disrupting to membrane crossing, an effective strategy in designing antibacterial polypeptide.

Drug-resistant bacterial infections have caused serious threats to human health and call for effective antibacterial agents that have low propensity to induce antimicrobial resistance. Host defense peptide-mimicking peptides are actively explored, among which poly-ß-l-lysine displays potent antibacterial activity but high cytotoxicity due to the helical structure and strong membrane disruption effect. Here, we report an effective strategy to optimize antimicrobial peptides by switching membrane disrupting to membrane penetrating and intracellular targeting by breaking the helical structure using racemic residues. Introducing ß-homo-glycine into poly-ß-lysine effectively reduces the toxicity of resulting poly-ß-peptides and affords the optimal poly-ß-peptide, ßLys50HG50, which shows potent antibacterial activity against clinically isolated methicillin-resistant Staphylococcus aureus (MRSA) and MRSA persister cells, excellent biosafety, no antimicrobial resistance, and strong therapeutic potential in both local and systemic MRSA infections. The optimal poly-ß-peptide demonstrates strong therapeutic potential and implies the success of our approach as a generalizable strategy in designing promising antibacterial polypeptides.

A Nomogram for Predicting the Cancer-Specific Survival of Patients with Initially Diagnosed Metastatic Gastric Cancer.

Background: There are few models to predict the survival of patients of different ethnicities initially diagnosed with metastatic gastric cancer (mGC). Therefore, the aim of this study was to construct a nomogram to predict the cancer-specific survival (CSS) of these patients. Methods: Data for 994 patients initially diagnosed with mGC between 2000 and 2013 were extracted from the Surveillance, Epidemiology, and End Results database. Patients were randomly classified into a training (n = 696) or internal validation (n = 298) cohort, and a cohort of 133 patients from Fudan cohort was used for external validation. A nomogram to predict the CSS of mGC patients was derived and validated using a concordance index (C-index), calibration curves, and decision-curve analysis (DCA). Results: Multivariate Cox regression indicated that five factors were independent predictors of CSS: differentiation grade, T stage, N stage, metastatic site at diagnosis, and with or without chemotherapy. Thus, these factors were integrated into the nomogram model. The C-index value of the nomogram model was 0.63 (95% CI: 0.60-0.65), and those of the internal and external validation cohorts were 0.60 (95%: CI 0.55-0.64) and 0.63 (95%: CI 0.57-0.69), respectively. The calibration curves showed good consistency between the actual and predicted survival rates in both the internal and external validation cohorts. The DCA also showed the clinical utility of the nomogram model. Conclusions: We established a practical nomogram to predict the CSS of patients initially diagnosed with mGC. The nomogram can be used for individualized prediction of survival and to guide clinicians in making treatment decisions.

Damage Detection for Rotating Blades Using Digital Image Correlation with an AC-SURF Matching Algorithm.

The motion information of blades is a key reflection of the operation state of an entire wind turbine unit. However, the special structure and operation characteristics of rotating blades have become critical obstacles for existing contact vibration monitoring technologies. Digital image correlation performs powerfully in non-contact, full-field measurements, and has increasingly become a popular method for solving the problem of rotating blade monitoring. Aiming at the problem of large-scale rotation matching for blades, this paper proposes a modified speeded-up robust features (SURF)-enhanced digital image correlation algorithm to extract the full-field deformation of blades. Combining an angle compensation (AC) strategy, the AC-SURF algorithm is developed to estimate the rotation angle. Then, an iterative process is presented to calculate the accurate rotation displacement. Subsequently, with reference to the initial state of rotation, the relative strain distribution caused by flaws is determined. Finally, the sensitivity of the strain is validated by comparing the three damage indicators including unbalanced rotational displacement, frequency change, and surface strain field. The performance of the proposed algorithm is verified by laboratory tests of blade damage detection and wind turbine model deformation monitoring. The study demonstrated that the proposed method provides an effective and robust solution for the operation status monitoring and damage detection of wind turbine blades. Furthermore, the strain-based damage detection algorithm is more advantageous in identifying cracks on rotating blades than one based on fluctuated displacement or frequency change.

Corrigendum: Large-scale preparation of highly stable recombinant human acidic fibroblast growth factor in Escherichia coli BL21(DE3) plysS strain.

[This corrects the article DOI: 10.3389/fbioe.2021.641505.].

Protein Subcellular Localization Prediction Model Based on Graph Convolutional Network.

