Search

The model of level set based on the local offset field solves the problem that the traditional local model cannot deal with the uneven grayscale images. But the performance of the model on noisy images is still not excellent. In response to this problem, this paper introduces a convolutional autoencoder to design an unsupervised noise separation mechanism to model the noise field based on the local model, so that the additive noise field can be separated from the image and avoid its interference to the image segmentation process. The results show that the proposed method can separate the additive image noise effectively and improve the segmentation accuracy in a noisy environment, which is superior to traditional noise robust models and offset field models.

Background Cumulative evidence points to abnormal spindle-like microcephaly-associated (ASPM) protein being overexpressed in various cancers, and the aberrant expression of ASPM has been shown to promote cancer tumorigenicity and progression. However, its role and clinical significance in lung adenocarcinoma (LUAD) remains unclear. This study aimed to determine the expression patterns of ASPM and its clinical significance in LUAD. Methods In total, 4 original worldwide LUAD microarray mRNA expression datasets (N=1,116) with clinical and follow-up annotations were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The expression of ASPM protein in LUAD patients was detected by immunohistochemistry. Survival analysis and Cox regression analysis were used to examine the prognostic value of ASPM expression. Gene set enrichment analysis (GSEA) was performed to investigate the relationship between ASPM and LUAD. Results Dataset analyses and immunohistochemistry revealed that ASPM expression was significantly higher in the LUAD tissues compared with normal lung tissues, especially in the advanced tumor stage. Additionally, overexpression of ASPM was significantly correlated with shorter overall survival (OS) and relapse-free survival (RFS) in LUAD. Univariate and multivariate Cox regression analyses revealed that the overexpression of ASPM was a potential independent predictor of poor OS and RFS. However, ASPM overexpression was not significantly associated with predicting OS in lung squamous cell carcinoma. GSEA analysis demonstrated that ASPM was significantly enriched in the cell cycle, DNA replication, homologous recombination, RNA degradation, mismatch repair, and p53 signaling pathways. Conclusions These findings demonstrate the important role of ASPM in the tumorigenesis and progression of LUAD.

Landscape pattern analysis is an important topic of landscape ecology.The ultimate goal of landscape pattern analysis is to link spatial patterns of landscape with ecological processes,and detect status of processes using landscape pattern information.Landscape metrics can represent the spatial distribution of landscape and it has been used as a common tool in landscape pattern analysis.There are some inherent limitations of landscape metrics.Multi-distance spatial cluster analysis based on the Ripley′s k-function can fetch up the landscape metrics faults.Based on the image data of Land Resources Satellite TM(2000),China-Brazil Earth Resources Satellite(2004),and Environment Disaster Monitoring and Forecasting Small Satellite(2011),future landscape change were forecasted with bringing forward simplified process on data transform of landscape pattern change prediction and multi-distance spatial cluster analysis,via analyzing landscape characteristics and its spatial pattern in Xi′an city,using ENVI 4.7,ARCGIS 9.2,and IDRISI 15 in this study..Results showed that,tremendous landscape patterns changes have taken place in Xi′an during the past two decades.Complex landscape matrix consisted of forest land and cropland accounted for 67.98% of the total landscape in 2000 and 54.46% in 2011 in the study area.Built-up land area increased from 33213.91 hm2 to 68380.79hm2,and amount of increased area between 2000 and 2004 was more than that between 2004 and 2011.The area of forest land decreased insignificantly.The shift of water and unused land area was insignificant.Patch number in the class and landscape level decreased obviously,which showed that decreasing landscape fragmentation,enhancing connectivity of forest landscape,and reducing connectivity of farmland was taking place during the study interval.Landscape spatial pattern had a significant aggregation in the expected maximum distance(40 km).The critical thresholds discrepancy of clustered,random,and dispersed distribution between different landscape types in different years were relatively evident;the spatial aggregation levels of water and unused land were significantly higher than those of farmland,woodland,grassland,and built-up land;there were the largest characteristic scale of heterogeneous spatial distribution of aggregated distribution,random distribution,and dispersed distribution for cropland and grassland.The effect of landscape pattern characteristic study via coupling landscape index analysis and multi-distance spatial cluster analysis is better than that of using single landscape index analysis.The simulation of CA-Markov model could basically reflect the future landscape changes.The method and the simplified step of multi-distance spatial cluster analysis by ARCGIS and data conversion for landscape pattern forecasting by IDRISI were proposed in this paper.

Microplastics (MPs) are emerging contaminants in aquatic environments, yet their impact on sediment microbiota and biogeochemical processes were not well reported. Herein, microcosm experiments were performed to investigate the effects of MPs (Polystyrene, PS and Polyethylene, PE) with three size classes (ranging from 100 nm to 150–200 μm) on sediment bacterial and fungal communities over 60-day incubation from Taihu Lake. High-throughput sequencing revealed the alpha diversities of bacterial and fungal communities were reduced by MPs, dependent on MPs’ size and type. Bacterial community structures were significantly altered under all MPs treatments, with clustering for the same size class for PS and PE. Fungal community structures were significantly affected for all MPs, with PS and PE exhibiting different effects. Co-occurrence network analysis suggested MPs changed bacterial and fungal network complexities. Proteobacteria and Ascomycota formed strong associations with other phyla and demonstrated tolerance to MPs exposure. Actinobacteria, Firmicutes, and Chytridiomycota were the main respondents to MPs. The enzyme concentrations were stimulated by MPs, indicating carbon and nitrogen uptakes might be increased. Therefore, PS and PE had similar impacts on the microbial community (particularly bacteria), and sizes of MPs were the main influencing factors. MPs shifted community structure and network with distinct responses from bacteria and fungi, likely leading to the alteration of microbial-involved carbon and nitrogen cycling. Refereed/Peer-reviewed