Fold change based approach for identification of significant network markers in breast,lung and prostate cancer

Cancer belongs to a class of highly aggressive diseases and a leading cause of death in the world. With more than 100 types of cancers, breast, lung and prostate cancer remain to be the most common types. To identify essential network markers (NMs) and therapeutic targets in these cancers, the authors present a novel approach which uses gene expression data from microarray and RNA‐seq platforms and utilises the results from this data to evaluate protein–protein interaction (PPI) network. Differentially expressed genes (DEGs) are extracted from microarray data using three different statistical methods in R, to produce a consistent set of genes. Also, DEGs are extracted from RNA‐seq data for the same three cancer types. DEG sets found to be common in both platforms are obtained at three fold change (FC) cut‐off levels to accurately identify the level of change in expression of these genes in all three cancers. A cancer network is built using PPI data characterising gene sets at log‐FC (LFC)>1, LFC>1.5 and LFC>2, and interconnection between principal hub nodes of these networks is observed. Resulting network of hubs at three FC levels highlights prime NMs with high confidence in multiple cancers as validated by Gene Ontology functional enrichment and maximal complete subgraphs from CFinder.Inspec keywords: cancer, proteins, RNA, bioinformatics, statistical analysis, genetics, molecular biophysics, ontologies (artificial intelligence), lungOther keywords: cancer network, PPI data, gene sets, multiple cancers, Gene Ontology functional enrichment, prostate cancer, gene expression data, RNA‐seq platforms, protein–protein interaction network, DEG, microarray data, RNA‐seq data, cancer types, lung cancer, diseases, breast cancer, network markers, differentially expressed genes, fold change based approach, CFinder, statistical methods     

Multigrid approach to predictive wave-front reconstruction in adaptive optical systems

A computationally efficient approach, based on the principles of multigrid methods, to predictive wave-front reconstruction in adaptive optical systems is described. Local predictive estimators are computed by use of recursive least squares on multiple grids. Each grid is increasingly coarse, allowing for temporal prediction of the behavior of both high- and low-spatial-frequency aberrations. Example numerical simulation results are given, showing that implementing the recursive least-squares algorithm for predictive estimation in a multigrid fashion greatly accelerates convergence to the steady-state optimal estimator condition. By implementation of the multigrid predictive reconstructor in parallel, the computational cost of implementing a predictive wave-front reconstruction scheme that uses recursive least squares for each processor at each cycle can be reduced from [symbol: see text](m2) to [symbol: see text](2m), where m is the number of actuators.     

Neural network approach to sensitivity and uncertainty analysis

Computer simulation of the dynamic evolution of complex systems has become a fundamental tool for many modern engineering activities. In particular, risk-informed design projects and safety analyses require that the system behavior be analyzed under several diverse conditions in the presence of substantial model and parameter uncertainty which must be accounted for. In this paper we investigate the capabilities of artificial neural networks of providing both a first-order sensitivity measure of the importance of the various parameters of a model and a fast, efficient tool for dynamic simulation, to be used in uncertainty analyses. The dynamic simulation of a steam generator is considered as a test-bed to show the potentialities of these tools and to point out the difficulties and crucial issues which typically arise when attempting to establish an efficient neural network structure for sensitivity and uncertainty analyses.     

Extreme learning machine based approach for diagnosis and analysis of breast cancer

Due to the heterogeneous and complex nature of clinical data, the need to use sophisticated diagnosis techniques has increased significantly in recent years. The proposed approach for diagnosis of breast cancer exploits the potential of an extreme learning machine (ELM) and analyzes its performance after classification into benign and malignant cases. To optimize the ELM network in terms of computation time and memory resources, weight pruning is used without performance compromise. Using real data sets, numerical experiments have been conducted. With an accuracy of 93%, the optimum numbers of node layers for breast cancer diagnosis has been found to be 20. Comparative results demonstrate over-performance of the proposed ELM approach.     

Using the Rasch measurement model to investigate the construct of motor ability in young children

This paper reports the use of a Rasch measurement model, the Extended Logistic Model of Rasch (Andrich, 1988), to explore the construct of a general motor ability in young children. Data were collected from 332 five and six year old children performing 24 motor skills, including run, hop, balance and ball skills. The data were categorised based on threshold estimates provided by the measurement model. Gender differences in performances on items were hypothesised to contribute to initial item and person misfit for the total sample. The data for boys and for girls were separated and independently analysed resulting in improved item and person fit. Two different, unidimensional scales for boys and for girls were created.     

A combinatorial approach to involution and δ-regularity I: involutive bases in polynomial algebras of solvable type

Involutive bases are a special form of non-reduced Gröbner bases with additional combinatorial properties. Their origin lies in the Janet–Riquier theory of linear systems of partial differential equations. We study them for a rather general class of polynomial algebras including also non-commutative algebras like those generated by linear differential and difference operators or universal enveloping algebras of (finite-dimensional) Lie algebras. We review their basic properties using the novel concept of a weak involutive basis and present concrete algorithms for their construction. As new original results, we develop a theory for involutive bases with respect to semigroup orders (as they appear in local computations) and over coefficient rings, respectively. In both cases it turns out that generally only weak involutive bases exist.     