Protein subcellular localization prediction is an important research area in bioinformatics, which plays an essential role in understanding protein function and mechanism. Many machine learning and deep learning algorithms have been employed for this task, but most of them do not use structural information of proteins. With the advances in protein structure research in recent years, protein contact map prediction has been dramatically enhanced. In this paper, we present GraphLoc, a deep learning model that predicts the localization of proteins at the subcellular level. The cores of the model are a graph convolutional neural network module and a multi-head attention module. The protein topology graph is constructed based on a contact map predicted from protein sequences, which is used as the input of the GCN module to take full advantage of the structural information of proteins. Multi-head attention module learns the weighted contribution of different amino acids to subcellular localization in different feature representation subspaces. Experiments on the benchmark dataset show that the performance of our model is better than others. The code can be accessed at https://github.com/GoodGuy398/GraphLoc . The proposed GraphLoc model consists of three parts. The first part is a graph convolutional network (GCN) module, which utilizes the predicted contact maps to construct protein graph, taking benefit of protein information accordingly. The second part is the multi-head attention module, which learns the weighted contribution of different amino acids in different feature representation subspace, and weighted average the feature map across all amino acid nodes. The last part is a fully connected layer that maps the flatten graph representation vector to another vector with a category number dimension, followed by a softmax layer to predict the protein subcellular localization.

Refined Contact Map Prediction of Peptides Based on GCN and ResNet.

Predicting peptide inter-residue contact maps plays an important role in computational biology, which determines the topology of the peptide structure. However, due to the limited number of known homologous structures, there is still much room for inter-residue contact map prediction. Current models are not sufficient for capturing the high accuracy relationship between the residues, especially for those with a long-range distance. In this article, we developed a novel deep neural network framework to refine the rough contact map produced by the existing methods. The rough contact map is used to construct the residue graph that is processed by the graph convolutional neural network (GCN). GCN can better capture the global information and is therefore used to grasp the long-range contact relationship. The residual convolutional neural network is also applied in the framework for learning local information. We conducted the experiments on four different test datasets, and the inter-residue long-range contact map prediction accuracy demonstrates the effectiveness of our proposed method.

Microbial Metabolite Inspired ß-Peptide Polymers Displaying Potent and Selective Antifungal Activity.

Potent and selective antifungal agents are urgently needed due to the quick increase of serious invasive fungal infections and the limited antifungal drugs available. Microbial metabolites have been a rich source of antimicrobial agents and have inspired the authors to design and obtain potent and selective antifungal agents, poly(DL-diaminopropionic acid) (PDAP) from the ring-opening polymerization of ß-amino acid N-thiocarboxyanhydrides, by mimicking ε-poly-lysine. PDAP kills fungal cells by penetrating the fungal cytoplasm, generating reactive oxygen, and inducing fungal apoptosis. The optimal PDAP displays potent antifungal activity with minimum inhibitory concentration as low as 0.4 µg mL-1 against Candida albicans, negligible hemolysis and cytotoxicity, and no susceptibility to antifungal resistance. In addition, PDAP effectively inhibits the formation of fungal biofilms and eradicates the mature biofilms. In vivo studies show that PDAP is safe and effective in treating fungal keratitis, which suggests PDAPs as promising new antifungal agents.

Amino acid environment affinity model based on graph attention network.

Proteins are engines involved in almost all functions of life. They have specific spatial structures formed by twisting and folding of one or more polypeptide chains composed of amino acids. Protein sites are protein structure microenvironments that can be identified by three-dimensional locations and local neighborhoods in which the structure or function exists. Understanding the amino acid environment affinity is essential for additional protein structural or functional studies, such as mutation analysis and functional site detection. In this study, an amino acid environment affinity model based on the graph attention network was developed. Initially, we constructed a protein graph according to the distance between amino acid pairs. Then, we extracted a set of structural features for each node. Finally, the protein graph and the associated node feature set were set to input the graph attention network model and to obtain the amino acid affinities. Numerical results show that our proposed method significantly outperforms a recent 3DCNN-based method by almost 30%.

The Predictive Value of Myoglobin for COVID-19-Related Adverse Outcomes: A Systematic Review and Meta-Analysis.

