High Degree Of Connectivity Research Articles

DAGsim

Scalability of distributed ledgers is a key adoption factor. As an alternative to blockchain-based protocols, directed acyclic graph (DAG) protocols are proposed with the intention to allow a higher volume of transactions to be processed. However, there is still limited understanding of the behaviour and security considerations of DAG-based systems. We present an asynchronous, continuous time, and multi-agent simulation framework for DAG-based cryptocurrencies. We model honest and semi-honest actors in the system to analyse the behaviour of one specific cryptocurrency, IOTA. Our simulations show that the agents that have low latency and a high connection degree have a higher probability of having their transactions accepted in the network with honest and semi-honest strategies. Last, the simulator is built with extensibility in mind. We are in the process of implementing SPECTRE as well as including malicious agents.

Visualizing modules of coordinated structural brain atrophy during the course of conversion to Alzheimer's disease by applying methodology from gene co-expression analysis.

ObjectiveWe aimed to identify modularized structural atrophy of brain regions with a high degree of connectivity and its longitudinal changes associated with the progression of Alzheimer's disease (AD) using weighted gene co-expression network analysis (WGCNA), which is an unsupervised hierarchical clustering method originally used in genetic analysis.MethodsWe included participants with late mild cognitive impairment (MCI) at baseline from the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) study. We imputed normalized and Z-transformed structural volume or cortical thickness data of 164 parcellated brain regions/structures based on the calculations of the FreeSurfer software. We applied the WGCNA to extract modules with highly interconnected structural atrophic patterns and examined the correlation between the identified modules and clinical AD progression.ResultsWe included 204 participants from the baseline dataset, and performed a follow-up with 100 in the 36-month dataset of MCI cohort participants from the J-ADNI. In the univariate correlation or variable importance analysis, baseline atrophy in temporal lobe regions/structures significantly predicted clinical AD progression. In the WGCNA consensus analysis, co-atrophy modules associated with MCI conversion were first distributed in the temporal lobe and subsequently extended to adjacent parietal cortical regions in the following 36 months.ConclusionsWe identified coordinated modules of brain atrophy and demonstrated their longitudinal extension along with the clinical course of AD progression using WGCNA, which showed a good correspondence with previous pathological studies of the tau propagation theory. Our results suggest the potential applicability of this methodology, originating from genetic analyses, for the surrogate visualization of the underlying pathological progression in neurodegenerative diseases not limited to AD.

Connectivity as the capacity to improve an organization’s decision-making

This paper describes the development of a new computational model to predict the desirability of decision consequences in an organization, and the development of a prototype tool to enable real-time interaction and decision support when changes occur simultaneously. A tool, called Decision Propagation System, is developed in response to the needs of BT Group plc in understanding the most effective set of interventions in the organization where the high degree of connectivity between system components and the uncertainty in connectivity data are two critical issues. Designed on a case study of the Fields Operations Engineering, this research demonstrates that a knowledge of overlapping decision propagation paths can direct the organizational decisions towards mitigating the risk of unintended consequences.

Identification of Key Genes and Pathways in Cervical Cancer by Bioinformatics Analysis.

Cervical cancer is a common malignant tumour of the female reproductive system that seriously threatens the health of women. The aims of this study were to identify key genes and pathways and to illuminate new molecular mechanisms underlying cervical cancer. Altogether, 1829 DEGs were identified, including 794 significantly down-regulated DEGs and 1035 significantly up-regulated DEGs. GO analysis suggested that the up-regulated DEGs were mainly enriched in mitotic cell cycle processes, including DNA replication, organelle fission, chromosome segregation and cell cycle phase transition, and that the down-regulated DEGs were primarily enriched in development and differentiation processes, such as tissue development, epidermis development, skin development, keratinocyte differentiation, epidermal cell differentiation and epithelial cell differentiation. KEGG pathway analysis showed that the DEGs were significantly enriched in cell cycle, DNA replication, the p53 signalling pathway, pathways in cancer and oocyte meiosis. The top 9 hub genes with a high degree of connectivity (over 72 in the PPI network) were down-regulated TSPO, CCND1, and FOS and up-regulated CDK1, TOP2A, CCNB1, PCNA, BIRC5 and MAD2L1. Module analysis indicated that the top 3 modules were significantly enriched in mitotic cell cycle, DNA replication and regulation of cell cycle (P < 0.01). The heat map based on TCGA database preliminarily demonstrated the expression change of the key genes in cervical cancer. GSEA results were basically coincident with the front enrichment analysis results. By comprehensive analysis, we confirmed that cell cycle was a key biological process and a critical driver in cervical cancer. In conclusion, this study identified DEGs and screened the key genes and pathways closely related to cervical cancer by bioinformatics analysis, simultaneously deepening our understanding of the molecular mechanisms underlying the occurrence and progression of cervical cancer. These results might hold promise for finding potential therapeutic targets of cervical cancer.

