Human and nonhuman primate brains: Are they allometrically scaled versions of the same design?

Allometric analyses of brain structure sizes across the primate order demonstrate that human, ape, and other anthropoid brains are not simply allometrically scaled versions of the same generalized design. Both human and ape brains exhibit specializations with respect to other anthropoid brains. Ape specializations include elaboration of the cerebellum (all apes) and frontal lobes (great apes only), and probably connectivity between them. Human brain specializations include an overall larger proportion of neocortex, with disproportionate enlargement of prefrontal and temporal association cortices; an apparent increase in cerebellar connections with cerebral cortical association areas involved in cognition; and a probable augmentation of intracortical connectivity in prefrontal cortex.     

A modelling framework to simulate foliar fungal epidemics using functional–structural plant models

Background and Aims

Sustainable agriculture requires the identification of new, environmentally responsible strategies of crop protection. Modelling of pathosystems can allow a better understanding of the major interactions inside these dynamic systems and may lead to innovative protection strategies. In particular, functional–structural plant models (FSPMs) have been identified as a means to optimize the use of architecture-related traits. A current limitation lies in the inherent complexity of this type of modelling, and thus the purpose of this paper is to provide a framework to both extend and simplify the modelling of pathosystems using FSPMs.

Methods

Different entities and interactions occurring in pathosystems were formalized in a conceptual model. A framework based on these concepts was then implemented within the open-source OpenAlea modelling platform, using the platform''s general strategy of modelling plant–environment interactions and extending it to handle plant interactions with pathogens. New developments include a generic data structure for representing lesions and dispersal units, and a series of generic protocols to communicate with objects representing the canopy and its microenvironment in the OpenAlea platform. Another development is the addition of a library of elementary models involved in pathosystem modelling. Several plant and physical models are already available in OpenAlea and can be combined in models of pathosystems using this framework approach.

Key Results

Two contrasting pathosystems are implemented using the framework and illustrate its generic utility. Simulations demonstrate the framework''s ability to simulate multiscaled interactions within pathosystems, and also show that models are modular components within the framework and can be extended. This is illustrated by testing the impact of canopy architectural traits on fungal dispersal.

Conclusions

This study provides a framework for modelling a large number of pathosystems using FSPMs. This structure can accommodate both previously developed models for individual aspects of pathosystems and new ones. Complex models are deconstructed into separate ‘knowledge sources’ originating from different specialist areas of expertise and these can be shared and reassembled into multidisciplinary models. The framework thus provides a beneficial tool for a potential diverse and dynamic research community.     

An approach to multiscale modelling with graph grammars

Background and Aims

Functional–structural plant models (FSPMs) simulate biological processes at different spatial scales. Methods exist for multiscale data representation and modification, but the advantages of using multiple scales in the dynamic aspects of FSPMs remain unclear. Results from multiscale models in various other areas of science that share fundamental modelling issues with FSPMs suggest that potential advantages do exist, and this study therefore aims to introduce an approach to multiscale modelling in FSPMs.

Methods

A three-part graph data structure and grammar is revisited, and presented with a conceptual framework for multiscale modelling. The framework is used for identifying roles, categorizing and describing scale-to-scale interactions, thus allowing alternative approaches to model development as opposed to correlation-based modelling at a single scale. Reverse information flow (from macro- to micro-scale) is catered for in the framework. The methods are implemented within the programming language XL.

Key Results

Three example models are implemented using the proposed multiscale graph model and framework. The first illustrates the fundamental usage of the graph data structure and grammar, the second uses probabilistic modelling for organs at the fine scale in order to derive crown growth, and the third combines multiscale plant topology with ozone trends and metabolic network simulations in order to model juvenile beech stands under exposure to a toxic trace gas.

Conclusions

The graph data structure supports data representation and grammar operations at multiple scales. The results demonstrate that multiscale modelling is a viable method in FSPM and an alternative to correlation-based modelling. Advantages and disadvantages of multiscale modelling are illustrated by comparisons with single-scale implementations, leading to motivations for further research in sensitivity analysis and run-time efficiency for these models.     

