Accuracy improvement in protein complex prediction from protein interaction networks by refining cluster overlaps

<p>Abstract</p> <p>Background</p> <p>Recent computational techniques have facilitated analyzing genome-wide protein-protein interaction data for several model organisms. Various graph-clustering algorithms have been applied to protein interaction networks on the genomic scale for predicting the entire set of potential protein complexes. In particular, the density-based clustering algorithms which are able to generate overlapping clusters, i.e. the clusters sharing a set of nodes, are well-suited to protein complex detection because each protein could be a member of multiple complexes. However, their accuracy is still limited because of complex overlap patterns of their output clusters.</p> <p><b>Results</b></p> <p>We present a systematic approach of refining the overlapping clusters identified from protein interaction networks. We have designed novel metrics to assess cluster overlaps: overlap coverage and overlapping consistency. We then propose an overlap refinement algorithm. It takes as input the clusters produced by existing density-based graph-clustering methods and generates a set of refined clusters by parameterizing the metrics. To evaluate protein complex prediction accuracy, we used the <it>f</it>-measure by comparing each refined cluster to known protein complexes. The experimental results with the yeast protein-protein interaction data sets from BioGRID and DIP demonstrate that accuracy on protein complex prediction has increased significantly after refining cluster overlaps.</p> <p><b>Conclusions</b></p> <p>The effectiveness of the proposed cluster overlap refinement approach for protein complex detection has been validated in this study. Analyzing overlaps of the clusters from protein interaction networks is a crucial task for understanding of functional roles of proteins and topological characteristics of the functional systems.</p

Revisiting the Marketing of the Indonesian Batik and the Nigerian Adire

This paper aims to fill the gap in the marketing of Batik and Adire. The methodology deployed is a comparative analysis of literature and legal instruments. The paper also uses data to comparatively analyze the impact of the marketing of Batik and Adire in the creative industries in Indonesia and Nigeria. The paper's findings show that both fabrics share some commonalities in making them, and their designs have sociocultural meanings. They contribute to sustainable socio-economic and cultural development of both countries. Moreover, both fabrics have cultural, aesthetic, artistic, and religious values. They play significant roles in tourism, art-craft, the creative industry, and the way of life of Indonesians and Nigerians. Both fabrics require similar measures to enhance their marketing strategies given their marketing limitations. Conclusively, common measures can be used to enhance the marketing of both fabrics in terms of the use of technology. Hence, both countries should put appropriate legal regimes, regulatory frameworks, facilities, and infrastructure in place to achieve that. Also, the creation of textile cottage industries, the establishment of small and medium enterprises, and public-private partnerships are key in bolstering the marketing of Batik and Adire

CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions

<p>Abstract</p> <p>Background</p> <p>Quantitative characterization of the topological characteristics of protein-protein interaction (PPI) networks can enable the elucidation of biological functional modules. Here, we present a novel clustering methodology for PPI networks wherein the biological and topological influence of each protein on other proteins is modeled using the probability distribution that the series of interactions necessary to link a pair of distant proteins in the network occur within a time constant (the occurrence probability).</p> <p>Results</p> <p>CASCADE selects representative nodes for each cluster and iteratively refines clusters based on a combination of the occurrence probability and graph topology between every protein pair. The CASCADE approach is compared to nine competing approaches. The clusters obtained by each technique are compared for enrichment of biological function. CASCADE generates larger clusters and the clusters identified have <it>p</it>-values for biological function that are approximately 1000-fold better than the other methods on the yeast PPI network dataset. An important strength of CASCADE is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches which have an average discard rate of 45% on the yeast protein-protein interaction network.</p> <p>Conclusion</p> <p>CASCADE is effective at detecting biologically relevant clusters of interactions.</p

A novel functional module detection algorithm for protein-protein interaction networks

