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From Hubble Frontier Fields photometry, and data from the Multi Unit Spectroscopic Explorer on the Very Large Telescope, we build the Fundamental Plane (FP) relation for the early-type galaxies of the cluster Abell S1063. We use this relation to develop an improved strong lensing model of the total mass distribution of the cluster, determining the velocity dispersions of all 222 cluster members included in the model from their measured structural parameters. Fixing the hot gas component from X-ray data, the mass density distributions of the diffuse dark matter haloes are optimised by comparing the observed and model-predicted positions of 55 multiple images of 20 background sources, distributed over the redshift range $0.73-6.11$. We determine the uncertainties on the model parameters with Monte Carlo Markov chains. Compared to previous works, our model allows for the inclusion of a scatter on the relation between the total mass and the velocity dispersion of cluster members, which also shows a shallower slope. We notice a lower statistical uncertainty on the value of some parameters, such as the core radius, of the diffuse mass component of the cluster. Thanks to a new estimate of the stellar mass of all members, we measure the cumulative projected mass profiles out to a radius of 350 kpc, for all baryonic and dark matter components of the cluster. At the outermost radius, we find a baryon fraction of $0.147 \pm 0.002$. We compare the sub-haloes as described by our model with recent hydrodynamical cosmological simulations. We find good agreement in terms of the stellar over total mass fraction. On the other hand, we report some discrepancies in terms of the maximum circular velocity, which is an indication of their compactness, and the sub-halo mass function in the central cluster regions., Comment: 15 pages, 13 figures, 5 tables, Astronomy and Astrophysics, in press

The stability of rhythmic interlimb coordination is governed by the coupling between limb movements. While it is amply documented how coordinative performance depends on movement frequency, theoretical considerations and recent empirical findings suggest that interlimb coupling (and hence coordinative stability) is actually mediated more by movement amplitude. Here, we present the results of a reanalysis of the data of Post, Peper, and Beek (2000), which were collected in an experiment aimed at teasing apart the effects of frequency and amplitude on coordinative stability of both steady-state and perturbed in-phase and antiphase interlimb coordination. The dataset in question was selected because we found indications that the according results were prone to artifacts, which may have obscured the potential effects of amplitude on the post-perturbation stability of interlimb coordination. We therefore redid the same analysis based on movement signals that were normalized each half-cycle for variations in oscillation center and movement frequency. With this refined analysis we found that (1) stability of both steady-state and perturbed coordination indeed seemed to depend more on amplitude than on movement frequency per se, and that (2) whereas steady-state antiphase coordination became less stable with increasing frequency for prescribed amplitudes, in-phase coordination became more stable at higher frequencies. Such effects may have been obscured in previous studies due to (1) unnoticed changes in performed amplitudes, and/or (2) artifacts related to inappropriate data normalization. The results of the present reanalysis therefore give cause for reconsidering the relation between the frequency, amplitude, and stability of interlimb coordination.

We present results of three-dimensional direct numerical simulations (DNS) and large eddy simulations (LES) of turbulent gravity currents with a discontinuous Galerkin finite elements method. In particular, we consider the lock-exchange test case as a benchmark for gravity currents. Since, to the best of our knowledge, non-Boussinesq three-dimensional reference DNS are not available in the literature for this test case, we first perform a DNS experiment. The DNS provides an accurate description of the turbulent phenomena and highlights some differences with respect to the Boussinesq regime, like the non-symmetric pattern in the evolution of instabilities at the interfacial region and the fact that less turbulent structures are present due to greater stratification. A periodic pattern is also evident in the time evolution of turbulent mixing. The DNS is then employed to assess the performance of different LES models. In particular, we have considered the isotropic dynamic model and an anisotropic dynamic model. The LES results provide a first indication about the superiority of dynamic models with respect to no-model LES. However, the considered Reynolds numbers in the non-Boussinesq context are still too low to draw firm conclusions about the superiority of the present explicit LES approach with respect to an implicit LES approach.

The famous Hamiltonian amplitude equation (HAE) and the well known cubic nonlinear Schrödinger equation (CNLSE) with repulsive delta potential have been taken into account for integrability analysis, establishment of conservation laws (CLs) and traveling wave (soliton) solutions in the polynomial forms. The integrability investigation has been done with the help of Painlevé test (P-test), conserved densities and associated fluxes required for the establishment of CLs are found with the help of scaling invariance approach whereas the traveling wave solutions in the form of polynomials are derived by unified method (UM).

Social media consume an increasing portion of people’s daily lives and are important platforms in the realms of politics and marketing for reaching out to voters and consumers. Describing and predicting the behaviour of users on social media is thus of interest for companies and politicians, as well as researchers studying information diffusion and human behaviour. Twitter is a fast-paced microblog that is host to debates, conversations, and campaigns between users as well as organisations all over the world. As all interactions on Twitter are publicly available, the platform has been used as a data source for many studies. While previous works have mainly focused on interaction dynamics for specific user groups or topics, or on predicting virality, the perspective we take in this thesis is to focus on the level of the individual conversation and to use dynamical models to characterise user interactions. The most prominent characteristic of Twitter conversations is the clear presence of peaks in engagement. We introduce a classification scheme based on peak configurations to quantify the interaction patterns present on Twitter and find that around 70% of conversations exhibit a single peak in user engagement, usually followed by a slower decay. A second order linear model describes the dynamics of the single peak scenario well, indicating that most conversations have two phases - an initial phase of rapid rise and decline in interaction rate, followed by a phase of slowly decreasing interaction rate. We quantify the characteristic life span of Twitter conversations in terms of the second order system time constants. Furthermore, we investigate the impact that users with many followers, so called influencers, have on conversation dynamics, and in particular on the emergence of interaction peaks. The data suggests that influencers do have a noticeable, albeit limited effect on the spreading of conversations to other users.

M105 is an early-type galaxy in the nearby Leo I group, the closest galaxy group to contain all galaxy types and therefore an excellent environment to explore the low-mass end of intra-group light (IGL) assembly. We present a new extended kinematic survey of planetary nebulae (PNe) in M105 and the surrounding 30'x30' in the Leo I group with the Planetary Nebula Spectrograph. We use PNe as kinematic tracers of the diffuse stellar light in the halo and IGL and employ Gaussian mixture models to separate contributions from the companion galaxy NGC 3384 and associate PNe with halo and IGL components around M105. We present a catalogue of 314 PNe and firmly associate 93 with NGC 3384 and 169 with M105. The PNe in M105 are further associated with its halo and the surrounding exponential envelope. We construct smooth velocity and velocity dispersion fields and calculate projected rotation, velocity dispersion, and $\lambda_R$ profiles for each component. Halo PNe exhibit declining velocity dispersion and rotation profiles, while the velocity dispersion and rotation of the exponential envelope increase notably at large radii. We identify three regimes with distinct kinematics that are linked to distinct stellar population properties: (i) the rotating core (within $1~R_\mathrm{eff}$) formed in situ and dominated by metal-rich ([M/H]~0) stars likely formed in situ, (ii) the halo from 1 to $7.5~R_\mathrm{eff}$ consisting of intermediate-metallicity stars ([M/H]>-1), either formed in situ or brought in through major mergers, and (iii) the exponential envelope reaching beyond our farthest data point at 16 $R_\mathrm{eff}$, predominately composed of metal-poor ([M/H], Comment: Submitted to A&A, comments welcome. 20 pages, 14 figures, abstract abridged