Apes in a changing world – the effects of global warming on the behaviour and distribution of African apes

Aim In this study we use a modelling approach to identify: (1) the factors responsible for the differences in ape biogeography, (2) the effects that global warming might have on distribution patterns of African apes, (3) the underlying mechanisms for these effects, and (4) the implications that behavioural flexibility might be expected to have for ape survival. All African apes are highly endangered, and the need for efficient conservation methods is a top priority. The expected changes in world climate are likely to further exacerbate the difficulties they face. Our study aims to further understand the mechanisms that link climatic conditions to the behaviour and biogeography of ape species. Location Africa. Method We use an existing validated time budgets model, derived from data on 20 natural populations of gorillas (Gorilla beringei and Gorilla gorilla) and chimpanzees (Pan troglodytes and Pan paniscus), which specifies the relationship between climate, group size, body weight and time available for various activities, to predict ape distribution across Africa under a uniform worst‐case climate change scenario. Results We demonstrate that a worst‐case global warming scenario is likely to alter the delicate balance between different time budget components. Our model points to the importance of annual temperature variation, which was found to have the strongest impact on ape biogeography. Our simulation indicates that rising temperatures and changes in rainfall patterns are likely to have strong effects on ape survival and distribution, particularly for gorillas. Even if they behaved with maximum flexibility, gorillas may not be able to survive in most of their present habitats if the climate was to undergo extreme changes. The survival of chimpanzees was found to be strongly dependent on the minimum viable group size required. Main conclusions Our model allows us to explore how climatic conditions, individual behaviour and morphological traits may interact to limit the biogeographical distributions of these species, thereby allowing us to predict the effects of climate change on African ape distributions under different climate change regimes. The model suggests that climate variability (i.e. seasonality) plays a more important role than the absolute magnitude of the change, but these data are not normally provided by climate models.     

A patch-dynamic framework for food web metacommunities

The metacommunity concept has proved to be a valuable tool for studying how space can affect the properties and assembly of competitive communities. However, the concept has not been as extensively applied to the study of food webs or trophically structured communities. Here, we demonstrate how to develop a modelling framework that permits food webs to be considered from a spatial perspective. We do this by broadening the classic metapopulation patch-dynamic framework so that it can also account for trophic interactions between many species and patches. Unlike previous metacommunity models, we argue that this requires a system of equations to track the changing patch occupancy of the various species interactions, not the patch occupancy of individual species. We then suggest how this general theoretical framework can be used to study complex and spatially extended food web metacommunities.     

A general framework to quantify and compare ecological impacts under temporal dynamics

Biodiversity is diminishing at alarming rates due to multiple anthropogenic drivers. To mitigate these drivers, their impacts must be quantified accurately and comparably across drivers. To enable that, we present a generally applicable framework introducing fundamental principles of ecological impact quantification, including the quantification of interactions between multiple drivers. The framework contrasts biodiversity variables in impacted against those in unimpacted or other reference situations while accounting for their temporal dynamics through modelling. Properly accounting for temporal dynamics reduces biases in impact quantification and comparison. The framework addresses key questions around ecological impacts in global change science, namely, how to compare impacts under temporal dynamics across stressors, how to account for stressor interactions in such comparisons, and how to compare the success of management actions over time.     

The brain and its main anatomical subdivisions in living hominoids using magnetic resonance imaging

