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Background This study evaluated the consequences in Europe of the COVID-19 outbreak on pathology laboratories orientated toward the diagnosis of thoracic diseases. Materials and methods A survey was sent to 71 pathology laboratories from 21 European countries. The questionnaire requested information concerning the organization of biosafety, the clinical and molecular pathology, the biobanking, the workload, the associated research into COVID-19, and the organization of education and training during the COVID-19 crisis, from 15 March to 31 May 2020, compared with the same period in 2019. Results Questionnaires were returned from 53/71 (75%) laboratories from 18 European countries. The biosafety procedures were heterogeneous. The workload in clinical and molecular pathology decreased dramatically by 31% (range, 3%-55%) and 26% (range, 7%-62%), respectively. According to the professional category, between 28% and 41% of the staff members were not present in the laboratories but did teleworking. A total of 70% of the laboratories developed virtual meetings for the training of residents and junior pathologists. During the period of study, none of the staff members with confirmed COVID-19 became infected as a result of handling samples. Conclusions The COVID-19 pandemic has had a strong impact on most of the European pathology laboratories included in this study. Urgent implementation of several changes to the organization of most of these laboratories, notably to better harmonize biosafety procedures, was noted at the onset of the pandemic and maintained in the event of a new wave of infection occurring in Europe., Highlights • Biosafety measures used in the first wave of the COVID-19 crisis were heterogeneous in 53 European pathology laboratories. • A dramatic decrease of the workload in pathology laboratories was noted. • No case of healthcare workers contaminated with SARS-CoV-2 associated with samples handling was identified.

To uncover the key cellular pathways associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity, Daniloski and coworkers used CRISPR-based whole-genome screening. Their results could propose new or repositioned drugs for the ongoing fight against COVID-19.

An increasing number of corporations aspire to meet demands to improve their environmental, social, and governance (ESG) performance. The stakeholders interested in ESG measures extend well beyond investors and the companies’ own management to include employees at the company and in its supply chain, consumers, regulators, and those subject to the company’s environmental and social impacts. Despite the huge differences between ESG and financial reporting, we believe that practices developed in financial reporting can contribute to achieving high-quality ESG reporting. In particular, we suggest that a comprehensive framework for ESG reporting must address the following three factors: (1) a limited set of metrics, primarily concerned with a company’s key environmental and social impacts; (2) a standard-setting body loosely modeled on the Financial Accounting Standards Board (FASB) to develop and particularize those metrics; and (3) reporting infrastructures that allow companies to collect, report, and verify the relevant metrics accurately.

Resume Objectif de l’etude La collaboration avec les etablissements medicosociaux accueillant des enfants polyhandicapes est inscrite dans les missions des equipes ressources regionales de soins palliatifs pediatriques. Cette etude visait a identifier la nature de la premiere demande d’intervention de l’equipe ressources regionale de soins palliatifs pediatriques, les beneficiaires, les actions realisees et la place de l’infirmiere/puericultrice diplomee d’etat dans cette collaboration. Materiels et methodes Il s’agissait d’une etude exploratoire retrospective sur 2 ans a partir de 97 dossiers d’enfants polyhandicapes suivis par neuf equipes ressources regionales de soins palliatifs pediatriques et accueillis dans differents types d’etablissements medicosociaux en France. Les publics concernes par les premieres demandes des etablissements medicosociaux etaient l’enfant, son entourage et les equipes. Resultats Soixante-neuf pour cent des demandes s’adressaient concurremment a deux, voire trois de ces publics. Les problematiques initiales etaient le soutien des professionnels (26 %), le soulagement des symptomes (21 %) et l’elaboration du parcours de soin (16 %). Cinquante-trois pour cent des interventions etaient effectuees en binome. Les actions des equipes ressources regionales de soins palliatifs pediatriques etaient : participation aux reunions pluridisciplinaires (18 %), aide a l’elaboration du projet de vie et de soins (16 %), lien avec l’institution (13 %). Conclusion Cette etude demontre l’existence et le developpement d’un esprit collaboratif entre les etablissements medicosociaux et les equipes ressources regionales de soins palliatifs pediatriques. Ces dernieres s’appuient sur leurs savoirs developpes en interprofessionnalite. Elles apportent leur expertise dans la prise en charge de ces enfants vulnerables, de leur entourage et des professionnels grâce a l’acculturation dans un cadre complexe. La premiere demande ouvre sur des problematiques variees sous-jacentes donnant lieu a diverses actions centrees sur l’enfant. L’infirmiere/puericultrice diplomee d’etat est impliquee dans cette collaboration en lien avec son role propre et les competences developpees pour cette fonction pivot.