Objective: Cardiac injury is detected in numerous patients with coronavirus disease 2019 (COVID-19) and has been demonstrated to be closely related to poor outcomes. However, an optimal cardiac biomarker for predicting COVID-19 prognosis has not been identified. Methods: The PubMed, Web of Science, and Embase databases were searched for published articles between December 1, 2019 and September 8, 2021. Eligible studies that examined the anomalies of different cardiac biomarkers in patients with COVID-19 were included. The prevalence and odds ratios (ORs) were extracted. Summary estimates and the corresponding 95% confidence intervals (95% CIs) were obtained through meta-analyses. Results: A total of 63 studies, with 64,319 patients with COVID-19, were enrolled in this meta-analysis. The prevalence of elevated cardiac troponin I (cTnI) and myoglobin (Mb) in the general population with COVID-19 was 22.9 (19-27%) and 13.5% (10.6-16.4%), respectively. However, the presence of elevated Mb was more common than elevated cTnI in patients with severe COVID-19 [37.7 (23.3-52.1%) vs.30.7% (24.7-37.1%)]. Moreover, compared with cTnI, the elevation of Mb also demonstrated tendency of higher correlation with case-severity rate (Mb, r = 13.9 vs. cTnI, r = 3.93) and case-fatality rate (Mb, r = 15.42 vs. cTnI, r = 3.04). Notably, elevated Mb level was also associated with higher odds of severe illness [Mb, OR = 13.75 (10.2-18.54) vs. cTnI, OR = 7.06 (3.94-12.65)] and mortality [Mb, OR = 13.49 (9.3-19.58) vs. cTnI, OR = 7.75 (4.4-13.66)] than cTnI. Conclusions: Patients with COVID-19 and elevated Mb levels are at significantly higher risk of severe disease and mortality. Elevation of Mb may serve as a marker for predicting COVID-19-related adverse outcomes. Prospero Registration Number: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020175133, CRD42020175133.

GJA1 promotes hepatocellular carcinoma progression by mediating TGF-ß-induced activation and the epithelial-mesenchymal transition of hepatic stellate cells.

INTRODUCTION: Gap junction protein, alpha 1 (GJA1), which is correlated with recurrences and unfavorable prognoses in hepatocellular carcinomas (HCCs), is one of the specific proteins expressed by activated hepatic stellate cells (HSCs). METHODS: Expression of GJA1 was compared between HCCs and nontumor tissues (NTs), between hepatic cirrhosis and NTs, and between primary and metastatic HCCs using transcriptomic datasets from the Gene Expression Omnibus and the Integrative Molecular Database of Hepatocellular Carcinoma. The in vitro activities of GJA1 were investigated in cultured HSCs and HCC cells. The underlying mechanism was characterized using Gene Set Enrichment Analysis and validated by western blotting. RESULTS: The expression of GJA1 was significantly increased in HCCs and hepatic cirrhosis compared to that in NTs. GJA1 was also overexpressed in pulmonary metastases from HCCs when compared with HCCs without metastasis. Overexpression of GJA1 promoted while knockdown of GJA1 inhibited proliferation and transforming growth factor (TGF)-ß-mediated activation and migration of cultured HSCs. Overexpression of GJA1 by lentivirus infection promoted proliferation and migration, while conditioned medium from HSCs overexpressing GJA1 promoted migration but inhibited proliferation of Hep3B and PLC-PRF-5 cells. Lentivirus infection with shGJA1 or conditioned medium from shGJA1-infected HSCs inhibited the proliferation and migration of HCCLM3 cells that had a high propensity toward lung metastasis. Mechanistically, GJA1 induced the epithelial-mesenchymal transition (EMT) in HSCs and HCCLM3 cells. CONCLUSION: GJA1 promoted HCC progression by inducing HSC activation and the EMT in HSCs. GJA1 is potentially regulated by TGF-ß and thus may be a therapeutic target to inhibit HCC progression.

Subpixel Matching Using Double-Precision Gradient-Based Method for Digital Image Correlation.

Digital image correlation (DIC) for displacement and strain measurement has flourished in recent years. There are integer pixel and subpixel matching steps to extract displacement from a series of images in the DIC approach, and identification accuracy mainly depends on the latter step. A subpixel displacement matching method, named the double-precision gradient-based algorithm (DPG), is proposed in this study. After, the integer pixel displacement is identified using the coarse-fine search algorithm. In order to improve the accuracy and anti-noise capability in the subpixel extraction step, the traditional gradient-based method is used to analyze the data on the speckle patterns using the computer, and the influence of noise is considered. These two nearest integer pixels in one direction are both utilized as an interpolation center. Then, two subpixel displacements are extracted by the five-point bicubic spline interpolation algorithm using these two interpolation centers. A novel combination coefficient considering contaminated noises is presented to merge these two subpixel displacements to obtain the final identification displacement. Results from a simulated speckle pattern and a painted beam bending test show that the accuracy of the proposed method can be improved by four times that of the traditional gradient-based method that reaches the same high accuracy as the Newton-Raphson method. The accuracy of the proposed method efficiently reaches at 92.67%, higher than the Newton-Raphon method, and it has better anti-noise performance and stability.