Investigating the relationship between competence and patient outcome with CBT

Little is understood about the relationship between therapist competence and the outcome of patients treated for common mental health disorders. Understanding the relationship between competence and patient outcome is of fundamental importance to the dissemination and implementation of Cognitive Behavioural Therapy (CBT). The current study extends existing literature by exploring the relationship between CBT competence and patient outcome in routine clinical practice within the framework of the British Government’s Improving Access to Psychological Therapies (IAPT) programme. Participants comprised 43 therapists treating 1247 patients over a training period of one year. Results found little support of a general association between CBT competence and patient outcome; however significantly more patients of the most competent therapists demonstrated a reliable improvement in their symptoms of anxiety than would be expected by chance alone, and fewer experienced no reliable change. Conversely, significantly more patients treated by the least competent therapists experienced a reliable deterioration in their symptoms than would be expected. The implications of these results for the dissemination and implementation of CBT are discussed.

Is it significant? Guidelines for reporting BCI performance

Recent growth in brain-computer interface (BCI) research has increased pressure to report improved performance. However, different research groups report performance in different ways. Hence, it is essential that evaluation procedures are valid and reported in sufficient detail. In this chapter we give an overview of available performance measures such as classification accuracy, cohen’s kappa, information transfer rate (ITR), and written symbol rate. We show how to distinguish results from chance level using confidence intervals for accuracy or kappa. Furthermore, we point out common pitfalls when moving from offline to online analysis and provide a guide on how to conduct statistical tests on (BCI) results.

Recognition, assessment and treatment of social anxiety disorder: summary of NICE guidance

Social anxiety disorder is one of the most persistent and common of the anxiety disorders, with lifetime prevalence rates in Europe of 6.7% (range 3.9-13.7%).1 It often coexists with depression, substance use disorder, generalised anxiety disorder, panic disorder, and post-traumatic stress disorder.2 It can severely impair a person’s daily functioning by impeding the formation of relationships, reducing quality of life, and negatively affecting performance at work or school. Despite this, and the fact that effective treatments exist, only about half of people with this condition seek treatment, many after waiting 10-15 years.3 Although about 40% of those who develop the condition in childhood or adolescence recover before adulthood,4 for many the disorder persists into adulthood, with the chance of spontaneous recovery then limited compared with other mental health problems. This article summarises the most recent recommendations from the National Institute for Health and Care Excellence (NICE) on recognising, assessing, and treating social anxiety disorder in children, young people, and adults.5

Factors affecting dairy farmers' attitudes towards antimicrobial medicine usage in cattle in England and Wales

There has been growing concern about bacterial resistance to antimicrobials in the farmed livestock sector. Attention has turned to sub-optimal use of antimicrobials as a driver of resistance. Recent reviews have identified a lack of data on the pattern of antimicrobial use as an impediment to the design of measures to tackle this growing problem. This paper reports on a study that explored use of antibiotics by dairy farmers and factors influencing their decision-making around this usage. We found that respondents had either recently reduced their use of antibiotics, or planned to do so. Advice from their veterinarian was instrumental in this. Over 70% thought reducing antibiotic usage would be a good thing to do. The most influential source of information used was their own veterinarian. Some 50% were unaware of the available guidelines on use in cattle production. However, 97% thought it important to keep treatment records. The Theory of Planned Behaviour was used to identify dairy farmers’ drivers and barriers to reduce use of antibiotics. Intention to reduce usage was weakly correlated with current and past practice of antibiotic use, whilst the strongest driver was respondents’ belief that their social and advisory network would approve of them doing this. The higher the proportion of income from milk production and the greater the chance of remaining in milk production, the significantly higher the likelihood of farmers exhibiting positive intention to reduce antibiotic usage. Such farmers may be more commercially minded than others and thus more cost-conscious or, perhaps, more aware of possible future restrictions. Strong correlation was found between farmers’ perception of their social referents’ beliefs and farmers’ intent to reduce antibiotic use. Policy makers should target these social referents, especially veterinarians, with information on the benefits from, and the means to, achieving reductions in antibiotic usage. Information on sub-optimal use of antibiotics as a driver of resistance in dairy herds and in humans along with advice on best farm practice to minimise risk of disease and ensure animal welfare, complemented with data on potential cost savings from reduced antibiotic use would help improve poor practice.