En aquesta tesi, establirem la base teòrica d'alguns algoritmes de reducció de la dimensionalitat com el GPLVM i la seva aplicació a la reproducció d'una sèrie temporal d'observables amb el GPDM i la seva generalització amb control CGPDM. Finalment, anem a introduir un nou model computacionalment més eficient, el MoCGPDM aplicant una barreja d'experts. L'última secció consistirà en afinar el model i comparar-lo amb el model previ. En esta tesis, estableceremos la base teórica de algunos algoritmos de reducción de la dimensionalidad como el GPLVM y su aplicación a la reproducción de una serie temporal de observables con el GPDM y su generalización con control CGPDM. Finalmente, vamos a introducir un nuevo modelo computacionálmente más eficiente, el MoCGPDM aplicando una mezcla de expertos. La última sección consistirá en afinar el modelo y compararlo con el modelo previo. In this thesis, we establish the theoretical basis of some dimensional reduction algorithms like the GPLVM and their application to the reproduction of a time series of observable data with the GPDM and its generalization with control CGPDM. Finally, we are going to introduce a new more time efficient model MoCGPDM applying a mixture of experts. And the final section will consist in fine-tuning the model and compare it to the previous model.

The design and application of Soft Sensors (SSs) in the process industry is a growing research field, which needs to mediate problems of model accuracy with data availability and computational complexity. Black-box machine learning (ML) methods are often used as an efficient tool to implement SSs. Many efforts are, however, required to properly select input variables, model class, model order and the needed hyperparameters. The aim of this work was to investigate the possibility to transfer the knowledge acquired in the design of a SS for a given process to a similar one. This has been approached as a transfer learning problem from a source to a target domain. The implementation of a transfer learning procedure allows to considerably reduce the computational time dedicated to the SS design procedure, leaving out many of the required phases. Two transfer learning methods have been proposed, evaluating their suitability to design SSs based on nonlinear dynamical models. Recurrent neural structures have been used to implement the SSs. In detail, recurrent neural networks and long short-term memory architectures have been compared in regard to their transferability. An industrial case of study has been considered, to evaluate the performance of the proposed procedures and the best compromise between SS performance and computational effort in transferring the model. The problem of labeled data scarcity in the target domain has been also discussed. The obtained results demonstrate the suitability of the proposed transfer learning methods in the design of nonlinear dynamical models for industrial systems.

This book is a collection of selected research articles discussing the analysis of infectious diseases by using mathematical modelling in recent times. Divided into two parts, the book gives a general and country-wise analysis of Covid-19. Analytical and numerical techniques for virus models are presented along with the application of mathematical modelling in the analysis of their spreading rates and treatments. The book also includes applications of fractional differential equations as well as ordinary, partial and integro-differential equations with optimization methods. Probability distribution and their bio-mathematical applications have also been studied. This book is a valuable resource for researchers, scholars, biomathematicians and medical experts. SERB, NBHM and FCT published

A dynamical model consists of a continuous self-map $T:\mathcal{X}\to \mathcal{X}$ of a compact state space $\mathcal{X}$ and a continuous observation function $f:\mathcal{X}\to \mathbb{R}$. This paper considers the fitting of a parametrized family of dynamical models to an observed real-valued stochastic process using empirical risk minimization. The limiting behavior of the minimum risk parameters is studied in a general setting. We establish a general convergence theorem for minimum risk estimators and ergodic observations. We then study conditions under which empirical risk minimization can effectively separate signal from noise in an additive observational noise model. The key condition in the latter results is that the family of dynamical models has limited complexity, which is quantified through a notion of entropy for families of infinite sequences that connects covering number based entropies with topological entropy studied in dynamical systems. We establish close connections between entropy and limiting average mean widths for stationary processes, and discuss several examples of dynamical models.

We present a sound and automated approach to synthesizing safe, digital controllers for physical plants represented as time-invariant models. Models are linear differential equations with inputs, evolving over a continuous state space. The synthesis precisely accounts for the effects of finite-precision arithmetic introduced by the controller. The approach uses counterexample-guided inductive synthesis: an inductive generalization phase produces a controller that is known to stabilize the model but that may not be safe for all initial conditions of the model. Safety is then verified via bounded model checking: if the verification step fails, a counterexample is provided to the inductive generalization, and the process further iterates until a safe controller is obtained. We demonstrate the practical value of this approach by automatically synthesizing safe controllers for physical plant models from the digital control literature.

The H0LiCOW collaboration inferred via gravitational lensing time delays a Hubble constant $H_0=73.3^{+1.7}_{-1.8}$ km s$^{-1}{\rm Mpc}^{-1}$, describing deflector mass density profiles by either a power-law or stars plus standard dark matter halos. The mass-sheet transform (MST) that leaves the lensing observables unchanged is considered the dominant source of residual uncertainty in $H_0$. We quantify any potential effect of the MST with flexible mass models that are maximally degenerate with H0. Our calculation is based on a new hierarchical approach in which the MST is only constrained by stellar kinematics. The approach is validated on hydrodynamically simulated lenses. We apply the method to the TDCOSMO sample of 7 lenses (6 from H0LiCOW) and measure $H_0=74.5^{+5.6}_{-6.1}$ km s$^{-1}{\rm Mpc}^{-1}$. In order to further constrain the deflector mass profiles, we then add imaging and spectroscopy for 33 strong gravitational lenses from the SLACS sample. For 9 of the SLAC lenses we use resolved kinematics to constrain the stellar anisotropy. From the joint analysis of the TDCOSMO+SLACS sample, we measure $H_0=67.4^{+4.1}_{-3.2}$ km s$^{-1}{\rm Mpc}^{-1}$, assuming that the TDCOSMO and SLACS galaxies are drawn from the same parent population. The blind H0LiCOW, TDCOSMO-only and TDCOSMO+SLACS analyses are in mutual statistical agreement. The TDCOSMO+SLACS analysis prefers marginally shallower mass profiles than H0LiCOW or TDCOSMO-only. While our new analysis does not statistically invalidate the mass profile assumptions by H0LiCOW, and thus their $H_0$ measurement relying on those, it demonstrates the importance of understanding the mass density profile of elliptical galaxies. The uncertainties on $H_0$ derived in this paper can be reduced by physical or observational priors on the form of the mass profile, or by additional data, chiefly spatially resolved kinematics of lens galaxies., Comment: accepted by A&A. Full analysis available at https://github.com/TDCOSMO/hierarchy_analysis_2020_public updated permanent analysis script links