Forest fires in Portugal - case study, 18 june 2017

Forest fires that occurred in Portugal on June 18, 2017, caused several tens of human casualties. The cause of their emergence, as well as many others that occurred in western Europe at the same time remained unknown. Taking into account consequences, including loss of human lives and endangerment of ecosystem sustainability, discovering of the forest fires causes is the very significant question. The heliocentric hypothesis has indirectly been tested, according to which charged particles are a possible cause of forest fires. We must point out that it was not possible to verify whether in this specific case the particles by reaching the ground and burning the plant mass create the initial phase of the formation of the flame. Therefore, we have tried to determine whether during the critical period, i. e. from June 15-19 there is a certain statistical connection between certain parameters of the solar wind and meteorological elements. Based on the hourly values of the charged particles flow, a correlation analysis was performed with hourly values of individual meteorological elements including time lag at Monte Real station. The application of the Adaptive Neuro Fuzzy Inference System models has shown that there is a high degree of connection between the flow of protons and the analyzed meteorological elements in Portugal. However, further verification of this hypothesis requires further laboratory testing.

Identification of Core Gene Biomarkers in Patients with Diabetic Cardiomyopathy.

Diabetic cardiomyopathy (DCM) is a disorder of the myocardium in diabetic patients, which is one of the critical complications of diabetes giving rise to an increased mortality. However, the underlying mechanisms of DCM remain incompletely understood presently. This study was designed to screen the potential molecules and pathways implicated with DCM. GSE26887 involving 5 control individuals and 7 DCM patients was selected from the GEO database to identify the differentially expressed genes (DEGs). DAVID was applied to perform gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction (PPI) network was also constructed to visualize the interactions among these DEGs. To further validate significant genes and pathways, quantitative real-time PCR (qPCR) and Western blot were performed. A total of 236 DEGs were captured, including 134 upregulated and 102 downregulated genes. GO, KEGG, and the PPI network disclosed that inflammation, immune disorders, metabolic disturbance, and mitochondrial dysfunction were significantly enriched in the development of DCM. Notably, IL6 was an upregulated hub gene with the highest connectivity degree, suggesting that it may interact with a great many molecules and pathways. Meanwhile, SOCS3 was also one of the top 15 hub genes in the PPI network. Herein, we detected the protein level of STAT3 and SOCS3 in a mouse model with DCM. Western blot results showed that the protein level of SOCS3 was significantly lower while phosphorylated-STAT3 (P-STAT3) was activated in mice with DCM. In vitro results also uncovered the similar alterations of SOCS3 and P-STAT3 in cardiomyocytes and cardiac fibroblasts induced by high glucose (HG). However, overexpression of SOCS3 could significantly reverse HG-induced cardiomyocyte hypertrophy and collagen synthesis of cardiac fibroblasts. Taken together, our analysis unveiled potential biomarkers and molecular mechanisms in DCM, which could be helpful to the diagnosis and treatment of DCM.

