scholarly journals Prediction of seasonal patterns of porcine reproductive and respiratory syndrome virus RNA detection in the U.S. swine industry

2020 ◽  
Vol 32 (3) ◽  
pp. 394-400
Author(s):  
Giovani Trevisan ◽  
Leticia C. M. Linhares ◽  
Bret Crim ◽  
Poonam Dubey ◽  
Kent J. Schwartz ◽  
...  
Keyword(s):  
Robust Regression ◽  
Historical Data ◽  
The United States ◽  
Respiratory Syndrome Virus ◽  
Seasonal Patterns ◽  
Test Results ◽  
Swine Industry ◽  
Cyclic Pattern ◽  
Rna Detection ◽  
Virus Rna

We developed a model to predict the cyclic pattern of porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection by reverse-transcription real-time PCR (RT-rtPCR) from 4 major swine-centric veterinary diagnostic laboratories (VDLs) in the United States and to use historical data to forecast the upcoming year’s weekly percentage of positive submissions and issue outbreak signals when the pattern of detection was not as expected. Standardized submission data and test results were used. Historical data (2015–2017) composed of the weekly percentage of PCR-positive submissions were used to fit a cyclic robust regression model. The findings were used to forecast the expected weekly percentage of PCR-positive submissions, with a 95% confidence interval (CI), for 2018. During 2018, the proportion of PRRSV-positive submissions crossed 95% CI boundaries at week 2, 14–25, and 48. The relatively higher detection on week 2 and 48 were mostly from submissions containing samples from wean-to-market pigs, and for week 14–25 originated mostly from samples from adult/sow farms. There was a recurring yearly pattern of detection, wherein an increased proportion of PRRSV RNA detection in submissions originating from wean-to-finish farms was followed by increased detection in samples from adult/sow farms. Results from the model described herein confirm the seasonal cyclic pattern of PRRSV detection using test results consolidated from 4 VDLs. Wave crests occurred consistently during winter, and wave troughs occurred consistently during the summer months. Our model was able to correctly identify statistically significant outbreak signals in PRRSV RNA detection at 3 instances during 2018.

scholarly journals Identification of SARS-CoV-2 in a Proficiency Testing Program

2020 ◽  
Vol 154 (4) ◽  
pp. 475-478
Author(s):  
Daniel C Edson ◽  
Danielle L Casey ◽  
Susan E Harmer ◽  
Frances P Downes
Keyword(s):  
The United States ◽  
Positive Sample ◽  
Test Results ◽  
Rna Detection ◽  
Virus Particles ◽  
Molecular Tests ◽  
P Gene ◽  
Control And Prevention ◽  
Overall Performance ◽  
Polymerase Chain

Abstract Objectives At the onset of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in the United States, testing was limited to the Centers for Disease Control and Prevention–developed reverse transcription polymerase chain reaction assay. The urgent and massive demand for testing prompted swift development of assays to detect SARS-CoV-2. The objective of this study was to assess the accuracy of these newly developed tests. Methods The American Proficiency Institute sent 2 test samples to 346 clinical laboratories in order to assess the accuracy of SARS-CoV-2 assays. The positive sample, containing 5,175 viral copies/mL, was fully extractable with SARS-CoV-2 viral capsid protein and RNA. The negative sample, with 3,951 viral copies/mL, contained recombinant virus particles with sequences for targeting human RNAase P gene sequences. Results Of the laboratories submitting results, 97.4% (302/310) correctly detected the virus when present and 98.3% (296/301) correctly indicated when the virus was not present. Among incorrect results reported in this proficiency challenge, 76.9% (10/13) were likely related to clerical error. This accounts for 1.6% (10/611) of all reported results. Conclusions Overall performance in this SARS-CoV-2 RNA detection challenge was excellent, providing confidence in the results of these new molecular tests and assurance for the clinical and public health decisions based on these test results.

scholarly journals PRRSV detection by qPCR in processing fluids and serum samples collected in a positive stable breeding herd following mass vaccination of sows with a modified live vaccine

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
A. Lebret ◽  
P. Berton ◽  
V. Normand ◽  
I. Messager ◽  
N. Robert ◽  
...  
Keyword(s):  
The United States ◽  
Live Vaccine ◽  
Mass Vaccination ◽  
Respiratory Syndrome Virus ◽  
Serum Samples ◽  
Blood Samples ◽  
Stability Monitoring ◽  
Breeding Herd ◽  
Modified Live Vaccine ◽  
Processing Fluid

AbstractIn the last two decades, in France, Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) stabilization protocols have been implemented using mass vaccination with a modified live vaccine (MLV), herd closure and biosecurity measures. Efficient surveillance for PRRSV is essential for generating evidence of absence of viral replication and transmission in pigs. The use of processing fluid (PF) was first described in 2018 in the United States and was demonstrated to provide a higher herd-level sensitivity compared with blood samples (BS) for PRRSV monitoring. In the meantime, data on vertical transmission of MLV viruses are rare even as it is a major concern. Therefore, veterinarians usually wait for several weeks after a sow mass vaccination before starting a stability monitoring. This clinical study was conducted in a PRRSV-stable commercial 1000-sow breed-to-wean farm. This farm suffered from a PRRS outbreak in January 2018. After implementing a stabilisation protocol, this farm was controlled as stable for more than 9 months before the beginning of the study. PF and BS at weaning were collected in four consecutive batches born after a booster sow mass MLV vaccination. We failed to detect PRRSV by qPCR on PF and BS collected in a positive-stable breeding herd after vaccination with ReproCyc® PRRS EU (Boehringer Ingelheim, Ingelheim, Germany).

