Cours d'Optique Ondulatoire adapté à une vidéo-projection, associé à un manuel, de niveau Licence deuxième année

Our own unique character traits make our behavior consistent and define our individuality. Yet, this consistency does not entail that we behave repetitively like machines. Like humans, animals also combine personality traits with spontaneity to produce adaptive behavior: consistent, but not fully predictable. Here, we study an iconically rigid behavioral trait – insect phototaxis – that that nevertheless also contains both components of individuality and spontaneity. In a light/dark T-maze, approximately 70% of a group of Drosophila fruit flies choose the bright arm of the T-Maze, while the remaining 30% walk into the dark. Taking the photopositive and the photonegative subgroups and re-testing them reveals the spontaneous component: a similar 70-30 distribution emerges in each of the two subgroups. Increasing the number of choices to ten choices, reveals the individuality component: flies with extremely negative first choices were more likely to show photonegative behavior in subsequent choices and vice versa . General behavioral traits, independent of light/dark preference, contributed to the development of this individuality. The interaction of individuality and spontaneity together explains why group averages, even for such seemingly stereotypical behaviors, are poor predictors of individual choices. ### Competing Interest Statement The authors have declared no competing interest.

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Abstract. Ice particle activation and evolution have important atmospheric implications for cloud formation, initiation of precipitation and radiative interactions. The initial formation of atmospheric ice by heterogeneous ice nucleation requires the presence of a nucleating seed, an ice-nucleating particle (INP), to facilitate its first emergence. Unfortunately, only a few long-term measurements of INPs exist, and as a result, knowledge about geographic and seasonal variations of INP concentrations is sparse. Here we present data from nearly 2 years of INP measurements from four stations in different regions of the world: the Amazon (Brazil), the Caribbean (Martinique), central Europe (Germany) and the Arctic (Svalbard). The sites feature diverse geographical climates and ecosystems that are associated with dissimilar transport patterns, aerosol characteristics and levels of anthropogenic impact (ranging from near pristine to mostly rural). Interestingly, observed INP concentrations, which represent measurements in the deposition and condensation freezing modes, do not differ greatly from site to site but usually fall well within the same order of magnitude. Moreover, short-term variability overwhelms all long-term trends and/or seasonality in the INP concentration at all locations. An analysis of the frequency distributions of INP concentrations suggests that INPs tend to be well mixed and reflective of large-scale air mass movements. No universal physical or chemical parameter could be identified to be a causal link driving INP climatology, highlighting the complex nature of the ice nucleation process. Amazonian INP concentrations were mostly unaffected by the biomass burning season, even though aerosol concentrations increase by a factor of 10 from the wet to dry season. Caribbean INPs were positively correlated to parameters related to transported mineral dust, which is known to increase during the Northern Hemisphere summer. A wind sector analysis revealed the absence of an anthropogenic impact on average INP concentrations at the site in central Europe. Likewise, no Arctic haze influence was observed on INPs at the Arctic site, where low concentrations were generally measured. We consider the collected data to be a unique resource for the community that illustrates some of the challenges and knowledge gaps of the field in general, while specifically highlighting the need for more long-term observations of INPs worldwide.

The lack of replicability and reproducibility of scientific studies based on computational methods has lead to serious mistakes in published scientific findings, some of which have been discovered and publicized recently. Many strategies are currently pursued to improve the situation. This article reports the first conclusions from the ActivePapers project, whose goal is the development and application of a computational platform that allows the publication of computational research in a form that enables installation-free deployment, encourages reuse, and permits the full integration of datasets and software into the scientific record. The main finding is that these goals can be achieved with existing technology, but that there is no straightforward way to adapt legacy software to such a framework.

Did you ever think about what your ideal work environment for scientific programming would be like? Here's my version of it.

Computational techniques have revolutionized many aspects of scientific research over the last few decades. Experimentalists use computation for data analysis, processing ever bigger data sets. Theoreticians compute predictions from ever more complex models. However, traditional articles do not permit the publication of big data sets or complex models. As a consequence, these crucial pieces of information no longer enter the scientific record. Moreover, they have become prisoners of scientific software: many models exist only as software implementations, and the data are often stored in proprietary formats defined by the software. In this article, I argue that this emphasis on software tools over models and data is detrimental to science in the long term, and I propose a means by which this can be reversed.

The Molecular Modeling Toolkit is a library that implements common molecular simulation techniques, with an emphasis on biomolecular simulations. It uses modern software engineering techniques (object-oriented design, a high-level language) to overcome limitations associated with the large monolithic simulation programs that are commonly used for biomolecules. Its principal advantages are (1) easy extension and combination with other libraries due to modular library design; (2) a single high-level general-purpose programming language (Python) is used for library implementation as well as for application scripts; (3) use of documented and machine-independent formats for all data files; and (4) interfaces to other simulation and visualization programs. © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 79–85, 2000

Normal mode analysis has become one of the standard techniques in the study of the dynamics of biological macromolecules. It is primarily used for identifying and characterizing the slowest motions in a macromolecular system, which are inaccessible by other methods. This text explains what normal mode analysis is and what one can do with it without going beyond its limit of validity. The focus is on proteins, although normal mode analysis can equally well be applied to other macromolecules (e. g. DNA) and to macromolecular assemblies ranging in size from protein-ligand complexes to a whole ribosome. By definition, normal mode analysis is the study of harmonic potential wells by analytic means.

Adopting a functional programming style could make your programs more robust, more compact, and more easily parallelizable. However, mastering it requires some effort. This article's purpose is to explain what functional programming is and how it differs from traditional imperative programming. The author also explains how functional programming helps with concurrent and parallel programming. The language I use in the examples is Clojure, a modern dialect of Lisp.