Tales of the unexpected: research discoveries that occurred by accident

Data from four experimental research projects are presented which have in common that unexpected results caused a change in direction of the research. A plant growth accelerator caused the appearance of white black bean aphids, a synthetic pyrethroid suspected of enhancing aphid reproduction proved to enhance plant growth, a chance conversation with a colleague initiated a search for fungal DNA in aphids, and the accidental invasion of aphid cultures by a parasitoid reversed the aphid population ranking of two Brussels sprout cultivars. This last result led to a whole series of studies on the plant odour preferences of emerging parasitoids which in turn revealed the unexpected phenomenon that chemical cues to the maternal host plant are left with the eggs at oviposition. It is pointed out that, too often, researchers fail to follow up unexpected results because they resist accepting flaws in their hypotheses; also that current application criteria for research funding make it hard to accommodate unexpected findings.

Interacting effects of climate change and habitat fragmentation on drought-sensitive butterflies

Climate change is expected to increase the frequency of some climatic extremes. These may have drastic impacts on biodiversity, particularly if meteorological thresholds are crossed, leading to population collapses. Should this occur repeatedly, populations may be unable to recover, resulting in local extinctions. Comprehensive time series data on butterflies in Great Britain provide a rare opportunity to quantify population responses to both past severe drought and the interaction with habitat area and fragmentation. Here, we combine this knowledge with future projections from multiple climate models, for different Representative Concentration Pathways (RCPs), and for simultaneous modelled responses to different landscape characteristics. Under RCP8.5, which is associated with ‘business as usual’ emissions, widespread drought-sensitive butterfly population extinctions could occur as early as 2050. However, by managing landscapes and particularly reducing habitat fragmentation, the probability of persistence until mid-century improves from around zero to between 6 and 42% (95% confidence interval). Achieving persistence with a greater than 50% chance and right through to 2100 is possible only under both low climate change (RCP2.6) and semi-natural habitat restoration. Our data show that, for these drought-sensitive butterflies, persistence is achieved more effectively by restoring semi-natural landscapes to reduce fragmentation, rather than simply focusing on increasing habitat area, but this will only be successful in combination with substantial emission reductions.

An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data.

A low mortality, high morbidity reduced intensity status epilepticus (RISE) model of epilepsy and epileptogenesis in the rat

Animal models of acquired epilepsies aim to provide researchers with tools for use in understanding the processes underlying the acquisition, development and establishment of the disorder. Typically, following a systemic or local insult, vulnerable brain regions undergo a process leading to the development, over time, of spontaneous recurrent seizures. Many such models make use of a period of intense seizure activity or status epilepticus, and this may be associated with high mortality and/or global damage to large areas of the brain. These undesirable elements have driven improvements in the design of chronic epilepsy models, for example the lithium-pilocarpine epileptogenesis model. Here, we present an optimised model of chronic epilepsy that reduces mortality to 1% whilst retaining features of high epileptogenicity and development of spontaneous seizures. Using local field potential recordings from hippocampus in vitro as a probe, we show that the model does not result in significant loss of neuronal network function in area CA3 and, instead, subtle alterations in network dynamics appear during a process of epileptogenesis, which eventually leads to a chronic seizure state. The model’s features of very low mortality and high morbidity in the absence of global neuronal damage offer the chance to explore the processes underlying epileptogenesis in detail, in a population of animals not defined by their resistance to seizures, whilst acknowledging and being driven by the 3Rs (Replacement, Refinement and Reduction of animal use in scientific procedures) principles.