305,123 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe 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
Dispelling the Myths Behind First-author Citation Counts
No full textWe 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
Evaluation of a Potential Bacteriophage Cocktail for the Control of Shiga-Toxin Producing Escherichia coli in Food
No full textShiga-toxin producing Escherichia coli (STEC) are important foodborne pathogens involved in gastrointestinal diseases. Furthermore, the recurrent use of antibiotics to treat different bacterial infections in animals has increased the spread of antibiotic-resistant bacteria, including E. coli, in foods of animal origin. The use of bacteriophages for the control of these microorganisms is therefore regarded as a valid alternative, especially considering the numerous advantages (high specificity, self-replicating, self-limiting, harmless to humans, animals, and plants). This study aimed to isolate bacteriophages active on STEC strains and to set up a suspension of viral particles that can be potentially used to control STEC food contamination. Thirty-one STEC of different serogroups (O26; O157; O111; O113; O145; O23, O76, O86, O91, O103, O104, O121, O128, and O139) were investigated for their antibiotic resistance profile and sensitivity to phage attack. Ten percent of strains exhibited a high multi-resistance profile, whereas ampicillin was the most effective antibiotic by inhibiting 65% of tested bacteria. On the other side, a total of 20 phages were isolated from feces, sewage, and bedding material of cattle. The viral particles proved not to carry genes codifying Shiga-toxins and intimin. No STEC was resistant to all phages, although some strains revealed weak sensitivity by forming turbid plaques. Three different bacteriophages (forming the “cocktail”) were selected considering their different RAPD (Random Amplification of Polymorphic DNA) profiles and the absence of virulence-encoding genes and antibiotic-resistance genes. The lytic ability against STEC strains was investigated at different multiplicity of infection (MOI = 0.1, 1, and 10). Significant differences (p < 0.05) among mean values of optical density were observed by comparing results of experiments at different MOI and controls. An effective reduction of bacterial population was obtained in 81% of cases, with top performance when the highest MOI was applied. The efficacy of the phage cocktail was tested on fresh cucumbers. Results showed a reduction in pathogenic E. coli by 1.97–2.01 log CFU/g at 25C and by 1.16–2.01 log CFU/g at 4C during 24 h, suggesting that the formulated cocktail could have the potential to be used in bio controlling STEC different serogroup
Author, publisher and bookseller : a tripartite synergy in Nigerian book industry
No full textThis work is about the roles of Author, Publisher and Bookseller in Book development in
Nigeria. The paper started by delving into the history of Book Publishing in Nigeria after
which it proceeded by defining who an author, a publisher, and a bookseller is and
expatiated on the indispensable roles of these key actors in Nigerian Book Industry and in
the emerging Information Society. Furthermore, the various constraints to book
development were identified while the paper advised on how the Book Industry can be
further promoted in Nigeria. However, the paper concluded and made recommendations
on how the Book sector can help in enhancing scholarship in the country
[Report to Chief J. E. Curry, by an unknown author #2]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
[Report to Chief J. E. Curry, by an unknown author #1]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
Mining e-mail content for author identification forensics
No full textWe describe an investigation into e-mail content mining for author identification, or authorship attribution, for the purpose of forensic investigation. We focus our discussion on the ability to discriminate between authors for the case of both aggregated e-mail topics as well as across different email topics. An extended set of e-mail document features including structural characteristics and linguistic patterns were derived and, together with a Support Vector Machine learning algorithm, were used for mining the e-mail content. Experiments using a number of e-mail documents generated by different authors on a set of topics gave promising results for both aggregated and multi-topic author categorisation
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