This paper presents a novel Differential Evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence speed and, therefore, when it is trapped into the local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our algorithm and that the proposed algorithm is superior to other state-of-the-art algorithms. It obtained a hit ratio of 100% for sequences up to 18 monomers, within a budget of $10^{11}$ solution evaluations. New best-known solutions were obtained for most of the sequences. The existence of the symmetric best-known solutions is also demonstrated in the paper., Comment: 22 pages, 8 figures, 10 tables, journal

Feature selection using Multi-Objective Differential Evolution (DEMO) is proposed.This efficient selection is integrated in advanced Facial Emotion Recognition system.Emotion-specific and more discriminative features over all emotions selection strategies.Emotion recognition accuracy of proposed algorithm comparable to state-of-the-art.Feature selection by using DEMO enormously reduces a feature vector length. This paper proposes an efficient feature selection system applied to a Facial Expression Recognition (FER) system. This system, capable of recognizing seven prototypical emotions including neutral expression, is based on a histogram of oriented gradient descriptor (HOG) and difference feature vectors. The emotion feature selection was carried out by using an appropriately modified multi-objective differential evolution algorithm. The number of used features was minimized, while the emotion recognition accuracy of the support vector machine classifiers was maximized simultaneously. The emotion-specific features and the more discriminative features over all emotions selection strategies were developed, whereby the latter strategy proved to be more efficient using the Friedman statistical test. This person-independent FER system with proposed feature selection was validated on three commonly used evaluation databases, where the mean emotion recognition rate was 98.37% on the Cohn Kanade database, 92.75% on the JAFFE database, and 84.07% on the MMI database, while the number of used features lowered up to 89% with respect to the original difference feature vector length. Compared to the state-of-the-art, the proposed FER method offers good results, while also greatly lowering the number of used features, which, in return, minimizes the computational cost of training the classifiers. The optimization proposed in this paper can be generalized easily to a feature selection for an arbitrary multi-objective, as well as many-objective, problem.

Graphical abstractDisplay Omitted HighlightsA new stochastic solver with self-avoiding walk strategy and random restarts.The new solver significantly dominates the current state-of-the-art solvers.New and improved best merit factors versus the ones reported in the literature.Asymptotic average-case predictions to achieve the best known merit factors. The search for binary sequences with a high figure of merit, known as the low autocorrelation binary sequence (labs) problem, represents a formidable computational challenge. To mitigate the computational constraints of the problem, we consider solvers that accept odd values of sequence length L and return solutions for skew-symmetric binary sequences only with the consequence that not all best solutions under this constraint will be optimal for each L. In order to improve both, the search for best merit factor and the asymptotic runtime performance, we instrumented three stochastic solvers, the first two are state-of-the-art solvers that rely on variants of memetic and tabu search (lssMAts and lssRRts), the third solver (lssOrel) organizes the search as a sequence of independent contiguous self-avoiding walk segments. By adapting a rigorous statistical methodology to performance testing of all three combinatorial solvers, experiments show that the solver with the best asymptotic average-case performance, lssOrel_8=0.000032*1.1504L, has the best chance of finding solutions that improve, as L increases, figures of merit reported to date. The same methodology can be applied to engineering new labs solvers that may return merit factors even closer to the conjectured asymptotic value of 12.3248.

