Publication trends of shared decision making in 15 high impact medical journals: a full-text review with bibliometric analysis.

BACKGROUND: Shared Decision Making (SDM) is increasingly advocated as a model for medical decision making. However, there is still low use of SDM in clinical practice. High impact factor journals might represent an efficient way for its dissemination. We aimed to identify and characterize publication trends of SDM in 15 high impact medical journals. METHODS: We selected the 15 general and internal medicine journals with the highest impact factor publishing original articles, letters and editorials. We retrieved publications from 1996 to 2011 through the full-text search function on each journal website and abstracted bibliometric data. We included publications of any type containing the phrase "shared decision making" or five other variants in their abstract or full text. These were referred to as SDM publications. A polynomial Poisson regression model with logarithmic link function was used to assess the evolution across the period of the number of SDM publications according to publication characteristics. RESULTS: We identified 1285 SDM publications out of 229,179 publications in 15 journals from 1996 to 2011. The absolute number of SDM publications by journal ranged from 2 to 273 over 16 years. SDM publications increased both in absolute and relative numbers per year, from 46 (0.32% relative to all publications from the 15 journals) in 1996 to 165 (1.17%) in 2011. This growth was exponential (P < 0.01). We found fewer research publications (465, 36.2% of all SDM publications) than non-research publications, which included non-systematic reviews, letters, and editorials. The increase of research publications across time was linear. Full-text search retrieved ten times more SDM publications than a similar PubMed search (1285 vs. 119 respectively). CONCLUSION: This review in full-text showed that SDM publications increased exponentially in major medical journals from 1996 to 2011. This growth might reflect an increased dissemination of the SDM concept to the medical community.

Tracking the ghost of coevolution in specific plant-insect interactions : the case of the pollination system between the globeflower Trollius europaeus and seven european Chiastocheta flies