Identification of key genes and pathways in chronic rhinosinusitis with nasal polyps using bioinformatics analysis.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a prevalent inflammatory disease of yet unknown etiology. The purpose of this study was to uncover key genes and pathways related to the pathogenesis of CRSwNP via bioinformatics approaches. The gene expression profile of GSE36830 extracted from Gene Expression Omnibus database was used to screen differentially expressed genes (DEGs) between nasal polyp samples and control samples. Furthermore, functional and pathway enrichment analysis was performed using the clusterProfiler package in R language. In addition, protein-protein interaction (PPI) network was constructed by STRING database and functional modules were detected using Molecular Complex Detection algorithm. A total of 538 DEGs (326 up-regulated and 212 down-regulated) were identified. The most significantly enriched pathways for up-regulated and down-regulated genes were hematopoietic cell lineage and salivary secretion, respectively. Moreover, twenty hub genes with high connectivity degrees were selected from the PPI network, such as TYRO protein tyrosine kinase binding protein (TYROBP), G protein subunit gamma 2 (GNG2), CCR7, and CCR3. Besides, six important modules were obtained, which were highly associated with chemokine signaling pathway, Th1 and Th2 cell differentiation, complement and coagulation cascades, cell cycle, systemic lupus erythematosus, and Staphylococcus aureus infection. The results of this study may provide new insights into potential molecular mechanisms of CRSwNP. Nevertheless, further experiments are needed to confirm these findings.

Identification of hub genes and outcome in colon cancer based on bioinformatics analysis.

BackgroundColon cancer is one of the leading malignant neoplasms worldwide. Until now, the concrete mechanisms of colonic cancerogenesis are largely unknown; identification of driven genes and pathways is, therefore, of great importance for monitoring and conquering this disease. This study aims to explore the potential biomarkers and therapeutic targets for colon cancer treatment.MethodsThe gene expression profile of GSE44076 from Gene Expression Omnibus database, including 98 primary colon cancers and 98 normal distant colon mucosa, was deeply analyzed. GEO2R tool was used to screen the differentially expressed genes (DEGs) between colon cancer tissues and normal samples. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed for screening DEGs using Database for Annotation, Visualization and Integrated Discovery database and Panther database. Moreover, Search Tool for the Retrieval of Interacting Genes, Cytoscape software, and Molecular Complex Detection plug-in were used to visualize the protein–protein interaction of these DEGs.ResultsA total of 497 DEGs were obtained, including 129 upregulated genes mainly enriched in Hippo signaling pathway, Wnt signaling pathway, and cytokine–cytokine receptor interaction and 368 downregulated genes enriched in retinol metabolism, steroid hormone biosynthesis, drug metabolism, and chemical carcinogenesis. Using Molecular Complex Detection software, three important modules were selected from the protein–protein interaction network. Moreover, 20 hub genes with high degree of connectivity were selected, including COL1A1, CXCL5, GNG4, TIMP1, and so on. The Kaplan–Meier analysis for overall survival and correlation analysis were applied among the hub genes.ConclusionTaken together, DEGs, especially the hub genes such as COL1A1, might be the driven genes in colon cancer progression. More importantly, they might be the novel biomarkers for diagnosis and guiding therapeutic strategies of colon cancer.

Quantitative Proteomic Profiling Identifies Differentially Regulated Pathways in Fanconi Anemia and Aplastic Anemia

Purpose: Prognosis and therapeutic treatment of Fanconi anemia (FA) and acquired aplastic anemia (AA) are different. However, delayed and incorrect treatments are frequently occurred in FA because its symptoms are very similar to those of AA. This study aimed to elucidate the differences between FA and AA at the molecular level and to identify biomarkers and pathways of FA through quantitative proteomics and bioinformatics analyses.Methods: Samples were obtained from the bone marrow of patients with FA and AA followed the institutional guideline. Proteins were extracted, digested with Lys-C and trypsin, and chemically labeled with tandem mass tag (TMT) duplex reagents (TMT126 and 127) for mass spectrometric analyses. Proteins with fold changes of > 1.5 or < 0.667 were considered as differentially expressed proteins (DEPs). Gene ontology analysis was performed using the Database for Annotation, Visualization and Integrated Discovery. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using the ClueGO plug-in in Cytoscape. A DEP-associated protein-protein interaction (PPI) network was constructed using STRING and visualized in Cytoscape. The degree and topological index of these DEPs were calculated using CentiScape. The PPI network was subjected to modular analysis using the Molecular Complex Detection plug-in in Cytoscape.Results: A total of 114 DEPs, including 71 upregulated and 43 downregulated proteins, were discovered in the FA sample compared with those in the AA sample. The upregulated proteins were enriched in nucleosome assembly, cell or subcellular component movement, glycolysis, and gluconeogenesis pathway. The downregulated proteins were enriched in immune response, negative regulation of apoptosis, proteolysis, phagosome, and pantothenate and CoA biosynthesis. Five important modules were detected from the PPI network of DEPs. Eight hub proteins, a-enolase (ENO1), phosphoglycerate kinase 1 (PGK1), HSP90AA1, HSP90AB1, ACTC1, ACTBL2, EEF1A1, and CFL1, with a high degree of connectivity were obtained.Conclusions: Quantitative proteomics coupled with bioinformatics analyses are useful for the identification of protein biomarkers and pathways associated with FA and AA. Some critical DEPs, such as ENO1, PGK1, ACTC1, ACTBL2, EEF1A1, and CFL1, likely play important roles in FA and could be used as serologic markers for its early diagnosis and to distinguish FA from AA. DisclosuresNo relevant conflicts of interest to declare.

