LittleDarwin: a Feature-Rich and Extensible Mutation Testing Framework for Large and Complex Java Systems

Mutation testing is a well-studied method for increasing the quality of a test suite. We designed LittleDarwin as a mutation testing framework able to cope with large and complex Java software systems, while still being easily extensible with new experimental components. LittleDarwin addresses two existing problems in the domain of mutation testing: having a tool able to work within an industrial setting, and yet, be open to extension for cutting edge techniques provided by academia. LittleDarwin already offers higher-order mutation, null type mutants, mutant sampling, manual mutation, and mutant subsumption analysis. There is no tool today available with all these features that is able to work with typical industrial software systems.Comment: Pre-proceedings of the 7th IPM International Conference on Fundamentals of Software Engineerin

Investigating the central executive in adult dyslexics: Evidence from phonological and visuospatial working memory performance

There is long-standing evidence for verbal working memory impairments in both children and adults with dyslexia. By contrast, spatial memory appears largely to be unimpaired. In an attempt to distinguish between phonological and central executive accounts of the impairments in working memory, a set of phonological and spatial working memory tasks was designed to investigate the key issues in working memory, task type, task demands (static, dynamic, and updating), and task complexity. Significant differences emerged between the dyslexic and nondyslexic participants on the verbal working memory tasks employed in Experiment 1, thereby providing further evidence for continuing dyslexic impairments of working memory into adulthood. The nature of the deficits suggested a problem with the phonological loop, with there being little evidence to implicate an impairment of the central executive. Due to the difficulties associated with separating verbal working memory and phonological processing, however, performance was investigated in Experiment 2 using visuospatial measures of working memory. The results of the visuospatial tasks indicated no between-group differences in static spatial memory, which requires the short-term storage of simultaneously presented information. In almost all conditions there were no between-group differences in dynamic spatial memory that demands the recall of both locationand order of stimuli presented sequentially. However, a significant impairment occurred on the dynamic task under high memory updating load, on which dyslexic adults showed nonphonological working memory deficits. In the absence of an explanation involving verbal recoding, this finding is interpreted in terms of a central executive or automaticity impairment in dyslexia

Dyslexic students have more everyday cognitive lapses.

There is a dearth of information about the everyday performance difficulties of adult dyslexic people. This study investigates the empirical support for anecdotal reports of increased vulnerability to distraction in dyslexia, using the self-report Cognitive Failures Questionnaire (CFQ). Two groups of university students, a dyslexic group and a non-dyslexic control group, were asked to complete the CFQ. The dyslexic group reported a higher frequency of everyday lapses in cognition, scoring significantly higher on a number of CFQ items. Representative problems include distractibility, over-focusing (so that relevant peripheral information is missed), and word-finding difficulties. A similar measure administered to close friends of dyslexic people, the CFQ-for-others, yielded results consistent with those of the CFQ, with major findings being that their friends considered them to be more disorganised, more distractible, and more absent-minded than normal. The results indicate clearly the continuing effects of dyslexia on cognition in adulthood and demonstrate that dyslexic impairments are not limited to "artificial" laboratory tasks or even literacy tasks but, instead, pervade everyday life

Classifying the posture and activity of ewes and lambs using accelerometers and machine learning on a commercial flock

This is the final version. Available on open access from Elsevier via the DOI in this recordData Availability: A censored version of the data is available upon request.Early decision making in commercial livestock systems is key to maximising animal welfare and production. Detailed information on an animal’s phenotype is needed to facilitate this, but can be difficult to obtain in a commercial setting. Research into the use of bio-logging on sheep to continuously monitor individual behaviour and indirectly inform health and production has seen rapid growth in recent years. Much of this research, however, has been conducted on small numbers of animals in an experimental setting and over limited time periods. Previous studies have also focused on ewes and there has been little research on the potential of bio-logging for collecting behavioural data on lambs, despite clear potential relevance for production. The present study aimed to test the feasibility of deploying accelerometers on a commercial sheep flock at a key point in the annual production cycle (lambing), to validate the viability of automated monitoring of sheep behaviour in a commercial setting. Also, we aimed to develop robust machine learning algorithms that can classify both the posture and physical activity of adult sheep and lambs. We used a Random Forest machine learning algorithm to predict: two mutually exclusive postures in ewes and lambs (standing and lying), achieving average accuracies of 83.7% and 85.9% respectively; four mutually exclusive activities in ewes (grazing, ruminating, inactive and walking), achieving an average accuracy of 70.9%; and three mutually exclusive activities in lambs (inactive, suckling, walking), achieving an average accuracy of 80.8%. These performance accuracies on large numbers of individuals afford the opportunity to provide a detailed understanding of the daily activity budget of ewes and lambs. Monitoring changes in daily patterns across the annual production cycle while capturing changes in environmental conditions such as weather, day length, terrain and management could reveal key indicator metrics that may inform production and health and provide early warning systems for key issues in commercial flocks.Biotechnology & Biological Sciences Research Council (BBSRC

