Neuroscience, quantum indeterminism and the Cartesian soul.

Quantum indeterminism is frequently invoked as a solution to the problem of how a disembodied soul might interact with the brain (as Descartes proposed), and is sometimes invoked in theories of libertarian free will even when they do not involve dualistic assumptions. Taking as example the Eccles-Beck model of interaction between self (or soul) and brain at the level of synaptic exocytosis, I here evaluate the plausibility of these approaches. I conclude that Heisenbergian uncertainty is too small to affect synaptic function, and that amplification by chaos or by other means does not provide a solution to this problem. Furthermore, even if Heisenbergian effects did modify brain functioning, the changes would be swamped by those due to thermal noise. Cells and neural circuits have powerful noise-resistance mechanisms, that are adequate protection against thermal noise and must therefore be more than sufficient to buffer against Heisenbergian effects. Other forms of quantum indeterminism must be considered, because these can be much greater than Heisenbergian uncertainty, but these have not so far been shown to play a role in the brain.

Déconstruction de la maîtrise théologique et nécessité éthique de l'instabilité normative

Dans une première partie, l'A. de cet article considère les relations instables qui existent entre l'éthique et la théologie, car il s'agit de deux problématiques distinctes. Dans le deuxième point, il montre que ces deux problématiques ne sont pas si distinctes qu'il eût été possible, traitant de la première, d'en taire complètement la seconde, qui aborde de front la tension entre la maîtrise et le chaos. La partie finale développe la reconstruction de l'avenir public de l'éthique théologique : signaler la transcendance, répondre du mal et résister aux injustices.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Additive functions in boolean models of gene regulatory network modules.

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.

Strategic behaviours in healthcare : evidence from primary care and pharmaceutical markets

We analyse the strategic behaviours of agents in a market through the appropriate¬ness of their skills to the market. If agents' skills are well adapted to market and they can reach their target, they will not need to adopt strategic behaviours. The agents will behave as selfish individuals. However, if their skills are not well adapted and they cannot attain their target alone, they will adopt strategic behaviours to reach their objectives. These behaviours will have a different impact on the utilities of other agents, depending on the skills and the objectives of the agent. If these agents need other agents to reach their objectives, they will behave as altruistic individuals who internalise the utilities of other agents in reaching their objectives and will adopt cooperative behaviours. However, if these agents fear that other agents could prevent them from reaching their target because they can foresee that the skills of other agents are better adapted than their own skills, the agents will then behave as predator individuals and will adopt destructive behaviours to attain their objective. It is in the interests of these agents to manipulate information to increase disorder and dissimulate their lack of skills. They will reproduce the strategies of animals that modify their appearance to escape predators or simulate being bait to attract their prey. These agents will seek to induce chaos into the behaviours of other agents to amplify the impact of their strategies. The appropriateness of skills to the market allows an understanding of the emer-gence of networks and associated strategies. The members of a networks are inputs who are excluded when their costs are higher than their benefits. A network simul-taneously allows cooperation and selfish, predatory behaviours among its members. A network may adopt informational strategies when seeking to become the leader in a market or when it cannot survive. The creation of networks and the manipulation of information are two overlapping evolutionary strategies, with the first strategy favouring the second. In our model, an agent does not behave like a firm that aims only to maximise the profits of the firm but rather as a member of a network who adopts strategic behaviours as a function of the interests of this network. If his skills are well adapted to the market and he can innovate, he will not invest in erroneous input; in contrast, if his skills are not adapted, the agent will invest in the erroneous input of information into the market in order to survive. Therefore, when any informational asymmetries between the agents and their principals characterise the market, the price cannot be the main element that allows equilibrium to be reached in the market; instead, the appropriateness of skills to the market enables equilibrium. We will now apply these hypotheses to explain the strategic behaviours of physicians and pharmaceutical companies.

Lessons learned after 2 full scale disaster exercises in a Swiss pediatric hospital

Introduction: Following a disaster, up to 50% of mass casualties are children. The number of disaster increases worldwide, including in Switzerland. Following national order, the mapping of the various risks of disaster in Switzerland will be completed by the end of 2012. Pre-hospital disaster drills and plans are well established and regularly tested. In-hospital disaster plans are much less frequently tested, if only available. Pediatric in-hospital full scale disaster exercises have never been reported in Switzerland. Based on our local constraints, we set up and evaluated a disaster plan during two full scale exercises. Methods: In a university hospital treating more than 35 000 pediatric emergencies per year, two exercises involving mock victims of a disaster aged 9 to 14 years old were successively set up over a period of 3 years. The exercises were planned during the day, without modification of the normal emergency room activities. The hospital staff was informed and trained in advance. Variables such as the alarm timing and transmission, triage set-up and function, special disaster medical records utilization, communication and victims' identification were assessed. Family members participated in the second exercise. An evaluation team observed and record exercises activities, identifying strength and weaknesses. Results: On two separate occasions, a total of 44 mock patients participated, were triaged, admitted and treated in the hospital according to usual standards of care. Alarm transmission was not appropriate during the first exercise. Triage overload occurred on one occasion. In-hospital communication needed readjustment. Identification and in-hospital tracking of the children remained problematic. Hospital employees showed great enthusiasm and stressed the positive effect of full scale exercises on their knowledge of the hospital disaster plan. Conclusions: Performing real life disaster exercises in a pediatric hospital was very beneficial. The disaster plan was adapted to local needs and updated accordingly. An alarm transmission protocol was elaborated and tested. Triage set-up was adapted and tested. A hospital identification plan for injured children was created and tested. Full scale hospital exercises evaluating disaster plans revealed several weaknesses in the system. Practice readjustments based on local experience were made. A tested pediatric disaster plan adapted to local constraints could minimize chaos, optimize care and support in the event of a real disaster. Children's identification and family reunification following a disaster remains a challenge.