LncRNAs expression in adriamycin-induced rats reveals the potential role of LncRNAs contributing to chronic glomerulonephritis pathogenesis

BackgroundChronic glomerulonephritis (CGN) is the most common form of primary glomerular disease with unclear molecular mechanisms. Currently, limited study on long non-coding RNAs (lncRNAs) in CGN is available. Our study aimed to identify potential lncRNAs and genes in the normal and adriamycin-induced CGN rats, which to explore the potential molecular mechanisms of CGN pathogenesis. MethodsTo identify LncRNAs specifically expressed in CGN, the expression of LncRNAs in glomerular tissues of rats from the adriamycin-induced group (n = 3) was compared with that in the control group (n = 3) using RNA-sequencing and real-time polymerase chain reaction (RT-PCR). Identification of differentially expressed lncRNAs and mRNAs were performed between the 2 groups. Gene ontology (GO) and pathway enrichment analyses were performed to analyze the biological functions and pathways for the differentially expressed mRNAs. LncRNA-mRNA co-expression network was constructed to analyses for the genes. The protein-protein interaction (PPI) network was visualized. ResultsA total of 114 significantly up-regulated and 86 down-regulated lncRNAs, 1038 up-regulated and 88 down-regulated mRNAs were identified. Additionally, Il6, with the highest connectivity degree in PPI network, was noteworthy enriched in various kinds pathways. Coding-non-coding gene co-expression networks (CNC network) were drawn based on the correlation analysis between the differentially expressed LncRNAs and mRNAs. Ten LncRNAs, NONRATT000964.2, NONRATT018086.2, NONRATT023684.2, NONRATT009530.2, NONRATT006315.2, NONRATT026805.2, MSTRG.9260.1, NONRATT009155.2, MSTRG.7681.1, NONRATT009275.2, were selected to analyze the relationship between LncRNAs and CGN via the CNC network and GO analysis. Real-time PCR result confirmed that the six LncRNAs were specifically expressed in the CGN rats. ConclusionsThe ten LncRNAs were differentially expressed and might play important roles in the development of CGN. Key genes, such as Il6, Ptprc, TOP2a, Fos, Myc, etc., may be crucial biomarkers for CGN.

Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis

Consensus strategy was proved to be highly efficient in the recognition of gene-disease association. Therefore, the main objective of this study was to apply theoretical approaches to explore genes and communities directly involved in breast cancer (BC) pathogenesis. We evaluated the consensus between 8 prioritization strategies for the early recognition of pathogenic genes. A communality analysis in the protein-protein interaction (PPi) network of previously selected genes was enriched with gene ontology, metabolic pathways, as well as oncogenomics validation with the OncoPPi and DRIVE projects. The consensus genes were rationally filtered to 1842 genes. The communality analysis showed an enrichment of 14 communities specially connected with ERBB, PI3K-AKT, mTOR, FOXO, p53, HIF-1, VEGF, MAPK and prolactin signaling pathways. Genes with highest ranking were TP53, ESR1, BRCA2, BRCA1 and ERBB2. Genes with highest connectivity degree were TP53, AKT1, SRC, CREBBP and EP300. The connectivity degree allowed to establish a significant correlation between the OncoPPi network and our BC integrated network conformed by 51 genes and 62 PPi. In addition, CCND1, RAD51, CDC42, YAP1 and RPA1 were functional genes with significant sensitivity score in BC cell lines. In conclusion, the consensus strategy identifies both well-known pathogenic genes and prioritized genes that need to be further explored.

