Test Model Predictions Research Articles

Testing model predictions of induced visual fading

Testing model predictions of induced visual fading

Reprint: Building and testing models of long-term agricultural intensification and population dynamics: A case study from the Leeward Kohala Field System, Hawai’i

The Malthusian and Boserupian phases of population change, and how these are linked to long-term processes of agricultural intensification, are themes of long-standing interest to anthropologists, archaeologists, demographers, economists, ecologists, and others. The Hawai’i Biocomplexity Project has used the Hawaiian archipelago and more specifically the Leeward Kohala Field System (LKFS) as a model system to investigate the dynamic, often non-linear interactions among soils, agricultural systems, populations, and sociopolitical systems over time scales of several centuries. Two major models have been developed: (1) a model of spatial and temporal variability in agricultural production; and (2) a model of food availability and its linkages to population fertility and mortality. Empirically derived field data were used both to parameterize the models, and to test their predictions. Archaeological data on the distribution and variation in ancient agricultural field infrastructure, derived from high-resolution LiDAR images, were used to test predictions of agricultural production and intensity. Similarly, chronological data on numbers of ancient households derived from archaeological excavation and radiocarbon dating were used to test model predictions of population growth over time. This iterative process of building and testing models has led to improved understanding of how pre-industrial agricultural systems were intensified, and how expansion and intensification were dynamically linked to demographic changes in farming populations.

Size matters: modeling the effects of body shape on locomotive behavior in the nematode C. elegans

The microscopic nematode worm, C. elegans, has a hard wired and comprehensively mapped neural circuit, with only 302 neurons in the adult. The organism's simplicity and the wealth of information available about it make C. elegans an inviting target for computer modeling. Locomotion is the clearest measure of the worm's behavior, and the undulatory waves used in the worm's movement are directly affected by the shape of the animal's body. As the worm develops from a larva to adulthood its body grows and changes in shape. More dramatically, mutant C. elegans exist with bodies that are considerably longer or shorter than in the wildtype, and these worms have severe locomotion phenotypes. Previous models for locomotion have often concentrated on either the worm's nervous system or body, modeling only one area in detail and greatly simplifying the other. This neglects important interactions between the physical and neural aspects of the worm's behavior, such as touch response, proprioception and the effects of its surroundings. We use an integrated neuromechanical model that incorporates the worm's neural circuit for locomotion, its body and its environment. We have previously used the model to predict that the L1 larva maintains robust forward locomotion using a similar gait to the adult worm, and now examine the behavior of the body shape mutants dpy-1, dpy-11 and lon-2, both in liquid and on gel. Testing model predictions against data from biological experiments, we can validate its robustness to such physical differences and its ability to capture the behavior of new phenotypes. Three candidates exist for the cause of behavioral abnormalities seen in such mutants; differences in the worm's neural circuit, muscle properties, or passive body properties. We make clear experimental predictions on the locus of change and the specific pathways through which it is effected.

Using Maximum Entropy modeling to predict the potential distributions of large trees for conservation planning

Large trees, as keystone structures, are functionally important in savanna ecosystems, and low recruitment and slow growth makes their conservation important. Understanding factors influencing their distribution is essential for mitigation of excessive mortality, for example from management fires or large herbivores. We recorded the locations of large trees in Hluhluwe‐Imfolozi Park (HiP) using GPS to record trees along 43 km of 10 m‐wide transects. Maximum entropy modeling (MaxEnt) uses niche modeling to predict the distribution of a species from the probability of finding it within raster squares, based on environmental variables and recorded locations. MaxEnt is typically applied at a regional spatial scale, and here we assessed its usefulness when predicting the distribution of species at a small (local) scale. HiP has variable topography, heterogeneous soils, and a strong rainfall gradient, resulting in a wide variety of habitat types. We used locations of 179 Acacia nigrescens and 106 Sclerocarya birrea (large trees ≥ 5m), and raster environmental layers for: aspect, elevation, geology, annual rainfall, slope, soil and vegetation. A. nigrescens was largely restricted to the Imfolozi section, while S. birrea had a wider distribution across the reserve. Understanding the interaction of environmental variables dictating tree distribution may facilitate habitat restoration, and will assist planning decisions for persistence of large trees within reserves, including options to reduce fire frequency or herbivore impacts. Though the AUC (Area Under the Curve) values used to test model predictions were high for both species, the ground truthing test data showed that distribution for A. nigrescens was more accurate than that for S. birrea, highlighting the need for independent test data to assess model accuracy. We emphasize that MaxEnt can be used at finer spatial scales than those typically used for species occurrence, but models must be tested using spatially independent test data.

