Biomarker identification and trans-regulatory network analyses in esophageal adenocarcinoma and Barrett's esophagus

BACKGROUND Esophageal adenocarcinoma(EAC) is an aggressive disease with high mortality and an overall 5-year survival rate of less than 20%. Barrett's esophagus(BE) is the only known precursor of EAC, and patients with BE have a persistent and excessive risk of EAC over time. Individuals with BE are up to 30-125 times more likely to develop EAC than the general population. Thus, early detection of EAC and BE could significantly improve the 5-year survival rate of EAC. Due to the limitations of endoscopic surveillance and the lack of clinical risk stratification strategies, molecular biomarkers should be considered and thoroughly investigated.AIM To explore the transcriptome changes in the progression from normal esophagus(NE) to BE and EAC.METHODS Two datasets from the Gene Expression Omnibus(GEO) in NCBI Database(https://www.ncbi.nlm.nih.gov/geo/) were retrieved and used as a training and a test dataset separately, since NE, BE, and EAC samples were included and the sample sizes were adequate. This study identified differentially expressed genes(DEGs) using the R/Bioconductor project and constructed trans-regulatory networks based on the Transcriptional Regulatory Element Database and Cytoscape software. Enrichment of Kyoto Encyclopedia of Genes and Genomes(KEGG) and Gene Ontology(GO) terms was identified using the Database for Annotation, Visualization, and Integrated Discovery(DAVID) Bioinformatics Resources. The diagnostic potential of certain DEGs was assessed in both datasets.RESULTS In the GSE1420 dataset, the number of up-regulated DEGs was larger than that of down-regulated DEGs when comparing EAC vs NE and BE vs NE. Among these DEGs, five differentially expressed transcription factors(DETFs) displayed the same trend in expression across all the comparison groups. Of these five DETFs,E2 F3, FOXA2, and HOXB7 were up-regulated, while PAX9 and TFAP2 C were down-regulated. Additionally, the majority of the DEGs in trans-regulatory networks were up-regulated. The intersection of these potential DEGs displayed the same direction of changes in expression when comparing the DEGs in the GSE26886 dataset to the DEGs in trans-regulatory networks above. The receiver operating characteristic curve analysis was performed for both datasets and found that TIMP1 and COL1 A1 could discriminate EAC from NE tissue, while REG1 A, MMP1, and CA2 could distinguish BE from NE tissue. DAVID annotation indicated that COL1 A1 and MMP1 could be potent biomarkers for EAC and BE, respectively, since they participate in the majority of the enriched KEGG and GO terms that are important for inflammation and cancer.CONCLUSION After the construction and analyses of the trans-regulatory networks in EAC and BE, the results indicate that COL1 A1 and MMP1 could be potential biomarkers for EAC and BE, respectively.

A Lambda Layer-Based Convolutional Sequence Embedding Model for Click-Through Rate Prediction

In the era of intelligent economy, the click-through rate(CTR) prediction system can evaluate massive service information based on user historical information, and screen out the products that are most likely to be favored by users, thus realizing customized push of information and achieve the ultimate goal of improving economic benefits. Sequence modeling is one of the main research directions of CTR prediction models based on deep learning. The user's general interest hidden in the entire click history and the short-term interest hidden in the recent click behaviors have different influences on the CTR prediction results, which are highly important. In terms of capturing the user's general interest, existing models paid more attention to the relationships between item embedding vectors(point-level), while ignoring the relationships between elements in item embedding vectors(union-level). The Lambda layer-based Convolutional Sequence Embedding(LCSE) model proposed in this paper uses the Lambda layer to capture features from click history through weight distribution, and uses horizontal and vertical filters on this basis to learn the user's general preferences from union-level and point-level. In addition, we also incorporate the user's short-term preferences captured by the embedding-based convolutional model to further improve the prediction results. The AUC(Area Under Curve) values of the LCSE model on the datasets Electronic, Movie & TV and MovieLens are 0.870 7, 0.903 6 and 0.946 7, improving 0.45%, 0.36% and 0.07% over the Caser model, proving the effectiveness of our proposed model.

Multi-Strategy Improvement of Sparrow Search Algorithm for Cloud Manufacturing Service Composition

In existing research,the optimization of algorithms applied to cloud manufacturing service composition based on the quality of service often suffers from decreased convergence rates and solution quality due to single-population searches in fixed spaces and insufficient information exchange.In this paper,we introduce an improved Sparrow Search Algorithm(ISSA)to address these issues.The fixed solution space is divided into multiple subspaces,allowing for parallel searches that expedite the discovery of target solutions.To enhance search efficiency within these subspaces and significantly improve population diversity,we employ multiple group evolution mechanisms and chaotic perturbation strategies.Furthermore,we incorporate adaptive weights and a global capture strategy based on the golden sine to guide individual discoverers more effectively.Finally,differential Cauchy mutation perturbation is utilized during sparrow position updates to strengthen the algorithm's global optimization capabilities.Simulation experiments on benchmark problems and service composition optimization problems show that the ISSA delivers superior optimization accuracy and convergence stability compared to other methods.These results demonstrate that our approach effectively balances global and local search abilities,leading to enhanced performance in cloud manufacturing service composition.

Five-channel frequency-division multiplexing using low-loss epsilon-near-zero metamaterial waveguide

The rapidly growing global data usage has demanded more efficient ways to utilize the scarce electromagnetic spectrum resource. Recent research has focused on the development of efficient multiplexing techniques in the millimeter-wave band(1-10 mm, or 30-300 GHz) due to the promise of large available bandwidth for future wireless networks. Frequency-division multiplexing is still one of the most commonly-used techniques to maximize the transmission capacity of a wireless network.Based on the frequency-selective tunnelling effect of the low-loss epsilon-near-zero metamaterial waveguide, we numerically and experimentally demonstrate five-channel frequency-division multiplexing and demultiplexing in the millimeter-wave range.We show that this device architecture offers great flexibility to manipulate the filter Q-factors and the transmission spectra of different channels, by changing of the epsilon-near-zero metamaterial waveguide topology and by adding a standard waveguide between two epsilon-near-zero channels. This strategy of frequency-division multiplexing may pave a way for efficiently allocating the spectrum for future communication networks.