Part-time hospitalisation and stigma experiences: a study in contemporary psychiatric hospitals

Background: Because numerous studies have revealed the negative consequences of stigmatisation, this study explores the determinants of stigma experiences. In particular, it examines whether or not part-time hospitalisation in contemporary psychiatric hospitals is associated with less stigma experiences than full-time hospitalisation. Methods: Survey data on 378 clients of 42 wards from 8 psychiatric hospitals are used to compare full-time clients, part-time clients and clients receiving part-time care as aftercare on three dimensions of stigma experiences, while controlling for symptoms, diagnosis and clients' background characteristics. Results: The results reveal that part-time clients without previous full-time hospitalisation report less social rejection than clients who receive full-time hospitalisation. In contrast, clients receiving part-time treatment as aftercare do not differ significantly from full-time clients concerning social rejection. No significant results for the other stigma dimensions were found. Conclusion: Concerning social rejection, immediate part-time hospitalisation could be recommended as a means of destigmatisation for clients of contemporary psychiatric hospitals

Visual parameter optimisation for biomedical image processing

Background: Biomedical image processing methods require users to optimise input parameters to ensure high quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output. Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm. Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches

Bayesian Methods for Exoplanet Science

Exoplanet research is carried out at the limits of the capabilities of current telescopes and instruments. The studied signals are weak, and often embedded in complex systematics from instrumental, telluric, and astrophysical sources. Combining repeated observations of periodic events, simultaneous observations with multiple telescopes, different observation techniques, and existing information from theory and prior research can help to disentangle the systematics from the planetary signals, and offers synergistic advantages over analysing observations separately. Bayesian inference provides a self-consistent statistical framework that addresses both the necessity for complex systematics models, and the need to combine prior information and heterogeneous observations. This chapter offers a brief introduction to Bayesian inference in the context of exoplanet research, with focus on time series analysis, and finishes with an overview of a set of freely available programming libraries.Comment: Invited revie

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior

Bayesian Multimodel Inference for Geostatistical Regression Models

The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC) method is investigated for the calculation of parameter estimates and posterior model probabilities for spatial regression models. The method can accommodate normal and non-normal response data and a large number of covariates. Thus the method is very flexible and can be used to fit spatial linear models, spatial linear mixed models, and spatial generalized linear mixed models (GLMMs). The Bayesian MCMC method also allows a priori unequal weighting of covariates, which is not possible with many model selection methods such as Akaike's information criterion (AIC). The proposed method is demonstrated on two data sets. The first is the whiptail lizard data set which has been previously analyzed by other researchers investigating model selection methods. Our results confirmed the previous analysis suggesting that sandy soil and ant abundance were strongly associated with lizard abundance. The second data set concerned pollution tolerant fish abundance in relation to several environmental factors. Results indicate that abundance is positively related to Strahler stream order and a habitat quality index. Abundance is negatively related to percent watershed disturbance

A Profile Likelihood Analysis of the Constrained MSSM with Genetic Algorithms

The Constrained Minimal Supersymmetric Standard Model (CMSSM) is one of the simplest and most widely-studied supersymmetric extensions to the standard model of particle physics. Nevertheless, current data do not sufficiently constrain the model parameters in a way completely independent of priors, statistical measures and scanning techniques. We present a new technique for scanning supersymmetric parameter spaces, optimised for frequentist profile likelihood analyses and based on Genetic Algorithms. We apply this technique to the CMSSM, taking into account existing collider and cosmological data in our global fit. We compare our method to the MultiNest algorithm, an efficient Bayesian technique, paying particular attention to the best-fit points and implications for particle masses at the LHC and dark matter searches. Our global best-fit point lies in the focus point region. We find many high-likelihood points in both the stau co-annihilation and focus point regions, including a previously neglected section of the co-annihilation region at large m_0. We show that there are many high-likelihood points in the CMSSM parameter space commonly missed by existing scanning techniques, especially at high masses. This has a significant influence on the derived confidence regions for parameters and observables, and can dramatically change the entire statistical inference of such scans.Comment: 47 pages, 8 figures; Fig. 8, Table 7 and more discussions added to Sec. 3.4.2 in response to referee's comments; accepted for publication in JHE

Radiation exposure in X-ray-based imaging techniques used in osteoporosis

Recent advances in medical X-ray imaging have enabled the development of new techniques capable of assessing not only bone quantity but also structure. This article provides (a) a brief review of the current X-ray methods used for quantitative assessment of the skeleton, (b) data on the levels of radiation exposure associated with these methods and (c) information about radiation safety issues. Radiation doses associated with dual-energy X-ray absorptiometry are very low. However, as with any X-ray imaging technique, each particular examination must always be clinically justified. When an examination is justified, the emphasis must be on dose optimisation of imaging protocols. Dose optimisation is more important for paediatric examinations because children are more vulnerable to radiation than adults. Methods based on multi-detector CT (MDCT) are associated with higher radiation doses. New 3D volumetric hip and spine quantitative computed tomography (QCT) techniques and high-resolution MDCT for evaluation of bone structure deliver doses to patients from 1 to 3 mSv. Low-dose protocols are needed to reduce radiation exposure from these methods and minimise associated health risks