Detecting the Molecular Signature of Social Conflict: Theory and a Test with Bacterial Quorum Sensing Genes

Extending social evolution theory to the molecular level opens the door to an unparalleled abundance of data and statistical tools for testing alternative hypotheses about the long-term evolutionary dynamics of cooperation and conflict. To this end, we take a collection of known sociality genes (bacterial quorum sensing [QS] genes), model their evolution in terms of patterns that are detectable using gene sequence data, and then test model predictions using available genetic data sets. Specifically, we test two alternative hypotheses of social conflict: (1) the "adaptive" hypothesis that cheaters are maintained in natural populations by frequency-dependent balancing selection as an evolutionarily stable strategy and (2) the "evolutionary null" hypothesis that cheaters are opposed by purifying kin selection yet exist transiently because of their recurrent introduction into populations by mutation (i.e., kin selection-mutation balance). We find that QS genes have elevated within- and among-species sequence variation, nonsignificant signatures of natural selection, and putatively small effect sizes of mutant alleles, all patterns predicted by our evolutionary null model but not by the stable cheater hypothesis. These empirical findings support our theoretical prediction that QS genes experience relaxed selection due to nonclonality of social groups, conditional expression, and the individual-level advantage enjoyed by cheaters. Furthermore, cheaters are evolutionarily transient, persisting in populations because of their recurrent introduction by mutation and not because they enjoy a frequency-dependent fitness advantage.

Active Vision: Fixational Eye Movements Help Seeing Space in Time

The significance of the miniature eye movements that we make during visual fixation has been intensely debated for the last 80 years. Recent studies have revealed that these motions of the eyes fulfill an important functional role: helping to extract useful information from natural scenes.

The dominant erosion processes supplying fine sediment to three major rivers in tropical Australia, the Daly (NT), Mitchell (Qld) and Flinders (Qld) Rivers

The tropics of northern Australia have received relatively little attention with regard to the impact of soil erosion on the many large river systems that are an important part of Australia's water resource, especially given the high potential for erosion when long dry seasons are followed by intense wet season rain. Here we use 137Cs concentrations to determine the erosion processes supplying sediment to two major northern Australian Rivers; the Daly River (Northern Territory), and the Mitchell River (Queensland). We also present data from five sediment samples collected from a 100km reach of the Cloncurry River, a major tributary of the Flinders River (Queensland). Concentrations of 137Cs in the surface soil and subsurface (channel banks and gully) samples were used to derive ‘best fit’ probability density functions describing their distributions. These modelled distributions are then used to estimate the relative contribution of these two components to the river sediments. Our results are consistent with channel and gully erosion being the dominant source of sediment, with more than 90% of sediment transported along the main stem of these rivers originating from subsoil. We summarize the findings of similar studies on tropical Australian rivers and conclude that the primary source of sediment delivered to these systems is gully and channel bank erosion. Previously, as a result of catchment scale modelling, sheet-wash and rill erosion was considered to be the major sediment source in these rivers. Identifying the relative importance of sediment sources, as shown in this paper, will provide valuable information for land management planning in the region. This study also reinforces the importance of testing model predictions before they are used to target investment in remedial action.

Building and testing models of long-term agricultural intensification and population dynamics: A case study from the Leeward Kohala Field System, Hawai’i

The Malthusian and Boserupian phases of population change, and how these are linked to long-term processes of agricultural intensification, are themes of long-standing interest to anthropologists, archaeologists, demographers, economists, ecologists, and others. The Hawai’i Biocomplexity Project has used the Hawaiian archipelago and more specifically the Leeward Kohala Field System (LKFS) as a model system to investigate the dynamic, often non-linear interactions among soils, agricultural systems, populations, and sociopolitical systems over time scales of several centuries. Two major models have been developed: (1) a model of spatial and temporal variability in agricultural production; and (2) a model of food availability and its linkages to population fertility and mortality. Empirically derived field data were used both to parameterize the models, and to test their predictions. Archaeological data on the distribution and variation in ancient agricultural field infrastructure, derived from high-resolution LiDAR images, were used to test predictions of agricultural production and intensity. Similarly, chronological data on numbers of ancient households derived from archaeological excavation and radiocarbon dating were used to test model predictions of population growth over time. This iterative process of building and testing models has led to improved understanding of how pre-industrial agricultural systems were intensified, and how expansion and intensification were dynamically linked to demographic changes in farming populations.

Striatal and hippocampal entropy and recognition signals in category learning: Simultaneous processes revealed by model-based fMRI.