In euxinic sediments, the reaction between iron and sulfides results in the formation of Fe(II)-sulfides, which are known to play a key role in trace metal sequestration. The present study investigates the sequestration of nickel during Fe(II)-sulfide formation mediated by the (thio)sulfate-reducing bacterium Desulfovibrio capillatus in the presence of soluble Fe(III)-citrate and thiosulfate as the terminal electron acceptor. XRD, HRTEM and Fe K-edge EXAFS data indicate that biogenic mackinawite (FeS) was the sole mineral formed in our experiments. These data also show that the kinetics of mackinawite crystal growth was significantly accelerated when nickel was present in the starting solution. In addition, the lack of detection of other Fe(II)-sulfides indicates inhibition of the mackinawite (FeS) to greigite (Fe3S4) and/or pyrite (FeS2) transformation that is likely related to (i) the efficiency of Desulfovibrio capillatus in reducing S(0) to H2S and (ii) the absence of O2 during the experiments. Finally, chemical analyses show that 98% of the nickel is associated with biogenic mackinawite and no release of nickel from mackinawite was observed after up to 10 months of incubation under anoxic conditions. This finding is consistent with Ni K-edge EXAFS data which show that Ni(II) substitutes for Fe(II) in the structure of biogenic mackinawite. This study shows that (thio)sulfate-reducing bacteria can efficiently promote the formation of mackinawite in euxinic sedimentary environments and that these Fe(II)-sulfides can act as efficient and long-term trapping minerals for nickel in such settings. Considering the capacity of mackinawite at incorporating other trace metals such as Mn, Co, Cu and Zn, this iron sulfide could also serve as a host for these elements as well. This study suggests that mackinawite likely plays a more important role in the biogeochemical cycles of Fe, S, and associated trace metals than considered up to now.

Real test images were used to perform thresholding using Otsu's method with a high level of thresholds.The proposed hybrid is compared with DE, jDE, PSO, ABC, and CS.Algorithms are compared based on PSNR and SSIM metrics.Friedman and Wilcoxon statistical tests are used to show the performances.Proposed hjDE shows superior performance in the quality of the results. Image thresholding is a process for separating interesting objects within an image from their background. An optimal threshold's selection can be regarded as a single objective optimization problem, where obtaining a solution can be computationally expensive and time-consuming, especially when the number of thresholds increases greatly. This paper proposes a novel hybrid differential evolution algorithm for selecting the optimal threshold values for a given gray-level input image, using the criterion defined by Otsu. The hybridization is done by adding a reset strategy, adopted from the Cuckoo Search, within the evolutionary loop of differential evolution. Additionally a study of different evolutionary or swarm-based intelligence algorithms for the purpose of thresholding, with a higher number of thresholds was performed, since many real-world applications require more than just a few thresholds for further processing. Experiments were performed on eleven real world images. The efficiency of the hybrid was compared to the cuckoo search and self-adaptive differential evolution, the original differential evolution, particle swarm optimization, and artificial bee colony where the results showed the superiority of the hybrid in terms of better segmentation results with the increased number of thresholds. Since the proposed method needs only two parameters adjusted, it is by far a better choice for real-life applications.

The initial population of an evolutionary algorithm is an important factor which affects the convergence rate and ultimately its ability to find high quality solutions or satisfactory solutions for that matter. If composed of good individuals it may bias the search towards promising regions of the search space right from the beginning. Although, if no knowledge about the problem at hand is available, the initial population is most often generated completely random, thus no such behavior can be expected. This paper proposes a method for initializing the population that attempts to identify i.e., to get close to promising parts of the search space and to generate (relatively) good solutions in their proximity. The method is based on clustering and a simple Cauchy mutation. The results obtained on a broad set of standard benchmark functions suggest that the proposed method succeeds in the aforementioned which is most noticeable as an increase in convergence rate compared to the usual initialization approach and a method from the literature. Also, insight into the usefulness of advanced initialization methods in higher-dimensional search spaces is provided, at least to some degree, by the results obtained on higher-dimensional problem instances—the proposed method is beneficial in such spaces as well. Moreover, results on several very high-dimensional problem instances suggest that the proposed method is able to provide a good starting position for the search.

Graphical abstractDisplay Omitted HighlightsThe novel genetic algorithm for the protein structure prediction problem.The local search for improving the energy values of the conformations with local movements of one or two consecutive monomers throughout the entire conformation.Opposition-based mechanism that transforms conformations into opposite directions and corresponding amino acid sequence to inverse sequence. This paper presents a genetic algorithm applied to the protein structure prediction in a hydrophobic-polar model on a cubic lattice. The proposed genetic algorithm is extended with crowding, clustering, repair, local search and opposition-based mechanisms. The crowding is responsible for maintaining the good solutions to the end of the evolutionary process while the clustering is used to divide a whole population into a number of subpopulations that can locate different good solutions. The repair mechanism transforms infeasible solutions to feasible solutions that do not occupy the lattice point for more than one monomer. In order to improve convergence speed the algorithm uses local search. This mechanism improves the quality of conformations with the local movement of one or two consecutive monomers through the entire conformation. The opposition-based mechanism is introduced to transform conformations to the opposite direction. In this way the algorithm easily improves good solutions on both sides of the sequence. The proposed algorithm was tested on a number of well-known hydrophobic-polar sequences. The obtained results show that the mechanisms employed improve the algorithm's performance and that our algorithm is superior to other state-of-the-art evolutionary and swarm algorithms.

