51 resultados para Bayesian hierarchical linear model

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.

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When tilted sideways participants misperceive the visual vertical assessed by means of a luminous line in otherwise complete dark- ness. A recent modeling approach (De Vrijer et al., 2009) claimed that these typical patterns of errors (known as A- and E-effects) could be explained by as- suming that participants behave in a Bayes optimal manner. In this study, we experimentally manipulate participants’ prior information about body-in-space orientation and measure the effect of this manipulation on the subjective visual vertical (SVV). Specifically, we explore the effects of veridical and misleading instructions about body tilt orientations on the SVV. We used a psychophys- ical 2AFC SVV task at roll tilt angles of 0 degrees, 16 degrees and 4 degrees CW and CCW. Participants were tilted to 4 degrees under different instruction conditions: in one condition, participants received veridical instructions as to their tilt angle, whereas in another condition, participants received the mis- leading instruction that their body position was perfectly upright. Our results indicate systematic differences between the instruction conditions at 4 degrees CW and CCW. Participants did not simply use an ego-centric reference frame in the misleading condition; instead, participants’ estimates of the SVV seem to lie between their head’s Z-axis and the estimate of the SVV as measured in the veridical condition. All participants displayed A-effects at roll tilt an- gles of 16 degrees CW and CCW. We discuss our results in the context of the Bayesian model by De Vrijer et al. (2009), and claim that this pattern of re- sults is consistent with a manipulation of precision of a prior distribution over body-in-space orientations. Furthermore, we introduce a Bayesian Generalized Linear Model for estimating parameters of participants’ psychometric function, which allows us to jointly estimate group level and individual level parameters under all experimental conditions simultaneously, rather than relying on the traditional two-step approach to obtaining group level parameter estimates.

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PURPOSE In patients with schizophrenia, premorbid psychosocial adjustment is an important predictor of functional outcome. We studied functional outcome in young clinical high-risk (CHR) patients and how this was predicted by their childhood to adolescence premorbid adjustment. METHODS In all, 245 young help-seeking CHR patients were assessed with the Premorbid Adjustment Scale, the Structured Interview for Prodromal Syndromes (SIPS) and the Schizophrenia Proneness Instrument (SPI-A). The SIPS assesses positive, negative, disorganised, general symptoms, and the Global Assessment of Functioning (GAF), the SPI-A self-experienced basic symptoms; they were carried out at baseline, at 9-month and 18-month follow-up. Transitions to psychosis were identified. In the hierarchical linear model, associations between premorbid adjustment, background data, symptoms, transitions to psychosis and GAF scores were analysed. RESULTS During the 18-month follow-up, GAF scores improved significantly, and the proportion of patients with poor functioning decreased from 74% to 37%. Poor premorbid adjustment, single marital status, poor work status, and symptoms were associated with low baseline GAF scores. Low GAF scores were predicted by poor premorbid adjustment, negative, positive and basic symptoms, and poor baseline work status. The association between premorbid adjustment and follow-up GAF scores remained significant, even when baseline GAF and transition to psychosis were included in the model. CONCLUSION A great majority of help-seeking CHR patients suffer from deficits in their functioning. In CHR patients, premorbid psychosocial adjustment, baseline positive, negative, basic symptoms and poor working/schooling situation predict poor short-term functional outcome. These aspects should be taken into account when acute intervention and long-term rehabilitation for improving outcome in CHR patients are carried out.

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Ecosystems are faced with high rates of species loss which has consequences for their functions and services. To assess the effects of plant species diversity on the nitrogen (N) cycle, we developed a model for monthly mean nitrate (NO3-N) concentrations in soil solution in 0-30 cm mineral soil depth using plant species and functional group richness and functional composition as drivers and assessing the effects of conversion of arable land to grassland, spatially heterogeneous soil properties, and climate. We used monthly mean NO3-N concentrations from 62 plots of a grassland plant diversity experiment from 2003 to 2006. Plant species richness (1-60) and functional group composition (1-4 functional groups: legumes, grasses, non-leguminous tall herbs, non-leguminous small herbs) were manipulated in a factorial design. Plant community composition, time since conversion from arable land to grassland, soil texture, and climate data (precipitation, soil moisture, air and soil temperature) were used to develop one general Bayesian multiple regression model for the 62 plots to allow an in-depth evaluation using the experimental design. The model simulated NO3-N concentrations with an overall Bayesian coefficient of determination of 0.48. The temporal course of NO3-N concentrations was simulated differently well for the individual plots with a maximum plot-specific Nash-Sutcliffe Efficiency of 0.57. The model shows that NO3-N concentrations decrease with species richness, but this relation reverses if more than approx. 25 % of legume species are included in the mixture. Presence of legumes increases and presence of grasses decreases NO3-N concentrations compared to mixtures containing only small and tall herbs. Altogether, our model shows that there is a strong influence of plant community composition on NO3-N concentrations.

