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.

An exploration of seniors’ motivation to use mobile brain-exercise software

This article reports on the development of an iPhone-based brain-exercise tool for seniors involving a series of focus groups (FGs) and field trials (FTs). Four FGs with 34 participants were conducted aimed at understanding the underlying motivational and de-motivational factors influencing seniors’ engagement with mobile brain-exercise software. As part of the FGs, participants had approximately 40 minutes hands-on experience with commercially available brain-exercise software. A content analysis was conducted on the data resulting in a ranking of 19 motivational factors, of which the top three were challenge, usefulness and familiarity and 15 de-motivational factors, of which the top-three were usability issues, poor communication and games that were too fast. Findings were used to inform the design of three prototype brain-exercise games for the iPhone contained within one overall application, named Brain jog. Subsequently, two FTs were conducted using Brain jog to investigate the part that time exposure has to play in shaping the factors influencing engagement. New factors arose with respect to the initial FGs including the motivational factor feedback and the de-motivational factor boring. The results of this research provide valuable guidelines for the design and evaluation of mobile brain-exercise software for seniors.

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.

The challenge to health professionals when carers resist truth telling at the end of life: a qualitative secondary analysis

Aims and objectives: To draw out the similar complexities faced by staff around
truth-telling in a children’s and adult population and to interrogate the dilemmas faced by staff when informal carers act to block truth-telling.

Background: Policy encourages normalisation of death, but carers may act to protect or prevent the patient from being told the truth. Little is known about the impact on staff.

Design: Secondary analysis of data using a supra-analysis design to identify commonality of experiences.

Methods: Secondary ‘supra-analysis’ was used to transcend the focus of two primary studies in the UK, which examined staff perspectives in a palliative children’s and a palliative adult setting, respectively. The analysis examined new theoretical questions relating to the commonality of issues independently derived in each primary study. Both primary studies used focus groups. Existing empirical data were analysed thematically and compared across the studies.

Results: Staff reported a hiding of the truth by carers and sustained use of activities aimed at prolonging life. Carers frequently ignored the advance of end of life, and divergence between staff and carer approaches to truth-telling challenged professionals. Not being truthful with patients had a deleterious effect on staff, causing anger and feelings of incompetence.

Conclusions: Both children’s and adult specialist palliative care staff found themselves caught in a dilemma, subject to policies that promoted openness in planning for death and informal carers who often prevented them from being truthful with patients about terminal prognosis. This dilemma had adverse psychological effects upon many staff.

Relevance to clinical practice: There remains a powerful death-denying culture in
many societies, and carers of dying patients may prevent staff from being truthful with their patients. The current situation is not ideal, and open discussion of this problem is the essential first step in finding a solution.

Individual Characteristics Associated with Mismatches between Self-Reported and Accelerometer-Measured Physical Activity

Background: Accurate assessment tools are required for the surveillance of physical activity (PA) levels and the assessment of the effect of interventions. In addition, increasing awareness of PA is often used as the first step in pragmatic behavioural interventions, as discrepancies between the amount of activity an individual perceives they do and the amount actually undertaken may act as a barrier to change. Previous research has demonstrated differences in the amount of activity individuals report doing, compared to their level of physical activity when measured with an accelerometer. Understanding the characteristics of those whose PA level is ranked differently when measured with either self-report or accelerometry is important as it may inform the choice of instrument for future research. The aim of this project was to determine which individual characteristics are associated with differences between self-reported and accelerometer measured physical activity.

Methods: Participant data from the 2009 wave of the Commuting and Health in Cambridge study were used. Quartiles of self-reported and accelerometer measured PA were derived by ranking each measure from lowest to highest. These quartiles were compared to determine whether individuals’ physical activity was ranked higher by either method. Multinomial logistic regression models were used to investigate the individual characteristics associated with different categories of mismatch.

Results: Data from 486 participants (70% female) were included in the analysis. In adjusted analyses, the physical activity of overweight or obese individuals was significantly more likely to be ranked higher by self-report than by accelerometer than that of normal-weight individuals (OR = 2.07, 95%CI = 1.28–3.34), particularly among women (OR = 3.97, 95%CI = 2.11–7.47).

Conclusions: There was a greater likelihood of mismatch between self-reported and accelerometer measured physical activity levels in overweight or obese adults. Future studies in overweight or obese adults should consider employing both methods of measurement.

Toward Unconstrained Ear Recognition From Two-Dimensional Images

Ear recognition, as a biometric, has several advantages. In particular, ears can be measured remotely and are also relatively static in size and structure for each individual. Unfortunately, at present, good recognition rates require controlled conditions. For commercial use, these systems need to be much more robust. In particular, ears have to be recognized from different angles ( poses), under different lighting conditions, and with different cameras. It must also be possible to distinguish ears from background clutter and identify them when partly occluded by hair, hats, or other objects. The purpose of this paper is to suggest how progress toward such robustness might be achieved through a technique that improves ear registration. The approach focuses on 2-D images, treating the ear as a planar surface that is registered to a gallery using a homography transform calculated from scale-invariant feature-transform feature matches. The feature matches reduce the gallery size and enable a precise ranking using a simple 2-D distance algorithm. Analysis on a range of data sets demonstrates the technique to be robust to background clutter, viewing angles up to +/- 13 degrees, and up to 18% occlusion. In addition, recognition remains accurate with masked ear images as small as 20 x 35 pixels.

Robust 2D Ear Registration and Recognition Based on SIFT Point Matching

Significant recent progress has shown ear recognition to be a viable biometric. Good recognition rates have been demonstrated under controlled conditions, using manual registration or with specialised equipment. This paper describes a new technique which improves the robustness of ear registration and recognition, addressing issues of pose variation, background clutter and occlusion. By treating the ear as a planar surface and creating a homography transform using SIFT feature matches, ears can be registered accurately. The feature matches reduce the gallery size and enable a precise ranking using a simple 2D distance algorithm. When applied to the XM2VTS database it gives results comparable to PCA with manual registration. Further analysis on more challenging datasets demonstrates the technique to be robust to background clutter, viewing angles up to +/- 13 degrees and with over 20% occlusion.

Population Mobility Dynamics Estimated from Mobile Telephony Data

In the last decade, mobile phones and mobile devices using mobile cellular telecommunication network connections have become ubiquitous. In several developed countries, the penetration of such devices has surpassed 100 percent. They facilitate communication and access to large quantities of data without the requirement of a fixed location or connection. Assuming mobile phones usually are in close proximity with the user, their cellular activities and locations are indicative of the user's activities and movements. As such, those cellular devices may be considered as a large scale distributed human activity sensing platform. This paper uses mobile operator telephony data to visualize the regional flows of people across the Republic of Ireland. In addition, the use of modified Markov chains for the ranking of significant regions of interest to mobile subscribers is investigated. Methodology is then presented which demonstrates how the ranking of significant regions of interest may be used to estimate national population, results of which are found to have strong correlation with census data.