1,721,168 research outputs found
A metallothionein gene from Tetrahymena thermophila with a copper inducible-repressible promoter.
No full text
Functional Characterization of the 5'-upstream Region of MTT5 Metallothionein Gene from Tetrahymena thermophila.
No full textIdentification of Selective Sweeps Through Deep Learning in Whole Genome Sequenced Malaria Parasites
No full textMachine learning predicts accurately mycobacterium tuberculosis drug resistance from whole genome sequencing data
Full text linkBackground: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major
public health problem. The emergence of M. tuberculosis strains resistant to existing
treatments threatens to derail control efforts. Resistance is mainly conferred by mutations
in genes coding for drug targets or converting enzymes, but our knowledge of these
mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common
approach to rapidly characterize isolates and identify mutations predicting antimicrobial
resistance and thereby providing a diagnostic tool to assist clinical decision making.
Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates
that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14
antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being
extensively drug resistant. We used non-parametric classification-tree and gradientboosted-tree models to predict drug resistance and uncover any associated novel putative
mutations. We fitted separate models for each drug, with and without “co-occurrent
resistance” markers known to be causing resistance to drugs other than the one of interest.
Predictive performance was measured using sensitivity, specificity, and the area under the
receiver operating characteristic curve, assuming DST results as the gold standard.
Results: The predictive performance was highest for resistance to first-line drugs,
amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis
(area under the receiver operating characteristic curve above 96%), and lowest for thirdline drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below
85%). The inclusion of co-occurrent resistance markers led to improved performance
for some drugs and superior results when compared to similar models in other largescale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models
performed better than the classification-tree models. The mutation-rank analysis detected
no new single nucleotide polymorphisms linked to drug resistance. Discordance between
DST and genotypically inferred resistance may be explained by DST errors, novel rare
mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation.
Conclusion: Our work demonstrates the utility of machine learning as a flexible approach
to drug resistance prediction that is able to accommodate a much larger number of
predictors and to summarize their predictive ability, thus assisting clinical decision
making and single nucleotide polymorphism detection in an era of increasing WGS data
generation
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
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