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OBJECTIVES: Obstructive sleep apnea (OSA) can have adverse effects on cognitive functioning, mood, and cardiovascular functioning. OSA brings with it disturbances in sleep architecture, oxygenation, sympathetic nervous system function, and inflammatory processes. It is not clear which of these mechanisms is linked to the decrease in cognitive functioning. This study examined the effect of inflammatory parameters on cognitive dysfunction. MATERIALS AND METHODS: Thirty-nine patients with untreated sleep apnea were evaluated by polysomnography and completed a battery of neuropsychological tests. After the first night of evaluation in the sleep laboratory, blood samples were taken for analysis of interleukin 6, tumor necrosis factor-alpha (TNF-alpha), and soluble TNF receptor 1 (sTNF-R1). RESULTS: sTNF-R1 significantly correlated with cognitive dysfunction. In hierarchical linear regression analysis, measures of obstructive sleep apnea severity explained 5.5% of the variance in cognitive dysfunction (n.s.). After including sTNF-R1, percentage of variance explained by the full model increased more than threefold to 19.6% (F = 2.84, df = 3, 36, p = 0.05). Only sTNF-R1 had a significant individual relationship with cognitive dysfunction (beta = 0.376 t = 2.48, p = 0.02). CONCLUSIONS: sTNF-R1 as a marker of chronic inflammation may be associated with diminished neuropsychological functioning in patients with OSA.

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BACKGROUND Pathology studies have shown delayed arterial healing in culprit lesions of patients with acute coronary syndrome (ACS) compared with stable coronary artery disease (CAD) after placement of drug-eluting stents (DES). It is unknown whether similar differences exist in-vivo during long-term follow-up. Using optical coherence tomography (OCT), we assessed differences in arterial healing between patients with ACS and stable CAD five years after DES implantation. METHODS AND RESULTS A total of 88 patients comprised of 53 ACS lesions with 7864 struts and 35 stable lesions with 5298 struts were suitable for final OCT analysis five years after DES implantation. The analytical approach was based on a hierarchical Bayesian random-effects model. OCT endpoints were strut coverage, malapposition, protrusion, evaginations and cluster formation. Uncovered (1.7% vs. 0.7%, adjusted p=0.041) or protruding struts (0.50% vs. 0.13%, adjusted p=0.038) were more frequent among ACS compared with stable CAD lesions. A similar trend was observed for malapposed struts (1.33% vs. 0.45%, adj. p=0.072). Clusters of uncovered or malapposed/protruding struts were present in 34.0% of ACS and 14.1% of stable patients (adj. p=0.041). Coronary evaginations were more frequent in patients with ST-elevation myocardial infarction compared with stable CAD patients (0.16 vs. 0.13 per cross section, p=0.027). CONCLUSION Uncovered, malapposed, and protruding stent struts as well as clusters of delayed healing may be more frequent in culprit lesions of ACS compared with stable CAD patients late after DES implantation. Our observational findings suggest a differential healing response attributable to lesion characteristics of patients with ACS compared with stable CAD in-vivo.

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In the literature, contrasting effects of plant species richness on the soil water balance are reported. Our objective was to assess the effects of plant species and functional richness and functional identity on soil water contents and water fluxes in the experimental grassland of the Jena Experiment. The Jena Experiment comprises 86 plots on which plant species richness (0, 1, 2, 4, 8, 16, and 60) and functional group composition (zero to four functional groups: legumes, grasses, tall herbs, and small herbs) were manipulated in a factorial design. We recorded meteorological data and soil water contents of the 0·0–0·3 and 0·3–0·7 m soil layers and calculated actual evapotranspiration (ETa), downward flux (DF), and capillary rise with a soil water balance model for the period 2003–2007. Missing water contents were estimated with a Bayesian hierarchical model. Species richness decreased water contents in subsoil during wet soil conditions. Presence of tall herbs increased soil water contents in topsoil during dry conditions and decreased soil water contents in subsoil during wet conditions. Presence of grasses generally decreased water contents in topsoil, particularly during dry phases; increased ETa and decreased DF from topsoil; and decreased ETa from subsoil. Presence of legumes, in contrast, decreased ETa and increased DF from topsoil and increased ETa from subsoil. Species richness probably resulted in complementary water use. Specific functional groups likely affected the water balance via specific root traits (e.g. shallow dense roots of grasses and deep taproots of tall herbs) or specific shading intensity caused by functional group effects on vegetation cover. Copyright © 2013 John Wiley & Sons, Ltd.

