MuSCA: A multi-scale source-sink carbon allocation model to explore carbon allocation in plants. An application to static apple tree structures

Background and aims: Carbon allocation in plants is usually represented at a topological scale, specific to each model. This makes the results obtained with different models, and the impact of their scales of representation, difficult to compare. In this study, we developed a multi-scale carbon allocation model (MuSCA) that allows the use of different, user-defined, topological scales of a plant, and assessment of the impact of each spatial scale on simulated results and computation time. Methods: Model multi-scale consistency and behaviour were tested on three realistic apple tree structures. Carbon allocation was computed at five scales, spanning from the metamer (the finest scale, used as a reference) up to first-order branches, and for different values of a sap friction coefficient. Fruit dry mass increments were compared across spatial scales and with field data. Key Results: The model was able to represent effects of competition for carbon assimilates on fruit growth. Intermediate friction parameter values provided results that best fitted field data. Fruit growth simulated at the metamer scale differed of ~1 % in respect to results obtained at growth unit scale and up to 60 % in respect to first order branch and fruiting unit scales. Generally, the coarser the spatial scale the more predicted fruit growth diverged from the reference. Coherence in fruit growth across scales was also differentially impacted, depending on the tree structure considered. Decreasing the topological resolution reduced computation time by up to four orders of magnitude. Conclusions: MuSCA revealed that the topological scale has a major influence on the simulation of carbon allocation. This suggests that the scale should be a factor that is carefully evaluated when using a carbon allocation model, or when comparing results produced by different models. Finally, with MuSCA, trade-off between computation time and prediction accuracy can be evaluated by changing topological scales

Exploring carbon allocation with a multi-scale model: the case of apple

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitièresUnderstanding the allocation of carbohydrates among organs is necessary to predict plant growth in relation to climatic conditions and agronomic practices. Despite the large number of studies on the subject of carbon allocation, no clear consensus exists on (i) the most appropriate topological scale (organ, metamer, compartment...) to represent this process on complex plant structures, and (ii) the importance of distances between organs in carbon transport. In this study, we implemented a generic source-sink based carbon allocation model, following the equation of the SIMWAL model, that takes into account the distances between sources and sinks, the sink strength and the availability of carbohydrates from photosynthesis. Our model makes use of multi-scale tree graph (MTG) to represent geometry and topology of a tree structure at different scales. Starting from the description of a plant at a given scale (e.g. metamer and growing unit scales), we defined additional grouping criteria (fruiting branches and main axis) that were used to represent the plant structure, and the process of carbon allocation at different spatial resolutions. Generic functions to determine the biomass and carbon demand of the individual organs described in an MTG were implemented and calibrated for apple trees (Fuji variety) by means of age and organ type dependent allometric equations and maximum potential Relative Growth Rate curves (RGR) obtained in a field experiment. Photosynthesis for individual leaves of the input MTG was estimated by means of a radiative model (RATP). The model was then applied to architectural mock-ups in the MTG format produced by the MappleT model, representing trees with high and low fruit loads. Simulations on simplified plant structures qualitatively showed the influence of the scale of representation and of the distance parameter on the predicted carbon allocation. In order to test assumptions regarding the effect of distance, the source-sink behavior and the suitability of the alternative scales of representation for predicting carbon allocation, the variability and spatial distribution of the simulated RGR were compared to field observations. Finally, a benchmarking was performed to compare the computational efficiency of the model when running at different scales. The presented multiscale model provides a framework to re-interpret the plant topology in order to test the influence of some assumptions at the basis of the carbon allocation process, such as branch autonomy or the effect of distance. It is also a mean to investigate the trade-offs between the detail at which a plant is described, and the accuracy and computational efficiency in predicting carbon allocation. The present work was developed on the OpenAlea platform, and will provide existing Functional Structural Plant Models with a new generic model to simulate carbon allocation in plants

An Insulin-to-Insulin Regulatory Network Orchestrates Phenotypic Specificity in Development and Physiology

Insulin-like peptides (ILPs) play highly conserved roles in development and physiology. Most animal genomes encode multiple ILPs. Here we identify mechanisms for how the forty Caenorhabditis elegans ILPs coordinate diverse processes, including development, reproduction, longevity and several specific stress responses. Our systematic studies identify an ILP-based combinatorial code for these phenotypes characterized by substantial functional specificity and diversity rather than global redundancy. Notably, we show that ILPs regulate each other transcriptionally, uncovering an ILP-to-ILP regulatory network that underlies the combinatorial phenotypic coding by the ILP family. Extensive analyses of genetic interactions among ILPs reveal how their signals are integrated. A combined analysis of these functional and regulatory ILP interactions identifies local genetic circuits that act in parallel and interact by crosstalk, feedback and compensation. This organization provides emergent mechanisms for phenotypic specificity and graded regulation for the combinatorial phenotypic coding we observe. Our findings also provide insights into how large hormonal networks regulate diverse traits

The Eps8/IRSp53/VASP Network Differentially Controls Actin Capping and Bundling in Filopodia Formation

There is a body of literature that describes the geometry and the physics of filopodia using either stochastic models or partial differential equations and elasticity and coarse-grained theory. Comparatively, there is a paucity of models focusing on the regulation of the network of proteins that control the formation of different actin structures. Using a combination of in-vivo and in-vitro experiments together with a system of ordinary differential equations, we focused on a small number of well-characterized, interacting molecules involved in actin-dependent filopodia formation: the actin remodeler Eps8, whose capping and bundling activities are a function of its ligands, Abi-1 and IRSp53, respectively; VASP and Capping Protein (CP), which exert antagonistic functions in controlling filament elongation. The model emphasizes the essential role of complexes that contain the membrane deforming protein IRSp53, in the process of filopodia initiation. This model accurately accounted for all observations, including a seemingly paradoxical result whereby genetic removal of Eps8 reduced filopodia in HeLa, but increased them in hippocampal neurons, and generated quantitative predictions, which were experimentally verified. The model further permitted us to explain how filopodia are generated in different cellular contexts, depending on the dynamic interaction established by Eps8, IRSp53 and VASP with actin filaments, thus revealing an unexpected plasticity of the signaling network that governs the multifunctional activities of its components in the formation of filopodia