Primary comparative data on the hominoid brain are scarce and major neuroanatomical differences between humans and apes have not yet been described satisfactorily, even at the gross level. Basic questions that involve the evolution of the human brain cannot be addressed adequately unless the brains of all extant hominoid species are analyzed. Contrary to the scarcity of original data, there is a rich literature on the topic of human brain evolution and several debates exist on the size of particular sectors of the brain, e.g., the frontal lobe.In this study we applied a non-invasive imaging technique (magnetic resonance) on living human, great ape and lesser ape subjects in order to investigate the overall size of the hominoid brain. The images were reconstructed in three dimensions and volumetric estimates were obtained for the brain and its main anatomical sectors, including the frontal and temporal lobes, the insula, the parieto-occipital sector and the cerebellum.A remarkable homogeneity is present in the relative size of many of the large sectors of the hominoid brain, but interspecific and intraspecific variation exists in certain parts of the brain. The human cerebellum is smaller than expected for an ape brain of human size. It is suggested that the cerebellum increased less than the cerebrum after the split of the human lineage from the African ancestral hominoid stock. In contrast, humans have a slightly larger temporal lobe and insula than expected, but differences are not statistically significant.Humans do not have a larger frontal lobe than expected for an ape brain of human size and gibbons have a relatively smaller frontal lobe than the rest of the hominoids. Given the fact that the frontal lobe in humans and great apes has similar relative size, it is parsimonious to suggest that the relative size of the whole of the frontal lobe has not changed significantly during hominid evolution in the Plio-Pleistocene.     

A mathematical modelling framework for understanding chemorepulsive signal transduction in Dictyostelium

Chemorepulsion is the process by which an organism or a cell moves in the direction of decreasing chemical concentration. While a few experimental studies have been performed, no mathematical models exist for this process. In this paper we have modelled gradient sensing, the first subprocess of chemorepulsion, in Dictyostelium discoideum-a well characterized model eukaryotic system. We take the first steps towards achieving a comprehensive mechanistic understanding of chemorepulsion in this system. We have used, as a basis, the biochemical network of the Keizer-Gunnink et al. (2007) to develop the mathematical modelling framework. This network describes the underlying pathways of chemorepellent gradient sensing in D. discoideum. Working within this modelling framework we address whether the postulated interactions of the pathways and species in this network can lead to a chemorepulsive response. We also analyse the possible role of additional regulatory effects (such as additional receptor regulation of enzymes in this network) and if this is necessary to achieve this behaviour. Thus we have investigated the receptor regulation of important enzymes and feedback effects in the network. This modelling framework generates important insights into and testable predictions regarding the role of key components and feedback loops in regulating chemorepulsive gradient sensing, and what factors might be important for generating a chemorepulsive response; it serves as a first step towards a comprehensive mechanistic understanding of this process.     

Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering

Large, naturally evolved biomolecular networks typically fulfil multiple functions. When modelling or redesigning such systems, functional subsystems are often analysed independently first, before subsequent integration into larger-scale computational models. In the design and analysis process, it is therefore important to quantitatively analyse and predict the dynamics of the interactions between integrated subsystems; in particular, how the incremental effect of integrating a subsystem into a network depends on the existing dynamics of that network. In this paper we present a framework for simulating the contribution of any given functional subsystem when integrated together with one or more other subsystems. This is achieved through a cascaded layering of a network into functional subsystems, where each layer is defined by an appropriate subset of the reactions. We exploit symmetries in our formulation to exhaustively quantify each subsystem’s incremental effects with minimal computational effort. When combining subsystems, their isolated behaviour may be amplified, attenuated, or be subject to more complicated effects. We propose the concept of mutual dynamics to quantify such nonlinear phenomena, thereby defining the incompatibility and cooperativity between all pairs of subsystems when integrated into any larger network. We exemplify our theoretical framework by analysing diverse behaviours in three dynamic models of signalling and metabolic pathways: the effect of crosstalk mechanisms on the dynamics of parallel signal transduction pathways; reciprocal side-effects between several integral feedback mechanisms and the subsystems they stabilise; and consequences of nonlinear interactions between elementary flux modes in glycolysis for metabolic engineering strategies. Our analysis shows that it is not sufficient to just specify subsystems and analyse their pairwise interactions; the environment in which the interaction takes place must also be explicitly defined. Our framework provides a natural representation of nonlinear interaction phenomena, and will therefore be an important tool for modelling large-scale evolved or synthetic biomolecular networks.     