Using LASSO regression based SVM classification to improve the predictive performance of radiation-induced pneumonitis complication in breast cancer

ABSTRACT

Radiation-induced pneumonitis (RP) is a common complication in breast cancer patients after radiation therapy (RT). In the present study, least absolute shrinkage and selection operator (LASSO) and five classification algorithms were used to improve both the predictive ability of RP and the quality of patients’ daily life. A total of 106 breast cancer patients were enrolled in this study. All of the patients were treated with volumetric modulated arc therapy (VMAT). A total of 19 risk factors were included in this study. The present study found that the area under receiver operating characteristics curve (AUC) and accuracy (ACC) of LASSO selected factors (F_LASSO) for each of the five classification algorithms were generally higher than those of all selected factors (F_all) and dose selected factors (F_dose). We propose to use LASSO with support vector machine (SVM) to assess the risk of complications, to improve the predictive ability for breast cancer patients with complications after RT, and to reduce the cost of assessing the risk of complications.     

A hybrid clustering approach to identify network positions and roles through social network and multivariate analysis

Scientometrics - This study proposes a hybrid clustering approach to identify the positions and roles in a relational network by integrating multivariate and social network analysis. First, an...     

An inverse problem approach to stiffness mapping for early detection of breast cancer: tissue phantom experiments

Early detection of breast cancer will continue to be crucial in improving patient survival rates. Our ultimate goal is to develop an electro-mechanical device to automate and refine the manual breast exam process, and use inverse techniques to generate a tissue stiffness map of the breast tissue. We have previously presented computational simulations of the stiffness mapping approach, which employs static indentations of the tissue and measurements of surface displacements. In this paper, we report on experimental validation of the technique with tissue phantom experiments. We tested 12 tissue phantom samples without simulated tumours and 14 tissue phantom samples with simulated tumours. Our stiffness mapping approach correctly identified all 26 samples.     

Modular systems approach to understanding the interaction of adaptive and monostable and bistable threshold processes

Adaptive signalling is a common and important element in cellular systems. Threshold processes are also ubiquitous in signal transduction. This study takes a modular systems approach to systematically understand the interaction of adaptive modules and threshold modules (both monostable and bistable). The authors employ representative modules of adaptive and threshold elements and use these to examine and analyse various aspects of their interaction including the order of interconnection, the role of relative time scales, the difference between monostable and bistable thresholds in this context and how threshold modules may act as a switch induced by transient signals. Numerical simulations, bifurcation analysis and analytical work are employed to address these questions. Overall, the authors' analysis is a first step towards a detailed systems engineering understanding of the different kinds of interactions between these ubiquitous elements in cellular signal transduction. [Includes supplementary material].     

An approach to integrate production-transportation planning and scheduling in an aluminium supply chain network

This article is based on a real-life problem of a global aluminium supply chain network driven by an aluminium smelter. At each echelon of the aluminium supply chain network, several members are involved which are scattered around the world. Producing aluminium begins with bauxite mining. Next, aluminium oxide is made from bauxite and finally aluminium is produced from aluminium oxide. A novel type of mixed-integer decision-making model, including a time-continuous representation of the planning period, is presented. The model enables coordination of production quantities and times of all supply chain members in order to minimise production and transportation costs of the whole supply chain minus bonus payments for early deliveries which are stipulated between the supply chain network and its customers. Material flows can take place with or without temporary storage of intermediate products at supplying and/or receiving sites. Furthermore, relax-and-fix heuristics are presented. A number of randomly generated scenarios are presented to demonstrate that the heuristics can find nearly optimal solutions along with drastically reduced computation times. The relax-and-fix heuristic enables iterative planning between centralised and decentralised decision makers.     

