1,720,963 research outputs found
Models of topologically-complex self-assembling systems
No full textThe design and realization of supramolecular systems with complex topology is an active area of research in physics, chemistry and material science. These systems offers many promising applications, from molecular synthesis to switchable surfaces and nanomachines. Despite this, their realization is still quite challenging in many aspects. In this Thesis, we show how suitably designed theoretical and computational models can help overcome these challenges. First, we present a minimal model for the self-assembly of knotted constructs, where a single building block can be used to assemble more than one knotted geometry, by simply changing the system concentration. The study of this model allows to draw general conclusions which can help the design of self-assemblies with tunable multiple targets. Next, we develop a general strategy for the design, using DNA origami techniques, of mesoscopic building blocks which assemble into topologically-complex structures. In particular, as a first step in this endeavour, we target the realization of a trefoil knot. Finally, we describe a model system which assemble Olympic gels, networks of linked ring polymers whose realization has been so far very challenging. The structural and mechanical properties of the gel are then studied in details, highlighting the role played by mechanical bonding and topological connectivity. We conclude by studying the pore translocation of an exoribonucleases-resistant RNA, to investigate the relations between its three-dimensional structure and its remarkable mechanical features
Maximum information extraction via clustering and minimization of Shannon entropy
Full text linkIn the analysis of any type of system, granting maximum information extraction from its data is non-trivial. Confidence in successful information extraction typically builds on prior knowledge of the studied system or on the user's experience. However, a robust and objective criterion for ensuring maximum information extraction from data is difficult to define. Here, we introduce a data-driven approach that employs Shannon entropy as a transferable metric to assess and quantify Maximum Information Extraction (MInE) from data via their clustering into statistically-relevant micro-domains. The method is general and can be applied virtually to any type of data or system. We demonstrate its efficiency by analyzing, as a first example, time-series data extracted from molecular dynamics simulations of water and ice coexisting at the solid/liquid transition temperature. The method allows quantifying the information contained in the data distributions (time-independent component) and the additional information gain attainable by analyzing data as time-series (i.e., accounting for the information contained in data time-correlations). The different micro-domains that can be effectively resolved and classified in the system are characterized by own entropy, which are found consistent with experimentally known thermodynamic parameters. A second test case demonstrates how the MInE approach is also effective for high-dimensional datasets and clearly shows how including little informative, but noisy, extra components/features in high-dimensional analyses may be not only useless, but even detrimental to maximum information extraction. This provides a robust parameter-free approach and quantitative metrics for data-analysis, and for the study of any type of system from its data
Layer-by-layer Unsupervised Clustering of Statistically Relevant Fluctuations in Noisy Time-series Data of Complex Dynamical Systems
Full text linkComplex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often non-trivial. Here we describe Onion Clustering : a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic- to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection and classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical sub-domains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data.29 pages, 9 figures. Errors in labels in Fig5 correcte
Molecular layers in thin supported films exhibit the same scaling as the bulk between slow relaxation and vibrational dynamics
Full text linkWe perform molecular-dynamics simulations of a supported molecular thin film. By varying thickness and temperature, we observe anisotropic mobility as well as strong gradients of both the vibrational motion and the structural relaxation through film layers with monomer-size thickness. We show that the gradients of the fast and the slow dynamics across the layers (except the adherent layer to the substrate) comply, without any adjustment, with the same scaling between the structural relaxation time and the Debye-Waller factor originally observed in the bulk [Larini et al., Nat. Phys., 2008, 4, 42]. The scaling is not observed if the average dynamics of the film is inspected. Our results suggest that the solidification process of each layer may be tracked by knowing solely the vibrational properties of the layer and the bulk
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Density-tunable pathway complexity in a minimalistic self-assembly model
Full text linkAn open challenge in self-assembly is learning how to design systems that can be conditionally guided towards different target structures depending on externally-controlled conditions. Using a theoretical and numerical approach, here we discuss a minimalistic self-assembly model that can be steered towards different types of ordered constructs at the equilibrium by solely tuning a facile selection parameter, namely the density of building blocks. Metadynamics and Langevin dynamics simulations allow us to explore the behavior of the system in and out of equilibrium conditions. We show that the density-driven tunability is encoded in the pathway complexity of the system, and specifically in the competition between two different main self-assembly routes. A comprehensive set of simulations provides insight into key factors allowing to make one self-assembling pathway prevailing on the other (or vice versa), determining the selection of the final self-assembled products. We formulate and validate a practical criterion for checking whether a specific molecular design is predisposed for such density-driven tunability of the products, thus offering a new, broader perspective to realize and harness this facile extrinsic control of conditional self-assembly
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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