Hybrid PET-MRI Applications in Movement Disorders.

Even before the success of combined positron emission tomography and computed tomography (PET/CT), the neuroimaging community was conceiving the idea to integrate the positron emission tomography (PET), with very high molecular quantitative data but low spatial resolution, and magnetic resonance imaging (MRI), with high spatial resolution. Several technical limitations have delayed the use of a hybrid scanner in neuroimaging studies, including the full integration of the PET detector ring within the MRI system, the optimization of data acquisition, and the implementation of reliable methods for PET attenuation, motion correction, and joint image reconstruction. To be valid and useful in clinical and research settings, this instrument should be able to simultaneously acquire PET and MRI, and generate quantitative parametric PET images comparable to PET-CT. While post hoc co-registration of combined PET and MRI data acquired separately became the most reliable technique for the generation of "fused" PET-MRI images, only hybrid PET-MRI approach allows merging these measurements naturally and correlating them in a temporal manner. Furthermore, hybrid PET-MRI represents the most accurate tool to investigate in vivo the interplay between molecular and functional aspects of brain pathophysiology. Hybrid PET-MRI technology is still in the early stages in the movement disorders field, due to the limited availability of scanners with integrated optimized methodological models. This technology is ideally suited to investigate interactions between resting-state functional/arterial spin labeling MRI and [18F]FDG PET glucose metabolism in the evaluation of the brain "hubs" particularly vulnerable to neurodegeneration, areas with a high degree of connectivity and associated with an efficient synaptic neurotransmission. In Parkinson's disease, hybrid PET-MRI is also the ideal instrument to deeper explore the relationship between resting-state functional MRI and dopamine release at [11C]raclopride PET challenge, in the identification of early drug-naïve Parkinson's disease patients at higher risk of motor complications and in the evaluation of the efficacy of novel neuroprotective treatment able to restore at the same time the altered resting state and the release of dopamine. In this chapter, we discuss the key methodological aspects of hybrid PET-MRI; the evidence in movement disorders of the key resting-state functional and perfusion MRI; [18F]FDG PET and [11C]raclopride PET challenge studies; the potential advantages of using hybrid PET-MRI to investigate the pathophysiology of movement disorders and neurodegenerative diseases. Future directions of hybrid PET-MRI will be discussed alongside with up-to-date technological innovations on hybrid systems.

Identifying the key genes and microRNAs in colorectal cancer liver metastasis by bioinformatics analysis and invitro experiments.

Colorectal cancer (CRC) is one of the principal causes of cancer-associated mortality worldwide. The high incidence of liver metastasis is the leading risk factor of mortality in patients with CRC, and the mechanisms of CRC liver metastasis are poorly understood. In the present study, 7 datasets, including 3 gene expression profile datasets and 4 microRNA (miRNA) expression profile datasets were downloaded from the NCBI Gene Expression Omnibus (GEO) database to identify potential key genes and miRNAs, which may be candidate biomarkers for CRC liver metastasis. Differentially expressed (DE) genes (DEGs) and DE miRNAs of primary CRC tumor tissues and liver metastatic CRC tumor tissues were selected using the GEO2R tool. Gene Ontology and Kyoto Encyclopedia of Gene and Genome pathway enrichment analyses were conducted using the Database for Annotation, Visualization and Integrated Discovery online database. Furthermore, Cytoscape with cytoHubba and the Molecular Complex Detection (MCODE) plug-in were used to visualize a protein-protein interaction (PPI) network for these DEGs, and to screen hub genes and gene modules in the PPI network. In addition, the online databases, TargetScan, miRanda, PITA, miRWalk and miRDB, were used to identify the target genes of the DE miRNAs. In the present study, 141 DEGs (97 upregulated and 44 downregulated) and 3 DE miRNAs (2 upregulated and 1 downregulated) were screened from the 3 gene expression microarray datasets and 4 miRNA expression microarray datasets, respectively. In total, 10 hub genes with a high degree of connectivity were selected from the PPI network, including albumin (ALB), coagulation factor II (F2), thrombin, apolipoprotein H (APOH), serpin family C member 1 (SERPINC1), apolipoprotein A1 (APOA1), α-1-microglobulin/bikunin precursor (AMBP), apolipoprotein C3 (APOC3), plasminogen (PLG), α-2 HS glycoprotein (AHSG) and apolipoprotein B (APOB). The most important module was detected in the PPI network using the MCODE plug-in. A total of 20 DEGs were identified to be potential target genes of these DE miRNAs, and novel miRNA-DEGs regulatory axes were constructed. In vitro experiments were performed to demonstrate that miR-885 promoted CRC cell migration by, at least partially, decreasing the expression of von Willebrand factor (vWF) and insulin-like growth factor binding protein 5 (IGFBP5). In conclusion, by using integrated bioinformatics analysis and in vitro experiments, key candidate genes were identified and novel miRNA-mRNA regulatory axes in CRC liver metastasis were constructed, which may improve understanding of the molecular mechanisms underlying CRC liver metastasis.