Using the local approach to evaluate scaling effects in ductile fracture

This paper uses a local model to predict ductile fracture in geometrically similar structures of different sizes containing either sharp cracks or blunt stress concentrators. Simple theoretical considerations suggest that when fracture occurs by quasi-isotropic void growth, fracture initiation at blunt notches follows replica scaling, whereas fracture initiation at sharp cracks does not. Simulations with a local fracture model of fracture events in (1) fatigue precracked compact specimens and (2) three-point-bend bars containing blunt notches confirm these conclusions. However, a comparison of simulations with actual experimental results with HY-130 steel specimens leads to mixed conclusions. Predicted and observed behaviors for fracture at sharp cracks agree well, but the discrepancy is considerable for fracture initiating at blunt notches loaded in bending. Significant scaling effects are observed in the experiments for the conditions of fracture initiation at blunt notches. Fractographic analysis reveals that the reason for this discrepancy is a difference in the micromechanisms controlling fracture at sharp cracks as opposed to blunt notches. At sharp cracks, quasi-isotropic void growth dominates, whereas fracture initiates at blunt notches by a shear localization process and the nucleation, growth, and coalescence of voids in a mixed shear and tensile deformation field. The transition from one mode to the other may be governed by the hardening rate and, if so, is material dependent. Therefore, when using local fracture models for predicting fracture under generalized geometric and loading conditions, care must be taken, that the micromechanisms of ductile fracture invoked in the actual material match those assumed by the local fracture model. If this correspondence is verified, local fracture models can be used to predict fracture conditions and associated scaling effects for situations not amenable to treatment by classical elasto-plastic fracture mechanics. However, new or expanded models that can treat ductile fracture in localized shear zones should be developed to realize the full potential of these local fracture methodologies. This revised version was published online in August 2006 with corrections to the Cover Date.     

Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics

Scientometrics - Altmetric indicators allow exploring and profiling individuals who discuss and share scientific literature in social media. But it is still a challenge to identify and characterize...     

Understanding scientific communities: a social network approach to collaborations in Talent Management research

Synthetic biology is an emerging domain that combines biological and engineering concepts and which has seen rapid growth in research, innovation, and policy interest in recent years. This paper contributes to efforts to delineate this emerging domain by presenting a newly constructed bibliometric definition of synthetic biology. Our approach is dimensioned from a core set of papers in synthetic biology, using procedures to obtain benchmark synthetic biology publication records, extract keywords from these benchmark records, and refine the keywords, supplemented with articles published in dedicated synthetic biology journals. We compare our search strategy with other recent bibliometric approaches to define synthetic biology, using a common source of publication data for the period from 2000 to 2015. The paper details the rapid growth and international spread of research in synthetic biology in recent years, demonstrates that diverse research disciplines are contributing to the multidisciplinary development of synthetic biology research, and visualizes this by profiling synthetic biology research on the map of science. We further show the roles of a relatively concentrated set of research sponsors in funding the growth and trajectories of synthetic biology. In addition to discussing these analyses, the paper notes limitations and suggests lines for further work.     

RGD-conjugated PLA-PLL nanoparticles targeting to Bacp-37 breast cancer xenografts in vivo

Targeted delivery carriers are receiving considerable attention, the development of a more precise targeted delivery carrier is critical for the advancement of cancer chemotherapy. In this study, we evaluated the effects of RGD-conjugated poly (lactic acid-co-lysine)-(Arginine-Glycine-Aspartic) nanoparticles (PLA-PLL-RGD NPs) on targeted delivery to Bacp-37 breast cancer bearing mice. PLA-PLL-RGD NPs were prepared by using the emulsion-solvent evaporation method. A subsequent MTT assay indicated that the NPs were non-toxic and had good biocompatibility. In vitro, the results of Confocal Laser Scanning Microscope (CLSM) and FAC Scan flow cytometry (FACS) indicated that the PLA-PLL-RGD NPs can bind more significantly to human umbilical vein endothelial cells, compared to PLA-PLL NPs. In vivo, the results of target imaging and biodistribution showed that PLA-PLL-RGD can significantly target to tumor of Bacp-37 breast cancer bearing mice. These results demonstrated that PLA-PLL-RGD NPs can effectively enhance targeted efficiency in vivo, and have the potential to be used as targeted delivery carrier.     

Using state-dependent processing rates to emulate SPT queue discipline in an FCFS queueing network

Using the 'shortest processing time' queue discipline results in long queues being quickly reduced as the short jobs are quickly pushed through the system. We approximate this effect under the 'first-come, first-served' queue discipline by having the service rate increase with the queue length. That is, instead of picking the shortest job, the server instead picks the first job, but then processes it at a higher rate. This new load-dependent queue is compared with the original queue both individually and within open queueing networks.     

Regulatory network motifs and hotspots of cancer genes in a mammalian cellular signalling network

Mutations or overexpression of signalling genes can result in cancer development and metastasis. In this study, we manually assembled a human cellular signalling network and developed a robust bioinformatics strategy for extracting cancer-associated single nucleotide polymorphisms (SNPs) using expressed sequence tags (ESTs). We then investigated the relationships of cancer-associated genes [cancer-associated SNP genes, known as cancer genes (CG) and cell mobility genes (CMGs)] in a signalling network context. Through a graph-theory-based analysis, we found that CGs are significantly enriched in network hub proteins and cancer-associated genes are significantly enriched or depleted in some particular network motif types. Furthermore, we identified a substantial number of hotspots, the three- and four-node network motifs in which all nodes are either CGs or CMGs. More importantly, we uncovered that CGs are enriched in the convergent target nodes of most network motifs, although CMGs are enriched in the source nodes of most motifs. These results have implications for the foundations of the regulatory mechanisms of cancer development and metastasis.