Mutations in PIK3CA are infrequent in neuroblastoma

BACKGROUND: Neuroblastoma is a frequently lethal pediatric cancer in which MYCN genomic amplification is highly correlated with aggressive disease. Deregulated MYC genes require co-operative lesions to foster tumourigenesis and both direct and indirect evidence support activated Ras signaling for this purpose in many cancers. Yet Ras genes and Braf, while often activated in cancer cells, are infrequent targets for activation in neuroblastoma. Recently, the Ras effector PIK3CA was shown to be activated in diverse human cancers. We therefore assessed PIK3CA for mutation in human neuroblastomas, as well as in neuroblastomas arising in transgenic mice with MYCN overexpressed in neural-crest tissues. In this murine model we additionally surveyed for Ras family and Braf mutations as these have not been previously reported. METHODS: Sixty-nine human neuroblastomas (42 primary tumors and 27 cell lines) were sequenced for PIK3CA activating mutations within the C2, helical and kinase domain "hot spots" where 80% of mutations cluster. Constitutional DNA was sequenced in cases with confirmed alterations to assess for germline or somatic acquisition. Additionally, Ras family members (Hras1, Kras2 and Nras) and the downstream effectors Pik3ca and Braf, were sequenced from twenty-five neuroblastomas arising in neuroblastoma-prone transgenic mice. RESULTS: We identified mutations in the PIK3CA gene in 2 of 69 human neuroblastomas (2.9%). Neither mutation (R524M and E982D) has been studied to date for effects on lipid kinase activity. Though both occurred in tumors with MYCN amplification the overall rate of PIK3CA mutations in MYCN amplified and single-copy tumors did not differ appreciably (2 of 31 versus 0 of 38, respectively). Further, no activating mutations were identified in a survey of Ras signal transduction genes (including Hras1, Kras2, Nras, Pik3ca, or Braf genes) in twenty-five neuroblastic tumors arising in the MYCN-initiated transgenic mouse model. CONCLUSION: These data suggest that activating mutations in the Ras/Raf-MAPK/PI3K signaling cascades occur infrequently in neuroblastoma. Further, despite compelling evidence for MYC and RAS cooperation in vitro and in vivo to promote tumourigenesis, activation of RAS signal transduction does not constitute a preferred secondary pathway in neuroblastomas with MYCN deregulation in either human tumors or murine models

Design of the PROCON trial: a prospective, randomized multi – center study comparing cervical anterior discectomy without fusion, with fusion or with arthroplasty

BACKGROUND: PROCON was designed to assess the clinical outcome, development of adjacent disc disease and costs of cervical anterior discectomy without fusion, with fusion using a stand alone cage and implantation of a Bryan's disc prosthesis. Description of rationale and design of PROCON trial and discussion of its strengths and limitations. METHODS/DESIGN: Since proof justifying the use of implants or arthroplasty after cervical anterior discectomy is lacking, PROCON was designed. PROCON is a multicenter, randomized controlled trial comparing cervical anterior discectomy without fusion, with fusion with a stand alone cage or with implantation of a disc. The study population will be enrolled from patients with a single level cervical disc disease without myelopathic signs. Each treatment arm will need 90 patients. The patients will be followed for a minimum of five years, with visits scheduled at 6 weeks, 3 months, 12 months, and then yearly. At one year postoperatively, clinical outcome and self reported outcomes will be evaluated. At five years, the development of adjacent disc disease will be investigated. DISCUSSION: The results of this study will contribute to the discussion whether additional fusion or arthroplasty is needed and cost effective. TRIAL REGISTRATION: Current Controlled Trials ISRCTN4168184

Conservation of pattern as a tool for inference on spatial snapshots in ecological data

As climate change and other anthropogenic factors increase the uncertainty of vegetation ecosystem persistence, the ability to rapidly assess their dynamics is paramount. Vegetation and sessile communities form a variety of striking regular spatial patterns such as stripes, spots and labyrinths, that have been used as indicators of ecosystem current state, through qualitative analysis of simple models. Here we describe a new method for rigorous quantitative estimation of biological parameters from a single spatial snapshot. We formulate a synthetic likelihood through consideration of the expected change in the correlation structure of the spatial pattern. This then allows Bayesian inference to be performed on the model parameters, which includes providing parameter uncertainty. The method was validated against simulated data and then applied to real data in the form of aerial photographs of seagrass banding. The inferred parameters were found to be able to reproduce similar patterns to those observed and able to detect strength of spatial competition, competition-induced mortality and the local range of reproduction. This technique points to a way of performing rapid inference of spatial competition and ecological stability from a single spatial snapshots of sessile communities