The outflow ranking method for weighted directed graphs

A ranking method assigns to every weighted directed graph a (weak) ordering of the nodes. In this paper we axiomatize the ranking method that ranks the nodes according to their outflow using four independent axioms. Besides the well-known axioms of anonymity and positive responsiveness we introduce outflow monotonicity – meaning that in pairwise comparison between two nodes, a node is not doing worse in case its own outflow does not decrease and the other node’s outflow does not increase – and order preservation – meaning that adding two weighted digraphs such that the pairwise ranking between two nodes is the same in both weighted digraphs, then this is also their pairwise ranking in the ‘sum’ weighted digraph. The outflow ranking method generalizes the ranking by outdegree for directed graphs, and therefore also generalizes the ranking by Copeland score for tournaments.

Ranking of microRNA target prediction scores by Pareto front analysis

Over the past ten years, a variety of microRNA target prediction methods has been developed, and many of the methods are constantly improved and adapted to recent insights into miRNA-mRNA interactions. In a typical scenario, different methods return different rankings of putative targets, even if the ranking is reduced to selected mRNAs that are related to a specific disease or cell type. For the experimental validation it is then difficult to decide in which order to process the predicted miRNA-mRNA bindings, since each validation is a laborious task and therefore only a limited number of mRNAs can be analysed. We propose a new ranking scheme that combines ranked predictions from several methods and - unlike standard thresholding methods - utilises the concept of Pareto fronts as defined in multi-objective optimisation. In the present study, we attempt a proof of concept by applying the new ranking scheme to hsa-miR-21, hsa-miR-125b, and hsa-miR-373 and prediction scores supplied by PITA and RNAhybrid. The scores are interpreted as a two-objective optimisation problem, and the elements of the Pareto front are ranked by the STarMir score with a subsequent re-calculation of the Pareto front after removal of the top-ranked mRNA from the basic set of prediction scores. The method is evaluated on validated targets of the three miRNA, and the ranking is compared to scores from DIANA-microT and TargetScan. We observed that the new ranking method performs well and consistent, and the first validated targets are elements of Pareto fronts at a relatively early stage of the recurrent procedure. which encourages further research towards a higher-dimensional analysis of Pareto fronts. (C) 2010 Elsevier Ltd. All rights reserved.

Ranking Spatial and Temporal Hotspots for Road Safety Interventions Using a Nonlinear Aggregation Approach

In recent years, the concept of a composite performance index, brought from economic and business statistics, has gained popularity in the field of road safety. The construction of the Composite Safety Performance Index (CSPI) involves the following key steps: the selection of the most appropriate indicators to be aggregated and the method used to aggregate them.

Over the last decade, various aggregation methods for estimating the CSPI have been suggested in the literature. However, recent studies indicates that most of these methods suffer from many deficiencies at both the theoretical and operational level; these include the correlation and compensability between indicators, as well as their high “degree of freedom” which enables one to readily manipulate them to produce desired outcomes.

The purpose of this study is to introduce an alternative aggregation method for the estimation of the CSPI, which is free from the aforementioned deficiencies. In contrast with the current aggregation methods, which generally use linear combinations of road safety indicators to estimate a CSPI, the approach advocated in this study is based on non-linear combinations of indicators and can be summarized into the following two main steps: the pairwise comparison of road safety indicators and the development of marginal and composite road safety performance functions. The introduced method has been successfully applied to identify and rank temporal and spatial hotspots for Northern Ireland, using road traffic collision data recorded in the UK STATs19 database. The obtained results highlight the promising features of the proposed approach including its stability and consistency, which enables significantly reduced deficiencies associated with the current aggregation methods. Progressively, the introduced method could evolve into an intelligent support system for road safety assessment.

Iso-Quality of Service: Fairly Ranking Servers for Real-Time Data Analytics

We present a mathematically rigorous Quality-of-Service (QoS) metric which relates the achievable quality of service metric (QoS) for a real-time analytics service to the server energy cost of offering the service. Using a new iso-QoS evaluation methodology, we scale server resources to meet QoS targets and directly rank the servers in terms of their energy-efficiency and by extension cost of ownership. Our metric and method are platform-independent and enable fair comparison of datacenter compute servers with significant architectural diversity, including micro-servers. We deploy our metric and methodology to compare three servers running financial option pricing workloads on real-life market data. We find that server ranking is sensitive to data inputs and desired QoS level and that although scale-out micro-servers can be up to two times more energy-efficient than conventional heavyweight servers for the same target QoS, they are still six times less energy efficient than high-performance computational accelerators.

