1,720,964 research outputs found
Binding Characteristics of Anticancer Drug Doxorubicin with Two-Dimensional Graphene and Graphene Oxide : Insights from Density Functional Theory Calculations and Fluorescence Spectroscopy
No full textThere has been a perpetual interest in identifying suitable nano-carriers for drug delivery. In this regard, graphene-based two-dimensional materials have been proposed and demonstrated as drug carriers. In this paper, we have investigated the adsorption characteristics of a widely used anticancer drug, doxorubicin (DOX), on graphene (G) and graphene oxide (GO) by density functional theory calculations and fluorescence and X-ray photoelectron spectroscopies. From the calculated structural and electronic properties, we have concluded that G is a better binder of DOX compared to GO, which is also supported by our fluorescence measurements. The binding of DOX to G is mainly based on strong pi-pi stacking interactions. Consistent with this result, we also found that the sp(2) regions of GO interact with DOX stronger than the sp(3) regions attached with the functional groups; the binding is characterized by pi-pi and hydrogen-bonding interactions, respectively.</p
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
Statistical mechanical theory of equilibrium structure and miscibility of polymer nanocomposites: effects of polymer chemical heterogeneity and architecture, and nanoparticle surface corrugation and softness
Full text linkMotivated by the persistent interest in different nanoparticles added to various polymer matrices, the Polymer Reference Interaction Site Model (PRISM) theory is extended and applied to study the thermodynamics, statistical structure, and miscibility of diverse polymer nanocomposites (PNCs). Under chemistry-matched conditions and in the absence of interfacial attractions between a spherically smooth nanoparticle and the matrix fluid, the polymer-induced depletion attraction is dominant and induces entropic phase separation. The depletion attraction can be potentially reduced by modifying the nanoparticle surface topography as recently observed in experiments. Two types of surface-modified nanoparticles have been considered in this thesis – (1) spheres with ordered roughness on the surface and (2) soft polymeric nanoparticles with surface fluctuations and fuzziness. Monte Carlo integration and other computational techniques have been developed to compute the effective interactions between such particles. The morphologically diverse particles introduce additional length scales, making the physics non-monotonic, subtle, and rich. The common advantage with using either of the particles is reduced contact aggregation and enhanced miscibility. Optimal surface corrugation and/or particle softness allow monomer penetration resulting in favourable (entropic) mixing. However, high enough degree of corrugation/softness can also result in destabilization by excluding the polymer from its interior.
Another route of developing new nanocomposites is by tuning the polymer-particle interfacial chemistry. Prior work has established three states of spatial organization, namely depletion, steric stabilization and bridging, depending upon the effective interfacial attraction strengths. Introducing polymer chemical heterogeneity via the use of AB copolymers offers additional control over the equilibrium structure. Specifically, two types of copolymers are considered – (1) random copolymers (RCP) of disordered sequence and (2) ordered, alternating multiblock copolymers (MBCP). Quantum chemical calculations are combined with the polymer liquid state theory to predict structure and miscibility. The chain connectivity, monomer sequence, copolymer composition and differential wettability results in unique frustration in the system leading to novel states of organization of the polymer around the nanoparticles. In the context of strongly attractive nanoscopic fullerenes, this results in improved miscibility relative to the corresponding homopolymers. For some of the systems studied, maximum dispersion is predicted at an intermediate copolymer composition due to packing correlations and differential wetting effects with favourable comparison to experiments.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2017-12-01The student, Debapriya Banerjee, accepted the attached license on 2015-07-29 at 10:23.The student, Debapriya Banerjee, submitted this Dissertation for approval on 2015-07-29 at 10:24.This Dissertation was approved for publication on 2015-07-31 at 15:42.DSpace SAF Submission Ingestion Package generated from Vireo submission #8651 on 2016-03-08 at 11:04:52Made available in DSpace on 2016-03-08T17:21:18Z (GMT). No. of bitstreams: 2
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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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Semi-supervised Learning using Triple-Siamese Network
Full text linkMissing data problem is inevitable in mostly all research areas including Artificial Intelligence, Machine Learning and Computer Vision where we have modicum knowledge about the complete dataset. One of the key reasons of missing data in AI is insufficiency of accurately labeled data. To solve a classification problem using ML or training a Deep Neural Network model, we need a huge amount of labeled data. It is difficult to get labeled data but unlabeled data is inexpensive and available easily. It is usual that we get no more than a single element per class to train our models due to unavailability of enough labeled training data. Strict privacy control or accidental loss may also cause missing data problem. One of the ways of getting training data labeled is using human-in-the-loop, but budget constraints can prevent that option. The objective of this research is to recover the complete signal or missing labels of the dataset using state-of-the-art Machine Learning and Computer Vision techniques. We propose a novel network trained with a few instances of a class to perform Metric Learning. We then convert our dataset to a graph signal and recover the graph completely using Recovery algorithm in Graph Fourier Transform. Our approach performs significantly better than Graph Neural Network and other state-of-the-art techniques
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