We tested the implementation of different IMFs in our model for the chemical evolution of ellipticals, with the aim of reproducing the observed relations of [Fe/H] and [Mg/Fe] abundances with galaxy mass in a sample of early-type galaxies selected from the SPIDER-SDSS catalog. Abundances in the catalog were derived from averaged spectra, obtained by stacking individual spectra according to central velocity dispersion, as a proxy of galaxy mass. We tested initial mass functions already used in a previous work, as well as two new models, based on low-mass tapered ("bimodal") IMFs, where the IMF becomes either (1) bottom-heavy in more massive galaxies, or (2) is time-dependent, switching from top-heavy to bottom-heavy in the course of galactic evolution. We found that observations could only be reproduced by models assuming either a constant, Salpeter IMF, or a time-dependent distribution, as other IMFs failed. We further tested the models by calculating their M/L ratios. We conclude that a constant, time-independent bottom-heavy IMF does not reproduce the data, especially the increase of the $[\alpha/Fe]$ ratio with galactic stellar mass, whereas a variable IMF, switching from top to bottom-heavy, can match observations. For the latter models, the IMF switch always occurs at the earliest possible considered time, i.e. $t_{\text{switch}}= 0.1$ Gyr., Comment: 24 pages, 25 figures, accepted for publication on MNRAS

Data-driven modeling of stochastic nonlinear systems is recognized as a very challenging problem, even when reduced to a parameter estimation problem. A main difficulty is the intractability of the likelihood function, which renders favored estimation methods, such as the maximum likelihood method, analytically intractable. During the last decade, several numerical methods have been developed to approximately solve the maximum likelihood problem. A class of algorithms that attracted considerable attention is based on sequential Monte Carlo algorithms (also known as particle filters/smoothers) and particle Markov chain Monte Carlo algorithms. These algorithms were able to obtain impressive results on several challenging benchmark problems; however, their application is so far limited to cases where fundamental limitations, such as the sample impoverishment and path degeneracy problems, can be avoided. This thesis introduces relatively simple alternative parameter estimation methods that may be used for fairly general stochastic nonlinear dynamical models. They are based on one-step-ahead predictors that are linear in the observed outputs and do not require the computations of the likelihood function. Therefore, the resulting estimators are relatively easy to compute and may be highly competitive in this regard: they are in fact defined by analytically tractable objective functions in several relevant cases. In cases where the predictors are analytically intractable due to the complexity of the model, it is possible to resort to {plain} Monte Carlo approximations. Under certain assumptions on the data and some conditions on the model, the convergence and consistency of the estimators can be established. Several numerical simulation examples and a recent real-data benchmark problem demonstrate a good performance of the proposed method, in several cases that are considered challenging, with a considerable reduction in computational time in comparison with state-of-the-art sequential Monte Carlo implementations of the ML estimator. Moreover, we provide some insight into the asymptotic properties of the proposed methods. We show that the accuracy of the estimators depends on the model parameterization and the shape of the unknown distribution of the outputs (via the third and fourth moments). In particular, it is shown that when the model is non-Gaussian, a prediction error method based on the Gaussian assumption is not necessarily more accurate than one based on an optimally weighted parameter-independent quadratic norm. Therefore, it is generally not obvious which method should be used. This result comes in contrast to a current belief in some of the literature on the subject. Furthermore, we introduce the estimating functions approach, which was mainly developed in the statistics literature, as a generalization of the maximum likelihood and prediction error methods. We show how it may be used to systematically define optimal estimators, within a predefined class, using only a partial specification of the probabilistic model. Unless the model is Gaussian, this leads to estimators that are asymptotically uniformly more accurate than linear prediction error methods when quadratic criteria are used. Convergence and consistency are established under standard regularity and identifiability assumptions akin to those of prediction error methods. Finally, we consider the problem of closed-loop identification when the system is stochastic and nonlinear. A couple of scenarios given by the assumptions on the disturbances, the measurement noise and the knowledge of the feedback mechanism are considered. They include a challenging case where the feedback mechanism is completely unknown to the user. Our methods can be regarded as generalizations of some classical closed-loop identification approaches for the linear time-invariant case. We provide an asymptotic analysis of the methods, and demonstrate their properties in a simulation example. Det är välkänt att datadriven modellering av icke-linjära stokastiska system är ett utmanande problem, även i fallen där det kan reduceras till ren parameterskattning. Den huvudsakliga svårigheten är att likelihoodfunktionen inte är analytiskt hanterbar, vilket medför problem vid tillämpning av standardmetoder såsom maximum likelihood. Under det senaste decenniet har numeriska algoritmer baserade på sekventiell Monte Carlo (partikelfilter) rönt stort intresse. Dessa algoritmer har imponerande prestanda på en rad benchmarkproblem; dock så är deras praktiska tillämpning än så länge begränsad till specialfall där fundamentala begränsningar kan undvikas. Den här avhandlingen introducerar nya metoder som kan användas för parameterestimering i en stor klass av icke-linjära stokastiska system. Metoderna baseras på enstegsprediktorer som är linjära i systemets observerade utsignal. Våra nya metoder kräver inte att likelihoodfunktionen beräknas; istället använder de, i en rad relevanta fall, analytiskt hanterbara uttryck som gör dem högst attraktiva. I fallen där prediktorerna är analytiskt ohanterbara (på grund av modellens komplexitet) kan man använda vanliga Monte Carlo-approximationer. Vi visar att klassiska resultat från asymptotisk teori kan användas under rimliga antaganden, och via dessa, att våra föreslagna skattare är konsistenta samt asymptotiskt normalfördelade. Skattarnas prestanda utvärderas i numeriska simulationer, samt nyligen föreslagna benchmarkproblem baserade på verklig data, med bra resultat. Vidare diskuterar vi de föreslagna metodernas asymptotiska egenskaper: deras nogrannhet beror inte enbart på hur modellen har parametriserats, utan även på datans sannolikhetsdistribution (via dess tredje och fjärde ordningens moment). Speciellt visar vi att när modellen inte uppfyller antaganden om normalfördelning, så är en prediktionsfelsmetod baserad på ett normalfördelningsantagande inte nödvändigtvis bättre än en prediktionsfelsmetod baserad på en viktad parameteroberonde kvadratisk norm. Vår slutsats är att det därför inte är uppenbart vilken prediktionsfelsmetod som bör användas. Detta resultat står i kontrast mot den vedertagna uppfattningen som finns i delar av litteraturen. Avhandlingen introducerar även den så kallade skattningsfunktionsmetoden (främst utvecklad inom statistiklitteraturen) som en generalisering av maximum likelihood- och prediktionsfelsmetoderna. Vi visar hur denna metod kan användas för att systematiskt konstruera optimala skattare, inom en specifierad modellklass, från enbart partiella specifikationer på den underliggande probabilistiska modellen. Detta ger skattare som asymptotiskt är likformigt mer noggrannare än linjära prediktionsfelsmetoder baserade på kvadratiska optimeringsobjektiv. Vi härleder konvergensresultat, såsom konsistens, för dessa skattare under standardantaganden. Slutligen behandlar vi identifieringsproblemet för återkopplade system som är stokastiska och icke-linjära. Vi behandlar ett par varianter på antaganden om mät- samt processbrus, och på kunskap om hur systemets återkoppling sker. Ett speciellt utmanande fall är när återkopplingsmekanismen är helt okänd. Metoderna vi föreslår kan ses som generaliseringar av klassiska metoder för identifiering av återkopplade system med linjärt tidsvarierande parametrar. Vi utför en asymptotisk analys av metoderna, och demonstrerar deras prestanda i numeriska experiment. QC 20190816

