Identification of key candidate genes and biological pathways in bladder cancer

Background Bladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms. Methods The GSE7476, GSE13507, GSE37815 and GSE65635 expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer. Results In total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA. Conclusions This study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer

Recognition of regions of stroke injury using multi-modal frequency features of electroencephalogram

ObjectiveNowadays, increasingly studies are attempting to analyze strokes in advance. The identification of brain damage areas is essential for stroke rehabilitation.ApproachWe proposed Electroencephalogram (EEG) multi-modal frequency features to classify the regions of stroke injury. The EEG signals were obtained from stroke patients and healthy subjects, who were divided into right-sided brain injury group, left-sided brain injury group, bilateral brain injury group, and healthy controls. First, the wavelet packet transform was used to perform a time-frequency analysis of the EEG signal and extracted a set of features (denoted as WPT features). Then, to explore the nonlinear phase coupling information of the EEG signal, phase-locked values (PLV) and partial directed correlations (PDC) were extracted from the brain network, and the brain network produced a second set of features noted as functional connectivity (FC) features. Furthermore, we fused the extracted multiple features and used the resnet50 convolutional neural network to classify the fused multi-modal (WPT + FC) features.ResultsThe classification accuracy of our proposed methods was up to 99.75%.SignificanceThe proposed multi-modal frequency features can be used as a potential indicator to distinguish regions of brain injury in stroke patients, and are potentially useful for the optimization of decoding algorithms for brain-computer interfaces

The inception of credit default swap trading and corporate cost structure

Prior literature shows that how creditors monitor borrowers and exercise control rights affect borrowers’ investment and financial policies, but little is known about their impact on borrowers’ operating decisions. The availability of a credit default swap (CDS) reduces creditors’ monitoring incentives ex ante but increases their liquidation incentives in the events of default ex post. After the inception of CDS trading, reference firms exhibit an increase in the elasticity of cost structure. Results are consistent in instrumental variable analyses and are robust with alternative matching samples. The increase in cost structure elasticity is more pronounced for firms with greater credit risk and more restrictive covenants and financially constrained firms, and those face greater product market competition and provide higher convexity in managers’ compensation. We provide the first evidence showing that managers choose a more elastic cost structure when creditors become less forgiving

Optimized SCMA Codebook Design by QAM Constellation Segmentation With Maximized MED

An optimized design of a sparse code multiple access (SCMA) codebook for uplink wireless communications is presented by dividing an optimized 16-point round quadrature amplitude modulation (QAM) into several subsets. The main goal of the scheme is to maximize the minimum Euclidean distance and thus reduces the collisions of the information bits on the resources. The final SCMA codebook is obtained with the mapping matrix, which indicates the sub-constellations generating by dividing the mother QAM constellation. Simulation results show that, in a Nakagami fading channel, the optimized SCMA scheme by the proposed design method achieves significantly performance gains approximately 1.0, 1.4, 2.5, 3.5, and 4.0 dB at bit error rate of $10^{-4}$ , respectively, when compared with those of an undivided 16-QAM constellation, a trellis code modulation (TCM) division, an original SCMA codebooks, a low-density signature (LDS), and an irregular LDS (IrLDS) schemes. In addition, at the signal-to-noise ratio ranging from 0 to 10 dB, the constellation constrained capacity of the scheme by the proposed method achieves more gains over those of the original SCMA, TCM, the undivided star-QAM, the LDS, and the IrLDS schemes. Thus, it can be combined with the grant-free random access mechanism to obtain rapid and low-cost access in next-generation wireless packet services and other applications

Recognition of Thyroid Ultrasound Standard Plane Images Based on Residual Network

