Empirical evidence of nonlinearity in bottom up effect in a marine predator-prey system.

The strength of species interactions may have profound effects on population dynamics. Empirical estimates of interaction strength are often based on the assumption that the interaction strengths are constant. Barents Sea (BS) cod and capelin are two fish populations for which such an interaction has been acknowledged and used, under the assumption of constant interaction strength, when studying their population dynamics. However, species interactions can often be nonlinear in marine ecosystems and might profoundly change our understanding of food chains. Analysing long-term time series data comprising a survey over 37 years in the Arcto-boreal BS, using a state-space modelling framework, we demonstrate that the effect of capelin on cod is not linear but shifts depending on capelin abundance: while capelin is beneficial for cod populations at high abundance; below the threshold, it becomes less important for cod. Our analysis therefore shows the importance of investigating nonlinearity in species interactions and may contribute to an improved understanding on species assemblages.

A Bayesian Method for Dynamic Origin-Destination Demand Estimation Synthesizing Multiple Sources of Data.

In this paper a Bayesian method is proposed to estimate dynamic origin-destination (O-D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O-D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O-D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O-D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O-D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen-Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O-D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O-D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and "true" O-D demands is relatively small, and the O-D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O-D demands with fine accuracy.

Nonlinearity in interspecific interactions in response to climate change: Cod and haddock as an example.

Climate change has profound ecological effects, yet our understanding of how trophic interactions among species are affected by climate change is still patchy. The sympatric Atlantic haddock and cod are co-occurring across the North Atlantic. They compete for food at younger stages and thereafter the former is preyed by the latter. Climate change might affect the interaction and coexistence of these two species. Particularly, the increase in sea temperature (ST) has been shown to affect distribution, population growth and trophic interactions in marine systems. We used 33-year long time series of haddock and cod abundances estimates from two data sources (acoustic and trawl survey) to analyse the dynamic effect of climate on the coexistence of these two sympatric species in the Arcto-Boreal Barents Sea. Using a Bayesian state-space threshold model, we demonstrated that long-term climate variation, as expressed by changes of ST, affected species demography through different influences on density-independent processes. The interaction between cod and haddock has shifted in the last two decades due to an increase in ST, altering the equilibrium abundances and the dynamics of the system. During warm years (ST over ca. 4°C), the increase in the cod abundance negatively affected haddock abundance while it did not during cold years. This change in interactions therefore changed the equilibrium population size with a higher population size during warm years. Our analyses show that long-term climate change in the Arcto-Boreal system can generate differences in the equilibrium conditions of species assemblages.

A co-alteration parceling of the cingulate cortex.

The cingulate cortex is known to be a complex structure, involved in several cognitive and emotional functions, as well as being altered by a variety of brain disorders. This heterogeneity is reflected in the multiple parceling models proposed in the literature. At the present, sub-regions of the cingulate cortex had been identified taking into account functional and structural connectivity, as well as cytological and electrochemical properties. In the present work, we propose an innovative node-wise parceling approach based on meta-analytic Bayesian co-alteration. To this aim, 193 case-control voxel-based morphometry experiments were analyzed, and the Patel's κ index was used to assess probability of morphometric co-alteration between nodes placed in the cingulate cortex and in the rest of the brain. Hierarchical clustering was then applied to identify nodes in the cingulate cortex exhibiting a similar pattern of whole-brain co-alteration. The obtained dendrogram highlighted a robust fronto-parietal cluster compatible with the default mode network, and being supported by the interplay between the retrosplenial cortex and the anterior and posterior cingulate cortex, rarely described in the literature. This ensemble was further confirmed by the analysis of functional patterns. Leveraging on co-alteration to investigate cortical organization could, therefore, allow to combine multimodal information, resolving conflicting results sometimes coming from the separate use of singular modalities. Crucially, this provides a valuable way to understand the pathological brain using data driven, whole-brain informed and context-specific evidence in a way not yet explored in the field.

Bayesian analysis of Ag thin films formation.

