A client focused perspective of the effectiveness of Counselling for Depression (CfD)

Background In the UK, only one in four people with a diagnosis of depression receive any form of treatment. To address this, the Improving Access to Psychological Therapies (IAPT) programme was established with the main therapeutic approach being cognitive behavioural therapy (CBT). This raised concern regarding client choice, prompting the development of a new evidence-based manualised therapy, namely ‘Counselling for Depression’ [CfD]. To date, the client's view of the effectiveness of CfD has not been researched. Aims The aims of this study were twofold: (1) to explore and evaluate CfD from the perspective of the client and (2) to inform the counselling profession of the client's perception of what is occurring within this therapeutic approach. Methodology This qualitative study used Interpretative Phenomenological Analysis, the ideographic aspect valuing each individual narrative and the contribution it makes towards a larger account of the phenomenon from a small group of people. Twelve participants receiving CfD completed a Helpful Aspects of Therapy questionnaire after each counselling session, with ten participating in a semi-structured interview post counselling. Findings Four superordinate themes were identified: A helpful process, Client's view of a counsellor, Gains and Negative aspects. Participants perceived this model of therapy as helpful, feeling understood by their counsellors and able to work through issues in a safe therapeutic relationship. Negative findings related to counselling being ‘hard work’ and a dislike of the time limitation that curtailed the work. Implications Participants believed this type of counselling met their needs, reassuring practitioners that CfD is helpful to their clients

Agile methods for agile universities

We explore a term, Agile, that is being used in various workplace settings, including the management of universities. The term may have several related but slightly different meanings. Agile is often used in the context of facilitating more creative problem-solving and advocating for the adoption, design, tailoring and continual updating of more innovative organizational processes. We consider a particular set of meanings of the term from the world of software development. Agile methods were created to address certain problems with the software development process. Many of those problems have interesting analogues in the context of universities, so a reflection on agile methods may be a useful heuristic for generating ideas for enabling universities to be more creative

Correcting the Bias of Empirical Frequency Parameter Estimators in Codon Models

Markov models of codon substitution are powerful inferential tools for studying biological processes such as natural selection and preferences in amino acid substitution. The equilibrium character distributions of these models are almost always estimated using nucleotide frequencies observed in a sequence alignment, primarily as a matter of historical convention. In this note, we demonstrate that a popular class of such estimators are biased, and that this bias has an adverse effect on goodness of fit and estimates of substitution rates. We propose a “corrected” empirical estimator that begins with observed nucleotide counts, but accounts for the nucleotide composition of stop codons. We show via simulation that the corrected estimates outperform the de facto standard estimates not just by providing better estimates of the frequencies themselves, but also by leading to improved estimation of other parameters in the evolutionary models. On a curated collection of sequence alignments, our estimators show a significant improvement in goodness of fit compared to the approach. Maximum likelihood estimation of the frequency parameters appears to be warranted in many cases, albeit at a greater computational cost. Our results demonstrate that there is little justification, either statistical or computational, for continued use of the -style estimators

Limit on Continuous Neutrino Emission from Neutron Stars

The timing data of the binary pulsar PSR1913+16, are used to establish an upper limit on the rate of continuous neutrino emission from neutron stars. Neutrino emission from each of the neutron stars of the binary system, increases the star binding energy and thus translates to a decrease in their masses. This in turn implies an increase with time of the binary period. Using the pulsar data we obtain an upper limit on the allowed rate of mass reduction : $| \dot{M}| <1.1 \times 10^{-12} yr^{-1} M$, where $M$ is the total mass of the binary. This constrains exotic nuclear equations of state that predict continuous neutrino emissions. The limit applies also to other channels of energy loss, e.g. axion emission. Continued timing measurements of additional binary pulsars, should yield a stronger limit in the future.Comment: 5 pages, Added a section on energy transport in the neutron star, JHEP publishe

Modeling HIV-1 Drug Resistance as Episodic Directional Selection

The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance

HIV-Specific Probabilistic Models of Protein Evolution

Comparative sequence analyses, including such fundamental bioinformatics techniques as similarity searching, sequence alignment and phylogenetic inference, have become a mainstay for researchers studying type 1 Human Immunodeficiency Virus (HIV-1) genome structure and evolution. Implicit in comparative analyses is an underlying model of evolution, and the chosen model can significantly affect the results. In general, evolutionary models describe the probabilities of replacing one amino acid character with another over a period of time. Most widely used evolutionary models for protein sequences have been derived from curated alignments of hundreds of proteins, usually based on mammalian genomes. It is unclear to what extent these empirical models are generalizable to a very different organism, such as HIV-1–the most extensively sequenced organism in existence. We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution. Our procedure pools the information from multiple sequence alignments, and provided software implementation can be run efficiently in parallel on a computer cluster. We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches. Our models had a consistently superior fit relative to the best existing models and to parameter-rich data-driven models when benchmarked on independent HIV-1 alignments, demonstrating evolutionary biases in amino-acid substitution that are unique to HIV, and that are not captured by the existing models. The scoring matrices derived from the models showed a marked difference from common amino-acid scoring matrices. The use of an appropriate evolutionary model recovered a known viral transmission history, whereas a poorly chosen model introduced phylogenetic error. We argue that our model derivation procedure is immediately applicable to other organisms with extensive sequence data available, such as Hepatitis C and Influenza A viruses

CodonTest: Modeling Amino Acid Substitution Preferences in Coding Sequences

Codon models of evolution have facilitated the interpretation of selective forces operating on genomes. These models, however, assume a single rate of non-synonymous substitution irrespective of the nature of amino acids being exchanged. Recent developments have shown that models which allow for amino acid pairs to have independent rates of substitution offer improved fit over single rate models. However, these approaches have been limited by the necessity for large alignments in their estimation. An alternative approach is to assume that substitution rates between amino acid pairs can be subdivided into rate classes, dependent on the information content of the alignment. However, given the combinatorially large number of such models, an efficient model search strategy is needed. Here we develop a Genetic Algorithm (GA) method for the estimation of such models. A GA is used to assign amino acid substitution pairs to a series of rate classes, where is estimated from the alignment. Other parameters of the phylogenetic Markov model, including substitution rates, character frequencies and branch lengths are estimated using standard maximum likelihood optimization procedures. We apply the GA to empirical alignments and show improved model fit over existing models of codon evolution. Our results suggest that current models are poor approximations of protein evolution and thus gene and organism specific multi-rate models that incorporate amino acid substitution biases are preferred. We further anticipate that the clustering of amino acid substitution rates into classes will be biologically informative, such that genes with similar functions exhibit similar clustering, and hence this clustering will be useful for the evolutionary fingerprinting of genes

Three-Dimensional Radiofrequency Tissue Tightening: A Proposed Mechanism and Applications for Body Contouring

The use of radiofrequency energy to produce collagen matrix contraction is presented. Controlling the depth of energy delivery, the power applied, the target skin temperature, and the duration of application of energy at various soft tissue levels produces soft tissue contraction, which is measurable. This technology allows precise soft tissue modeling at multiple levels to enhance the result achieved over traditional suction-assisted lipectomy as well as other forms of energy such as ultrasonic and laser-generated lipolysis

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies

<p>Abstract</p> <p>Background</p> <p>Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta.</p> <p>Methods</p> <p>Daily measures of twelve ambient air pollutants were analyzed: NO₂, NO_x, O₃, SO₂, CO, PM₁₀mass, PM_2.5mass, and PM_2.5components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits.</p> <p>Results</p> <p>Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed.</p> <p>Conclusions</p> <p>For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.</p