Measurement of IFN-γ in the mitogen-stimulated ferret PBMC culture supernatants by ELISA. (A) Standard curve for ferret IFN-γ ELISA. ELISA plate well was coated with a monoclonal anti-ferret antibody generated in our laboratory. Recombinant ferret IFN-γ was sequentially diluted and loaded to the antibody-coated wells. Captured ferret IFN-γ was detected by a second monoclonal anti-ferret IFN-γ antibody generated in our laboratory conjugated to biotin, using the avidin-HRP detection method. Logarithmic dilution was used to derive a standard curve for downstream applications of the ELISA. (B) IFN-γ in mitogen stimulated ferret PBMC supernatants. ELISA utilizing the monoclonal ferret IFN-γ antibody as a capture antibody was performed on ferret PBMC cells treated with PMA, ionomycin or both. Results represent the mean values of triplicate samples.
ferret copy detection software 29
Our primary scope in developing the ferret IFN-γ-specific monoclonal antibodies was to develop a reagent with which to measure IFN-γ immune responses in tissues or cells derived from influenza A virus-infected ferrets. To this end, an ELISA using the capture-detection monoclonal antibody pair was used to assess the level of IFN-γ in sera obtained from influenza A-infected ferrets. The assay showed substantial levels of circulating IFN-γ on day 6 post-infection (Figure 5). The level of IFN-γ in serum from the non-infected control ferret was below the detection limit. These results show that the ELISA assay using our anti-ferret IFN-γ monoclonal antibodies will be invaluable in monitoring systemic IFN-γ responses during a host response against virus infection.
ELISPOT assay for the IFN-γ producing cells in mitogen-stimulated ferret PBMCs. ELISPOT assay was performed in the same manner outlined for Figure 4 for capture and detection. Ferret PBMCs were plated in serial dilution and stimulated with PMA plus ionomycin for 18 h and IFN-γ secreting cells were detected by biotinylated capture antibody. The y-axis depicts the number of IFN-γ spot forming cells per well; total cells per well are indicated on the x-axis. Data shown are the average of triplicate samples.
(a, b) Infectious virus titres of WT (orange) or ΔCS (blue) taken from donor ferret (a) and direct contact ferret (b) nasal washes as determined by TCID50. Dotted line indicates limit of detection of infectious virus. (c) Microneutralisation assay showing ferret post-infection serology. Threshold of detection was a neutralisation titre of 10 (dotted line). Serum taken 14 days post-infection. (d) Changes in donor and direct contact ferret body weights during the duration of the infection. (e) Changes in donor and direct contact ferret body temperatures during the duration of the infection.
The findings and conclusions in this report are those of the author(s) and do not necessarily represent the views of the Department of Health and Human Services, its components, or the FDA. Dose-dependent response to infection with Ebola virus in the ferret model and evidence of viral evolution in the eye is under Crown copyright and is reproduced with the permission of Public Health England under delegated authority from the Controller of HMSO.
Plagiarism detection or content similarity detection is the process of locating instances of plagiarism or copyright infringement within a work or document. The widespread use of computers and the advent of the Internet have made it easier to plagiarize the work of others.[1][2]
Detection of plagiarism can be undertaken in a variety of ways. Human detection is the most traditional form of identifying plagiarism from written work. This can be a lengthy and time-consuming task for the reader[2] and can also result in inconsistencies in how plagiarism is identified within an organization.[3] Text-matching software (TMS), which is also referred to as "plagiarism detection software" or "anti-plagiarism" software, has become widely available, in the form of both commercially available products as well as open-source[examples needed] software. TMS does not actually detect plagiarism per se, but instead finds specific passages of text in one document that match text in another document.
A study was conducted to test the effectiveness of similarity detection software in a higher education setting. One part of the study assigned one group of students to write a paper. These students were first educated about plagiarism and informed that their work was to be run through a content similarity detection system. A second group of students was assigned to write a paper without any information about plagiarism. The researchers expected to find lower rates in group one but found roughly the same rates of plagiarism in both groups.[15]
Literal copies, aka copy and paste (c&p) plagiarism or blatant copyright infringement, or modestly disguised plagiarism cases can be detected with high accuracy by current external PDS if the source is accessible to the software. Especially substring matching procedures achieve a good performance for c&p plagiarism, since they commonly use lossless document models, such as suffix trees. The performance of systems using fingerprinting or bag of words analysis in detecting copies depends on the information loss incurred by the document model used. By applying flexible chunking and selection strategies, they are better capable of detecting moderate forms of disguised plagiarism when compared to substring matching procedures.