Natural ferrihydrites (Fh) often contain impurities such as aluminum, especially in acid mine drainage, and these impurities can potentially impact the chemical reactivity of Fh with respect to metal (loid) adsorption. In the present study, we have investigated the influence of aluminum on the sorption properties of ferrihydrite with respect to environmentally relevant aqueous arsenic species, arsenite and arsenate. We have conducted sorption experiments by reacting aqueous As(III) and As(V) with synthetic Al-free and Al-bearing ferrihydrite at pH 6.5. Our results reveal that, when increasing the Al:Fe molar ratio in Fh, the sorption density dramatically decreased for As(III), whereas it increased for As(V). Extended X-ray Absorption Fine Structure (EXAFS) spectroscopy analysis at the As K-edge indicated that the As III O 3 pyramid binds to FeO 6 octahedra on both Al-free Fh and Al-bearing Fh, by forming bidentate mononuclear edge-sharing ( 2 E ) and bidentate binuclear corner-sharing ( 2 C ) surface complexes characterized by As–Fe distances of 2.9 A and 3.4 A, respectively. The decrease in As(III) sorption density with increasing Al:Fe ratio in Fh could thus be explained by a low affinity of the As(OH) 3 molecule for Al surface sites compared to Fe ones. In contrast, on the basis of available literature on As(V) adsorption mechanisms, we suggest that, in addition to inner-sphere 2 C arsenate surface complexes, outer-sphere arsenate surface complexes forming hydrogen bonds with both Al–OH and Fe–OH surface sites could explain the enhancement of As(V) sorption onto aluminous Fh relative to Al-free Fh, as observed in the present study. The presence of aluminum in Fh may thus enhance the mobility of arsenite with respect to arsenate in Acid Mine Drainage impacted systems, while mixed Al:Fe systems could present an alternative for arsenic removal from impacted waters, provided that As(III) would be oxidized to As(V).

This paper presents insight into an adaptation and self-adaptation mechanism within differential evolution, covering not only how but moreover – when this mechanism generates new values for control parameters, focusing on the iteration-temporal randomness of the self-adaptive control parameters. In particular, this randomness is controlled by a randomness level parameter, which influences the control parameters values׳ dynamics and their propagation through suitable individuals׳ improvement contributions during ellitistic selection. Thereby, the randomness level parameter defines the chaotic behavior of self-adaptive control parameter values׳ instances. A Differential Evolution (DE) algorithm for Real Parameter Single Objective Optimization is utilized as an application of this mechanism, to analyze the impact of the randomness level parameter as used inside the evolutionary algorithm parameter adaptation and control mechanism, yielding statistically significant different algorithm performances and ranks on different randomness level parameter values. Moreover, the impacts of different randomness configurations on the number of improvements, improvement scales, and adaptation frequencies, are shown, in order to present a deeper insight into the influences and causes using different randomness level parameter configurations, to present the influence of randomization frequency on propagation stability. Since DE variant algorithms with the mechanism of control parameters self-adaptation are widely applied, this study might help in increasing the performances of these different variants and their applications.

Most investors have a single goal: to earn the highest financial return. These socially-neutral investors maximize their risk-adjusted returns and would not accept a lower financial return from an investment that also produced social benefits. An increasing number of socially-motivated investors have goals beyond maximizing profits. Some seek investments that are aligned with their social values (value alignment), for example by only owning stock in companies whose activities are consistent with the investor’s moral or social values. Others may also want their investment to make portfolio companies create more social value (social value creation). The thrust of this essay is that while it is relatively easy to achieve value alignment, creating social value is far more difficult. The literature published by asset managers, foundations, and trade associations voices considerable optimism that socially-motivated investors can create social value, particularly through non-concessionary investments. We are skeptical about many of these assertions; their language is often too loose to support a disciplined assessment whether social value was created, and the absence of fees keyed to social, rather than financial, value creation fuels that skepticism. To address this problem, we first offer a taxonomy of socially-motivated investments so that investors can clearly articulate their goals, and asset managers can clearly articulate what they offer and how their performance should be measured. We then address three big questions. First, can investments in public companies create social value whether or not with concessions on return? Second, can investments in private companies create social value, again whether or not with return concessions? Third, can investors, working with socially motivated stakeholders, cause public companies to create social value?