International audience; The entry, descent and landing of Schiaparelli, the ExoMars Entry, descent and landing Demonstrator Module (EDM), offered a rare (once-per-mission) opportunity for in situ investigations of the martian environment over a wide altitude range. The aim of the ExoMars AMELIA experiment was to exploit the Entry, Descent and Landing System (EDLS) engineering measurements for scientific investigations of Mars’ atmosphere and surface. Here we present the simulations, modelling and the planned investigations prior to the Entry, Descent and Landing (EDL) event that took place on 19th October 2016. Despite the unfortunate conclusion of the Schiaparelli mission, flight data recorded during the entry and the descent until the loss of signal, have been recovered. These flight data, although limited and affected by transmission interruptions and malfunctions, are essential for investigating the anomaly and validating the EDL operation, but can also contribute towards the partial achievement of AMELIA science objectives

Riflemen’s trainers are efficiently applied for the training of the armed forces, police, and shooting sportsmen. However, the training quality directly depends on the perfection of trainers as well as on their functional facilities. One of them is the shooting possibility by using various weapons. The new modification of a practise range is created: it’s included three types of weapons imitators for 5.56 mm G-36 assault rifle, 7.62 mm FN MAG machine gun and, 84 mm CARL GUSTAF recoilless rifle. The work presents the universal dynamical and mathematical models of the one way operation pneumatic drive for recoil imitation, results of its investigation and description of the new modification of laser range for riflemen.

International audience; All surveys include observational biases, which makes it impossible to directly compare properties of discovered trans-Neptunian Objects (TNOs) with dynamical models. However, by carefully keeping track of survey pointings on the sky, detection limits, tracking fractions, and rate cuts, the biases from a survey can be modeled in Survey Simulator software. A Survey Simulator takes an intrinsic orbital model (from, for example, the output of a dynamical Kuiper belt emplacement simulation) and applies the survey biases, so that the biased simulated objects can be directly compared with real discoveries. This methodology has been used with great success in the Outer Solar System Origins Survey (OSSOS) and its predecessor surveys. In this chapter, we give four examples of ways to use the OSSOS Survey Simulator to gain knowledge about the true structure of the Kuiper Belt. We demonstrate how to statistically compare different dynamical model outputs with real TNO discoveries, how to quantify detection biases within a TNO population, how to measure intrinsic population sizes, and how to use upper limits from non-detections. We hope this will provide a framework for dynamical modelers to statistically test the validity of their models.

We present a deep, low-frequency radio continuum study of the nearby Fanaroff--Riley class I (FR I) radio galaxy 3C 31 using a combination of LOw Frequency ARray (LOFAR; 30--85 and 115--178 MHz), Very Large Array (VLA; 290--420 MHz), Westerbork Synthesis Radio Telescope (WSRT; 609 MHz) and Giant Metre Radio Telescope (GMRT; 615 MHz) observations. Our new LOFAR 145-MHz map shows that 3C 31 has a largest physical size of $1.1$ Mpc in projection, which means 3C 31 now falls in the class of giant radio galaxies. We model the radio continuum intensities with advective cosmic-ray transport, evolving the cosmic-ray electron population and magnetic field strength in the tails as functions of distance to the nucleus. We find that if there is no in-situ particle acceleration in the tails, then decelerating flows are required that depend on radius $r$ as $v\propto r^{\beta}$ ($\beta\approx -1$). This then compensates for the strong adiabatic losses due to the lateral expansion of the tails. We are able to find self-consistent solutions in agreement with the entrainment model of Croston & Hardcastle, where the magnetic field provides $\approx$$1/3$ of the pressure needed for equilibrium with the surrounding intra-cluster medium (ICM). We obtain an advective time-scale of $\approx$$190$ Myr, which, if equated to the source age, would require an average expansion Mach number ${\cal M} \approx 5$ over the source lifetime. Dynamical arguments suggest that instead, either the outer tail material does not represent the oldest jet plasma or else the particle ages are underestimated due to the effects of particle acceleration on large scales., Comment: MNRAS accepted, 17 pages, 9 figures, 2 tables + 3 pages appendix with 3 figures and 2 tables

Although forest succession has traditionally been approached as a deterministic process, successional trajectories of vegetation change vary widely, even among nearby stands with similar environmental conditions and disturbance histories. Here, we provide the first attempt, to our knowledge, to quantify predictability and uncertainty during succession based on the most extensive long-term datasets ever assembled for Neotropical forests. We develop a novel approach that integrates deterministic and stochastic components into different candidate models describing the dynamical interactions among three widely used and interrelated forest attributes - stem density, basal area, and species density. Within each of the seven study sites, successional trajectories were highly idiosyncratic, even when controlling for prior land use, environment, and initial conditions in these attributes. Plot factors were far more important than stand age in explaining successional trajectories. For each site, the best-fit model was able to capture the complete set of time series in certain attributes only when both the deterministic and stochastic components were set to similar magnitudes. Surprisingly, predictability of stem density, basal area, and species density did not show consistent trends across attributes, study sites, or land use history, and was independent of plot size and time series length. The model developed here represents the best approach, to date, for characterizing autogenic successional dynamics and demonstrates the low predictability of successional trajectories. These high levels of uncertainty suggest that the impacts of allogenic factors on rates of change during tropical forest succession are far more pervasive than previously thought, challenging the way ecologists view and investigate forest regeneration. © 2015, National Academy of Sciences. All rights reserved.

