Facilitating insight into a simulation model using visualization and dynamic model previews

This paper shows how model simplification, by replacing iterative steps with unitary predictive equations, can enable dynamic interaction with a complex simulation process. Model previews extend the techniques of dynamic querying and query previews into the context of ad hoc simulation model exploration. A case study is presented within the domain of counter-current chromatography. The relatively novel method of insight evaluation was applied, given the exploratory nature of the task. The evaluation data show that the trade-off in accuracy is far outweighed by benefits of dynamic interaction. The number of insights gained using the enhanced interactive version of the computer model was more than six times higher than the number of insights gained using the basic version of the model. There was also a trend for dynamic interaction to facilitate insights of greater domain importance

Human and animal models for translational research on neurodegeneration: Challenges and opportunities from South America

Facing the alarming growth of dementia and neurodegenerative conditions has become a critical priority across the globe (Alzheimer´s Disease International, 2009;Lancet, 2015;Shah et al., 2016;Parra et al., 2018). Neurodegenerative diseases are the most frequent cause of dementia, representing a burden for public health systems (especially in middle and middle-high income countries). Although most research on this subject is concentrated in first-world centers, growing efforts in South American countries (SACs) are affording important breakthroughs. This emerging agenda poses not only new challenges for the region, but also new opportunities for the field at large. SACs have witnessed a promising development of relevant research in humans and animals, giving rise to new regional challenges. As highlighted in a recent experts? consensus paper Latin-American countries (LAC), and SACs in particular (Parra et al., 2018), face a critical situation. Higher demographic rates and the predicted prevalence of dementia have reached and even exceeded those of developing countries. In SACs, low- and middle-income countries (e.g., Bolivia, Paraguay), the prevalence of dementia will double that of high-income countries, while upper-middle-income countries in the region (e.g., Argentina, Brazil, Chile, Colombia, Peru, Uruguay, and Venezuela) will experience the greatest impact of dementia. The WHO estimated that the standardized prevalence of dementia in Latin America was 8.5%, but multiple SACs have been underrepresented or underestimated in such a calculation (Parra et al., 2018). Moreover, raw prevalence rates across studies are characterized by high variability within and between countries (e.g., Argentina: 8.3; Brazil: 7.1-2.0; Chile: 4.4-7.0; Colombia: 6.0; Peru: 6.72-9.3; Uruguay: 3.1; Venezuela: 5.7-13,7) (Parra et al., 2018). In addition, most of these studies are undermined by various limitations and methodological problems. Even considering these data, SACs possess the highest global prevalence of dementia after North Africa/Middle East in people above the age of 60 (Parra et al., 2018). Moreover, the harmonization of global strategies against dementia in these contexts is hindered not only by reduced epidemiological data, but also by the lack of standardized clinical practice, insufficient training of physicians, limited resources, and poor governmental support, let alone poverty and more general cultural barriers and stigmas. All of these factors have impacted the type and amount of research conducted in SACs. A regional network, based on multiinstitutional actors from research, governmental, and private sectors is fundamental to overcome these challenges (Parra et al., 2018).Fil: Ibanez Barassi, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Universidad Autónoma del Caribe; Colombia. Universidad Adolfo Ibañez; ChileFil: Sedeño, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; ArgentinaFil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; ArgentinaFil: Deacon, Robert. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Universidad de Chile; ChileFil: Cogram, Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Universidad de Chile; Chil

Topological Analysis of Metabolic Networks Integrating Co-Segregating Transcriptomes and Metabolomes in Type 2 Diabetic Rat Congenic Series

Background: The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus is caused by complex organ-specific cellular mechanisms contributing to impaired insulin secretion and insulin resistance. Methods: We used systematic metabotyping by 1H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualise shortest paths between metabolites and genes significantly associated with each genomic block. Results: Despite strong genomic similarities (95-99%) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific metabotypes (mQTL) and genome-wide expression traits (eQTL). Variation in key metabolites like glucose, succinate, lactate or 3-hydroxybutyrate, and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing shortest path length drove prioritization of biological validations by gene silencing. Conclusions: These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulations and to characterize novel functional roles for genes determining tissue-specific metabolism

Subanesthetic ketamine treatment promotes abnormal interactions between neural subsystems and alters the properties of functional brain networks

Acute treatment with subanesthetic ketamine, a non-competitive N-methyl-D-aspartic acid (NMDA) receptor antagonist, is widely utilized as a translational model for schizophrenia. However, how acute NMDA receptor blockade impacts on brain functioning at a systems level, to elicit translationally relevant symptomatology and behavioral deficits, has not yet been determined. Here, for the first time, we apply established and recently validated topological measures from network science to brain imaging data gained from ketamine-treated mice to elucidate how acute NMDA receptor blockade impacts on the properties of functional brain networks. We show that the effects of acute ketamine treatment on the global properties of these networks are divergent from those widely reported in schizophrenia. Where acute NMDA receptor blockade promotes hyperconnectivity in functional brain networks, pronounced dysconnectivity is found in schizophrenia. We also show that acute ketamine treatment increases the connectivity and importance of prefrontal and thalamic brain regions in brain networks, a finding also divergent to alterations seen in schizophrenia. In addition, we characterize how ketamine impacts on bipartite functional interactions between neural subsystems. A key feature includes the enhancement of prefrontal cortex (PFC)-neuromodulatory subsystem connectivity in ketamine-treated animals, a finding consistent with the known effects of ketamine on PFC neurotransmitter levels. Overall, our data suggest that, at a systems level, acute ketamine-induced alterations in brain network connectivity do not parallel those seen in chronic schizophrenia. Hence, the mechanisms through which acute ketamine treatment induces translationally relevant symptomatology may differ from those in chronic schizophrenia. Future effort should therefore be dedicated to resolve the conflicting observations between this putative translational model and schizophrenia