Category learning is a complex phenomenon that engages multiple cognitive processes, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and adjust their representations to support behavior in future encounters. Many techniques that are available to understand the neural basis of category learning assume that the multiple processes that subserve it can be neatly separated between different trials of an experiment. Model-based functional magnetic resonance imaging offers a promising tool to separate multiple, simultaneously occurring processes and bring the analysis of neuroimaging data more in line with category learning's dynamic and multifaceted nature. We use model-based imaging to explore the neural basis of recognition and entropy signals in the medial temporal lobe and striatum that are engaged while participants learn to categorize novel stimuli. Consistent with theories suggesting a role for the anterior hippocampus and ventral striatum in motivated learning in response to uncertainty, we find that activation in both regions correlates with a model-based measure of entropy. Simultaneously, separate subregions of the hippocampus and striatum exhibit activation correlated with a model-based recognition strength measure. Our results suggest that model-based analyses are exceptionally useful for extracting information about cognitive processes from neuroimaging data. Models provide a basis for identifying the multiple neural processes that contribute to behavior, and neuroimaging data can provide a powerful test bed for constraining and testing model predictions.

Self-Organization of Motor-Propelled Cytoskeletal Filaments at Topographically Defined Borders

Self-organization phenomena are of critical importance in living organisms and of great interest to exploit in nanotechnology. Here we describe in vitro self-organization of molecular motor-propelled actin filaments, manifested as a tendency of the filaments to accumulate in high density close to topographically defined edges on nano- and microstructured surfaces. We hypothesized that this “edge-tracing” effect either (1) results from increased motor density along the guiding edges or (2) is a direct consequence of the asymmetric constraints on stochastic changes in filament sliding direction imposed by the edges. The latter hypothesis is well captured by a model explicitly defining the constraints of motility on structured surfaces in combination with Monte-Carlo simulations [cf. Nitta et al. (2006)] of filament sliding. In support of hypothesis 2 we found that the model reproduced the edge tracing effect without the need to assume increased motor density at the edges. We then used model simulations to elucidate mechanistic details. The results are discussed in relation to nanotechnological applications and future experiments to test model predictions.

A Framework for Elucidating the Temperature Dependence of Fitness

Climate warming is predicted to cause large-scale extinctions, particularly of ectothermic species. A striking difference between tropical and temperate ectotherms is that tropical species experience a mean habitat temperature that is closer to the temperature at which fitness is maximized (T(opt)) and an upper temperature limit for survival (T(max)) that is closer to T(opt) than do temperate species. Thus, even a small increase in environmental temperature could put tropical ectotherms at high risk of extinction, whereas temperate ectotherms have a wider temperature cushion. Although this pattern is widely observed, the mechanisms that produce it are not well understood. Here we develop a mathematical framework to partition the temperature response of fitness into its components (fecundity, mortality, and development) and test model predictions with data for insects. We find that fitness declines at high temperatures because the temperature responses of fecundity and mortality act in opposite ways: fecundity decreases with temperature when temperatures exceed the optimal range, whereas mortality continues to increase. The proximity of T(opt) to T(max) depends on how the temperature response of development mediates the interaction between fecundity and mortality. When development is highly temperature sensitive, mortality exceeds reproduction only after fecundity has started to decline with temperature, which causes fitness to decline rapidly to zero when temperatures exceed T(opt). The model correctly predicts empirically observed fitness-temperature relationships in insects from different latitudes. It also suggests explanations for the widely reported phenological shifts in many ectotherms and the latitudinal differences in fitness responses.

A reduced-order, single-bubble cavitation model with applications to therapeutic ultrasound

Cavitation often occurs in therapeutic applications of medical ultrasound such as shock-wave lithotripsy (SWL) and high-intensity focused ultrasound (HIFU). Because cavitation bubbles can affect an intended treatment, it is important to understand the dynamics of bubbles in this context. The relevant context includes very high acoustic pressures and frequencies as well as elevated temperatures. Relative to much of the prior research on cavitation and bubble dynamics, such conditions are unique. To address the relevant physics, a reduced-order model of a single, spherical bubble is proposed that incorporates phase change at the liquid-gas interface as well as heat and mass transport in both phases. Based on the energy lost during the inertial collapse and rebound of a millimeter-sized bubble, experimental observations were used to tune and test model predictions. In addition, benchmarks from the published literature were used to assess various aspects of model performance. Benchmark comparisons demonstrate that the model captures the basic physics of phase change and diffusive transport, while it is quantitatively sensitive to specific model assumptions and implementation details. Given its performance and numerical stability, the model can be used to explore bubble behaviors across a broad parameter space relevant to therapeutic ultrasound.

Evaluating the adaptive-filter model of the cerebellum.