This study aims at understanding the evolutionary processes at work in specialized species interactions. Prom the macroevolutionary perspective, coevolution among specialized taxa was proposed to be one of the major processes generating biodiversity. We challenge this idea from the theoretical and practical perspective and through a literature review and show that the major hypotheses linking coevolutionary process with macroevolutionary patterns do not necessarily predict lineage co diversification and parallel speciation, limit¬ing the utility of the comparative phylogenenetic approach for investigating coevolution¬ary processes. We also point to the rarity of observed long-term coevolutionary dynamics among lineages and propose that coevolution rather occurs in shorter timescales, followed by ecological fitting. Prom the empirical point, we focus on the nursery pollination interaction between the European globeflower Trollius europaeus (Ranunculaceae) and its associated Chiastocheta flies (Anthomyiidae; Diptera) as a model system of evolution and maintenance of special¬ized interactions. The flies are obligate parasites of the seeds, but also pollinate the plant - it was thus proposed that both species are mutually dependent. Contrasting with the paradigm used for two decades of research on this system, we show that the female fitness component of the plant is similar in the populations with and without Chiastocheta. The plant is thus not exclusively dependent on the flies for reproduction. We discuss this result in the context of the factors responsible for the evolution of mutualistic systems. Understanding the evolution of a biological system requires understanding of its phylo- genetic context. Previous studies showed large mismatch between mtDNA phylogeny and morphological taxonomy in Chiastocheta. By using a large set of RAD-sequencing loci, we delineate the species limits that are congruent with morphology, and show that the discordance is best explained by the scenario of mitochondrial capture among fly species. Finally, we examine this system from a phylogeographic perspective, and identify the lack of congruence in spatial genetic structures of the plant and associated insects across their whole geographic range. The flies show lower numbers of spatial genetic groups than the plant, indicating that not all of the plant réfugia were shared by all the fly species or that the migration dynamics homogenized some of the groups. The incongruence in spatial genetic patterns indicates that fly migrations were largely independent from the genetic background of the plant, following rather a scenario of resource tracking, without the signature of coevolutionary process at this scale. Indeed, while the flies require the plant to survive climatic oscillations, the opposite is not true. Eventually, we show that there is no phylogenetic signal of spatial genetic structures, meaning that neither histories nor life- history traits are shared among closely related species and that species are characterized by unique trajectories of their genes. -- Cette étude vise à comprendre les processus évolutifs à l'oeuvre au sein d'interactions en¬tre espèces spécialisées. Du point de vue macroévolutif, la coévolution entre les taxons spécialisée a été considérée comme l'un des principaux processus générateur de biodiversité. Nous contestons cette idée du point de vue théorique et pratique à travers une revue de la littérature. Nous montrons que les hypothèses majeures reliant les processus coévolutifs avec les patterns de diversité au niveau macroévolutif ne prédisent pas nécessairement la co- diversification des lignées et leur spéciation parallèle, ce qui limite l'utilité de l'approche de phylogénie comparative pour étudier les processus coévolutifs . Nous rappelons également le peu d'exemples de dynamique coévolutive à long terme et proposons que la coévolution se produit plutôt dans des intervalles courts, suivis d'ajustements écologiques. Du point empirique, nous nous concentrons sur l'interaction de pollinisation entre le Trolle d'Europe Trollius europaeus (Ranunculaceae) et ses pollinisateurs associés, du genre Chiastocheta (Anthomyiidae; Diptera) en tant que système-modèle pour étudier l'évolution et le maintien des interactions spécialisées. Les mouches sont des parasites obligatoires des semences, mais pollinisent également la plante. Il a donc été proposé que les deux espèces soient mutuellement dépendantes. Contrastant avec le paradigme utilisé pendant deux décennies de recherche sur ce système, nous montrons, que la composante de fitness femelle de la plante est similaire dans les populations avec et sans Chiastocheta. La plante ne dépend donc pas exclusivement de son interaction avec les mouches pour la reproduction. Nous discutons de ce résultat dans le contexte des facteurs responsables de l'évolution des systèmes mutualistes. Comprendre l'évolution d'un système biologique nécessite la compréhension de son con- texte phylogénétique. Des études antérieures ont montré, chez Chiastocheta, de grandes disparités entre les phylogénies obtenues à partir d'ADN mitochondrial et la taxonomie basée sur les critères morphologiques. En utilisant un grand nombre de loci obtenus par RAD-sequencing, nous traçons les limites des espèces, qui concordent avec les car¬actéristiques morphologies, et montrons que la discordance s'explique en fait par un scénario de capture mitochondriale entre espèces de mouches. Enfin, nous examinons le système d'un point de vue phylogéographique, et identi¬fions les incohérences entre structurations génétiques spatiales de la plante et des insectes associés dans toute leur aire de distribution géographique. Les mouches présentent un nombre de groupes génétiques inférieur à la plante, indiquant que tous les refuges de la plante n'étaient pas partagés par toutes les espèces de mouches ou que les dynamiques migratoires ont homogénéisés certains des groupes chez les mouches. Les différences ob¬servées dans les patrons de structuration génétique spatiale indique que les migrations et dispersions des mouches ont été indépendantes du contexte génétique de la plante, et ces dernières ont été uniquement tributaires de la disponibilité des ressources, sans qu'il n'y ait de signature du processus de coévolution à cette échelle. En effet, tandis que les mouches ont besoin de la plante pour survivre aux oscillations climatiques, le contraire n'est pas exact. Finalement, nous montrons qu'il n'y a pas de signal phylogénétique des structurations génétiques spatiales chez les mouches, ce qui signifie que ni l'histoire, ni les traits d'histoire de vie ne sont partagés entre les espèces phylogénétiquement proches et que les espèces sont caractérisées par des trajectoires uniques de leurs gènes.