Ultrasound is one of the critical methods for diagnosis and treatment in thyroid examination. In clinical application, many reasons, such as large outpatient traffic, time-consuming training of sonographers, and uneven professional level of physicians, often cause irregularities during the ultrasonic examination, leading to misdiagnosis or missed diagnosis. In order to standardize the thyroid ultrasound examination process, this paper proposes using a deep learning method based on residual network to recognize the Thyroid Ultrasound Standard Plane (TUSP). At first, referring to multiple relevant guidelines, eight TUSP were determined with the advice of clinical ultrasound experts. A total of 5,500 TUSP images of 8 categories were collected with the approval and review of the Ethics Committee and the patient’s informed consent. Then, after desensitizing and filling the images, the 18-layer residual network model (ResNet-18) was trained for TUSP image recognition, and five-fold cross-validation was performed. Finally, through indicators like accuracy rate, we compared the recognition effect of other mainstream deep convolutional neural network models. Experimental results showed that ResNet-18 has the best recognition effect on TUSP images with an average accuracy rate of 91.07%. The average macro precision, average macro recall, and average macro F1-score are 91.39%, 91.34%, and 91.30%, respectively. It proves that the deep learning method based on residual network can effectively recognize TUSP images, which is expected to standardize clinical thyroid ultrasound examination and reduce misdiagnosis and missed diagnosis

Transmission-Reflection-Integrated Multifunctional Passive Metasurface for Entire-Space Electromagnetic Wave Manipulation

In recent years, many intriguing electromagnetic (EM) phenomena have come into being utilizing metasurfaces (MSs). However, most of them operate in either transmission or reflection mode, leaving the other half of the EM space completely unmodulated. Here, a kind of transmission-reflection-integrated multifunctional passive MS is proposed for entire-space electromagnetic wave manipulation, which can transmit the x-polarized EM wave and reflect the y-polarized EM wave from the upper and lower space, respectively. By introducing an H-shaped chiral grating-like micro-structure and open square patches into the unit, the MS acts not only as an efficient converter of linear-to-left-hand circular (LP-to-LHCP), linear-to-orthogonal (LP-to-XP), and linear-to-right-hand circular (LP-to-RHCP) polarization within the frequency bands of 3.05–3.25, 3.45–3.8, and 6.45–6.85 GHz, respectively, under the x-polarized EM wave, but also as an artificial magnetic conductor (AMC) within the frequency band of 12.6–13.5 GHz under the y-polarized EM wave. Additionally, the LP-to-XP polarization conversion ratio (PCR) is up to −0.52 dB at 3.8 GHz. To discuss the multiple functions of the elements to manipulate EM waves, the MS operating in transmission and reflection modes is designed and simulated. Furthermore, the proposed multifunctional passive MS is fabricated and experimentally measured. Both measured and simulated results confirm the prominent properties of the proposed MS, which validates the design’s viability. This design offers an efficient way to achieve multifunctional meta-devices, which may have latent applications in modern integrated systems

RNA-Seq read mapping to the reference gene PDGFA.

<p>A: RNA-Seq read mapping to the UCSC reference genome (hg19) of the gene PDGFA for UCB and non-tumor tissues in this study. The UCB tracks are shown in red and non-tumor tissue in green. The pink band indicated the location of skipped exon. B: The detail of junction reads mapping to the skipped exon and its neighboring exons. The Ψ (”percentage spliced in”) indicates the ratio of reads supporting inclusion exon vs. total reads supporting both inclusion and exclusion exon. The Ψ posterior distributions <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091466#pone.0091466-Katz1" target="_blank">[25]</a> were shown in the right side.</p

Statistics of alternative splicing events in bladder cancer.

<p>Statistics of alternative splicing events in bladder cancer.</p

Gene Ontology terms of enriched differentially expressed genes in bladder cancer.

<p>#: Fold Enrichment  =  (number of differentially expressed genes with the GO term/number of differentially expressed genes)/(number of expressed genes with the GO term/number of expressed genes)</p><p>*: p value corrected by method of Bonferroni, and only GO terms of the corrected p value less than 0.05 were shown.</p

The qRT-PCR validation of differential splicing events detected by RNA-seq.

<p>qRT-PCR was performed for four genes that are identified as differential splicing genes between UCB and non-tumor tissues. The result of qRT-PCR is the relative expression level of the skipped exon and the neighboring constitutive exon. The expression level of each exon was normalized to the level in non-tumor tissue. The ΨMISO was the result of MISO, indicates the ratio of reads supporting inclusion exon vs. total reads supporting both inclusion and exclusion exon. A∼D: CD44, PDGFA, NUMB and GSK3B.</p