A cell based modelling framework for skeletal tissue engineering applications

In this study, a cell based lattice free modelling framework is proposed to study cell aggregate behaviour in bone tissue engineering applications. The model encompasses cell-to-cell and cell–environment interactions such as adhesion, repulsion and drag forces. Oxygen, nutrients, waste products, growth factors and inhibitors are explicitly represented in the model influencing cellular behaviour. Furthermore, a model for cell metabolism is incorporated representing the basic enzymic reactions of glycolysis and the Krebs cycle. Various types of cell death such as necrosis, apoptosis and anoikis are implemented. Finally, an explicit model of the cell cycle controls the proliferation process, taking into account the presence or absence of various metabolites, sufficient space and mechanical stress. Several examples are presented demonstrating the potential of the modelling framework. The behaviour of a synchronised cell aggregate under ideal circumstances is simulated, clearly showing the different stages of the cell cycle and the resulting growth of the aggregate. Also the difference in aggregate development under ideal (normoxic) and hypoxic conditions is simulated, showing hypoxia induced necrosis mainly in the centre of the aggregate grown under hypoxic conditions. The next step in this research will be the application of this modelling framework to specific experimental set-ups for bone tissue engineering applications.     

Clinical response Decision Tree for the Mountain Gorilla (Gorilla beringeii) as a model for great apes

Disease is one of the main threats to the remaining great ape populations of the world. The decision to intervene in the health of the great apes for population sustainability is controversial. Humans' increasing negative influence on great ape health has mandated the reevaluation of current management policies. The Mountain Gorilla Veterinary Project (MGVP) has been making health intervention decisions since 1986. The decision to intervene has often been made subjectively due to poorly defined criteria that are often influenced by emotion. This paper provides a consistent framework for evidence-based health intervention decision-making. The decision tree is a five-tier process consisting of routine sentinel health observation, intensive follow-up veterinary health observation, outbreak assessment, risk assessment, and risk management. Although this paper focuses on the mountain gorillas, it serves as a basis for evidence-based decision-making in other species.     

Integrating multiple lines of evidence into historical biogeography hypothesis testing: a Bison bison case study

One of the grand goals of historical biogeography is to understand how and why species'' population sizes and distributions change over time. Multiple types of data drawn from disparate fields, combined into a single modelling framework, are necessary to document changes in a species''s demography and distribution, and to determine the drivers responsible for change. Yet truly integrated approaches are challenging and rarely performed. Here, we discuss a modelling framework that integrates spatio-temporal fossil data, ancient DNA, palaeoclimatological reconstructions, bioclimatic envelope modelling and coalescence models in order to statistically test alternative hypotheses of demographic and potential distributional changes for the iconic American bison (Bison bison). Using different assumptions about the evolution of the bioclimatic niche, we generate hypothetical distributional and demographic histories of the species. We then test these demographic models by comparing the genetic signature predicted by serial coalescence against sequence data derived from subfossils and modern populations. Our results supported demographic models that include both climate and human-associated drivers of population declines. This synthetic approach, integrating palaeoclimatology, bioclimatic envelopes, serial coalescence, spatio-temporal fossil data and heterochronous DNA sequences, improves understanding of species'' historical biogeography by allowing consideration of both abiotic and biotic interactions at the population level.     

Safe space in the woods: Mechanistic spatial models for predicting risks of human–bear conflicts in India

Human–wildlife interactions can have negative consequences when they involve large carnivores. Spatial risk modelling could serve as a useful management approach for predicting and pre-emptively mitigating negative interactions. We present a mechanistic modelling framework and examine interactions between humans and sloth bears (Melursus ursinus) in a multi-use forest landscape of central India. We first assessed patterns and determinants of bear distribution across the landscape using indirect sign surveys. At the same spatial scale, we then estimated spatial probabilities of bear attacks on people using information from 675 interviews with local residents, incorporating estimates of distribution probabilities from the previous step. We found the average occupancy probability across 128 grid-cells to be 0.77 (SE = 0.03). Bear occupancy was influenced by terrain ruggedness, forest composition and configuration, vegetation productivity and size of human settlements. The average probability of a bear attack in any given grid-cell was 0.61 (SE = 0.03), mostly determined by bear occurrence patterns, forest cover, terrain ruggedness, and size of human settlements. Using spatial information on people's dependence on forest resources, we identified locations with the highest risk of bear attacks. Our study demonstrates that human attacks by bears—generally believed to be random or incidental—in fact showed deterministic patterns. Our framework can be applied to other scenarios involving human–wildlife conflicts. Based on our findings, we propose that a proactive co-management approach which involves collaboration between wildlife managers and local residents could help better manage human–bear conflicts in central India and elsewhere across the species' range.     