International audience; Title: Utilisation de modèles dynamiques pour l'optimisation des traitements des patients infectés par le VIH: une approche par analysis Bayésienne séquentielle Mélanie Prague Résumé : The use of dynamic mechanistic models based on ordinary differential equations (ODE) has greatly improved the knowledge of the dynamics of HIV and of the immune system. Their flexibility for fitting data and prediction abilities make them a good tool for optimization of the design delivery and efficacy of new intervention in the HIV field. We present the problem of inference in ODE models with mixed effects on parameters. We introduce a Bayesian estimation procedure based on the maximization of the penalized likelihood and a normal approximation of posteriors, which is implemented in the NIMROD software. We investigate the impact of pooling different data by using a sequential bayesian analysis (SBA), which uses posteriors of a previous study as new priors. We show that the normal approximation of the posteriors, which constrains the shape of new priors, leads to gains in accuracy of estimation while reducing computation times. The illustration is from two clinical trials of combination of antiretroviral therapies (cART) : ALBI ANRS 070 and PUZZLE ANRS 104. This paper reproduces some unpublished work from my PhD thesis. It is an extension of my oral presentation on the same topic at the 47th Journées de Statistique organized by the French Statistical Society (SFdS) in Lille, France, May 2015, when being awarded the Marie-Jeanne Laurent-Duhamel prize. Abstract: L'utilisation des modèles mécanistes dynamiques basés sur des équations différentielles ordinaires (ODE) a considérablement amélioré les connaissances de la dynamique HIV-système immunitaire. Leur flexibilité à ajuster des données et leur capacité de predictions en font un bon outil pour l'optimization du plan d'expérience et de l'analyse d'efficacité d'interventions nouvelle dans le domaine du VIH. Nous traitons des méthodes d'estimation pour les ODEs dont les paramètres sont représentés par des modèles à effet mixtes. Nous proposons une estimation Bayésienne par maximisation de la vraisemblance pénalisée et basée sur l'approximation normale des a posteriori, implémentée dans le logiciel NIMROD. Nous discutons l'impact d'une analyse séquentielle Bayesienne (SBA) permettant d'analyser plusieurs jeux de données en utilisant comme nouvel a priori l'a posteriori des analyses précédentes. Nous illustrons que l'approximation normale de l'a posteriori, qui contraint la forme des nouveaux a priori, permet un gain en précision de l'estimation et diminue les temps de calculs. Nous illustrons la méthode avec des données issues de deux essais cliniques testant des combinaisons d'antirétroviraux (cART): ALBI ANRS 070 et PUZZLE ANRS 104. Cet article reproduit des résultats non publiés de mon manuscript de thèse. C'est une extension de la conférence sur le même sujet que j'ai eu l'honneur de donner lors de la réception du prix Marie-Jeanne Laurent-Duhamel, dans le cadre des 47èmes Journées de Statistique organisées par la Société Française de Statistique à Lille, France, en mai 2015.

If the measure of computational complexity, defined as the computation time required to solve a problem, is independent from the computer used, the measure reflects the complexity of the algorithm and the problem instance. If the measure further does not depend on the algorithm, it purely reflects the complexity of the problem instance. Focusing on the stable marriage problem, we propose a mapping from problem instances to dynamical systems. The computational time required for a dynamical system to reach equilibrium is used to measure the complexity. A diagram similar to a phase diagram of dynamical systems is proposed to indicate the structure of stable manifolds of dynamical systems. Based on computer simulations, we conjecture that the diagram is qualitatively invariant to the mapping and naturally reflects the complexity of the problem instance. The diagram indicates not only that the stable equilibrium corresponds to the stable matchings but also that the contour structure corresponds to the discrete structure of the lattice formed by the stable matchings.

This work deals with Direct Numerical Simulations (DNS) and Large Eddy Simulations (LES) of turbulent gravity currents, performed by means of a Discontinuous Galerkin (DG) Finite Element method. In particular, a DG-LES approach in which the filter operation is built in the numerical discretization has been employed, similarly to VMS approaches. Numerical simulations of non-Boussinesq lock-exchange benchmark problems show that, in the DNS case, the proposed method allows to correctly reproduce relevant features of variable density flows with gravity. Moreover LES results highlight the excessively high dissipation of the Smagorinsky model with respect to the Germano dynamic procedure, providing a first indication of the superiority of dynamic models in the context of gravity currents.

Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box&ndash, Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box&ndash, Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box&ndash, Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative binomial regression model was formulated for fitting malaria incidence in Mopani and Vhembe―two of the epidemic district municipalities in Limpopo, South Africa. In particular, a zero-inflated negative binomial regression model was formulated for daily malaria counts as a function of some climate variables, with the aim of identifying the model that best predicts reported malaria cases. Results from this study show that daily rainfall amount and the average temperature at various lags have a significant influence on malaria incidence in the study areas. The significance of zero inflation on the malaria count was examined using the Vuong test and the result shows that zero-inflated negative binomial regression model fits the data better. A dynamical climate-based model was further used to investigate the population dynamics of mosquitoes over the two regions. Findings highlight the significant roles of Anopheles arabiensis on malaria transmission over the regions and suggest that vector control activities should be intense to eradicate malaria in Mopani and Vhembe districts. Although An. arabiensis has been identified as the major vector over these regions, our findings further suggest the presence of additional vectors transmitting malaria in the study regions. The findings from this study offer insight into climate-malaria incidence linkages over Limpopo province of South Africa.

The aim of this article is to study the discrete mathematical model of transport network dynamics, recently built by author. The study showed some drawbacks of the basic model and the ways of overcoming these drawbacks, and an improved version of the model was proposed. Simulation systems, created on the basis of this new model were used to do test calculations similar to those previously done with the help of the basic model. The results of these calculations with both models are compared.

This note characterizes the dynamic structure of stable matchings of the stable marriage problem (SMP). The characterization focuses on the dynamic process of how the stable matching will be attained. To this end, the discrete problem of the SMP will be mapped to nonlinear dynamical models whose attractors include the counterparts of the stable matchings in the original SMP. A simple measure of decision-making difficulty is introduced. We use two types of diagram: a cross section diagram (and its 3D imaging) of a lattice to visualize the decision-making difficulty, and a radiation diagram to visualize the time evolution to a matching. Both diagrams are used to examine the dynamic structure of the neighborhood of an attractor and its basin as well as the region between basins in the dynamical model mapped from the SMP.

International audience; The use of dynamic mechanistic models based on ordinary differential equations (ODE) has greatly improved the knowledge of the dynamics of HIV and of the immune system. Their flexibility for fitting data and prediction abilities make them a good tool for optimization of the design delivery and efficacy of new intervention in the HIV field. We present the problem of inference in ODE models with mixed effects on parameters. We introduce a Bayesian estimation procedure based on the maximization of the penalized likelihood and a normal approximation of posteriors, which is implemented in the NIMROD software. We investigate the impact of pooling different data by using a sequential Bayesian analysis (SBA), which uses posteriors of a previous study as new priors. We show that the normal approximation of the posteriors, which constrains the shape of new priors, leads to gains in accuracy of estimation while reducing computation times. The illustration is from two clinical trials of combination of antiretroviral therapies (cART): ALBI ANRS 070 and PUZZLE ANRS 104. This paper reproduces some unpublished work from my PhD thesis. It is an extension of my oral presentation on the same topic at the 47th Journées de Statistique organized by the French Statistical Society (SFdS) in Lille, France, May 2015, when being awarded the Marie-Jeanne Laurent-Duhamel prize.