BACKGROUND: The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM. RESULTS: STM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique, quasi-clique, minimum cut, betweeness cut and Markov Clustering (MCL) algorithms. The clusters obtained by each technique are compared for enrichment of biological function. STM generates larger clusters and the clusters identified have p-values that are approximately 125-fold better than the other methods on biological function. An important strength of STM is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches. CONCLUSION: STM outperforms competing approaches and is capable of effectively detecting both densely and sparsely connected, biologically relevant functional modules with fewer discards

Ultrathin petal-like NiAl layered double oxide/sulfide composites as an advanced electrode for high-performance asymmetric supercapacitors

Layered double hydroxide (LDH) is an important layer-structured material for supercapacitors because of its versatile compositions, high theoretical capacitance, environmental friendliness, and low cost. However, the high resistivity of this material results in capacity fading, limiting its application in energy storage. Herein, we develop a facile approach to synthesize ultrathin petal-like NiAl layered double oxide/sulfide (LDO/LDS) composites with high electrochemical activity using hydrothermal reaction followed by sulfidation process. Scanning electron micrograph shows that the petal-like NiAl LDO/LDS composites are as thin as ~10 nm with a mean lateral dimension of ~1 µm. The NiAl LDO/LDS electrode delivers remarkably high specific capacitance of 2250.5 F g−1 at 1 A g−1 compared with that of NiAl LDH (1740.5 F g−1 at 1 A g−1) and possesses good cycling ability of 88.9% capacitance retention over 5000 cycles at 5 A g−1. Asymmetric supercapacitor (ASC) is fabricated using NiAl LDO/LDS and graphene as positive and negative electrodes, respectively. NiAl LDO/LDS//G ASC exhibits specific capacitance of 153.3 F g−1 at 1 A g−1, high energy density of 47.9 Wh kg−1 at a power density of 750 W kg−1, and reliable cycling stability of 95.68% capacitance retention after 5000 cycles. Results highlight that NiAl LDO/LDS composites are promising materials for energy storage devices with long cycling stability

The Effect of Environmental Enrichment on Glutathione-Mediated Xenobiotic Metabolism and Antioxidation in Normal Adult Mice

Olfactory bulb (OB) plays an important role in protecting against harmful substances via the secretion of antioxidant and detoxifying enzymes. Environmental enrichment (EE) is a common rehabilitation method and known to have beneficial effects in the central nervous system. However, the effects of EE in the OB still remain unclear. At 6 weeks of age, CD-1® (ICR) mice were assigned to standard cages or EE cages. After 2 months, we performed proteomic analysis. Forty-four up-regulated proteins were identified in EE mice compared to the control mice. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes Pathway demonstrated that the upregulated proteins were mainly involved in metabolic pathways against xenobiotics. Among those upregulated proteins, 9 proteins, which participate in phase I or II of the xenobiotic metabolizing process and are known to be responsible for ROS detoxification, were validated by qRT-PCR. To explore the effect of ROS detoxification mediated by EE, glutathione activity was measured by an ELISA assay. The ratio of reduced glutathione to oxidized glutathione was significantly increased in EE mice. Based on a linear regression analysis, GSTM2 and UGT2A1 were found to be the most influential genes in ROS detoxification. For further analysis of neuroprotection, the level of iNOS and the ratio of Bax to Bcl-2 were significantly decreased in EE mice. While TUNEL+ cells were significantly decreased, Ki67+ cells were significantly increased in EE mice, implicating that EE creates an optimal state for xenobiotic metabolism and antioxidant activity. Taken together, our results suggested that EE protects olfactory layers via the upregulation of glutathione-related antioxidant and xenobiotic metabolizing enzymes, eventually lowering ROS-mediated inflammation and apoptosis and increasing neurogenesis. This study may provide an opportunity for a better understanding of the beneficial effects of EE in the OB

Facile synthesis of NiAl layered double hydroxide nanoplates for high-performance asymmetric supercapacitor

