HYDRA: Distributed Multi-Objective Optimization for Designers

Architectural design problems can be quite involved, as there is a plethora of – usually conflicting – criteria that one has to address in order to find an optimal, performative solution. Multi-Objective Optimization (MOO) techniques can thus prove very useful, as they provide solution spaces which can traverse the different trade-offs of convoluted design options. Nevertheless, they are not widely used as (a) they are computationally expensive and (b) the resulting solution space can be proven difficult to visualize and navigate, particularly when dealing with higher dimensional spaces. This paper will present a system, which merges bespoke multi-objective optimization with a parametric CAD system, enhanced by supercomputing, into a single, coherent workflow, in order to address the above issues. The system architecture ensures optimal use of existing compute resources and enables massive performance speed-up, allowing for fast review and delivery cycles. The application aims to provide architects, designers and engineers with a better understanding of the design space, aiding the decision-making process by procuring tangible data from different objectives and finally providing fit (and sometimes unforeseen) solutions to a design problem. This is primarily achieved by a graphical interface of easy to navigate solution spaces of design options, derived from their respective Pareto fronts, in the form of a web-based interactive dashboard. Since understanding high-dimensionality data is a difficult task, multivariate analysis techniques were implemented to post-process the data before displaying it to end users. Visual Data Mining (VDM) and Machine Learning (ML) techniques were incorporated to facilitate knowledge discovery and exploration of large sets of design options at an early design stage. The system is demonstrated and assessed on an applied design case study of a master-planning project, where the benefits of the process are more evident, especially due to its complexity and size

Internal hernia and volvulus of the small bowel following liver transplantation.

Internal herniation with volvulus of the small intestine is an uncommon, but potentially fatal, complication after liver transplantation. We present here four cases in which the herniation occurred around the Roux-en-Y loop used for the biliary reconstruction. One patient died due to intestinal and liver allograft necrosis; another lost almost the entire small intestine and has since undergone successful intestinal transplantation. Two patients survived following surgery that involved reduction of the hernia and closure of the mesenteric defect. Clinical diagnostic implications emphasize early diagnosis and appropriate operative intervention

Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain

Background: Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network. Methodology/Principal Findings: Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm's locomotion. Conclusions/Significance: The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available

Individualism and the extended-self: cross-cultural differences in the valuation of authentic objects

The current studies examine how valuation of authentic items varies as a function of culture. We find that U.S. respondents value authentic items associated with individual persons (a sweater or an artwork) more than Indian respondents, but that both cultures value authentic objects not associated with persons (a dinosaur bone or a moon rock) equally. These differences cannot be attributed to more general cultural differences in the value assigned to authenticity. Rather, the results support the hypothesis that individualistic cultures place a greater value on objects associated with unique persons and in so doing, offer the first evidence for how valuation of certain authentic items may vary cross-culturally

Analyzing Ideological Communities in Congressional Voting Networks

We here study the behavior of political party members aiming at identifying how ideological communities are created and evolve over time in diverse (fragmented and non-fragmented) party systems. Using public voting data of both Brazil and the US, we propose a methodology to identify and characterize ideological communities, their member polarization, and how such communities evolve over time, covering a 15-year period. Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties

REFERQUAL: A pilot study of a new service quality assessment instrument in the GP Exercise Referral scheme setting

Background The development of an instrument accurately assessing service quality in the GP Exercise Referral Scheme (ERS) industry could potentially inform scheme organisers of the factors that affect adherence rates leading to the implementation of strategic interventions aimed at reducing client drop-out. Methods A modified version of the SERVQUAL instrument was designed for use in the ERS setting and subsequently piloted amongst 27 ERS clients. Results Test re-test correlations were calculated via Pearson's 'r' or Spearman's 'rho', depending on whether the variables were Normally Distributed, to show a significant (mean r = 0.957, SD = 0.02, p < 0.05; mean rho = 0.934, SD = 0.03, p < 0.05) relationship between all items within the questionnaire. In addition, satisfactory internal consistency was demonstrated via Cronbach's 'α'. Furthermore, clients responded favourably towards the usability, wording and applicability of the instrument's items. Conclusion REFERQUAL is considered to represent promise as a suitable tool for future evaluation of service quality within the ERS community. Future research should further assess the validity and reliability of this instrument through the use of a confirmatory factor analysis to scrutinise the proposed dimensional structure

Emerging pharmacotherapy of tinnitus

Tinnitus, the perception of sound in the absence of an auditory stimulus, is perceived by about 1 in 10 adults, and for at least 1 in 100, tinnitus severely affects their quality of life. Because tinnitus is frequently associated with irritability, agitation, stress, insomnia, anxiety and depression, the social and economic burdens of tinnitus can be enormous. No curative treatments are available. However, tinnitus symptoms can be alleviated to some extent. The most widespread management therapies consist of auditory stimulation and cognitive behavioral treatment, aiming at improving habituation and coping strategies. Available clinical trials vary in methodological rigor and have been performed for a considerable number of different drugs. None of the investigated drugs have demonstrated providing replicable long-term reduction of tinnitus impact in the majority of patients in excess of placebo effects. Accordingly, there are no FDA or European Medicines Agency approved drugs for the treatment of tinnitus. However, in spite of the lack of evidence, a large variety of different compounds are prescribed off-label. Therefore, more effective pharmacotherapies for this huge and still growing market are desperately needed and even a drug that produces only a small but significant effect would have an enormous therapeutic impact. This review describes current and emerging pharmacotherapies with current difficulties and limitations. In addition, it provides an estimate of the tinnitus market. Finally, it describes recent advances in the tinnitus field which may help overcome obstacles faced in the pharmacological treatment of tinnitus. These include incomplete knowledge of tinnitus pathophysiology, lack of well-established animal models, heterogeneity of different forms of tinnitus, difficulties in tinnitus assessment and outcome measurement and variability in clinical trial methodology. © 2009 Informa UK Ltd.Fil: Langguth, Berthold. Universitat Regensburg; AlemaniaFil: Salvi, Richard. State University of New York; Estados UnidosFil: Elgoyhen, Ana Belen. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; Argentin

Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

Atypical onset of diabetes in a teenage girl: a case report

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