Designing theoretically-informed implementation interventions

Clinical and health services research is continually producing new findings that may contribute to effective and efficient patient care. However, the transfer of research findings into practice is unpredictable and can be a slow and haphazard process. Ideally, the choice of implementation strategies would be based upon evidence from randomised controlled trials or systematic reviews of a given implementation strategy. Unfortunately, reviews of implementation strategies consistently report effectiveness some, but not all of the time; possible causes of this variation are seldom reported or measured by the investigators in the original studies. Thus, any attempts to extrapolate from study settings to the real world are hampered by a lack of understanding of the effects of key elements of individuals, interventions, and the settings in which they were trialled. The explicit use of theory offers a way of addressing these issues and has a number of advantages, such as providing: a generalisable framework within which to represent the dimensions that implementation studies address, a process by which to inform the development and delivery of interventions, a guide when evaluating, and a way to allow for an exploration of potential causal mechanisms. However, the use of theory in designing implementation interventions is methodologically challenging for a number of reasons, including choosing between theories and faithfully translating theoretical constructs into interventions. The explicit use of theory offers potential advantages in terms of facilitating a better understanding of the generalisability and replicability of implementation interventions. However, this is a relatively unexplored methodological area

Integrating personality research and animal contest theory: aggressiveness in the green swordtail <i>Xiphophorus helleri</i>

&lt;p&gt;Aggression occurs when individuals compete over limiting resources. While theoretical studies have long placed a strong emphasis on context-specificity of aggression, there is increasing recognition that consistent behavioural differences exist among individuals, and that aggressiveness may be an important component of individual personality. Though empirical studies tend to focus on one aspect or the other, we suggest there is merit in modelling both within-and among-individual variation in agonistic behaviour simultaneously. Here, we demonstrate how this can be achieved using multivariate linear mixed effect models. Using data from repeated mirror trials and dyadic interactions of male green swordtails, &lt;i&gt;Xiphophorus helleri&lt;/i&gt;, we show repeatable components of (co)variation in a suite of agonistic behaviour that is broadly consistent with a major axis of variation in aggressiveness. We also show that observed focal behaviour is dependent on opponent effects, which can themselves be repeatable but were more generally found to be context specific. In particular, our models show that within-individual variation in agonistic behaviour is explained, at least in part, by the relative size of a live opponent as predicted by contest theory. Finally, we suggest several additional applications of the multivariate models demonstrated here. These include testing the recently queried functional equivalence of alternative experimental approaches, (e. g., mirror trials, dyadic interaction tests) for assaying individual aggressiveness.&lt;/p&gt

Selective serotonin reuptake inhibitors in the treatment of generalized anxiety disorder

Selective serotonin reuptake inhibitors have proven efficacy in the treatment of panic disorder, obsessive–compulsive disorder, post-traumatic stress disorder and social anxiety disorder. Accumulating data shows that selective serotonin reuptake inhibitor treatment can also be efficacious in patients with generalized anxiety disorder. This review summarizes the findings of randomized controlled trials of selective serotonin reuptake inhibitor treatment for generalized anxiety disorder, examines the strengths and weaknesses of other therapeutic approaches and considers potential new treatments for patients with this chronic and disabling anxiety disorder

Mutations in the autoregulatory domain of β-tubulin 4a cause hereditary dystonia.

Dystonia type 4 (DYT4) was first described in a large family from Heacham in Norfolk with an autosomal dominantly inherited whispering dysphonia, generalized dystonia, and a characteristic hobby horse ataxic gait. We carried out a genetic linkage analysis in the extended DYT4 family that spanned 7 generations from England and Australia, revealing a single LOD score peak of 6.33 on chromosome 19p13.12-13. Exome sequencing in 2 cousins identified a single cosegregating mutation (p.R2G) in the β-tubulin 4a (TUBB4a) gene that was absent in a large number of controls. The mutation is highly conserved in the β-tubulin autoregulatory MREI (methionine-arginine-glutamic acid-isoleucine) domain, highly expressed in the central nervous system, and extensive in vitro work has previously demonstrated that substitutions at residue 2, specifically R2G, disrupt the autoregulatory capability of the wild-type β-tubulin peptide, affirming the role of the cytoskeleton in dystonia pathogenesis

Computational fact checking from knowledge networks

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation