Predator-prey models: an application for the plankton dynamics of lake Geneva

In this paper we present a hierarchical Bayesian analysis for a predator-prey model applied to ecology considering the use of Markov Chain Monte Carlo methods. We consider the introduction of a random effect in the model and the presence of a covariate vector. An application to ecology is considered using a data set related to the plankton dynamics of lake Geneva for the year 1990. We also discuss some aspects of discrimination of the proposed models.

Growth and reproductive dynamics of the South American red shrimp, Pleoticus muelleri (Crustacea: Solenoceridae), from the southeastern coast of Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Structure and dynamics of a Cariniana estrellensis (Lecythidaceae) population in a fragment of Atlantic Forest in Minas Gerais, Brazil

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Patch-size and isolation effects in the Fisher-Kolmogorov equation

We examine the classical problem of the existence of a threshold size for a patch to allow for survival of a given population in the case where the patch is not completely isolated. The surrounding habitat matrix is characterized by a non-zero carrying capacity. We show that a critical patch size cannot be strictly defined in this case. We also obtain the saturation density in such a patch as a function of the size of the patch and the relative carrying capacity of the outer region. We argue that this relative carrying capacity is a measure of the isolation of the patch. Our results are then compared with conclusions drawn from observations of the population dynamics of understorey birds in fragments of the Amazonian forest and shown to qualitatively agree with them, offering an explanation for the importance of dispersal and isolation in these observations. Finally, we show that a generalized critical patch size can be introduced resorting to threshold densities for the observation of a given species.

Oviposition of a leaf-miner on Erythroxylum tortuosum (Erythroxylaceae) leaves: hierarchical variation of physical leaf traits

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

International technological dynamics in production sectors: An empirical analysis

A new methodology is proposed for evaluating the economic development opportunities associated with the different industries making up a country’s economic structure. To this end, neo-Schumpeterian concepts are used to reinterpret the tools afforded by the “product space” literature in an attempt to assess the technological pervasiveness and sophistication of different production sectors. The ultimate objective is to develop a description of today’s techno-productive paradigm and the differential role that the various sectors play in it. An analysis of export data from 113 countries and territories for 2005-2009 indicates that the key sectors in the world economy are: industrial machinery, scientific and medical instruments, and pharmaceuticals. The strong performance of sectors based on mature technologies suggests that key sectors originating in different stages in history can survive and overlap one another, much like geological strata, owing to the persistence of older technological systems.

Changes in plant species composition and functional traits along the successional trajectory of a restored patch of Atlantic Forest

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

How population loss through habitat boundaries determines the dynamics of a predator-prey system

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multi-planet extrasolar systems - detection and dynamics

20 years after the discovery of the first planets outside our solar system, the current exoplanetary population includes more than 700 confirmed planets around main sequence stars. Approximately 50% belong to multiple-planet systems in very diverse dynamical configurations, from two-planet hierarchical systems to multiple resonances that could only have been attained as the consequence of a smooth large-scale orbital migration. The first part of this paper reviews the main detection techniques employed for the detection and orbital characterization of multiple-planet systems, from the (now) classical radial velocity (RV) method to the use of transit time variations (TTV) for the identification of additional planetary bodies orbiting the same star. In the second part we discuss the dynamical evolution of multi-planet systems due to their mutual gravitational interactions. We analyze possible modes of motion for hierarchical, secular or resonant configurations, and what stability criteria can be defined in each case. In some cases, the dynamics can be well approximated by simple analytical expressions for the Hamiltonian function, while other configurations can only be studied with semi-analytical or numerical tools. In particular, we show how mean-motion resonances can generate complex structures in the phase space where different libration islands and circulation domains are separated by chaotic layers. In all cases we use real exoplanetary systems as working examples.

A decaying factor accounts for contained activity in neuronal networks with no need of hierarchical or modular organization

The mechanisms responsible for containing activity in systems represented by networks are crucial in various phenomena, for example, in diseases such as epilepsy that affect the neuronal networks and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided into modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory, through which containment of activity is achieved by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, namely, random, Barabasi-Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not spread out to the entire neuronal network, even when no special topological organization exists.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Time-dependent hierarchical organization of spatial working memory: a transcranial magnetic stimulation study

The performance of memory-guided saccades with two different delays (3 and 30 s of memorization) was studied in seven healthy subjects. Double-pulse transcranial magnetic stimulation (dTMS) with an interstimulus interval of 100 ms was applied over the right dorsolateral prefrontal cortex (DLPFC) early (1 s after target presentation) and late (28 s after target presentation). Early stimulation significantly increased in both delays the percentage of error in amplitude (PEA) of contralateral memory-guided saccades compared to the control experiment without stimulation. dTMS applied late in the delay had no significant effect on PEA. Furthermore, we found a significantly smaller effect of early stimulation in the long-delay paradigm. These results suggest a time-dependent hierarchical organization of the spatial working memory with a functional dominance of DLPFC during the early memorization, independent from the memorization delay. For a long memorization delay, however, working memory seems to have an additional, DLPFC-independent component.