We show the benefits of using Electron-Multiplying CCDs and the shift-and-add technique as a tool to minimise the effects of the atmospheric turbulence such as blending between stars in crowded fields and to avoid saturated stars in the fields observed. We intend to complete, or improve, the census of the variable star population in globular cluster NGC~6715. Our aim is to obtain high-precision time-series photometry of the very crowded central region of this stellar system via the collection of better angular resolution images than has been previously achieved with conventional CCDs on ground-based telescopes. Observations were carried out using the Danish 1.54-m Telescope at the ESO La Silla observatory in Chile. The telescope is equipped with an Electron-Multiplying CCD that allowed to obtain short-exposure-time images (ten images per second) that were stacked using the shift-and-add technique to produce the normal-exposure-time images (minutes). The high precision photometry was performed via difference image analysis employing the DanDIA pipeline. We attempted automatic detection of variable stars in the field. We statistically analysed the light curves of 1405 stars in the crowded central region of NGC~6715 to automatically identify the variable stars present in this cluster. We found light curves for 17 previously known variable stars near the edges of our reference image (16 RR Lyrae and 1 semi-regular) and we discovered 67 new variables (30 RR Lyrae, 21 long-period irregular, 3 semi-regular, 1 W Virginis, 1 eclipsing binary, and 11 unclassified). Photometric measurements for these stars are available in electronic form through the Strasbourg Astronomical Data Centre., Accepted for publication in Astronomy & Astrophysics, 18 pages, 7 figures, 4 tables

Olfactory transduction exhibits two distinct types of adaptation, which we denote multipulse and step adaptation. In terms of measured transduction current, multipulse adaptation appears as a decrease in the amplitude of the second of two consecutive responses when the olfactory neuron is stimulated with two brief pulses. Step adaptation occurs in response to a sustained steplike stimulation and is characterized by a return to a steady-state current amplitude close to the prestimulus value, after a transient peak. In this article, we formulate a dynamical model of the olfactory transduction pathway, which includes the kinetics of the CNG channels, the concentration of Ca ions flowing through them, and the Ca-complexes responsible for the regulation. Based on this model, a common dynamical explanation for the two types of adaptation is suggested. We show that both forms of adaptation can be well described using different time constants for the kinetics of Ca ions (faster) and the kinetics of the feedback mechanisms (slower). The model is validated on experimental data collected in voltage-clamp conditions using different techniques and animal species.

We present neutral hydrogen, ultraviolet, optical, and near-infrared imaging, and optical spectroscopy, of Minkowski's Object (MO), a star-forming peculiar galaxy near NGC 541. The observations strengthen evidence that star formation in MO was triggered by the radio jet from NGC 541. Key new results are the discovery of a 4.9 X 10(8) M-circle dot double H I cloud straddling the radio jet downstream from MO, where the jet changes direction and decollimates; strong detections of MO, also showing double structure, in UV and H alpha; and numerous H II regions and associated clusters in MO. In UV, MO resembles the radio-aligned, rest-frame UV morphologies in many high-redshift radio galaxies (HzRGs), also thought to be caused by jet-induced star formation. MO's stellar population is dominated by a 7.5 Myr old, 1.9 X 10(7) M-circle dot instantaneous burst, with a current star formation rate of 0.52 M-circle dot yr(-1) (concentrated upstream from where the H I column density is high). This is unlike the jet-induced star formation in Centaurus A, where the jet interacts with preexisting cold gas; in MO, the H I may have cooled out of a warmer, clumpy intergalactic or interstellar medium as a result of jet interaction, followed by the collapse of the cooling clouds and subsequent star formation (consistent with numerical simulations). Since the radio source that triggered star formation in MO is much less luminous, and therefore more common than powerful HzRGs, and because the environment around MO is not particularly special in terms of abundant dense, cold gas, jet-induced star formation in the early universe might be even more prevalent than previously thought.

We present neutral hydrogen, ultraviolet, optical, and near-infrared imaging, and optical spectroscopy, of Minkowski's Object (MO), a star-forming peculiar galaxy near NGC 541. The observations strengthen evidence that star formation in MO was triggered by the radio jet from NGC 541. Key new results are the discovery of a 4.9 X 10(8) M-circle dot double H I cloud straddling the radio jet downstream from MO, where the jet changes direction and decollimates; strong detections of MO, also showing double structure, in UV and H alpha; and numerous H II regions and associated clusters in MO. In UV, MO resembles the radio-aligned, rest-frame UV morphologies in many high-redshift radio galaxies (HzRGs), also thought to be caused by jet-induced star formation. MO's stellar population is dominated by a 7.5 Myr old, 1.9 X 10(7) M-circle dot instantaneous burst, with a current star formation rate of 0.52 M-circle dot yr(-1) (concentrated upstream from where the H I column density is high). This is unlike the jet-induced star formation in Centaurus A, where the jet interacts with preexisting cold gas; in MO, the H I may have cooled out of a warmer, clumpy intergalactic or interstellar medium as a result of jet interaction, followed by the collapse of the cooling clouds and subsequent star formation (consistent with numerical simulations). Since the radio source that triggered star formation in MO is much less luminous, and therefore more common than powerful HzRGs, and because the environment around MO is not particularly special in terms of abundant dense, cold gas, jet-induced star formation in the early universe might be even more prevalent than previously thought.

The majority of nearby early-type galaxies contain detectable amounts of emission-line gas at their centers. The nuclear gas kinematics form a valuable diagnostic of the central black hole (BH) mass. Here we analyze and model Hubble Space Telescope STIS observations of a sample of 27 galaxies; 16 Fanaroff-Riley Type I radio galaxies and 11 (more) normal early-type galaxies. We focus here on what can be learned from the nuclear velocity dispersion (line width) of the gas as a complement to the many studies dealing with gas rotation velocities. We find that the dispersion in a STIS aperture of similar to 0."1-0B2 generally exceeds the large-scale stellar velocity dispersion of the galaxy. This is qualitatively consistent with the presence of central BHs but raises the questions of whether the excess gas dispersion is of gravitational or nongravitational origin and whether the implied BH masses are consistent with our current understanding of BH demography (as predicted by the M-sigma relation between BH mass and stellar velocity dispersion). To address this we construct purely gravitational axisymmetric dynamical models for the gas, both thin-disk models and models with more general axis ratios and velocity anisotropies. For the normal galaxies the nuclear gas dispersions are adequately reproduced assuming disks around the BHs with masses that follow the M-sigma relation. In contrast, the gas dispersions observed for the radio galaxies generally exceed those predicted by any of the models. We attribute this to the presence of nongravitational motions in the gas that are similar to or larger than the gravitational motions. The nongravitational motions are presumably driven by the active galactic nucleus (AGN), but we do not find a relation between the radiative output of the AGN and the nongravitational dispersion. Given the uncertainties about the dynamical state of the gas, it is not possible to uniquely determine the BH mass for each galaxy from its nuclear gas dispersion. However, for the sample as a whole the observed dispersions do not provide evidence for significant deviations from the M-sigma relation.

