Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding

The uniform sampling of convex polytopes is an interesting computational problem with many applications in inference from linear constraints, but the performances of sampling algorithms can be affected by ill-conditioning. This is the case of inferring the feasible steady states in models of metabolic networks, since they can show heterogeneous time scales . In this work we focus on rounding procedures based on building an ellipsoid that closely matches the sampling space, that can be used to define an efficient hit-and-run (HR) Markov Chain Monte Carlo. In this way the uniformity of the sampling of the convex space of interest is rigorously guaranteed, at odds with non markovian methods. We analyze and compare three rounding methods in order to sample the feasible steady states of metabolic networks of three models of growing size up to genomic scale. The first is based on principal component analysis (PCA), the second on linear programming (LP) and finally we employ the lovasz ellipsoid method (LEM). Our results show that a rounding procedure is mandatory for the application of the HR in these inference problem and suggest that a combination of LEM or LP with a subsequent PCA perform the best. We finally compare the distributions of the HR with that of two heuristics based on the Artificially Centered hit-and-run (ACHR), gpSampler and optGpSampler. They show a good agreement with the results of the HR for the small network, while on genome scale models present inconsistencies.Comment: Replacement with major revision

Affordances of distractors and compatibility effects: a study with the computational model TRoPICALS

Seeing an object activates in the brain both visual and action codes. Crucial evidence supporting this view is offered by compatibility effect experiments (Ellis et al. (2007). J Exp Psychol: Hum Percept Perform): perception of an object can facilitate or interfere with the execution of an action (e.g. grasping) even when the viewer has no intention of interacting with the object. TRoPICALS (Caligiore et al. (2010). Psychol Rev) is a computational model developed to study compatibility effects. It provides a general hypothesis about the brain mechanisms underlying compatibility effects, suggesting that the top-down bias from prefrontal cortex (PFC), and its agreement or disagreement with the affordances of objects, plays a key role in such phenomena. Compatibility effects have been investigated in the presence of a distractor object in (Ellis et al. (2007). J Exp Psychol: Hum Percept Perform). The reaction times (RTs) results confirmed compatibility effects found in previous experiments without the distractor. Interestingly, results also showed an unexpected effect of the distractor: responding to a target with a grip compatible with the size of the distractor produced slower RTs in comparison to the incompatible case. Here we present an enhanced version of TRoPICALS that reproduces and explains these new results. This explanation is based on the idea according to which PFC might play a double role in its top-down guidance of action selection producing: (a) a positive bias in favor of the action requested by the experimental task; (b) a negative bias directed to inhibiting the action evoked by the distractor. The model also provides two testable predictions on the possible consequences on compatibilities effects of the target and distractor objects in Parkinsonian disease patients with damages of inhibitory circuits

Transition from confined to bulk dynamics in symmetric star-linear polymer mixtures

We report on the linear viscoelastic properties of mixtures comprising multiarm star (as model soft colloids) and long linear chain homopolymers in a good solvent. In contrast to earlier works, we investigated symmetric mixtures (with a size ratio of 1) and showed that the polymeric and colloidal responses can be decoupled. The adopted experimental protocol involved probing the linear chain dynamics in different star environments. To this end, we studied mixtures with different star mass fraction, which was kept constant while linear chains were added and their entanglement plateau modulus ($G_p$) and terminal relaxation time ($\tau_d$) were measured as functions of their concentration. Two distinct scaling regimes were observed for both $G_p$ and $\tau_d$: at low linear polymer concentrations, a weak concentration dependence was observed, that became even weaker as the fraction of stars in the mixtures increased into the star glassy regime. On the other hand, at higher linear polymer concentrations, the classical entangled polymer scaling was recovered. Simple scaling arguments show that the threshold crossover concentration between the two regimes corresponds to the maximum osmotic star compression and signals the transition from confined to bulk dynamics. These results provide the needed ingredients to complete the state diagram of soft colloid-polymer mixtures and investigate their dynamics at large polymer-colloid size ratios. They also offer an alternative way to explore aspects of the colloidal glass transition and the polymer dynamics in confinement. Finally, they provide a new avenue to tailor the rheology of soft composites.Comment: 9 Figure

The role of tactile input in learning to reach an object with one\u27s hand.