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Temperate C3-grasslands are of high agricultural and ecological importance in Central Europe. Plant growth and consequently grassland yields depend strongly on water supply during the growing season, which is projected to change in the future. We therefore investigated the effect of summer drought on the water uptake of an intensively managed lowland and an extensively managed sub-alpine grassland in Switzerland. Summer drought was simulated by using transparent shelters. Standing above- and belowground biomass was sampled during three growing seasons. Soil and plant xylem waters were analyzed for oxygen (and hydrogen) stable isotope ratios, and the depths of plant water uptake were estimated by two different approaches: (1) linear interpolation method and (2) Bayesian calibrated mixing model. Relative to the control, aboveground biomass was reduced under drought conditions. In contrast to our expectations, lowland grassland plants subjected to summer drought were more likely (43–68 %) to rely on water in the topsoil (0–10 cm), whereas control plants relied less on the topsoil (4–37 %) and shifted to deeper soil layers (20–35 cm) during the drought period (29–48 %). Sub-alpine grassland plants did not differ significantly in uptake depth between drought and control plots during the drought period. Both approaches yielded similar results and showed that the drought treatment in the two grasslands did not induce a shift to deeper uptake depths, but rather continued or shifted water uptake to even more shallower soil depths. These findings illustrate the importance of shallow soil depths for plant performance under drought conditions.

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This study aims at assessing the skill of several climate field reconstruction techniques (CFR) to reconstruct past precipitation over continental Europe and the Mediterranean at seasonal time scales over the last two millennia from proxy records. A number of pseudoproxy experiments are performed within the virtual reality ofa regional paleoclimate simulation at 45 km resolution to analyse different aspects of reconstruction skill. Canonical Correlation Analysis (CCA), two versions of an Analog Method (AM) and Bayesian hierarchical modeling (BHM) are applied to reconstruct precipitation from a synthetic network of pseudoproxies that are contaminated with various types of noise. The skill of the derived reconstructions is assessed through comparison with precipitation simulated by the regional climate model. Unlike BHM, CCA systematically underestimates the variance. The AM can be adjusted to overcome this shortcoming, presenting an intermediate behaviour between the two aforementioned techniques. However, a trade-off between reconstruction-target correlations and reconstructed variance is the drawback of all CFR techniques. CCA (BHM) presents the largest (lowest) skill in preserving the temporal evolution, whereas the AM can be tuned to reproduce better correlation at the expense of losing variance. While BHM has been shown to perform well for temperatures, it relies heavily on prescribed spatial correlation lengths. While this assumption is valid for temperature, it is hardly warranted for precipitation. In general, none of the methods outperforms the other. All experiments agree that a dense and regularly distributed proxy network is required to reconstruct precipitation accurately, reflecting its high spatial and temporal variability. This is especially true in summer, when a specifically short de-correlation distance from the proxy location is caused by localised summertime convective precipitation events.

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The spatial context is critical when assessing present-day climate anomalies, attributing them to potential forcings and making statements regarding their frequency and severity in a long-term perspective. Recent international initiatives have expanded the number of high-quality proxy-records and developed new statistical reconstruction methods. These advances allow more rigorous regional past temperature reconstructions and, in turn, the possibility of evaluating climate models on policy-relevant, spatio-temporal scales. Here we provide a new proxy-based, annually-resolved, spatial reconstruction of the European summer (June–August) temperature fields back to 755 CE based on Bayesian hierarchical modelling (BHM), together with estimates of the European mean temperature variation since 138 BCE based on BHM and composite-plus-scaling (CPS). Our reconstructions compare well with independent instrumental and proxy-based temperature estimates, but suggest a larger amplitude in summer temperature variability than previously reported. Both CPS and BHM reconstructions indicate that the mean 20th century European summer temperature was not significantly different from some earlier centuries, including the 1st, 2nd, 8th and 10th centuries CE. The 1st century (in BHM also the 10th century) may even have been slightly warmer than the 20th century, but the difference is not statistically significant. Comparing each 50 yr period with the 1951–2000 period reveals a similar pattern. Recent summers, however, have been unusually warm in the context of the last two millennia and there are no 30 yr periods in either reconstruction that exceed the mean average European summer temperature of the last 3 decades (1986–2015 CE). A comparison with an ensemble of climate model simulations suggests that the reconstructed European summer temperature variability over the period 850–2000 CE reflects changes in both internal variability and external forcing on multi-decadal time-scales. For pan-European temperatures we find slightly better agreement between the reconstruction and the model simulations with high-end estimates for total solar irradiance. Temperature differences between the medieval period, the recent period and the Little Ice Age are larger in the reconstructions than the simulations. This may indicate inflated variability of the reconstructions, a lack of sensitivity and processes to changes in external forcing on the simulated European climate and/or an underestimation of internal variability on centennial and longer time scales.