Covering more than 70% of tropical and subtropical coastlines, mangrove intertidal forests are well known to accumulate potentially toxic trace metals in their sediments, and thus are generally considered to play a protective role in marine and lagoon ecosystems. However, the chemical forms of these trace metals in mangrove sediments are still not well known, even though their molecular-level speciation controls their long-term behavior. Here we report the vertical and lateral changes in the chemical forms of nickel, which accumulates massively in mangrove sediments downstream from lateritized ultramafic deposits from New Caledonia, where one of nature’s largest accumulations of nickel occurs. To accomplish this we used Ni K-edge Extended X-ray Absorption Fine Structure (EXAFS) spectroscopy data in combination with microscale chemical analyses using Scanning Electron Microscopy coupled with Energy-Dispersive X-ray Spectroscopy (SEM-EDXS). After Principal Component and Target Transform analyses (PCA-TT), the EXAFS data of the mangrove sediments were reliably least-squares fitted by linear combination of 3-components chosen from a large model compound spectral database including synthetic and natural Ni-bearing sulfides, clay minerals, oxyhydroxides, and organic complexes. Our results show that in the inland salt flat Ni is hosted in minerals inherited from the eroded lateritic materials, i.e. Ni-poor serpentine (44–58%), Ni-rich talc (20–31%), and Ni-goethite (18–24%). In contrast, in the hydromorphic sediments beneath the vegetated Avicennia and Rhizophora stands, a large fraction of Ni is partly redistributed into a neoformed smectite pool (20–69% of Ni-montmorillonite), and Ni speciation significantly changes with depth in the sediment. Indeed, Ni-rich talc (25–56%) and Ni-goethite (15–23%) disappear below ∼15 cm depth in the sediment and are replaced by Ni-sorbed pyrite (23–52%) in redox-active intermediate depth layers and by pyrite (34–55%) in the deepest sediment layers. Ni-incorporation in pyrite is especially observed beneath an inland Avicennia stand where anoxic conditions are dominant. In contrast, beneath a Rhizophora stand closer to the ocean, where the redox cycle is intensified due to the tide cycle, partial re-oxidation of Ni-bearing pyrites favors nickel mobility, as confirmed by Ni-mass balance estimates and by higher Ni concentration in the pore waters. These findings have important environmental implications for better evaluating the protective role of mangroves against trace metal dispersion into marine ecosystems. They may also help in predicting the response of mangrove ecosystems to increasing anthropogenic pressure on coastal areas.

This paper presents a vectorized matrix parameters encoding aspect for an evolutionary computer vision approach to procedural tree modeling. A serialized fixed-size floating-point encoded tree parameter set consists of a set of auxiliary local and other global parameters. The main goal of paper is to lower problem dimensionality needed for encoding local parameters. For evolution simulation, differential evolution algorithm is used. The optimizer evolves a parameterized procedural model by fitting a set of its rendered images to a set of automatically preprocessed reference photo images. The reconstructed tree morphology is then used for reconstructed tree animation, to generate similar geometrical tree models based on similar morphology. Examples of reconstructed model animation are shown, such as simulation of its growth, sway in the wind, or adding leaves.

Multiaxial and heterogeneous mechanical experiments, by means of the diametral compression test (Brazilian test), were carried out on Zr-based Bulk Metallic Glass. Attention was notably focused on obtaining the displacement field in the area of interest (large strains) employing a Digital Image Correlation device, in addition to the measurement of the usual global load–displacement curve. In order to reproduce the behaviour of the BMG at stake, several constitutive equations were considered with growing complexity: von Mises, Drucker–Prager (pressure dependence), free volume based model as well as a viscoplastic Coulomb–Mohr type model. The two latter were implemented as user-material in a Finite Element computation code. The results of these investigations including experiments and computational simulations are discussed.