Ca2+-dependent repair of pneumolysin pores: A new paradigm for host cellular defense against bacterial pore-forming toxins

Pneumolysin (PLY), a key virulence factor of Streptococcus pneumoniae, permeabilizes eukaryotic cells by forming large trans-membrane pores. PLY imposes a puzzling multitude of diverse, often mutually excluding actions on eukaryotic cells. Whereas cytotoxicity of PLY can be directly attributed to the pore-mediated effects, mechanisms that are responsible for the PLY-induced activation of host cells are poorly understood. We show that PLY pores can be repaired and thereby PLY-induced cell death can be prevented. Pore-induced Ca2+ entry from the extracellular milieu is of paramount importance for the initiation of plasmalemmal repair. Nevertheless, active Ca2+ sequestration that prevents excessive Ca2+ elevation during the execution phase of plasmalemmal repair is of no less importance. The efficacy of plasmalemmal repair does not only define the fate of targeted cells but also intensity, duration and repetitiveness of PLY-induced Ca2+ signals in cells that were able to survive after PLY attack. Intracellular Ca2+ dynamics evoked by the combined action of pore formation and their elimination mimic the pattern of receptor-mediated Ca2+ signaling, which is responsible for the activation of host immune responses. Therefore, we postulate that plasmalemmal repair of PLY pores might provoke cellular responses that are similar to those currently ascribed to the receptor-mediated PLY effects. Our data provide new insights into the understanding of the complexity of cellular non-immune defense responses to a major pneumococcal toxin that plays a critical role in the establishment and the progression of life-threatening diseases. Therapies boosting plasmalemmal repair of host cells and their metabolic fitness might prove beneficial for the treatment of pneumococcal infections.

Mechanisms of recognition and binding of α-TTP to the plasma membrane by multi-scale molecular dynamics simulations

We used multiple sets of simulations both at the atomistic and coarse-grained level of resolution to investigate interaction and binding of α-tochoperol transfer protein (α-TTP) to phosphatidylinositol phosphate lipids (PIPs). Our calculations indicate that enrichment of membranes with such lipids facilitate membrane anchoring. Atomistic models suggest that PIP can be incorporated into the binding cavity of α-TTP and therefore confirm that such protein can work as lipid exchanger between the endosome and the plasma membrane. Comparison of the atomistic models of the α-TTP-PIPs complex with membrane-bound α-TTP revealed different roles for the various basic residues composing the basic patch that is key for the protein/ligand interaction. Such residues are of critical importance as several point mutations at their position lead to severe forms of ataxia with vitamin E deficiency (AVED) phenotypes. Specifically, R221 is main residue responsible for the stabilization of the complex. R68 and R192 exchange strong interactions in the protein or in the membrane complex only, suggesting that the two residues alternate contact formation, thus facilitating lipid flipping from the membrane into the protein cavity during the lipid exchange process. Finally, R59 shows weaker interactions with PIPs anyway with a clear preference for specific phosphorylation positions, hinting a role in early membrane selectivity for the protein. Altogether, our simulations reveal significant aspects at the atomistic scale of interactions of α-TTP with the plasma membrane and with PIP, providing clarifications on the mechanism of intracellular vitamin E trafficking and helping establishing the role of key residue for the functionality of α-TTP.

Network Dynamics in Natural Resource Governance: A Case Study of Swiss Landscape Management

Structural characteristics of social networks have been recognized as important factors of effective natural resource governance. However, network analyses of natural resource governance most often remain static, even though governance is an inherently dynamic process. In this article, we investigate the evolution of a social network of organizational actors involved in the governance of natural resources in a regional nature park project in Switzerland. We ask how the maturation of a governance network affects bonding social capital and centralization in the network. Applying separable temporal exponential random graph modeling (STERGM), we test two hypotheses based on the risk hypothesis by Berardo and Scholz (2010) in a longitudinal setting. Results show that network dynamics clearly follow the expected trend toward generating bonding social capital but do not imply a shift toward less hierarchical and more decentralized structures over time. We investigate how these structural processes may contribute to network effectiveness over time.