Citation-based plagiarism detection using citation pattern analysis is capable of identifying stronger paraphrases and translations with higher success rates when compared to other detection approaches, because it is independent of textual characteristics.[26][29] However, since citation-pattern analysis depends on the availability of sufficient citation information, it is limited to academic texts. It remains inferior to text-based approaches in detecting shorter plagiarized passages, which are typical for cases of copy-and-paste or shake-and-paste plagiarism; the latter refers to mixing slightly altered fragments from different sources.[44]
Most large-scale plagiarism detection systems use large, internal databases (in addition to other resources) that grow with each additional document submitted for analysis. However, this feature is considered by some as a violation of student copyright.[citation needed]
Various complications have been documented with the use of text-matching software when used for plagiarism detection. One of the more prevalent concerns documented centers on the issue of intellectual property rights. The basic argument is that materials must be added to a database in order for the TMS to effectively determine a match, but adding users' materials to such a database may infringe on their intellectual property rights. The issue has been raised in a number of court cases.
An additional complication with the use of TMS is that the software finds only precise matches to other text. It does not pick up poorly paraphrased work, for example, or the practice of plagiarizing by use of sufficient word substitutions to elude detection software, which is known as rogeting.
A(H1N1)pdm09 viruses with mutations at position 156 have been reported in human surveillance studies ([40], WHO Collaborating Centre for Reference and Research on Influenza, Melbourne), although they occur relatively rarely (0.15% of all samples on GISAID with passage information). We have demonstrated that this mutation from ferret respiratory samples does not persist in cell culture without further adaptation, or reversion to wildtype virus in mixed populations. The low HA titre in the initial passage, and negative association with cell culture, may prevent identification of original clinical samples containing N156K alone, hence laboratories may exclude these viruses from surveillance studies. Therefore conventional methods for isolation of human samples may select against detection of mutations at position 156. We also cannot discount the possibility that the ferret model has selected for a mutant that has compromised transmissibility in the human population, due to differences in the glycan receptor profiles between the human and ferret respiratory tract, or more complex immunity in humans with their extensive infection and vaccination history. Use of a different A(H1N1)pdm09 strain, cell-cultured virus or an alternate adjuvant/vaccine preparation may have also influenced the mutation profile. Further analysis of viruses from the lungs of MIV-immunized ferrets, where immune escape mutants may also emerge, would be worthwhile.
A(H1N1)pdm09 has circulated over the past four years with limited genetic diversification and no significant antigenic change. Rising population immunity is likely to drive antigenic drift in the A(H1N1)pdm09 virus eventually. Passage in immune ferrets demonstrated that an antigenic mutant can arise in a similar situation. Importantly, antigenic mutations in the HA may also influence receptor binding properties, affecting the efficiency of isolation of viruses for surveillance characterization purposes. Routine genetic analysis of original clinical specimens is therefore important. In vitro and/or in vivo passage of the 2012/2013 A(H1N1)pdm09 viruses under immune pressure would indicate whether N156K is permissive in the presence of additional mutations or whether other regions of the HA globular head are now under greater selective pressure. Continued examination of influenza virus escape mutants should aid in the identification of future vaccine-breakthrough viruses, enabling their rapid detection through influenza surveillance and ensuring appropriate changes to the A(H1N1)pdm09 component of seasonal influenza vaccine are made in a timely fashion.
Cells were seeded into T25 flasks or 24-well tissue culture plates and grown to confluence overnight. Monolayers were washed twice with Ca2+/Mg2+-free phosphate-buffered saline before incubation with 500 or 100 µl virus, respectively (nasal wash samples inoculated at 1/6 to 1/10 dilution; cell culture isolates inoculated neat; egg-grown virus inoculated at 1/100) at 37C and 5% CO2 for 30 min, or other temperatures, as indicated. The inoculum was removed and replaced with medium (without fetal calf serum) supplemented with 4 µg/ml trypsin (Sigma) [52]. 2 mU/ml exogenous neuraminidase (from Clostridium perfringens (Sigma) or 5 nM oseltamivir carboxylate (the active form of the ethyl ester prodrug oseltamivir phosphate, kindly provided by Hoffmann-La Roche Ltd, Switzerland) was added to cultures as indicated in the text. Supernatant was collected daily and the presence of virus was assessed by hemagglutination using 1% turkey RBC and by real time RT-PCR assays for the influenza A matrix gene. Infection was also assessed by surface staining of infected cells and analysis by flow cytometry. Cells were stained with anti-influenza A HA monoclonal antibodies (mAbs) (A(H1N1)pdm09: mAb174 clone 10F5.1D7 and mAb175 clone 2G10.1C11; A(H3N2): mAb131 clone 4E4.1F10 and mAb 132 clone 1G6.1G7; kindly provided by CSL Limited), ferret antisera, followed by anti-ferret Ig-FITC (Rockland Immunochemicals Inc., USA) or anti-influenza A matrix mAb (clone GA2B, AbD Serotec, UK), followed by anti-mouse Ig-FITC (SantaCruz Biotechnology and KPL). Staining was performed in the presence of 1 µM oseltamivir carboxylate. Samples were run on a FC500 Analyzer (Beckman Coulter) or FACSCanto II (BD Biosciences) with data analysis using FlowJo 7.5.5 software. 2ff7e9595c
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