Identification of potential key genes associated with ovarian clear cell carcinoma.

BackgroundOvarian cancer is the major cause of death from cancer among females worldwide. Ovarian clear cell carcinoma (OCCC) is considered a distinct histopathologic subtype with worse prognosis and resistance to conventional chemotherapy.Materials and methodsWe analyzed five microarray datasets derived from the Gene Expression Omnibus database. GEO2R tool was used to screen out differentially expressed genes (DEGs) between OCCC tumor and normal ovary tissue. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed using the g:Profiler database and Cytoscape. Based on Search Tool for the Retrieval of Interacting Genes, we performed protein–protein interaction (PPI) network analysis on the DEGs. Real-time PCR (RT-PCR) and Western blotting in frozen samples of normal ovary and OCCC were performed to verify the expression difference of hub genes in OCCC patients.ResultsThirty upregulated DEGs and 13 downregulated DEGs were identified by cross referencing. Six were chosen as hub genes with high connectivity degree via PPI network analysis, including two upregulated and four downregulated. RT-PCR and Western blotting results showed significant expression difference of the two upregulated genes, SPP1 and EPCAM, between tumor and normal tissues.ConclusionOur research suggests that SPP1 and EPCAM are overexpressed in OCCC compared with normal ovary tissue. Clinical study of large sample is required to evaluate the value of SPP1 and EPCAM in the precision treatment and prognostic influence on OCCC in the future.

Identification of hub genes and pathways in glioblastoma by bioinformatics analysis.

Glioblastoma (GBM) is the most common type of malignant brain tumor, and is associated with poor patient prognosis. A comprehensive understanding of the molecular mechanism underlying GBM may help to guide the identification of novel diagnoses and treatment targets. The gene expression profile of the GSE4290 GBM dataset was analyzed in order to identify differentially expressed genes (DEGs). Enriched pathways were identified through Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analyses. A protein-protein interaction network was constructed in order to identify hub genes and for module analysis. Expression and survival analyses were conducted in order to screen and validate critical genes. A total of 1,801 DEGs were recorded, including 620 upregulated and 1,181 downregulated genes. Upregulated DEGs were enriched in the terms ‘mitotic cell cycle process’, ‘mitotic cell cycle’ and ‘cell cycle process’. Downregulated genes were enriched in ‘transsynaptic signaling’, ‘anterograde transsynaptic signaling’ and ‘synaptic signaling’. A total of 15 hub genes, which displayed a high degree of connectivity, were selected. These genes included vascular endothelial growth factor A, cyclin-dependent kinase 1 (CDK1), cell-division cycle protein 20 (CDC20), aurora kinase A (AURKA), and budding uninhibited by benzimidazoles 1 (BUB1). The identified DEGs and hub genes may help guide investigations on the mechanisms underlying the development and progression of GBM. CDK1, CDC20, AURKA and BUB1, which are involved in cell cycle pathways, may be potential targets in the diagnosis and therapy of GBM.

