1,721,104 research outputs found
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
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
Computational and theoretical studies of co-operative phenomena in biology at different scales
No full text2024From the collective dynamics observed in starling murmuration to the intricate process of protein folding, co-operativity stands as a fundamental concept underlying biological phenomena across different scales. Consequently, the pursuit of a mechanistic comprehension of the complex co-operative phenomena in biology remains a central theme of biophysics. To this end, my research has been organized into two parts, which are dedicated to the understanding of co-operative phenomena in biology at two different scales respectively. The first part focuses on protein allostery (inter-residue co-operativity), a fundamental regulatory mechanism of protein function. Here, we first developed a machine learning model that helps us identify the defining molecular features of allosteric hotspots, where the results highlight the convergence and divergence of the allosteric mechanisms among homologous transcriptional factors. These findings further inspired us to build a statistical thermodynamic model, which quantitatively recapitulates in vivo experimental data and provides physical insights into the mutational effects on the allosteric response of two-domain systems. The second part focuses on studying biomolecular condensation (inter-molecular co-operativity), an emerging mechanism essential for the fast and reversible assembly of functional subcellular compartments characterized by diverse and distinct chemical features. Here, using molecular simulations and a mean-field theory, we demonstrate that biological membranes, by coupling its own phase separation to that of the surface biomolecules for co-operative domain growth, offers a robust mechanism for sensitive and selective condensate regulation. Our results add to the fundamental understanding of the mechanisms for spatiotemporal control of condensate assembly, and the revealed physical principle has broad implications beyond the specific biophysical problem
Analysis of solid/liquid interface and solution reactions with molecular simulations and machine learning
Full text linkUnderstanding the behavior of electrolytes at the solid-liquid interface is important to numerous applications, such as in batteries and fuel cells. The molecular organization of a liquid at the interface is usually distinct from the bulk, and frequency measurements from experimental techniques such as vibrational sum frequency generation can be used to provide a characterization of local electrostatics at the interface. However, to infer molecular features of the interface based on these measurements, detailed atomistic simulations are required.
Our approach to computationally studying solid-liquid interfaces involves a combination of classical molecular dynamics simulations with vibrational frequency calculations using semiempirical frequency maps. The first part of this work investigates the effect of anion size on structure and electrostatics at the nitrile-functionalized gold-ionic liquid interface. We observed that the intercalation of smaller ions into the nitrile layer leads to higher electric fields and experimentally, larger redshifts. In the second part, we aim to understand solvatochromic frequency shifts at the nitrile-functionalized gold-water interface in the presence of other ligands. The electrostatic environment, in this case, is highly heterogeneous and subject to ligand placement, surface density, and the chemical nature of the ligands. Despite the heterogeneity, water access to nitrile probes turned out to make an essential contribution to the trend in vibrational frequencies.
The second part of the thesis aims to understand chemical reactions in the condensed phase, which often requires sampling multiple degrees of freedom. Constructing multidimensional free energy surfaces with reliable accuracy requires extensive conformational sampling, which can be prohibitively expensive. To address this challenge, first, we compute a multidimensional free energy surface using QM/MM reinforced dynamics simulations with inexpensive but less accurate DFTB3 as the QM method. In the next step, we improve the accuracy of the free energy surface using delta machine learning trained on previously accessed conformations. Finally, we apply this scheme to study biologically relevant phosphoryl transfer and phosphate hydrolysis reactions to gain novel mechanistic insights.2024-02-2
Exploring free energy landscapes in complex biomolecular systems with advanced computer simulations and neural networks
No full text2023Recent advances in computer simulation and experimental techniques have motivated computational chemists and biophysicists to better understand the function of complex biomolecular systems by exploring the underlying free energy landscapes with extensive sampling and/or accurate potential functions. With creative application of existing techniques and continuing development of new methodologies, increasingly complex mechanistic problems can now be solved with computational techniques. In this dissertation, we take advantage of state-of-the-art molecular dynamics simulations and free energy approaches, as well as modern neural networks to tackle two major problems in the area of computational biophysics. The first topic was inspired by recent deep mutational scanning experiments on a transcription factor, the Tetracycline repressor (TetR), which revealed an unexpected distribution of allostery hotspots that cannot be explained by existing models. Accordingly, we have developed a new computational framework to understand the molecular basis of allostery and the broad distribution of hotspot residues in TetR. The key was to integrate long timescale molecular dynamics simulations, free energy computations and analyses of the structural and dynamical properties of TetR at both local and global scales. The mechanistic framework and multifaceted analysis strategy is expected to be applicable to many allostery systems. In the second part, we aim at improving the computational efficiency and accuracy of multi-level free energy simulations so that accurate quantum mechanical potential functions can be applied to complex biomolecular systems at the cost of an inexpensive method, such as a semi-empirical quantum mechanical approach. The solution we propose is an innovative combination of modern neural networks and enhanced sampling simulations, resulting in a computational framework that greatly improves the convergence and accuracy of multi-level free energy calculations for condensed phase systems.2025-08-05T00:00:00
Analysis of regulatory mechanism of protein functions with advanced computations
No full text2024Understanding the regulatory mechanisms of protein function is of major significance from both fundamental and biomedical points of view. Due to potential contributions from a multitude of physicochemical factors, analysis of regulatory mechanisms poses significant challenges to both experiments and computations. In this dissertation, a range of computational techniques have been developed and applied to better understand several representative regulatory mechanisms of protein function. One important regulatory mechanism of protein function emerged in recent studies concerns the hydration level of internal cavities. For example, water penetration was proposed to stabilize buried charges/dipoles, which play key roles in enzymes and bioenergetics systems. However, much uncertainty remains regarding the methodologies for describing the time scale and energetic driving forces for water penetration. Using extensive free energy simulations with polarizable force fields, we demonstrated that to properly describe the stability, hydration, dynamics, and therefore function of buried charges/dipoles, it is essential to explicitly include electronic polarization. Motivated by this observation, we have revised and implemented a grand canonical nonequilibrium candidate Monte Carlo approach to enable efficient sampling of cavity hydration level using a polarizable force field. These insights and method- ologies were essential to the analysis of the gating mechanism of the big potassium channel, in which the hydration level of the central hydrophobic cavity was proposed to regulate ion transport. Combined with nuclear magnetic resonance (NMR) spectroscopy, our enhanced sampling simulations also illustrated the roles and timescales of conformational change and internal hydration dynamics in determining the higher temperature-sensitivity of an engineered potassium channel. Another hallmark for biomolecules is that distal residues make significant cumulative contributions. However, their individual and specific roles remain difficult to predict and understand. We analyzed the contributions of second-shell residues in a metalloenzyme. By adopting a multifaceted approach that included both quantum mechanical and classical models, we probed the rate-limiting chemical step and structural properties of all relevant enzyme states. In combination with available experimental kinetics data, our results showed that mutations of those second-shell residues impact catalytic efficiency mainly by perturbation of the apo state and there- fore substrate binding, while they do not affect the ground state or transition state significantly. In another study, by examining a range of structural and dynamical properties in a transcription factor at both local and global scales in extensive molecular dynamics simulations, we showed that experimentally identified hotspot residues modulate allostery in distinct ways. The results motivated a thermodynamic model that qualitatively explained the broad distribution of hotspot residues observed in the experiment. We further demonstrated that the mutation effects of hotspot residues can be evaluated and ranked with functional free energy simulations. Collectively, these studies highlighted the power of integrating multiple computational approaches to better define the complex contributions of distal residues to function regulation
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