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A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
SLocX predicting subcellular localization of Arabidopsis proteins leveraging gene expression data
(2011)
Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mito-chondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.
Predicting the actions of other individuals is crucial for our daily interactions. Recent evidence suggests that the prediction of object-directed arm and full-body actions employs the dorsal premotor cortex (PMd). Thus, the neural substrate involved in action control may also be essential for action prediction. Here, we aimed to address this issue and hypothesized that disrupting the PMd impairs action prediction. Using fMRI-guided coil navigation, rTMS (five pulses, 10Hz) was applied over the left PMd and over the vertex (control region) while participants observed everyday actions in video clips that were transiently occluded for 1s. The participants detected manipulations in the time course of occluded actions, which required them to internally predict the actions during occlusion. To differentiate between functional roles that the PMd could play in prediction, rTMS was either delivered at occluder-onset (TMS-early), affecting the initiation of action prediction, or 300 ms later during occlusion(TMS-late), affecting the maintenance of anongoing prediction. TMS-early over the left PMd produced more prediction errors than TMS-early over the vertex. TMS-late had no effect on prediction performance, suggesting that the left PMd might be involved particularly during the initiation of internally guided action prediction but may play a subordinate role in maintaining ongoing prediction. These findings open a new perspective on the role of the left PMd in action prediction which is in line with its functions in action control and in cognitive tasks. In the discussion, there levance of the left PMd for integrating external action parameters with the observer's motor repertoire is emphasized. Overall, the results are in line with the notion that premotor functions are employed in both action control and action observation.
Aim Biotic interactions within guilds or across trophic levels have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of species interaction distribution models (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities.
We investigate spatio-temporal properties of earthquake patterns in the San Jacinto fault zone (SJFZ), California, between Cajon Pass and the Superstition Hill Fault, using a long record of simulated seismicity constrained by available seismological and geological data. The model provides an effective realization of a large segmented strike-slip fault zone in a 3D elastic half-space, with heterogeneous distribution of static friction chosen to represent several clear step-overs at the surface. The simulated synthetic catalog reproduces well the basic statistical features of the instrumental seismicity recorded at the SJFZ area since 1981. The model also produces events larger than those included in the short instrumental record, consistent with paleo-earthquakes documented at sites along the SJFZ for the last 1,400 years. The general agreement between the synthetic and observed data allows us to address with the long-simulated seismicity questions related to large earthquakes and expected seismic hazard. The interaction between m a parts per thousand yen 7 events on different sections of the SJFZ is found to be close to random. The hazard associated with m a parts per thousand yen 7 events on the SJFZ increases significantly if the long record of simulated seismicity is taken into account. The model simulations indicate that the recent increased number of observed intermediate SJFZ earthquakes is a robust statistical feature heralding the occurrence of m a parts per thousand yen 7 earthquakes. The hypocenters of the m a parts per thousand yen 5 events in the simulation results move progressively towards the hypocenter of the upcoming m a parts per thousand yen 7 earthquake.
Despite recent growth of research on the effects of prosocial media, processes underlying these effects are not well understood. Two studies explored theoretically relevant mediators and moderators of the effects of prosocial media on helping. Study 1 examined associations among prosocial- and violent-media use, empathy, and helping in samples from seven countries. Prosocial-media use was positively associated with helping. This effect was mediated by empathy and was similar across cultures. Study 2 explored longitudinal relations among prosocial-video-game use, violent-video-game use, empathy, and helping in a large sample of Singaporean children and adolescents measured three times across 2 years. Path analyses showed significant longitudinal effects of prosocial- and violent-video-game use on prosocial behavior through empathy. Latent-growth-curve modeling for the 2-year period revealed that change in video-game use significantly affected change in helping, and that this relationship was mediated by change in empathy.
Ecologists urgently need a better ability to predict how environmental change affects biodiversity. We examine individual-based ecology (IBE), a research paradigm that promises better a predictive ability by using individual-based models (IBMs) to represent ecological dynamics as arising from how individuals interact with their environment and with each other. A key advantage of IBMs is that the basis for predictions-fitness maximization by individual organisms-is more general and reliable than the empirical relationships that other models depend on. Case studies illustrate the usefulness and predictive success of long-term IBE programs. The pioneering programs had three phases: conceptualization, implementation, and diversification. Continued validation of models runs throughout these phases. The breakthroughs that make IBE more productive include standards for describing and validating IBMs, improved and standardized theory for individual traits and behavior, software tools, and generalized instead of system-specific IBMs. We provide guidelines for pursuing IBE and a vision for future IBE research.