Layered double hydroxide (LDH) is a promising electrode material for supercapacitor owing to its versatility in compositions, high theoretical capacitance, environmental benignity, and low cost. However, capacity fading of LDH hinders its application in energy storage. Herein, we develop a facile approach to synthesize NiAl-LDH nanoplates possessing high electrochemical activity and desirable morphology to improve ion diffusion kinetics and reduce charge transfer resistance, leading to enhanced specific capacitance compared to pristine NiAl-LDH. Scanning electron microscopy shows that the LDH nanoplates are as thin as ∼30 nm with a mean lateral dimension of ∼150 nm. The NiAl-LDH nanoplates electrode delivers remarkably high specific capacitance of 1713.2 F g−1 at 1 A g−1 and good cycling ability of 88% capacitance retention over 5000 cycles compared to only 757.1 F g−1 at 1 A g−1 and 76.4% of the pristine NiAl-LDH. An asymmetric supercapacitor (ASC) is assembled using NiAl-LDH nanoplates and graphene as positive and negative electrodes, respectively. The ASC operating at 1.4 V delivers a high specific capacitance of 125 F g−1 at 1 A g−1 with a high energy density of 34.1 Wh kg−1 at a power density of 700 W kg−1 and outstanding cyclic stability (91.8% capacitance retention after 5000 cycles)

THERMAL HYDRAULIC ISSUES OF CONTAINMENT FILTERED VENTING SYSTEM FOR A LONG OPERATING TIME

This study investigated the thermal hydraulic issues in the Containment Filtered Venting System (CFVS) for a long operating time using the MELCOR computer code. The modeling of the CFVS, including the models for pool scrubbing and the filter, was added to the input file for the OPR-1000, and a Station Blackout (SBO) was chosen as an accident scenario. Although depressurization in the containment building as a primary objective of the CFVS was successful, the decontamination feature by scrubbing and filtering in the CFVS for a long operating time could fail by the continuous evaporation of the scrubbing solution. After the operation of the CFVS, the atmosphere temperature in the CFVS became slightly above the water saturation temperature owing to the release of an amount of steam with high temperature from the containment building to the scrubbing solution. Reduced pipe diameters at the inlet and outlet of the CFVS vessel mitigated the evaporation of scrubbing water by controlling the amount of high-temperature steam and the water saturation temperature

High-performance solid-state flexible supercapacitor based on reduced graphene oxide/hierarchical core-shell Ag nanowire@NiAl layered double hydroxide film electrode

All-solid-state flexible supercapacitor (AFSC) is a promising energy storage device due to its high flexibility, security, and environmental friendliness. However, high electrical resistance and low specific capacitance of electrodes limit its application for potential portable electronic devices. In this study, we design a novel hybrid film electrode composed of reduced graphene oxide (rGO)/silver nanowire (Ag NW)@nickel aluminum layered double hydroxide (NiAl LDH; herein, GAL) possessing high electrochemical performance by using hydrothermal and vacuum filtration techniques. The Ag NW@NiAl LDH (AL) composites with hierarchical core-shell structure are utilized to increase electroactive surface area and improve electrical conductivity, while the rGO nanosheets serve as a prominent carbon material with outstanding electrical conductivity and mechanical flexibility. The freestanding GAL electrode shows high specific capacitance of 1148 F g−1 at 1 A g−1 compared with rGO/NiAl LDH (GL) of 765.2 F g−1 at 1 A g−1. Furthermore, the bind-free symmetric AFSC device is successfully prepared using GAL hybrid film as electrodes and PVA-KOH as solid-state gel electrolyte. The GAL//GAL AFSC device delivers a superior specific capacitance of 127.2 F g−1 at 1 A g−1, a high energy density of 35.75 mWh cm−3 at a power density of 1.01 W cm−3, and great cycling ability of 83.2% over 10,000 cycles at 5 A g−1. This study introduces a novel design of flexible electrode structure for advanced energy storage applications