Bayesian Networks for the Age Classification of Living Individuals: a Study on Transition Analysis

Over the past few decades, age estimation of living persons has represented a challenging task for many forensic services worldwide. In general, the process for age estimation includes the observation of the degree of maturity reached by some physical attributes, such as dentition or several ossification centers. The estimated chronological age or the probability that an individual belongs to a meaningful class of ages is then obtained from the observed degree of maturity by means of various statistical methods. Among these methods, those developed in a Bayesian framework offer to users the possibility of coherently dealing with the uncertainty associated with age estimation and of assessing in a transparent and logical way the probability that an examined individual is younger or older than a given age threshold. Recently, a Bayesian network for age estimation has been presented in scientific literature; this kind of probabilistic graphical tool may facilitate the use of the probabilistic approach. Probabilities of interest in the network are assigned by means of transition analysis, a statistical parametric model, which links the chronological age and the degree of maturity by means of specific regression models, such as logit or probit models. Since different regression models can be employed in transition analysis, the aim of this paper is to study the influence of the model in the classification of individuals. The analysis was performed using a dataset related to the ossifications status of the medial clavicular epiphysis and results support that the classification of individuals is not dependent on the choice of the regression model.

On the longitudinal component of paraxial fields

The analysis of paraxial Gaussian beams features in most undergraduate courses in laser physics, advanced optics and photonics. These beams provide a simple model of the field generated in the resonant cavities of lasers, thus constituting a basic element for understanding laser theory. Usually, uniformly polarized beams are considered in the analytical calculations, with the electric field vibrating at normal planes to the propagation direction. However, such paraxial fields do not verify the Maxwell equations. In this paper we discuss how to overcome this apparent contradiction and evaluate the longitudinal component that any paraxial Gaussian beam should exhibit. Despite the fact that the assumption of a purely transverse paraxial field is useful and accurate, the inclusion of the above issue in the program helps students to clarify the importance of the electromagnetic nature of light, thus providing a more complete understanding of the paraxial approach.

Image quality in CT: From physical measurements to model observers.

Evaluation of image quality (IQ) in Computed Tomography (CT) is important to ensure that diagnostic questions are correctly answered, whilst keeping radiation dose to the patient as low as is reasonably possible. The assessment of individual aspects of IQ is already a key component of routine quality control of medical x-ray devices. These values together with standard dose indicators can be used to give rise to 'figures of merit' (FOM) to characterise the dose efficiency of the CT scanners operating in certain modes. The demand for clinically relevant IQ characterisation has naturally increased with the development of CT technology (detectors efficiency, image reconstruction and processing), resulting in the adaptation and evolution of assessment methods. The purpose of this review is to present the spectrum of various methods that have been used to characterise image quality in CT: from objective measurements of physical parameters to clinically task-based approaches (i.e. model observer (MO) approach) including pure human observer approach. When combined together with a dose indicator, a generalised dose efficiency index can be explored in a framework of system and patient dose optimisation. We will focus on the IQ methodologies that are required for dealing with standard reconstruction, but also for iterative reconstruction algorithms. With this concept the previously used FOM will be presented with a proposal to update them in order to make them relevant and up to date with technological progress. The MO that objectively assesses IQ for clinically relevant tasks represents the most promising method in terms of radiologist sensitivity performance and therefore of most relevance in the clinical environment.

The effect of learning on the evolution of new courtship behavior: a simulation model

The fact that individuals learn can change the relationship between genotype and phenotype in the population, and thus affect the evolutionary response to selection. Here we ask how male ability to learn from female response affects the evolution of a novel male behavioral courtship trait under pre-existing female preference (sensory drive). We assume a courtship trait which has both a genetic and a learned component, and a two-level female response to males. With individual-based simulations we show that, under this scenario, learning generally increases the strength of selection on the genetic component of the courtship trait, at least when the population genetic mean is still low. As a consequence, learning not only accelerates the evolution of the courtship trait, but also enables it when the trait is costly, which in the absence of learning results in an adaptive valley. Furthermore, learning can enable the evolution of the novel trait in the face of gene flow mediated by immigration of males that show superior attractiveness to females based on another, non-heritable trait. However, rather than increasing monotonically with the speed of learning, the effect of learning on evolution is maximized at intermediate learning rates. This model shows that, at least under some scenarios, the ability to learn can drive the evolution of mating behaviors through a process equivalent to Waddington's genetic assimilation.