Predicting Paris: Multi-Method Approaches to Forecast the Outcomes of Global Climate Negotiations
(2016)
We examine the negotiations held under the auspices of the United Nations Framework Convention of Climate Change in Paris, December 2015. Prior to these negotiations, there was considerable uncertainty about whether an agreement would be reached, particularly given that the world’s leaders failed to do so in the 2009 negotiations held in Copenhagen. Amid this uncertainty, we applied three different methods to predict the outcomes: an expert survey and two negotiation simulation models, namely the Exchange Model and the Predictioneer’s Game. After the event, these predictions were assessed against the coded texts that were agreed in Paris. The evidence suggests that combining experts’ predictions to reach a collective expert prediction makes for significantly more accurate predictions than individual experts’ predictions. The differences in the performance between the two different negotiation simulation models were not statistically significant.
Background:
Endomyocardial biopsy is considered as the gold standard in patients with suspected myocarditis. We aimed to evaluate the impact of bioptic findings on prediction of successful return to work.
Methods:
In 1153 patients (48.9 ± 12.4 years, 66.2% male), who were hospitalized due to symptoms of left heart failure between 2005 and 2012, an endomyocardial biopsy was performed. Routine clinical and laboratory data, sociodemographic parameters, and noninvasive and invasive cardiac variables including endomyocardial biopsy were registered. Data were linked with return to work data from the German statutory pension insurance program and analyzed by Cox regression.
Results:
A total of 220 patients had a complete data set of hospital and insurance information. Three quarters of patients were virus-positive (54.2% parvovirus B19, other or mixed infection 16.7%). Mean invasive left ventricular ejection fraction was 47.1% ± 18.6% (left ventricular ejection fraction <45% in 46.3%). Return to work was achieved after a mean interval of 168.8 ± 347.7 days in 220 patients (after 6, 12, and 24 months in 61.3%, 72.2%, and 76.4%). In multivariate regression analysis, only age (per 10 years, hazard ratio, 1.27; 95% confidence interval, 1.10–1.46; p = 0.001) and left ventricular ejection fraction (per 5% increase, hazard ratio, 1.07; 95% confidence interval, 1.03–1.12; p = 0.002) were associated with increased, elevated work intensity (heavy vs light, congestive heart failure, 0.58; 95% confidence interval, 0.34–0.99; p < 0.049) with decreased probability of return to work. None of the endomyocardial biopsy–derived parameters was significantly associated with return to work in the total group as well as in the subgroup of patients with biopsy-proven myocarditis.
Conclusion:
Added to established predictors, bioptic data demonstrated no additional impact for return to work probability. Thus, socio-medical evaluation of patients with suspected myocarditis furthermore remains an individually oriented process based primarily on clinical and functional parameters.
We propose a reduced dynamical system describing the coupled evolution of fluid flow and magnetic field at the top of the Earth's core between the years 1900 and 2014. The flow evolution is modeled with a first-order autoregressive process, while the magnetic field obeys the classical frozen flux equation. An ensemble Kalman filter algorithm serves to constrain the dynamics with the geomagnetic field and its secular variation given by the COV-OBS.x1 model. Using a large ensemble with 40,000 members provides meaningful statistics including reliable error estimates. The model highlights two distinct flow scales. Slowly varying large-scale elements include the already documented eccentric gyre. Localized short-lived structures include distinctly ageostophic features like the high-latitude polar jet on the Northern Hemisphere. Comparisons with independent observations of the length-of-day variations not only validate the flow estimates but also suggest an acceleration of the geostrophic flows over the last century. Hindcasting tests show that our model outperforms simpler predictions bases (linear extrapolation and stationary flow). The predictability limit, of about 2,000 years for the magnetic dipole component, is mostly determined by the random fast varying dynamics of the flow and much less by the geomagnetic data quality or lack of small-scale information.