The adaptive-filter model of the cerebellar microcircuit is in widespread use, combining as it does an explanation of key microcircuit features with well-specified computational power. Here we consider two methods for its evaluation. One is to test its predictions concerning relations between cerebellar inputs and outputs. Where the relevant experimental data are available, e.g. for the floccular role in image stabilization, the predictions appear to be upheld. However, for the majority of cerebellar microzones these data have yet to be obtained. The second method is to test model predictions about details of the microcircuit. We focus on features apparently incompatible with the model, in particular non-linear patterns in Purkinje cell simple-spike firing. Analysis of these patterns suggests the following three conclusions. (i) It is important to establish whether they can be observed during task-related behaviour. (ii) Highly non-linear models based on these patterns are unlikely to be universal, because they would be incompatible with the (approximately) linear nature of floccular function. (iii) The control tasks for which these models are computationally suited need to be identified. At present, therefore, the adaptive filter remains a candidate model of at least some cerebellar microzones, and its evaluation suggests promising lines for future enquiry.

Brown Dwarf Binaries

AbstractNearly 500 brown dwarfs have been discovered in recent years. The majority of these brown dwarfs exist in the solar neighborhood, yet determining their fundamental properties (mass, age, temperature & metallicity) has proved to be quite difficult, with current estimates relying heavily on theoretical models. Binary brown dwarfs provide a unique opportunity to empirically determine fundamental properties, which can then be used to test model predictions. In addition, the observed binary fractions, separations, mass ratios, & orbital eccentricities can provide insight into the formation mechanism of these low-mass objects. I will review the results of various brown dwarf multiplicity studies, and will discuss what we have learned about the formation and evolution of brown dwarfs by examining their binary properties as a function of age and mass.

Magnetic effects of the substorm current wedge in a “spread-out wire” model and their comparison with ground, geosynchronous, and tail lobe data

[1] Although the substorm current wedge (SCW) is recognized as a basic 3-D current system of the substorm expansion phase, its existing models still do not extend beyond a cartoon-like sketch, and very little is known of how well they reproduce magnetic variations observed in the magnetosphere during substorms. A lack of a realistic quantitative SCW model hampers testing model predictions against large sets of spacecraft data. This paper (1) presents a computationally efficient and flexible model with a realistic geometry of field-aligned currents, conveniently parameterized by the SCW strength, longitudinal width, and position, all derived from ground-based midlatitude magnetic variations; and (2) tests the model against INTERMAGNET network observations during substorms and compares its predictions with space magnetometer data. The testing demonstrated significant and systematic discrepancies between the observed and predicted magnetic variations, depending on spacecraft location, concurrent magnetotail configuration, and substorm phase. In particular, we found that the net SCW current derived from the midlatitude field variations corresponds to only a relatively small and variable fraction of the distant 3-D substorm current, inferred from spacecraft data in the lobe and at geosynchronous distance. The discrepancy can be partly attributed to additional region 2 polarity field-aligned currents in the same longitudinal sector, associated with azimuthal diversion of the earthward plasma flow when it encounters the region of strong quasi-dipolar field in the inner magnetosphere.

Analysis and Validation of a Unified Slip Factor Model for Impellers at Design and Off-Design Conditions

This paper presents a unified slip model for axial, radial, and mixed-flow impellers. The core assumption of the model is that the flow deviation or the slip velocity at the impeller exit is mainly originated from the blade loading near the discharge of an impeller and its subsequent relative eddy in the impeller passage. The blade loading is estimated and then used to derive the slip velocity using Stodola’s assumption. The final form of the slip factor model can be successfully related to Carter’s rule for axial impellers and Stodola’s slip model for radial impellers, making the case for this model applicable to axial, radial, and mixed-flow impellers. Unlike conventional slip factor models for radial impellers, the new slip model suggests that the flow coefficient at the impeller exit is an important variable for the slip factor when there is significant blade turning at the impeller discharge. This explains the interesting off-design trends for slip factor observed from experiments, such as the rise of the slip factor with flow coefficient in the Eckardt A impeller. Extensive validation results for this new model are presented in this paper. Several cases are studied in detail to demonstrate how this new model can capture the slip factor variation at the off-design conditions. Furthermore, a large number of test data from more than 90 different compressors, pumps, and blowers were collected. Most cases are radial impellers, but a few axial impellers are also included. The test data and model predictions of the slip factor are compared at both design and off-design flow conditions. In total, over 1650 different flow conditions are evaluated. The unified model shows a clear advantage over the traditional slip factor correlations, such as the Busemann–Wiesner model, when off-design conditions are considered.