The majority of nearby early-type galaxies contain detectable amounts of emission-line gas at their centers. The nuclear gas kinematics form a valuable diagnostic of the central black hole (BH) mass. Here we analyze and model Hubble Space Telescope STIS observations of a sample of 27 galaxies; 16 Fanaroff-Riley Type I radio galaxies and 11 (more) normal early-type galaxies. We focus here on what can be learned from the nuclear velocity dispersion (line width) of the gas as a complement to the many studies dealing with gas rotation velocities. We find that the dispersion in a STIS aperture of similar to 0."1-0B2 generally exceeds the large-scale stellar velocity dispersion of the galaxy. This is qualitatively consistent with the presence of central BHs but raises the questions of whether the excess gas dispersion is of gravitational or nongravitational origin and whether the implied BH masses are consistent with our current understanding of BH demography (as predicted by the M-sigma relation between BH mass and stellar velocity dispersion). To address this we construct purely gravitational axisymmetric dynamical models for the gas, both thin-disk models and models with more general axis ratios and velocity anisotropies. For the normal galaxies the nuclear gas dispersions are adequately reproduced assuming disks around the BHs with masses that follow the M-sigma relation. In contrast, the gas dispersions observed for the radio galaxies generally exceed those predicted by any of the models. We attribute this to the presence of nongravitational motions in the gas that are similar to or larger than the gravitational motions. The nongravitational motions are presumably driven by the active galactic nucleus (AGN), but we do not find a relation between the radiative output of the AGN and the nongravitational dispersion. Given the uncertainties about the dynamical state of the gas, it is not possible to uniquely determine the BH mass for each galaxy from its nuclear gas dispersion. However, for the sample as a whole the observed dispersions do not provide evidence for significant deviations from the M-sigma relation.

International audience; The Jump Markov state-space system (JMSS) is a well known model for representing dynamical models with jumps. However inference in a JMSS model is NP-hard, even in the conditionally linear and Gaussian case. Suboptimal solutions include Sequential Monte Carlo (SMC) and Interacting Multiple Models (IMM) methods. In this paper, we build a constrained Triplet Markov Chain (TMC) model which is close to the given JMSS model, and in which moments of interest can be computed exactly (without resorting to numerical nor Monte Carlo approximations) and at a computational cost which is linear in the number of observations. Additionally, a side advantage of our technique is that it can be used easily in a partially known model context

A systems-biology approach to complex disease (such as cancer) is now complementing traditional experience-based approaches, which have typically been invasive and expensive. The rapid progress in biomedical knowledge is enabling the targeting of disease with therapies that are precise, proactive, preventive, and personalized. In this paper, we summarize and classify models of systems biology and model checking tools, which have been used to great success in computational biology and related fields. We demonstrate how these models and tools have been used to study some of the twelve biochemical pathways implicated in but not unique to pancreatic cancer, and conclude that the resulting mechanistic models will need to be further enhanced by various abstraction techniques to interpret phenomenological models of cancer progression.

Cortical neurons operate within recurrent neuronal circuits. Dissecting their operation is key to understanding information processing in the cortex and requires transparent and adequate dynamical models of circuit function. Convergent evidence from experimental and theoretical studies indicates that strong feedback inhibition shapes the operating regime of cortical circuits. For circuits operating in inhibition-dominated regimes, mathematical and computational studies over the past several years achieved substantial advances in understanding response modulation and heterogeneity, emergent stimulus selectivity, inter-neuron correlations, and microstate dynamics. The latter indicate a surprisingly strong dependence of the collective circuit dynamics on the features of single neuron action potential generation. New approaches are needed to definitely characterize the cortical operating regime. peerReviewed

In this paper a new model of growing and dividing protocells is described, whose main features are (i) a lipid container that grows according to the composition of the molecular milieu (ii) a set of "genetic memory molecules" (GMMs) that undergo catalytic reactions in the internal aqueous phase and (iii) a set of stochastic kinetic equations for the GMMs. The mass exchange between the external environment and the internal phase is described by simulating a semipermeable membrane and a flow driven by the differences in chemical potentials, thereby avoiding to resort to sometimes misleading simplifications, e.g., that of a flow reactor. Under simple assumptions, it is shown that synchronization takes place between the rate of replication of the GMMs and that of the container, provided that the set of reactions hosts a so-called RAF (Reflexive Autocatalytic, Food-generated) set whose influence on synchronization is hereafter discussed. It is also shown that a slight modification of the basic model that takes into account a rate-limiting term, makes possible the growth of novelties, allowing in such a way suitable evolution: so the model represents an effective basis for understanding the main abstract properties of populations of protocells.

The characterization of the generic properties underlying the complex interplay ruling cell differentiation is one of the goals of modern biology. To this end, we rely on a powerful and general dynamical model of cell differentiation, which defines differentiation hierarchies on the basis of the stability of gene activation patterns against biological noise. In particular, in this work we investigate the role of the topology (i.e. scale-free or random) and of the dynamical regime (i.e. ordered, critical or disordered) of gene regulatory networks on the model dynamics. Two real lineage commitment trees, i.e. intestinal crypts and hematopoietic cells, are compared with the hierarchies emerging from the dynamics of ensembles of randomly simulated networks. Briefly, critical networks with random topology seem to display a wider range of possible behaviours as compared to the others, hence suggesting an intrinsic dynamical heterogeneity that may be fundamental in defining different differentiation trees. Conversely, scale-free networks show a generally more ordered dynamics, which limit the overall variability, yet containing the effect of possible genomic perturbations. Interestingly, a considerable number of networks across all types show emergent trees that are biologically plausible, suggesting that a relatively wide portion of the networks space may be suitable, without the need for a fine tuning of the parameters.

Sensory systems adapt, i.e., they adjust their sensitivity to external stimuli according to the ambient level. In this paper we show that single cell electrophysiological responses of vertebrate olfactory receptors and of photoreceptors to different input protocols exhibit several common features related to adaptation, and that these features can be used to investigate the dynamical structure of the feedback regulation responsible for the adaptation. In particular, we point out that two different forms of adaptation can be observed, in response to steps and to pairs of pulses. These two forms of adaptation appear to be in a dynamical trade-off: the more adaptation to a step is close to perfect, the slower is the recovery in adaptation to pulse pairs and viceversa. Neither of the two forms is explained by the dynamical models currently used to describe adaptation, such as the integral feedback model.