Analysis of cyclin E co-expression genes reveals nuclear transcription factor Y subunit alpha is an oncogene in gastric cancer

ObjectiveTo explore genes potentially co-expressed with cyclin E in gastric cancer and discover possible targets for gastric cancer treatment. MethodsThe Cancer Genome Atlas (TCGA) stomach adenocarcinoma sequencing data were used to predict genes co-expressed with cyclin E. Co-expression genes predicted by cBioPortal online analysis with Pearson correlation coefficient ≥0.4 were analyzed by gene ontology (GO) enrichment annotation using the PANTHER online platform (Ver. 7). Interactions between proteins encoded by these genes were analyzed using the STRING online platform (Ver. 10.5) and Cytoscape software (Ver. 3.5.1). Genes displaying a high degree of connection were analyzed by transcription factor enrichment prediction using FunRich software (Ver. 3). The significant transcription factor and cyclin E expression levels and their impact on gastric cancer progression were analyzed by Western blotting and Kaplan–Meier survival curve analysis. ResultsAfter filtering the co-expression gene prediction results, 78 predicted genes that included 73 protein coding genes and 5 non-coding genes with Pearson correlation coefficient ≥0.4 were selected. The expressions of the genes were considered to be correlated with cyclin E expression. Among the 78 genes co-expressed with cyclin E, 19 genes at the central of the regulatory network associated with cyclin E were discovered. Nuclear transcription factor Y subunit alpha (NF-YA) was identified as a significant transcription factor associated with cyclin E co-expressing genes. Analysis of specimen donors’ clinical records revealed that high expression of NF-YA tended to be associated with increased cyclin E expression. The expression of both was associated with progression of gastric cancer. Western blotting results showed that compared with normal tissues, NF-YA and cyclin E were highly expressed in tumor tissues (P < 0.001). Survival curve analysis clearly demonstrated relatively poor overall survival of gastric cancer patients with high cyclin E or high NF-YA expression level, compared to patients with low cyclin E or NF-YA expression (P < 0.05). ConclusionsNF-YA may promote gastric cancer progression by increasing the transcription of cyclin E and other cell cycle regulatory genes. NF-YA might be a potential therapeutically useful prognostic factor for gastric cancer.

Volatile segregation and generation of highly vesiculated explosive magmas by volatile-melt fining processes: The case of the Campanian Ignimbrite eruption

The late Pleistocene trachytic Campanian Ignimbrite (>300 km3 DRE, ca. 39 ka) covers the Campanian Plain (Italy) around Naples. It is found behind ridges >1000 m high at 80 km from the source in the Campi Flegrei caldera. This points towards very dilute currents, that together with the huge amount of discharged magmatic material, suggest a magma reservoir highly enriched in volatiles. The huge volume of magma that extruded during the Campanian Ignimbrite eruption differentiated and mixed at shallow depth (6–3 km), as shown by modelling of the petrologic and geochemical features of the erupted products together with melt inclusion-based studies of gas-melt saturation. With respect to compositionally similar but lower magnitude Phlegraean eruptions (e.g., Agnano-Monte Spina, >1 km3 DRE, ca. 4.6 ka), the large amount of volatiles discharged by Campanian Ignimbrite was likely due to fractional crystallization of the lowermost portion of its magmatic reservoir. Because of the long residence time of the Campanian Ignimbrite magmatic system within the crust, an efficient decoupling took place during the upward migration of volatile elements in response to a chromatographic fractionation between H2O and CO2. This caused early pulses of nearly pure CO2 (CO2-fluxing), followed by a long-lasting H2O enrichment which yielded dissolved H2O contents up to 6–7 wt%. An overpressurized CO2-dominated gas cap was consequently produced, uniformly distributed at the top of the magma chamber. The onset of the eruption tapped this cap and generated Plinian columns, causing depressurization and fast volume decrease that facilitated, or even drove, the caldera collapse. H2O-rich magma was discharged during the following phase, characterised by pyroclastic density currents. The uniform distribution of the high values of void fraction (>70%) and the high degree of vesicle connectivity throughout the magma body testify to the huge abundance of volatiles. These percolate from the crystallizing basal layers and determine the volatile-melt fining process yielding an efficient separation between H2O and CO2. The gas saturation-based estimates of the tapped foamy magma are compatible with the extent of magma chamber roof collapse, the strong expansion revealed by textural data, and the transport and deposition mechanisms, reflecting depressurization and inflation of the volatile-rich magma within the collapsed and laterally confined caldera.