Select, Link and Rank: Diversified Query Expansion and Entity Ranking using Wikipedia

A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. We propose a method, Select-Link-Rank, that exploits semantic information from Wikipedia to generate diversified query expansions. SLR does collective processing of terms and Wikipedia entities in an integrated framework, simultaneously diversifying query expansions and entity recommendations. SLR starts with selecting informative terms from search results of the initial query, links them to Wikipedia entities, performs a diversity-conscious entity scoring and transfers such scoring to the term space to arrive at query expansion suggestions. Through an extensive empirical analysis and user study, we show that our method outperforms the state-of-the-art diversified query expansion and diversified entity recommendation techniques.

Conservation of substrate specificities among coronavirus main proteases.

The key enzyme in coronavirus replicase polyprotein processing is the coronavirus main protease, 3CL(pro). The substrate specificities of five coronavirus main proteases, including the prototypic enzymes from the coronavirus groups I, II and III, were characterized. Recombinant main proteases of human coronavirus (HCoV), transmissible gastroenteritis virus (TGEV), feline infectious peritonitis virus, avian infectious bronchitis virus and mouse hepatitis virus (MHV) were tested in peptide-based trans-cleavage assays. The determination of relative rate constants for a set of corresponding HCoV, TGEV and MHV 3CL(pro) cleavage sites revealed a conserved ranking of these sites. Furthermore, a synthetic peptide representing the N-terminal HCoV 3CL(pro) cleavage site was shown to be effectively hydrolysed by noncognate main proteases. The data show that the differential cleavage kinetics of sites within pp1a/pp1ab are a conserved feature of coronavirus main proteases and lead us to predict similar processing kinetics for the replicase polyproteins of all coronaviruses.

A Hybrid Evolutionary Approach to the Nurse Rostering Problem

Nurse rostering is a difficult search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimisation benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better in finding feasible solutions but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridise it with a recently proposed simulated annealing hyper-heuristic within a local search and genetic algorithm framework. The hybrid algorithm shows significant improvement over both the genetic algorithm with stochastic ranking and the simulated annealing hyper-heuristic alone. The hybrid algorithm also considerably outperforms the methods in the literature which have the previously best known results.

Survival of laboratory-reared juvenile European lobster (Homarus gammarus) from three brood sources in southwestern Norway

Experiments were carried out from June 2000 to April 2001 to compare survival of European lobster (Homarus gammarus) offspring (larvae and juveniles) from three brood sources, Kvitsøy Wild (KW), Kvitsøy Cultured (KC), and Rogaland Wild (RW), Norway. In the first set of experiments, newly hatched larvae (stage I) were raised in separate family tanks. All larvae groups survived to stage III/IV, although large variation in relative survival was observed among families within each of the three different female groups. Highest overall survival was observed for the RW group (12.8%), whereas no differences in overall survival were found between the KW (9.0%) and KC groups (9.6%). From stage III/IV, larvae from single family tank experiments were mixed in five “common garden” juvenile experiments. These lasted for 9 months, and the surviving juveniles were identified to family/female group using microsatellite DNA profiling. Significantly higher survival of the KW families (7.0%) was found compared with the KC (3.7%) and the RW families (3.2%), and differences in family ranking of relative survival values were evident between the KW and KC groups. The relative survival rate of the different groups was independent of female lobster size. An estimate based on only stage IV larvae reduced the difference in survival between the KW (11.4%) and KC (8.3%) group. The experiments provided evidence that cultured females (KC) are producing viable offspring with lower, but comparable survival to that of offspring from wild females (KW).

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

Factors influencing patients with stage 5 chronic kidney disease to opt for conservative management: a practitioner research study

Aims and objectives. This study explored decision-making experiences of patients with stage 5 chronic kidney disease when opting for conservative management of their renal failure.

Background. Dialysis is an invasive treatment, and for some older patients, there is an associated treatment burden of dialysis-related symptoms. An alternative choice is conservative management, but little is known about those who make this decision and how they are supported through the process.

Design. Qualitative practitioner research study.

Method. Data were generated from nine patients' naturally occurring clinic consultations with a renal clinical nurse specialist between May 2010 - July 2010. Interviews were transcribed verbatim and findings fed back at three multi-disciplinary meetings to check for relevance and resonance. Common themes were identified and codes applied.