Wild-Born Orangutans (Pongo abelii) Engage in Triadic Interactions During Play

It has long been held that triadic interactions, or interactions between individuals that include shared perception and goals concerning an outside entity, require elaborate cognitive processes such as joint attention. With their connection to shared intentionality, triadic interactions have been a key topic of interest for developmental and evolutionary psychologists, notably when making comparisons between humans and other ape species. There is good evidence that chimpanzees and bonobos engage in triadic interactions; however, convincing evidence for orangutans are more limited and so far have been found only in the context of feeding. I engaged 11 wild-born sanctuary orangutans through the medium of a stick, allowing them to decide how to use the object and how to interact with me. The participating orangutans developed idiosyncratic ways of using the stick and engaging with me during the activity, and six of them alternated their gaze between the stick and me. When I interrupted the activity, the participating orangutans displayed more numerous and different behaviors than before the interruption to actively reengage me in the game. Much like human infants, they appeared more interested in the social interaction than in the stick. These findings confirm that triadic interactions occur in nonenculturated orangutans and are consistent with studies of other nonhuman great ape species, which also show triadic interactions, suggesting that joint attention and potentially shared intentionality may have an early origin in our evolutionary history.     

Biotic interactions boost spatial models of species richness

Biotic interactions are known to affect the composition of species assemblages via several mechanisms, such as competition and facilitation. However, most spatial models of species richness do not explicitly consider inter‐specific interactions. Here, we test whether incorporating biotic interactions into high‐resolution models alters predictions of species richness as hypothesised. We included key biotic variables (cover of three dominant arctic‐alpine plant species) into two methodologically divergent species richness modelling frameworks – stacked species distribution models (SSDM) and macroecological models (MEM) – for three ecologically and evolutionary distinct taxonomic groups (vascular plants, bryophytes and lichens). Predictions from models including biotic interactions were compared to the predictions of models based on climatic and abiotic data only. Including plant–plant interactions consistently and significantly lowered bias in species richness predictions and increased predictive power for independent evaluation data when compared to the conventional climatic and abiotic data based models. Improvements in predictions were constant irrespective of the modelling framework or taxonomic group used. The global biodiversity crisis necessitates accurate predictions of how changes in biotic and abiotic conditions will potentially affect species richness patterns. Here, we demonstrate that models of the spatial distribution of species richness can be improved by incorporating biotic interactions, and thus that these key predictor factors must be accounted for in biodiversity forecasts.     

Patterns of differences in brain morphology in humans as compared to extant apes

Although human evolution is characterized by a vast increase in brain size, it is not clear whether or not certain regions of the brain are enlarged disproportionately in humans, or how this enlargement relates to differences in overall neural morphology. The aim of this study is to determine whether or not there are specific suites of features that distinguish the morphology of the human brain from that of apes. The study sample consists of whole brain, in vivo magnetic resonance images (MRIs) of anatomically modern humans (Homo sapiens sapiens) and five ape species (gibbons, orangutans, gorillas, chimpanzees, bonobos). Twenty-nine 3D landmarks, including surface and internal features of the brain were located on 3D MRI reconstructions of each individual using MEASURE software. Landmark coordinate data were scaled for differences in size and analyzed using Euclidean Distance Matrix Analysis (EDMA) to statistically compare the brains of each non-human ape species to the human sample. Results of analyses show both a pattern of brain morphology that is consistently different between all apes and humans, as well as patterns that differ among species. Further, both the consistent and species-specific patterns include cortical and subcortical features. The pattern that remains consistent across species indicates a morphological reorganization of 1) relationships between cortical and subcortical frontal structures, 2) expansion of the temporal lobe and location of the amygdala, and 3) expansion of the anterior parietal region. Additionally, results demonstrate that, although there is a pattern of morphology that uniquely defines the human brain, there are also patterns that uniquely differentiate human morphology from the morphology of each non-human ape species, indicating that reorganization of neural morphology occurred at the evolutionary divergence of each of these groups.     