High-frequency electrical stimulation promotes reshaping of the functional connections and synaptic plasticity in in vitro cortical networks

High-frequency electrical stimulation (tetanus) promotes global synaptic potentiation in dissociated cortical networks coupled to multi-electrode arrays (MEAs). Since little is known about the functional changes induced by this protocol, this work aims to investigate the statistical dependences between the time series (i.e. functional links) of the network nodes involved pre- and post-tetanus. Specifically, we first show a strong reshaping of the functional connections induced by the stimulation and possibly associated with the global plasticity. Then, we find that about 30% of the nodes linked before and after electrical perturbation show high-connectivity degree (⩾9 links), occupying a central role in the neuronal communication. Finally, we observe that these functional units drive the global network plasticity showing more synaptic potentiation than the other nodes involved in the connectivity reshaping.

Gate‐Tunable Synaptic Plasticity through Controlled Polarity of Charge Trapping in Fullerene Composites

AbstractMotivated by the biological neuromorphic system with high degree of connectivity to process huge amounts of information, transistor‐based artificial synapses are expected to pave a way to overcome the von Neumann bottleneck for neuromorphic computing paradigm. Here, artificial flexible organic synaptic transistors capable of concurrently exhibiting signal transmission and learning functions are verified using C60/poly(methyl methacrylate) (PMMA) hybrid layer for the first time. C60 trapping sites are doped in PMMA by facile solution process to form the hybrid structure. The flexible synaptic transistor exhibits a memory window of 2.95 V, a currenton/currentoff ratio greater than 103, program/erase endurance cycle over 500 times. In addition, comprehensive synaptic functions of biosynapse including the excitatory postsynaptic current with different duration time, pulse amplitudes and temperatures, paired‐pulse facilitation/depression, potentiation and depression of the channel conductance modulation, transition from short‐term potentiation to long‐term potentiation, and repetitive learning processes are successfully emulated in this synaptic three‐terminal device. The realization of synaptic devices based on C60 with low operation voltage and controlled polarity of charge trapping is an important step toward future neuromorphic computing using organic electronics.

Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis.

Background and Objective: Non-small cell lung cancer (NSCLC) accounts for 80–85% of all patients with lung cancer and 5-year relative overall survival (OS) rate is less than 20%, so that identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study attempted to identify potential key genes associated with the pathogenesis and prognosis of NSCLC.Methods: Four GEO datasets (GSE18842, GSE19804, GSE43458, and GSE62113) were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NSCLC samples and normal ones were analyzed using limma package, and RobustRankAggreg (RRA) package was used to conduct gene integration. Moreover, Search Tool for the Retrieval of Interacting Genes database (STRING), Cytoscape, and Molecular Complex Detection (MCODE) were utilized to establish protein–protein interaction (PPI) network of these DEGs. Furthermore, functional enrichment and pathway enrichment analyses for DEGs were performed by Funrich and OmicShare. While the expressions and prognostic values of top genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan Meier-plotter (KM) online dataset.Results: A total of 249 DEGs (113 upregulated and 136 downregulated) were identified after gene integration. Moreover, the PPI network was established with 166 nodes and 1784 protein pairs. Topoisomerase II alpha (TOP2A), a top gene and hub node with higher node degrees in module 1, was significantly enriched in mitotic cell cycle pathway. In addition, Interleukin-6 (IL-6) was enriched in amb2 integrin signaling pathway. The mitotic cell cycle was the most significant pathway in module 1 with the highest P-value. Besides, five hub genes with high degree of connectivity were selected, including TOP2A, CCNB1, CCNA2, UBE2C, and KIF20A, and they were all correlated with worse OS in NSCLC. Conclusion: The results showed that TOP2A, CCNB1, CCNA2, UBE2C, KIF20A, and IL-6 may be potential key genes, while the mitotic cell cycle pathway may be a potential pathway contribute to progression in NSCLC. Further, it could be used as a new biomarker for diagnosis and to direct the synthesis medicine of NSCLC.