Social cognition in people with schizophrenia: A cluster-analytic approach

Background The study aimed to subtype patients with schizophrenia on the basis of social cognition (SC), and to identify cut-offs that best discriminate among subtypes in 809 out-patients recruited in the context of the Italian Network for Research on Psychoses. Method A two-step cluster analysis of The Awareness of Social Inference Test (TASIT), the Facial Emotion Identification Test and Mayer-Salovey-Caruso Emotional Intelligence Test scores was performed. Classification and regression tree analysis was used to identify the cut-offs of variables that best discriminated among clusters. Results We identified three clusters, characterized by unimpaired (42%), impaired (50.4%) and very impaired (7.5%) SC. Three theory-of-mind domains were more important for the cluster definition as compared with emotion perception and emotional intelligence. Patients more able to understand simple sarcasm (14 for TASIT-SS) were very likely to belong to the unimpaired SC cluster. Compared with patients in the impaired SC cluster, those in the very impaired SC cluster performed significantly worse in lie scenes (TASIT-LI <10), but not in simple sarcasm. Moreover, functioning, neurocognition, disorganization and SC had a linear relationship across the three clusters, while positive symptoms were significantly lower in patients with unimpaired SC as compared with patients with impaired and very impaired SC. On the other hand, negative symptoms were highest in patients with impaired levels of SC. Conclusions If replicated, the identification of such subtypes in clinical practice may help in tailoring rehabilitation efforts to the person's strengths to gain more benefit to the person

Social cognition in people with schizophrenia: A cluster-analytic approach

Background The study aimed to subtype patients with schizophrenia on the basis of social cognition (SC), and to identify cut-offs that best discriminate among subtypes in 809 out-patients recruited in the context of the Italian Network for Research on Psychoses. Method A two-step cluster analysis of The Awareness of Social Inference Test (TASIT), the Facial Emotion Identification Test and Mayer-Salovey-Caruso Emotional Intelligence Test scores was performed. Classification and regression tree analysis was used to identify the cut-offs of variables that best discriminated among clusters. Results We identified three clusters, characterized by unimpaired (42%), impaired (50.4%) and very impaired (7.5%) SC. Three theory-of-mind domains were more important for the cluster definition as compared with emotion perception and emotional intelligence. Patients more able to understand simple sarcasm (14 for TASIT-SS) were very likely to belong to the unimpaired SC cluster. Compared with patients in the impaired SC cluster, those in the very impaired SC cluster performed significantly worse in lie scenes (TASIT-LI <10), but not in simple sarcasm. Moreover, functioning, neurocognition, disorganization and SC had a linear relationship across the three clusters, while positive symptoms were significantly lower in patients with unimpaired SC as compared with patients with impaired and very impaired SC. On the other hand, negative symptoms were highest in patients with impaired levels of SC. Conclusions If replicated, the identification of such subtypes in clinical practice may help in tailoring rehabilitation efforts to the person's strengths to gain more benefit to the person

Social cognition in people with schizophrenia: A cluster-analytic approach

Background The study aimed to subtype patients with schizophrenia on the basis of social cognition (SC), and to identify cut-offs that best discriminate among subtypes in 809 out-patients recruited in the context of the Italian Network for Research on Psychoses. Method A two-step cluster analysis of The Awareness of Social Inference Test (TASIT), the Facial Emotion Identification Test and Mayer-Salovey-Caruso Emotional Intelligence Test scores was performed. Classification and regression tree analysis was used to identify the cut-offs of variables that best discriminated among clusters. Results We identified three clusters, characterized by unimpaired (42%), impaired (50.4%) and very impaired (7.5%) SC. Three theory-of-mind domains were more important for the cluster definition as compared with emotion perception and emotional intelligence. Patients more able to understand simple sarcasm (14 for TASIT-SS) were very likely to belong to the unimpaired SC cluster. Compared with patients in the impaired SC cluster, those in the very impaired SC cluster performed significantly worse in lie scenes (TASIT-LI <10), but not in simple sarcasm. Moreover, functioning, neurocognition, disorganization and SC had a linear relationship across the three clusters, while positive symptoms were significantly lower in patients with unimpaired SC as compared with patients with impaired and very impaired SC. On the other hand, negative symptoms were highest in patients with impaired levels of SC. Conclusions If replicated, the identification of such subtypes in clinical practice may help in tailoring rehabilitation efforts to the person's strengths to gain more benefit to the person

Primary Care Psychiatry in Italy

In Italy, the importance of integrating primary care and mental health has only recently been grasped. Several reasons may explain this delay: a) until 2005, primary care physicians worked individually instead of in group practices, without any functional network or structured contacts with colleagues; b) community mental health centers with multiprofessional teams were well structured and widespread in several regions but focused on people with severe and persistent mental disorders; and c) specific national government health policies were lacking. Only two regions have implemented explicit policies on this issue. The "G. Leggieri" program started by the Emilia-Romagna region health government in 1999 aims to coordinate unsolicited bottom-up cooperation initiatives developing since the 1980s. In Liguria, a regional work group was established in 2010 to boost the strategic role of collaborative programs between primary care and mental health services. This article describes the most innovative experiences relating to primary care psychiatry in Italy