We have tried to understand when an artificial organism controlled by a neural network receives tactile input when its hand touches an object, the evolution of capacity to reach the object, compared to a condition in which the organism has no tactile input

Integrating reinforcement learning, equilibrium points and minimum variance to understand the development of reaching: a computational model

Despite the huge literature on reaching behaviour we still lack a clear idea about the motor control processes underlying its development in infants. This article contributes to overcome this gap by proposing a computational model based on three key hypotheses: (a) trial-anderror learning processes drive the progressive development of reaching; (b) the control of the movements based on equilibrium points allows the model to quickly find the initial approximate solution to the problem of gaining contact with the target objects; (c) the request of precision of the end-movement in the presence of muscular noise drives the progressive refinement of the reaching behaviour. The tests of the model, based on a two degrees of freedom simulated dynamical arm, show that it is capable of reproducing a large number of empirical findings, most deriving from longitudinal studies with children: the developmental trajectory of several dynamical and kinematic variables of reaching movements, the time evolution of submovements composing reaching, the progressive development of a bell-shaped speed profile, and the evolution of the management of redundant degrees of freedom. The model also produces testable predictions on several of these phenomena. Most of these empirical data have never been investigated by previous computational models and, more importantly, have never been accounted for by a unique model. In this respect, the analysis of the model functioning reveals that all these results are ultimately explained, sometimes in unexpected ways, by the same developmental trajectory emerging from the interplay of the three mentioned hypotheses: the model first quickly learns to perform coarse movements that assure a contact of the hand with the target (an achievement with great adaptive value), and then slowly refines the detailed control of the dynamical aspects of movement to increase accuracy

Some adaptive advantages of the ability to make predictions

We describe some simple simulations showing two possible adaptive advantages of the ability to predict the consequences of one?s actions: predicted inputs can replace missing inputs and predicted success vs. failure can help deciding whether to actually executing a planned action or not. The neural networks controlling the organisms? behaviour include distinct modules whose connection weights are all genetically inherited and evolved using a genetic algorithm except those of the predictive module which are learned during life

Robotic double-loop reconstruction method following total gastrectomy

Minimally invasive surgery for gastric cancer is a challenge. The reconstructive time is a particular issue and researchers have adopted a large variety of solutions and produced heterogeneous data. The reconstructive phase can be divided into two major categories based on the approach adopted: the execution of extracorporeal versus intracorporeal anastomosis. In turn, the surgical team can perform the latter with laparoscopic or robotic assistance. However, the question is, how should a robotic esophagojejunal anastomosis be performed after total gastrectomy? Most articles in the literature have reported the execution of mechanical anastomoses [1] [2] [3] [4] [5] [6], especially with circular staplers via the creation of a manual purse-string around the anvil. Other solutions have described the use of the Orvil or the overlap technique. Only three authors have reported intracorporeal sutures with a completely robotic-sewn anastomosis [7] [8] [9]. A new robotic technique (the Parisi technique) was developed and adopted at St. Mary’s Hospital, Terni, Italy. A double-loop reconstruction method with an intracorporeal robot-sewn anastomosis is performe

Determining the depth of Jupiter's Great Red Spot with Juno: a Slepian approach

One of Jupiter's most prominent atmospheric features, the Great Red Spot (GRS), has been observed for more than two centuries, yet little is known about its structure and dynamics below its observed cloud-level. While its anticyclonic vortex appearance suggests it might be a shallow weather-layer feature, the very long time span for which it was observed implies it is likely deeply rooted, otherwise it would have been sheared apart by Jupiter's turbulent atmosphere. Determining the GRS depth will shed light not only on the processes governing the GRS, but on the dynamics of Jupiter's atmosphere as a whole. The Juno mission single flyby over the GRS (PJ7) discovered using microwave radiometer measurements that the GRS is at least a couple hundred kilometers deep (Li et al. 2017). The next flybys over the GRS (PJ18 and PJ21), will allow high-precision gravity measurements that can be used to estimate how deep the GRS winds penetrate below the cloud-level. Here we propose a novel method to determine the depth of the GRS based on the new gravity measurements and a Slepian function approach that enables an effective representation of the wind-induced spatially-confined gravity signal, and an efficient determination of the GRS depth given the limited measurements. We show that with this method the gravity signal of the GRS should be detectable for wind depths deeper than 300 kilometers, with reasonable uncertainties that depend on depth (e.g., $\pm$100km for a GRS depth of 1000km)

La diffusione di tratti culturali tra reti di agenti artificiali

No abstract availabl

Compatibility effects and affordances: a neural-network computational model

No abstract availabl