The mechanisms of tinnitus: perspectives from human functional neuroimaging

In this review, we highlight the contribution of advances in human neuroimaging to the current understanding of central mechanisms underpinning tinnitus and explain how interpretations of neuroimaging data have been guided by animal models. The primary motivation for studying the neural substrates of tinnitus in humans has been to demonstrate objectively its representation in the central auditory system and to develop a better understanding of its diverse pathophysiology and of the functional interplay between sensory, cognitive and affective systems. The ultimate goal of neuroimaging is to identify subtypes of tinnitus in order to better inform treatment strategies. The three neural mechanisms considered in this review may provide a basis for TI classification. While human neuroimaging evidence strongly implicates the central auditory system and emotional centres in TI, evidence for the precise contribution from the three mechanisms is unclear because the data are somewhat inconsistent. We consider a number of methodological issues limiting the field of human neuroimaging and recommend approaches to overcome potential inconsistency in results arising from poorly matched participants, lack of appropriate controls and low statistical power

Gene Expression Meta-Analysis Reveals Concordance in Gene Activation, Pathway, and Cell-Type Enrichment in Dermatomyositis Target Tissues.

ObjectiveWe conducted a comprehensive gene expression meta-analysis in dermatomyositis (DM) muscle and skin tissues to identify shared disease-relevant genes and pathways across tissues.MethodsSix publicly available data sets from DM muscle and two from skin were identified. Meta-analysis was performed by first processing data sets individually then cross-study normalization and merging creating tissue-specific gene expression matrices for subsequent analysis. Complementary single-gene and network analyses using Significance Analysis of Microarrays (SAM) and Weighted Gene Co-expression Network Analysis (WGCNA) were conducted to identify genes significantly associated with DM. Cell-type enrichment was performed using xCell.ResultsThere were 544 differentially expressed genes (FC ≥ 1.3, q < 0.05) in muscle and 300 in skin. There were 94 shared upregulated genes across tissues enriched in type I and II interferon (IFN) signaling and major histocompatibility complex (MHC) class I antigen-processing pathways. In a network analysis, we identified eight significant gene modules in muscle and seven in skin. The most highly correlated modules were enriched in pathways consistent with the single-gene analysis. Additional pathways uncovered by WGCNA included T-cell activation and T-cell receptor signaling. In the cell-type enrichment analysis, both tissues were highly enriched in activated dendritic cells and M1 macrophages.ConclusionThere is striking similarity in gene expression across DM target tissues with enrichment of type I and II IFN pathways, MHC class I antigen-processing, T-cell activation, and antigen-presenting cells. These results suggest IFN-γ may contribute to the global IFN signature in DM, and altered auto-antigen presentation through the class I MHC pathway may be important in disease pathogenesis

An integrated ranking algorithm for efficient information computing in social networks

Social networks have ensured the expanding disproportion between the face of WWW stored traditionally in search engine repositories and the actual ever changing face of Web. Exponential growth of web users and the ease with which they can upload contents on web highlights the need of content controls on material published on the web. As definition of search is changing, socially-enhanced interactive search methodologies are the need of the hour. Ranking is pivotal for efficient web search as the search performance mainly depends upon the ranking results. In this paper new integrated ranking model based on fused rank of web object based on popularity factor earned over only valid interlinks from multiple social forums is proposed. This model identifies relationships between web objects in separate social networks based on the object inheritance graph. Experimental study indicates the effectiveness of proposed Fusion based ranking algorithm in terms of better search results.Comment: 14 pages, International Journal on Web Service Computing (IJWSC), Vol.3, No.1, March 201

Bridging the gap between research and agile practice: an evolutionary model

There is wide acceptance in the software engineering field that industry and research can gain significantly from each other and there have been several initiatives to encourage collaboration between the two. However there are some often-quoted challenges in this kind of collaboration. For example, that the timescales of research and practice are incompatible, that research is not seen as relevant for practice, and that research demands a different kind of rigour than practice supports. These are complex challenges that are not always easy to overcome. Since the beginning of 2013 we have been using an approach designed to address some of these challenges and to bridge the gap between research and practice, specifically in the agile software development arena. So far we have collaborated successfully with three partners and have investigated three practitioner-driven challenges with agile. The model of collaboration that we adopted has evolved with the lessons learned in the first two collaborations and been modified for the third. In this paper we introduce the collaboration model, discuss how it addresses the collaboration challenges between research and practice and how it has evolved, and describe the lessons learned from our experience