This paper gives a review of recent extensions of the Differential Evolution (DE) algorithm for use in Large-Scale Global Optimization (LSGO) and presents an empirical analysis of DE-based and some other state-of-the-art algorithms for LSGO on the CEC 2013 LSGO benchmark suite. Since witnessing the first successful applications of DE for a wide variety of optimization problems in the early nineties, researchers have developed several new algorithms in this field. In this paper, we are especially interested in algorithms for solving LSGO. As LSGO is one of the most active research lines, not only in DE, but in many evolutionary and meta-heuristic algorithms, we discuss general approaches for dealing with LSGO first. The main focus of the paper is DE. We review its basic algorithm and discuss several extensions used for coping with large-scale problems. This paper has two main objectives: (1) To propose, from a theoretical point of view, the grouping of DE mechanisms for dealing with LSGO into nine groups, and (2) To evaluate sixteen recently proposed algorithms for LSGO empirically. Many benchmark suites were designed with the aim of providing a suitable evaluation platform for testing and comparing large-scale optimization algorithms. In this paper, the CEC 2013 LSGO benchmark suite was chosen for comparison, because it resembles the following features of real-world problems: Non-uniform subcomponent sizes; imbalance in the contribution of subcomponents; and functions with interdependent overlapping subcomponents. The performances of state-of-the-art algorithms are compared, and the algorithms are ranked using three different metrics, which evaluate the performance from different perspectives. The conducted research shows that DE is among the best algorithms for LSGO on the CEC 2013 LSGO benchmark suite, especially when used with other mechanisms for dealing with large numbers of variables. Finally, the analysis has shown that there is still some room for further improvements in DE towards the solution of LSGO problems.

Quaternions are a number system, which extends complex numbers. They are especially useful in areas where fast rotation calculations are needed, e.g., programming video games or controllers of spacecraft. This paper proposes to use quaternion for the representation of individuals in firefly algorithm so as to enhance the performance of the firefly algorithm and to avoid any stagnation. The preliminary results of our experiments after optimizing a test-suite consisting of ten standard functions, showed that the proposed firefly algorithms using quaternion’s representation improved the results of the original firefly algorithm.

Introduction L’equipe des Ateliers de la Peau du service de dermatologie du CHRU de Brest a concu un nouvel outil therapeutique destine aux enfants et adolescents atteints de dermatite atopique : la « randonnee de la peau ». C’est un jeu de plateau pour apprendre a mieux se debrouiller avec sa peau, a travers differents paysages et differentes saisons. Les participants apprennent sur leur maladie, comment faire avec elle, et trouvent des reponses le long du chemin et dans les villages. Patients et methodes Un plateau de jeu ; 150 cartes : « emoticones », « remplis ton sac », « ecusson », « joker », « objectifs », « ? » ; 8 chevalets ; 12 figurines ; 1 de, allant de 1 a 3 ; cremes, lotions, pansements, bandages, dermocorticoides, emollients et antalgiques ; photos de peau. Autour de 3 animateurs (dermatologue, infirmiere et psychologue), 6 patients maximum (travail en groupe restreint) « randonnent » et s’arretent sur les cases pour apprendre a mieux prendre soin de leur peau, de maniere plus autonome. Le plateau de jeu, les cartes, les personnages, les outils utilises sont presentes. Une explication de la regle du jeu est donnee, pas a pas, et permet d’apprehender son utilisation et sa visee. Le randonneur choisit un objectif dont il partagera les solutions trouvees avec les autres joueurs. Resultats Les mises en situation permettent des echanges entre joueurs, et entre joueurs et animateurs. Quand le randonneur a atteint son objectif et souhaite partager ses solutions avec les autres joueurs, il deplace son pion jusqu’a la case « objectif » : il explique et commente avec les autres autour d’un temps de discussion ( Fig. 1 et 2 ). Discussion C’est a la suite de 14 annees d’experience en education therapeutique que l’equipe des Ateliers de la Peau du service de dermatologie du CHRU de Brest a decide de creer son propre outil de travail, afin de repondre au plus pres aux attentes et demandes des patients et de leur famille. Il s’agit d’aider l’equipe dans sa demarche educative groupale, d’une part, et d’envoyer d’autre part, un message simple et clair aux dermatologues adressant leurs jeunes patients. Conclusion La Randonnee de la Peau est l’aboutissement de deux annees de travail : conception et creation. Cela a contribue a renforcer la cohesion de l’equipe et la dynamique de groupe. L’objectif est d’ajuster au mieux notre demarche au reseau des medecins de ville (dermatologues, allergologues, pediatres et medecins generalistes), d’une part, aux patients et a leur famille, d’autre part.