Assessing endocranial variations in great apes and humans using 3D data from virtual endocasts

Modern humans are characterized by their large, complex, and specialized brain. Human brain evolution can be addressed through direct evidence provided by fossil hominid endocasts (i.e. paleoneurology), or through indirect evidence of extant species comparative neurology. Here we use the second approach, providing an extant comparative framework for hominid paleoneurological studies. We explore endocranial size and shape differences among great apes and humans, as well as between sexes. We virtually extracted 72 endocasts, sampling all extant great ape species and modern humans, and digitized 37 landmarks on each for 3D generalized Procrustes analysis. All species can be differentiated by their endocranial shape. Among great apes, endocranial shapes vary from short (orangutans) to long (gorillas), perhaps in relation to different facial orientations. Endocranial shape differences among African apes are partly allometric. Major endocranial traits distinguishing humans from great apes are endocranial globularity, reflecting neurological reorganization, and features linked to structural responses to posture and bipedal locomotion. Human endocasts are also characterized by posterior location of foramina rotunda relative to optic canals, which could be correlated to lesser subnasal prognathism compared to living great apes. Species with larger brains (gorillas and humans) display greater sexual dimorphism in endocranial size, while sexual dimorphism in endocranial shape is restricted to gorillas, differences between males and females being at least partly due to allometry. Our study of endocranial variations in extant great apes and humans provides a new comparative dataset for studies of fossil hominid endocasts.     

Acetylation and phosphorylation processes modulate Tau’s binding to microtubules: A molecular dynamics study

The microtubule-associated protein Tau has its normal function impaired when undergoing post-translational modifications. In this work, molecular modelling techniques were used to infer the effects of acetylation and phosphorylation in Tau's overall conformation, electrostatics, and interactions, but mostly in Tau's ability to bind microtubules. Reported harmful Lys sites were mutated by its acetylated form, generating eight different acetylated Tau (aTau) analogues. Similarly, phosphorylation sites found in normal brains and in Alzheimer’s lesioned brains were considered to design phosphorylated Tau (pTau) analogues. All these designed variants were evaluated in intracellular fluid and near a microtubule (MT) model. Our in silico findings demonstrated that the electrostatic changes, due to the absence of positive Lys’ charges in acetylation cases, or the increasingly negative charge in the phosphorylated forms, hamper the association to the MT tubulins in most cases. Post-translational modifications also pose very distinct conformations to the ones described for native Tau, which hinders the microtubule-binding region (MTBR) and turns difficult the expected binding. Our study elucidates important molecular processes behind Tau abnormal function which can inspire novel therapeutics to address Alzheimer’s disease.     

Evidence for Finely-Regulated Asynchronous Growth of Toxoplasma gondii Cysts Based on Data-Driven Model Selection

Toxoplasma gondii establishes a chronic infection by forming cysts preferentially in the brain. This chronic infection is one of the most common parasitic infections in humans and can be reactivated to develop life-threatening toxoplasmic encephalitis in immunocompromised patients. Host-pathogen interactions during the chronic infection include growth of the cysts and their removal by both natural rupture and elimination by the immune system. Analyzing these interactions is important for understanding the pathogenesis of this common infection. We developed a differential equation framework of cyst growth and employed Akaike Information Criteria (AIC) to determine the growth and removal functions that best describe the distribution of cyst sizes measured from the brains of chronically infected mice. The AIC strongly support models in which T. gondii cysts grow at a constant rate such that the per capita growth rate of the parasite is inversely proportional to the number of parasites within a cyst, suggesting finely-regulated asynchronous replication of the parasites. Our analyses were also able to reject the models where cyst removal rate increases linearly or quadratically in association with increase in cyst size. The modeling and analysis framework may provide a useful tool for understanding the pathogenesis of infections with other cyst producing parasites.