Waddlia: An emerging pathogen and a model organism to study the biology of chlamydiae.

Waddlia chondrophila is an emerging pathogen associated with abortion in cattle. In humans, a growing body of evidence supports its pathogenic role in miscarriage and in respiratory tract infection. The human pathogenicity of W. chondrophila is further supported by the presence of several virulence factors including a catalase, a functional T3SS and several adhesins. Despite this medical importance, no commercial tests are available and diagnostic of this strict intracellular bacterium mainly relies on serology, PCR and immunohistochemistry. So far, the epidemiology of W. chondrophila remains largely unexplored and zoonotic, waterborne or interhuman transmission has been considered. Apart from its pathogenic role, chlamydiologists are also interested in W. chondrophila in order to better understand biological mechanisms conserved and shared with Chlamydia spp. Indeed, W. chondrophila proved to be a useful model organism to study the pathobiology of chlamydiae thanks to its rapid replication, its large size allowing precise subcellular protein localization, as well as its growth in Dictyostelium amoebae.

Bayesian Estimation of Noise

In mathematical modeling the estimation of the model parameters is one of the most common problems. The goal is to seek parameters that fit to the measurements as well as possible. There is always error in the measurements which implies uncertainty to the model estimates. In Bayesian statistics all the unknown quantities are presented as probability distributions. If there is knowledge about parameters beforehand, it can be formulated as a prior distribution. The Bays’ rule combines the prior and the measurements to posterior distribution. Mathematical models are typically nonlinear, to produce statistics for them requires efficient sampling algorithms. In this thesis both Metropolis-Hastings (MH), Adaptive Metropolis (AM) algorithms and Gibbs sampling are introduced. In the thesis different ways to present prior distributions are introduced. The main issue is in the measurement error estimation and how to obtain prior knowledge for variance or covariance. Variance and covariance sampling is combined with the algorithms above. The examples of the hyperprior models are applied to estimation of model parameters and error in an outlier case.

Statistical Analysis of an SEIR Epidemic Model

This thesis was focussed on statistical analysis methods and proposes the use of Bayesian inference to extract information contained in experimental data by estimating Ebola model parameters. The model is a system of differential equations expressing the behavior and dynamics of Ebola. Two sets of data (onset and death data) were both used to estimate parameters, which has not been done by previous researchers in (Chowell, 2004). To be able to use both data, a new version of the model has been built. Model parameters have been estimated and then used to calculate the basic reproduction number and to study the disease-free equilibrium. Estimates of the parameters were useful to determine how well the model fits the data and how good estimates were, in terms of the information they provided about the possible relationship between variables. The solution showed that Ebola model fits the observed onset data at 98.95% and the observed death data at 93.6%. Since Bayesian inference can not be performed analytically, the Markov chain Monte Carlo approach has been used to generate samples from the posterior distribution over parameters. Samples have been used to check the accuracy of the model and other characteristics of the target posteriors.

Searching for consensus in ahp-group decision making. A bayesian perspective

This paper sets out to identify the initial positions of the different decisionmakers who intervene in a group decision making process with a reducednumber of actors, and to establish possible consensus paths between theseactors. As a methodological support, it employs one of the most widely-knownmulticriteria decision techniques, namely, the Analytic Hierarchy Process(AHP). Assuming that the judgements elicited by the decision makers follow theso-called multiplicative model (Crawford and Williams, 1985; Altuzarra et al.,1997; Laininen and Hämäläinen, 2003) with log-normal errors and unknownvariance, a Bayesian approach is used in the estimation of the relative prioritiesof the alternatives being compared. These priorities, estimated by way of themedian of the posterior distribution and normalised in a distributive manner(priorities add up to one), are a clear example of compositional data that will beused in the search for consensus between the actors involved in the resolution ofthe problem through the use of Multidimensional Scaling tools