A detailed numerical study of the formation of metallic silver thin films ranged from 8 up to 50 nm on thickness is presented. The topography of these films was imaged by Atomic Force Microscopy, and starting from these images, some surface parameters were obtained. We characterized the root mean square roughness evolution by a simple power-law model with a coefficient α=0.74±0.01 consistent with the theoretical results of Family and Vicsek (1985), Family (1990). Additionally, we considered different models to describe the distributions of the grains' heights and sizes, and analyzed them via Bayesian statistics and a Markov Chain Monte Carlo numerical method. This Bayesian analysis has been significantly helpful in this work for allowing the study of the models that represent our data best and considering the experimental errors as instrumental data. The results of this analysis suggest an individual grains' growth followed by a collapse between neighboring grains.

A Bayesian control chart based on the beta distribution for monitoring the two-dimensional gamma index pass rate in the context of patient-specific quality assurance.

PURPOSE: In the context of quality assurance in intensity modulated radiation therapy (IMRT), the aim of this work was two-fold: (a) to show that the beta distribution characterizes the two-dimensional gamma index pass rate (GIPR), and that the quantiles of the distribution should be used in order to compute the control limit (CL) for the detection of abnormally low GIPR, and (b) to introduce a Bayesian control chart that allows calculation of CLs from the first measurement. METHODS: In order to enable monitoring of the GIPR from the first measurement, we developed a Bayesian control chart based on the beta distribution, elaborated according to the following two steps: (a) an iterative bayesian inference approach without any prior information on the GIPR distribution was used at the start of monitoring and the CL was progressively updated; and (b) when sufficient in-control arcs had been recorded and the estimators of the parameters of the beta distribution were sufficiently accurate, the CL of the chart was fixed to a constant value corresponding to the quantile of the beta distribution. The clinical utility of this approach is illustrated through a real data case study: monitoring the GIPR of patients treated with a moving gantry IMRT technique RapidArcTM on a Novalis TrueBeam STx (Varian Medical Systems) linear accelerator equipped with an aS1200 electronic portal imager device. RESULTS: We showed that some commonly used distributions for monitoring GIPR in the literature, such as normal or logarithm transformation, are not appropriate. We compared the CLs of those solutions with the CL of our chart based on the BD (CL = 95.14%). The comparison revealed that the CL for the normal law (CL = 97.62%) generated too many false positives, and that the CL of the Logarithm transformation (CL = 83.74%) could fail to efficiently detect (i.e., sufficiently early on or faster) changes in the process. CONCLUSIONS: Successful GIPR monitoring requires careful and rigorous application of well-established statistical concepts in the field of statistical process control. In this paper, we stress the importance of carefully analyzing the distribution of the monitored characteristic that is plotted on the control chart. We propose a Bayesian control chart that can be viewed as a practical solution for early implementation of GIPR monitoring, starting from the first arc. We demonstrate that beta distribution is a better method for characterizing the GIPR, and thus, the use of this approach is expected to improve patient-specific quality assurance plans in radiotherapy.

Inhibitory control is not lateralized in Parkinson's patients.

Parkinson's disease (PD) is often characterized by asymmetrical symptoms, which are more prominent on the side of the body contralateral to the most extensively affected brain hemisphere. Therefore, lateralized PD presents an opportunity to examine the effects of asymmetric subcortical dopamine deficiencies on cognitive functioning. As it has been hypothesized that inhibitory control relies upon a right-lateralized pathway, we tested whether left-dominant PD (LPD) patients suffered from a more severe deficit in this key executive function than right-dominant PD patients (RPD). To this end, via a countermanding task, we assessed both proactive and reactive inhibition in 20 LPD and 20 RPD patients, and in 20 age-matched healthy subjects. As expected, we found that PD patients were significantly more impaired in both forms of inhibitory control than healthy subjects. However, there were no differences either in reactive or proactive inhibition between LPD and RPD patients. All in all, these data support the idea that brain regions affected by PD play a fundamental role in subserving inhibitory function, but do not sustain the hypothesis according to which this executive function is predominantly or solely computed by the brain regions of the right hemisphere.