Un model híbrid transitòria fotovoltaic / tèrmic ha estat desenvolupat i validat experimentalment. La metodologia s'estén el model tèrmic quasi-dinàmica s'indica a la norma EN 12975 per involucrar el rendiment elèctric i estudiar el comportament dinàmic minimitzar les limitacions de l'hora de caracteritzar el col • lector. Una es mou cap enrere procediment mitjà de filtrat s'ha aplicat per millorar la resposta del model de condicions de treball variables. Quant a la part elèctrica, el model inclou les dependències tèrmiques i la radiació en les seves variables. Els resultats van revelar que els paràmetres característics inclosos en el model raonablement d'acord amb els valors experimentals obtinguts de la norma d'estat estacionari i els mesuraments de la corba característica IV. Després d'un procés de calibració del model proposat és una eina adequada per predir el comportament tèrmic i elèctric d'un col • lector solar híbrida, per un temps determinat conjunt de dades., A hybrid photovoltaic/thermal transient model has been developed and validated experimentally. The methodology extends the quasi-dynamic thermal model stated in the EN 12975 to involve the electrical performance and to consider the dynamic behaviour minimising constraints when characterising the collector. A backward moving average filtering procedure has been applied to improve the model response for variable working conditions. Concerning the electrical part, the model includes the thermal and radiation dependences in its variables. The results revealed that the characteristic parameters included in the model reasonably agree with the experimental values obtained from standard steady-state and IV characteristic curve measurements. After a calibration process the proposed model is a suitable tool to predict the thermal and electrical performance of a hybrid solar collector, for a specific weather data set, Se ha desarrollado un modelo dinámico para caracterizar colectores solares híbridos térmofotovoltaicos. La metodología extiende el modelo térmico estipulado en la norma EN 12975 involucrando la aportación eléctrica y estudiando el comportamiento dinámico para minimizar las restricciones a la hora de caracterizar el módulo. Se han implementado procedimientos de filtrado que mejoran la respuesta del modelo bajo condiciones variables. En cuanto a la parte eléctrica, el modelo incluye las dependencias térmicas y la radiación en sus variables. Los resultados obtenidos a partir de caracterización dinámica del colector híbrido PV/T revelaron que los parámetros característicos incluidos en el modelo concuerdan razonablemente bien con los valores experimentales obtenidos siguiendo el estándar de caracterización estacionaria, la capacidad calorífica efectiva y las mediciones de la curva característica IV. Después de un proceso de calibración, el modelo es una herramienta adecuada para predecir el comportamiento de un colector solar híbrido, para unas condiciones externas determinadas.

Intermediate mass black holes (IMBHs) with expected masses M_BH ~ 10^4 M_sun are thought to bridge the gap between stellar mass black holes (M_BH ~ 3 - 100 M_sun) and supermassive black holes found at the centre of galaxies (M_BH > 10^6 M_sun). Until today, no IMBH has been confirmed observationally. The most promising objects to host an IMBH as their central mass are globular clusters. Here, we present high sensitivity multi-epoch 1.6 GHz very long baseline interferometry observations of the globular cluster M15 that has been suggested to host an IMBH. Assuming the IMBH to be accreting matter from its surrounding we expect to detect it as a point source moving with the global motion of the cluster. However, we do not detect any such object within a radius of 6000 AU of the cluster centre in any of the five observations spread over more than one year. This rules out any variability of the putative IMBH on the time scale of one to two months. To get the most stringent upper limit for the flux density of the putative IMBH we concatenate the data of all five epochs. In this data we measure a 3{\sigma} upper flux limit of 10 {\mu}Jy for a central source. We employ the fundamental plane of black hole activity to estimate the mass of the central IMBH candidate. Based on previous X-ray observations of M15 our measurements indicate a 3{\sigma} upper mass limit of ~500 M_sun., Comment: 6 pages, 2 figures, accepted for publication in A&A

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This paper presents a dynamical model of avian immune response, for antibody production purposes. Antibodies are fundamental tools for research, diagnosis, and the treatment of several diseases. It has been recognized for some time that antibodies obtained from poultry are an efficient alternative for applications in medicine and biology. Therefore, a dynamical model for this process might be very useful. The model being proposed consists of seven non-linear ordinary differential equations with constant coefficients that represent the main cellular and molecular populations in avian immune response. Values for the parameters were obtained from avian and mammalian literature. In silico-generated responses in terms of antibody concentrations are presented and compared to reported kinetics. En este artículo se presenta un modelo dinámico de la respuesta inmune humoral de aves para la producción de anticuerpos específicos. Los anticuerpos son fundamentales para la investigación, diagnóstico y tratamiento de diversas enfermedades. Se ha reconocido que los anticuerpos obtenidos de aves de corral son una opción eficiente para aplicaciones en medicina y biología. Por tanto, un modelo que represente la dinámica de este proceso resulta de gran utilidad. El modelo está compuesto de siete ecuaciones diferenciales ordinarias, no lineales, con coeficientes constantes y representa las principales poblaciones celulares y moleculares de la respuesta inmune de las aves. Los parámetros se obtuvieron a partir de datos reportados en el área de inmunología aviar y mamífera. Las respuestas obtenidas "in silico" en términos de la concentración de anticuerpos se presentan y se comparan contra la cinética reportada.

This paper presents a dynamical model of avian immune response, for antibody production purposes. Antibodies are fundamental tools for research, diagnosis, and the treatment of several diseases. It has been recognized for some time that antibodies obtained from poultry are an efficient alternative for applications in medicine and biology. Therefore, a dynamical model for this process might be very useful. The model being proposed consists of seven non-linear ordinary differential equations with constant coefficients that represent the main cellular and molecular populations in avian immune response. Values for the parameters were obtained from avian and mammalian literature. In silico-generated responses in terms of antibody concentrations are presented and compared to reported kinetics.

Recently, some intriguing results have led to speculations whether the central density slope-velocity dispersion anisotropy inequality (An & Evans) actually holds at all radii for spherical dynamical systems. We extend these studies by providing a complete analysis of the global slope-anisotropy inequality for all spherical systems in which the augmented density is a separable function of radius and potential. We prove that these systems indeed satisfy the global inequality if their central anisotropy is beta(0) 1/2 for which the inequality does not hold, thus demonstrating that the global density slope-anisotropy inequality is not a universal property. This analysis is a significant step toward an understanding of the relation for general spherical systems.

In most theories concerning the origin of life autocatalytic sets are supposed to play an important role in the phase transition between non-living and living matter. Although several theoretical models describe this phase transition, it is very hard to recreate the experimental conditions in wet lab. We here introduce a stochastic model of catalytic reaction networks with energy constraints, devoted to the study of the emergence of autocatalytic sets, in which some of the assumptions of the already existing model are relaxed in order to explore the possible reasons which make the emergence of autocatalytic cycles difficult or which make them unstable. Moreover, since living systems operate with a continuous exchange of matter and energy with the environment, we investigate the effects on the model behavior of changes in the rate of the energy intake.

Classical random Boolean networks (RBN) are not well suited to describe experimental data from time-course microarray, mainly because of the strict assumptions about the synchronicity of the regulatory mechanisms. In order to overcome this setback, a generalization of the RBN model is described and analyzed. Gene products (e.g., regulatory proteins) are introduced, with each one characterized by a specific decay time, thereby introducing a form of memory in the system. The dynamics of these networks is analyzed, and it is shown that the distribution of the decay times has a strong effect that can be adequately described and understood. The implications for the dynamical criticality of the networks are also discussed. © 2011, Mary Ann Liebert, Inc.