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The present study investigated how ease of imagery influences source monitoring accuracy. Two experiments were conducted in order to examine how ease of imagery influences the probability of source confusions of perceived and imagined completions of natural symmetric shapes. The stimuli consisted of binary pictures of natural objects, namely symmetric pictures of birds, butterflies, insects, and leaves. The ease of imagery (indicating the similarity of the sources) and the discriminability (indicating the similarity of the items) of each stimulus were estimated in a pretest and included as predictors of the memory performance for these stimuli. It was found that confusion of the sources becomes more likely when the imagery process was relatively easy. However, if the different processes of source monitoring-item memory, source memory and guessing biases-are disentangled, both experiments support the assumption that the effect of decreased source memory for easily imagined stimuli is due to decision processes and misinformation at retrieval rather than encoding processes and memory retention. The data were modeled with a Bayesian hierarchical implementation of the one high threshold source monitoring model.

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BACKGROUND: Despite long-standing calls to disseminate evidence-based treatments for generalized anxiety (GAD), modest progress has been made in the study of how such treatments should be implemented. The primary objective of this study was to test three competing strategies on how to implement a cognitive behavioral treatment (CBT) for out-patients with GAD (i.e., comparison of one compensation vs. two capitalization models). METHODS: For our three-arm, single-blinded, randomized controlled trial (implementation of CBT for GAD [IMPLEMENT]), we recruited adults with GAD using advertisements in high-circulation newspapers to participate in a 14-session cognitive behavioral treatment (Mastery of your Anxiety and Worry, MAW-packet). We randomly assigned eligible patients using a full randomization procedure (1:1:1) to three different conditions of implementation: adherence priming (compensation model), which had a systematized focus on patients' individual GAD symptoms and how to compensate for these symptoms within the MAW-packet, and resource priming and supportive resource priming (capitalization model), which had systematized focuses on patients' strengths and abilities and how these strengths can be capitalized within the same packet. In the intention-to-treat population an outcome composite of primary and secondary symptoms-related self-report questionnaires was analyzed based on a hierarchical linear growth model from intake to 6-month follow-up assessment. This trial is registered at ClinicalTrials.gov (identifier: NCT02039193) and is closed to new participants. FINDINGS: From June 2012 to Nov. 2014, from 411 participants that were screened, 57 eligible participants were recruited and randomly assigned to three conditions. Forty-nine patients (86%) provided outcome data at post-assessment (14% dropout rate). All three conditions showed a highly significant reduction of symptoms over time. However, compared with the adherence priming condition, both resource priming conditions indicated faster symptom reduction. The observer ratings of a sub-sample of recorded videos (n = 100) showed that the therapists in the resource priming conditions conducted more strength-oriented interventions in comparison with the adherence priming condition. No patients died or attempted suicide. INTERPRETATION: To our knowledge, this is the first trial that focuses on capitalization and compensation models during the implementation of one prescriptive treatment packet for GAD. We have shown that GAD related symptoms were significantly faster reduced by the resource priming conditions, although the limitations of our study included a well-educated population. If replicated, our results suggest that therapists who implement a mental health treatment for GAD might profit from a systematized focus on capitalization models. FUNDING: Swiss Science National Foundation (SNSF-Nr. PZ00P1_136937/1) awarded to CF. KEYWORDS: Cognitive behavioral therapy; Evidence-based treatment; Implementation strategies; Randomized controlled trial

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Although evidence suggests that the benefits of psychodynamic treatments are sustained over time, presently it is unclear whether these sustained benefits are superior to non-psychodynamic treatments. Additionally, the extant literature comparing the sustained benefits of psychodynamic treatments compared to alternative treatments is limited with methodological shortcomings. The purpose of the current study was to conduct a rigorous test of the growth of the benefits of psychodynamic treatments relative to alternative treatments across distinct domains of change (i.e., all outcome measures, targeted outcome measures, non-targeted outcome measures, and personality outcome measures). To do so, the study employed strict inclusion criteria to identify randomized clinical trials that directly compared at least one bona fide psychodynamic treatment and one bona fide non-psychodynamic treatment. Hierarchical linear modeling (Raudenbush, Bryk, Cheong, Congdon, & du Toit, 2011) was used to longitudinally model the impact of psychodynamic treatments compared to non-psychodynamic treatments at post-treatment and to compare the growth (i.e., slope) of effects beyond treatment completion. Findings from the present meta-analysis indicated that psychodynamic treatments and non-psychodynamic treatments were equally efficacious at post-treatment and at follow-up for combined outcomes (k=20), targeted outcomes (k=19), non-targeted outcomes (k=17), and personality outcomes (k=6). Clinical implications, directions for future research, and limitations are discussed.

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The noxious stimulation response index (NSRI) is a novel anesthetic depth index ranging between 100 and 0, computed from hypnotic and opioid effect-site concentrations using a hierarchical interaction model. The authors validated the NSRI on previously published data.