Results. Patients reported age and having to travel three times a week to hospital for dialysis as reasons not to opt for treatment. Others felt well without dialysis not wanting to upset the 'status quo' or to burden loved ones. Most felt equipped to make the decision following explanation and discussion with the clinical nurse specialist in the renal clinic.

Conclusions. Patients opting for conservative management give numerous reasons for this including old age, travel limitations, feeling well without dialysis and not wanting to be a burden, but appear content with their decision. One-to-one discussions with the clinical nurse specialist appear helpful during the decision-making process presenting an opportunity for advancing nursing roles in the chronic kidney disease service.

Relevance to clinical practice. Understanding patients' reasons for refusing dialysis assists in supporting until death. There is an opportunity for developing nursing practice to meet the multi-faceted needs of this group.

Mechanical fatigue and spectrum analysis of small-satellite

Mechanical fatigue due to environmental loads and spectrum analysis due to launch loads of the primary structure of a low cost, low-earth orbit small satellite intended for earth observation missions are presented. The payload of the satellite under consideration is a precise optical unit to image the earth’s surface having a mass of 45 kg. 3-D Finite Element Model for the satellite structure is generated by applying substructure method. Modal analysis is required to determine natural frequencies of the satellite and define its mode shape. Then, ranking of mode shapes according to specific constraint is performed. Harmonic analysis at resonance frequencies with the highest ranking is done and cumulative fatigue damage analysis is performed. Spectrum analysis is performed for Small Sat structure to verify the satellite structure reliability under all dynamic random vibration loads applied during transportation and launch cases.

On the Qualitative Comparison of Decisions Having Positive and Negative Features

Making a decision is often a matter of listing and comparing positive and negative arguments. In such cases, the evaluation scale for decisions should be considered bipolar, that is, negative and positive values should be explicitly distinguished. That is what is done, for example, in Cumulative Prospect Theory. However, contrary to the latter framework that presupposes genuine numerical assessments, human agents often decide on the basis of an ordinal ranking of the pros and the cons, and by focusing on the most salient arguments. In other terms, the decision process is qualitative as well as bipolar. In this article, based on a bipolar extension of possibility theory, we define and axiomatically characterize several decision rules tailored for the joint handling of positive and negative arguments in an ordinal setting. The simplest rules can be viewed as extensions of the maximin and maximax criteria to the bipolar case, and consequently suffer from poor decisive power. More decisive rules that refine the former are also proposed. These refinements agree both with principles of efficiency and with the spirit of order-of-magnitude reasoning, that prevails in qualitative decision theory. The most refined decision rule uses leximin rankings of the pros and the cons, and the ideas of counting arguments of equal strength and cancelling pros by cons. It is shown to come down to a special case of Cumulative Prospect Theory, and to subsume the “Take the Best” heuristic studied by cognitive psychologists.

Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis

Integrating evidence from multiple domains is useful in prioritizing disease candidate genes for subsequent testing. We ranked all known human genes (n = 3819) under linkage peaks in the Irish Study of High-Density Schizophrenia Families using three different evidence domains: 1) a meta-analysis of microarray gene expression results using the Stanley Brain collection, 2) a schizophrenia protein-protein interaction network, and 3) a systematic literature search. Each gene was assigned a domain-specific p-value and ranked after evaluating the evidence within each domain. For comparison to this
ranking process, a large-scale candidate gene hypothesis was also tested by including genes with Gene Ontology terms related to neurodevelopment. Subsequently, genotypes of 3725 SNPs in 167 genes from a custom Illumina iSelect array were used to evaluate the top ranked vs. hypothesis selected genes. Seventy-three genes were both highly ranked and involved in neurodevelopment (category 1) while 42 and 52 genes were exclusive to neurodevelopment (category 2) or highly ranked (category 3), respectively. The most significant associations were observed in genes PRKG1, PRKCE, and CNTN4 but no individual SNPs were significant after correction for multiple testing. Comparison of the approaches showed an excess of significant tests using the hypothesis-driven neurodevelopment category. Random selection of similar sized genes from two independent genome-wide association studies (GWAS) of schizophrenia showed the excess was unlikely by chance. In a further meta-analysis of three GWAS datasets, four candidate SNPs reached nominal significance. Although gene ranking using integrated sources of prior information did not enrich for significant results in the current experiment, gene selection using an a priori hypothesis (neurodevelopment) was superior to random selection. As such, further development of gene ranking strategies using more carefully selected sources of information is warranted.