1,721,038 research outputs found
Livestock production drivers of antibiotic resistance
No full textThere are three main datasets i.e A) House_Hold_PAIRS, B) HOUSE_HOLD_PAIRS_SHARED_MDR, C) E_COLI_MDR_SHARED, We also have a list of paired farmer and pig(HOUSE_HOLD_PAIRS), the multi-drug resistant(MDR) strains they share (HOUSE_HOLD_PAIRS_MDR_SHARED), and the MDR patterns carried by E.coli recovered from the paired farmer and pig(E_COLI_MDR_SHARED). Farmer-pig_carriage this dataset compares the gene carriage of a farmer at time T and thier pig at time T+1 in the same household.
METADATA dataset has 476 rows and 14 columns This data set is named PHENOTYPE DATA it contains 8 columns and 2830 rows This data is output from analysis of bacteria culture from farmers and their pigs in the one-year longitudinal study. We collected these samples 6 times growing three bacteria E.coli, Klebsiella, and Salmonella Pig and Human ID SAMPLE ID E.G KLAMAK011P The first three letters District, Second three letter Sub-county The two number digits is for the household next number is for the visit number the last number is for the host (Pig or Human)
Controls KLANANCTR021 CRT means control 02 = Negative control 01= Positive control The last number is for the visit number For analysis use column 3. SAMPLE_ID2 We often generate variables out of the ID as a data integrity validation process usually in a data set as ID3 SETTING Means District (Urban =Kampala(Intensive & Semi intensive pig), Rural=Mubende(free range or semi-free range)) Sub-county is an administrative division in the district SAMPLING.... is the visit time (1-6) Species mean the Host (Human or Pig) Isolate means Is a tracking number for the laboratory Bacteria is the Bacteria isolated Antibiotics, these will have three states(R, I, S) resistant, intermediate and Susceptible.E_COLI_MDR_SHARED.csv
Farmer_pig_carriage.csv
FLF_QPCR_DB.csv
HOUSE_HOLD_PAIRS_MDR_SHARED.csv
HOUSE_HOLD_PAIRS.csv
META_DATA.csv
PHENOTYPE_DB.cs
SUPERSEDED - AMR dynamics in gut microbiome of farmers and animals
No full text## This item has been replaced by the one which can be found at [https://doi.org/10.7488/ds/7792] ##' This dataset and code are part of supplementary data for a manuscript Muwonge, A., Kakooza, T., Johnson, P.C.D., Kisuule, L., Kimaanga, M., Kankya, C., de Clare Bronsvoort, B.M., Lembo, T., "Production system drivers of antibiotic resistance at the human-animal interface in Uganda", The Lancet Planetary Health (in submission). They explore the role of livestock production systems in the epidemiology of antibacterial resistance (ABR) in sympatric human and livestock populations, which is poorly understood. Here, they examine ABR at the farmer-pig interface of Uganda, where the pig sector is rapidly growing, to quantify rates of resistance, understand associated human- and livestock-related factors, and investigate cross-species transmission.
The motivation of this is to improve our understanding of the role of livestock production systems in the emergence and transmission of AMR, this paper uses phenotypic resistance profile from sentinel bacteria E.coli and Klebsiella, recovered from a faecal sample collected from farmers and their pigs across a one-year longitudinal study. This is mapped to AMR gene carriage of four selected genes measured using QPCR. Using the Metadata, they examine drivers of resistance in this setting, and also use the prevalence and sharing of MDR profiles to infer transmission.There are three main datasets: Metadata, Phenotype and Qpcr datases.
Three other subsets: A) House_Hold_PAIRS, B) HOUSE_HOLD_PAIRS_SHARED_MDR,C) E_COLI_MDR_SHARED
METADATA.csv has 476 rows and 14 columns.
This data set is named FLF_PHENOTYPE DATA. It contains 8 columns and 2830 rows This data is output from analysis of bacteria culture from farmers and thier pigs in the one year longitudinal study. We collected these sample 6 times growing three bacteria E.coli, Klebsiella and Salmonella.
Pig and Human ID, SAMPLE ID E.G KLAMAK011P (The first three letters= District; second three letter= Sub county; the two number digit= household; next number= visit number; last number= the host, Pig or Human)
Controls:
E.g. KLANANCTR021. CRT= control; 02= Negative control; 01= Positive control; last number= visit number.
For analysis, use column 3: SAMPLE_ID2. We often generate variables out of the ID as data integrity validation process, usually in a data set as ID3.
SETTING= District (Urban=Kampala (intensive & semi intensive pig), Rural= Mubende (free range or semi free range));
Subcounty= administrative division in the district;
SAMPLING= the visit time (1-6);
Species= the Host (Human or Pig);
Isolate= tracking number for the laboratory;
Bacteria= the Bacteria isolated.
Antibiotics, these will have three states (R,I,S)= resistant, intermediate and susceptible.
Note that, we did not have results for Imipenem for the first sampling poin
Repurposing data to support livestock disease control in developing countries
No full textThe use of network analysis to support livestock disease control in low middle-income countries (LMICs) has historically been hampered by the cost of generating empirical data in the absence of animal movement recording schemes. To fill this gap, we have adopted methods which exploit freely available demographic and archived molecular data to generate livestock networks based on phylogeographic and gravity modelling techniques. We compare output from these network methodologies to empirical and randomly generated data. We simulate disease scenarios on the networks to evaluate the potential utility of our methodologies to inform robust livestock disease control strategies.
The molecular network was the closest approximation to the empirical network, both in relation to topological and epidemic characteristics. The gravity network tended to overestimated disease epidemics. However, better agreement across all three networks was observed if less specific epidemic characteristics such as the size of outbreak were investigated. Moreover, these methods consistently identified the same important animal movement and trade hotspots as the empirical networks. We therefore consider this proof-of-concept that demographic data such as censuses and archived molecular data could be repurposed to inform livestock disease management in LMICs
Preventing future pandemics and epidemics through a North-South collaboration on genomic surveillance in Africa
Full text linkThe editorial describes the measures for preventing future pandemics and epidemics through a North-South collaboration on genomic surveillance in Afric
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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