The Role of Bending Stiffness on the Rheology of Fibrin Networks

During the formation of a blood clot, fibrinogen is converted to fibrin which in turn polymerizes to form a biopolymer network whose mechanical properties are partially imparted to the clot itself. When such a network is deformed, it has an initial, low strain linear regime followed by a nonlinear, high strain regime. In the body, blood clots must function under a large range of stresses. For this reason, both the linear and nonlinear properties of a fibrin network are important for proper function. Previous work has studied the relation between calcium concentration and the linear modulus and fiber radius. Here we extend this work by also characterizing the effect of calcium concentration on the nonlinear modulus using rheology as well as on the hydrated structure using confocal microscopy. Unlike previous work, using this information, we can also directly test model predictions of the role of bending stiffness on both the linear and nonlinear moduli.

Maxwell–Stefan diffusivities in liquid mixtures: Using molecular dynamics for testing model predictions

AbstractMulticomponent diffusion is ubiquitous in (bio)chemical processes. The Maxwell–Stefan (M–S) framework provides a sound theoretical basis for describing transport diffusion as it correctly accounts for the gradient in chemical potential as driving force. Unfortunately, M–S diffusivities Đij cannot be measured directly in experiments. The use of predictive models based on easily measurable quantities like Fick- or self-diffusivities in diluted systems is therefore desirable. In this study, equilibrium molecular dynamics (EMD) simulations are used to study M–S diffusivities in liquid mixtures containing n-hexane, cyclohexane and/or toluene. Predictive models for estimating M–S diffusivities Đijxk→1 in ternary systems are investigated. The following analysis are carried out. First, these predictive models are used to calculate the self-diffusivity in the infinite dilution limit using the well-known Vignes approximation. The predicted self-diffusivity is compared to the self-diffusivity directly calculated from EMD simulations. Second, we investigated the quality of the Vignes approximation using diffusivities obtained from EMD simulations. Third, we directly compared the predictive models for Đijxk→1 with EMD simulations. Our results show that: (1) predicted self-diffusivities are not very sensitive to the choice of the predictive model for Đijxk→1; (2) the Vignes equation results in only reasonable predictions for M–S diffusivities, yielding errors of 13% on average; (3) the interaction between solutes and solvent cannot be neglected in predictive models for Đijxk→1; (4) present predictive models for calculating Đijxk→1 from binary data results in errors of 8% for the systems under investigation.

Structural analysis of high-rise reinforced concrete building structures during construction

This paper presents a three-dimensional finite element method based structural analysis model for structural analysis of reinforced concrete high-rise buildings during construction. The model considered the time-dependency of the structural configuration and material properties as well as the effect of the construction rate and shoring stiffness. Uniaxial compression tests of young concrete within 28 days of age were conducted to establish the time-dependent compressive stress-strain relationship of concrete, which was then used as input parameters to the structural analysis model. In-situ tests of a RC high-rise building were conducted, the results of which were used for model verification. Good agreement between the test results and model predictions was achieved. At the end, a parametric study was conducted using the verified model. The results indicated that the floor position and construction rate had significant effect on the shore load, whereas the influence of the shore removal timing and shore stiffness have much smaller. It was also found that the floors are more prone to cracking during construction than is ultimate bending failure.

Is the climate right for pleistocene rewilding? Using species distribution models to extrapolate climatic suitability for mammals across continents.

Species distribution models (SDMs) are increasingly used for extrapolation, or predicting suitable regions for species under new geographic or temporal scenarios. However, SDM predictions may be prone to errors if species are not at equilibrium with climatic conditions in the current range and if training samples are not representative. Here the controversial “Pleistocene rewilding” proposal was used as a novel example to address some of the challenges of extrapolating modeled species-climate relationships outside of current ranges. Climatic suitability for three proposed proxy species (Asian elephant, African cheetah and African lion) was extrapolated to the American southwest and Great Plains using Maxent, a machine-learning species distribution model. Similar models were fit for Oryx gazella, a species native to Africa that has naturalized in North America, to test model predictions. To overcome biases introduced by contracted modern ranges and limited occurrence data, random pseudo-presence points generated from modern and historical ranges were used for model training. For all species except the oryx, models of climatic suitability fit to training data from historical ranges produced larger areas of predicted suitability in North America than models fit to training data from modern ranges. Four naturalized oryx populations in the American southwest were correctly predicted with a generous model threshold, but none of these locations were predicted with a more stringent threshold. In general, the northern Great Plains had low climatic suitability for all focal species and scenarios considered, while portions of the southern Great Plains and American southwest had low to intermediate suitability for some species in some scenarios. The results suggest that the use of historical, in addition to modern, range information and randomly sampled pseudo-presence points may improve model accuracy. This has implications for modeling range shifts of organisms in response to climate change.