The natural hallucinogen 5-MeO-DMT, component of Ayahuasca, disrupts cortical function in rats: reversal by antipsychotic drugs

5-Methoxy-N,N-dimethyltryptamine (5-MeO-DMT) is a natural hallucinogen component of Ayahuasca, an Amazonian beverage traditionally used for ritual, religious and healing purposes that is being increasingly used for recreational purposes in US and Europe. 5MeO-DMT is of potential interest for schizophrenia research owing to its hallucinogenic properties. Two other psychotomimetic agents, phencyclidine and 2,5-dimethoxy-4-iodo-phenylisopropylamine (DOI), markedly disrupt neuronal activity and reduce the power of low frequency cortical oscillations (<4 Hz, LFCO) in rodent medial prefrontal cortex (mPFC). Here we examined the effect of 5-MeO-DMT on cortical function and its potential reversal by antipsychotic drugs. Moreover, regional brain activity was assessed by blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI). 5-MeO-DMT disrupted mPFC activity, increasing and decreasing the discharge of 51 and 35% of the recorded pyramidal neurons, and reducing (−31%) the power of LFCO. The latter effect depended on 5-HT1A and 5-HT2A receptor activation and was reversed by haloperidol, clozapine, risperidone, and the mGlu2/3 agonist LY379268. Likewise, 5-MeO-DMT decreased BOLD responses in visual cortex (V1) and mPFC. The disruption of cortical activity induced by 5-MeO-DMT resembles that produced by phencyclidine and DOI. This, together with the reversal by antipsychotic drugs, suggests that the observed cortical alterations are related to the psychotomimetic action of 5-MeO-DMT. Overall, the present model may help to understand the neurobiological basis of hallucinations and to identify new targets in antipsychotic drug development.

Towards open access networks – prototyping with the Lappeenranta model

The provision of Internet access to large numbers has traditionally been under the control of operators, who have built closed access networks for connecting customers. As the access network (i.e. the last mile to the customer) is generally the most expensive part of the network because of the vast amount of cable required, many operators have been reluctant to build access networks in rural areas. There are problems also in urban areas, as incumbent operators may use various tactics to make it difficult for competitors to enter the market. Open access networking, where the goal is to connect multiple operators and other types of service providers to a shared network, changes the way in which networks are used. This change in network structure dismantles vertical integration in service provision and enables true competition as no service provider can prevent others fromcompeting in the open access network. This thesis describes the development from traditional closed access networks towards open access networking and analyses different types of open access solution. The thesis introduces a new open access network approach (The Lappeenranta Model) in greater detail. The Lappeenranta Model is compared to other types of open access networks. The thesis shows that end users and service providers see local open access and services as beneficial. In addition, the thesis discusses open access networking in a multidisciplinary fashion, focusing on the real-world challenges of open access networks.

Comparison of a deterministic and a data driven model to describe MBR fouling

Membrane bioreactors (MBRs) are a combination of activated sludge bioreactors and membrane filtration, enabling high quality effluent with a small footprint. However, they can be beset by fouling, which causes an increase in transmembrane pressure (TMP). Modelling and simulation of changes in TMP could be useful to describe fouling through the identification of the most relevant operating conditions. Using experimental data from a MBR pilot plant operated for 462days, two different models were developed: a deterministic model using activated sludge model n°2d (ASM2d) for the biological component and a resistance in-series model for the filtration component as well as a data-driven model based on multivariable regressions. Once validated, these models were used to describe membrane fouling (as changes in TMP over time) under different operating conditions. The deterministic model performed better at higher temperatures (>20°C), constant operating conditions (DO set-point, membrane air-flow, pH and ORP), and high mixed liquor suspended solids (>6.9gL-1) and flux changes. At low pH (<7) or periods with higher pH changes, the data-driven model was more accurate. Changes in the DO set-point of the aerobic reactor that affected the TMP were also better described by the data-driven model. By combining the use of both models, a better description of fouling can be achieved under different operating conditions

Analysis of patterns in mining time series using Qlucore

Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.