scholarly journals Structural comparison of CD163 SRCR5 from different species sheds some light on its involvement in porcine reproductive and respiratory syndrome virus-2 infection in vitro

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Hongfang Ma ◽  
Rui Li ◽  
Longguang Jiang ◽  
Songlin Qiao ◽  
Xin-xin Chen ◽  
...  
Keyword(s):  
Scavenger Receptor ◽  
Host Cells ◽  
Respiratory Syndrome Virus ◽  
Swine Industry ◽  
Prrs Virus ◽  
Rich Domain ◽  
Serious Disease ◽  
The One ◽  
And Control

AbstractPorcine reproductive and respiratory syndrome (PRRS) is a serious disease burdening global swine industry. Infection by its etiological agent, PRRS virus (PRRSV), shows a highly restricted tropism of host cells and has been demonstrated to be mediated by an essential scavenger receptor (SR) CD163. CD163 fifth SR cysteine-rich domain (SRCR5) is further proven to play a crucial role during viral infection. Despite intense research, the involvement of CD163 SRCR5 in PRRSV infection remains to be elucidated. In the current study, we prepared recombinant monkey CD163 (moCD163) SRCR5 and human CD163-like homolog (hCD163L1) SRCR8, and determined their crystal structures. After comparison with the previously reported crystal structure of porcine CD163 (pCD163) SRCR5, these structures showed almost identical structural folds but significantly different surface electrostatic potentials. Based on these differences, we carried out mutational research to identify that the charged residue at position 534 in association with the one at position 561 were important for PRRSV-2 infection in vitro. Altogether the current work sheds some light on CD163-mediated PRRSV-2 infection and deepens our understanding of the viral pathogenesis, which will provide clues for prevention and control of PRRS.

scholarly journals Numerical and Experimental Investigation on Concrete Splitting Failure of Anchor Channels

CivilEng ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 502-522
Author(s):  
Anton Bogdanić ◽  
Daniele Casucci ◽  
Joško Ožbolt
Keyword(s):  
Construction Industry ◽  
Failure Mode ◽  
Current Trend ◽  
The United States ◽  
Embedment Depth ◽  
Test Results ◽  
Concrete Members ◽  
Splitting Failure ◽  
Edge Distance ◽  
Numerical Parametric Study

Concrete splitting failure due to tension load can occur when fastening systems are located close to an edge or corner of a concrete member, especially in thin members. This failure mode has not been extensively investigated for anchor channels. Given the current trend in the construction industry towards more slender concrete members, this failure mode will become more and more relevant. In addition, significantly different design rules in the United States and Europe indicate the need for harmonization between codes. Therefore, an extensive numerical parametric study was carried out to evaluate the influence of member thickness, edge distance, and anchor spacing on the capacity of anchor channels in uncracked and unreinforced concrete members. One of the main findings was that the characteristic edge distance depends on the member thickness and can be larger than 3hef (hef = embedment depth) for thin members. Based on the numerical and experimental test results, modifications of the design recommendations for the splitting failure mode are proposed. Overall, the authors recommend performing the splitting verification separately from the concrete breakout to design anchor channels in thin members more accurately.

An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

2021 ◽  
pp. 1-11
Author(s):  
Yuan Zou ◽  
Daoli Yang ◽  
Yuchen Pan
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Historical Data ◽  
Markov Chain Model ◽  
Back Propagation ◽  
The United States ◽  
Domestic Product ◽  
Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

A Nationwide Survey of COVID-19 Testing in LGBTQ+ Populations in the United States

2021 ◽  
pp. 003335492110181
Author(s):  
Richard J. Martino ◽  
Kristen D. Krause ◽  
Marybec Griffin ◽  
Caleb LoSchiavo ◽  
Camilla Comer-Carruthers ◽  
...  
Keyword(s):  
United States ◽  
Gay Men ◽  
Antibody Test ◽  
Nationwide Survey ◽  
The United States ◽  
Sociodemographic Characteristics ◽  
Full Time ◽  
Test Results ◽  
Antibody Testing ◽  
And Gender

Objectives Lesbian, gay, bisexual, transgender, or queer and questioning (LGBTQ+) people and populations face myriad health disparities that are likely to be evident during the COVID-19 pandemic. The objectives of our study were to describe patterns of COVID-19 testing among LGBTQ+ people and to differentiate rates of COVID-19 testing and test results by sociodemographic characteristics. Methods Participants residing in the United States and US territories (N = 1090) aged ≥18 completed an internet-based survey from May through July 2020 that assessed COVID-19 testing and test results and sociodemographic characteristics, including sexual orientation and gender identity (SOGI). We analyzed data on receipt and results of polymerase chain reaction (PCR) and antibody testing for SARS-CoV-2 and symptoms of COVID-19 in relation to sociodemographic characteristics. Results Of the 1090 participants, 182 (16.7%) received a PCR test; of these, 16 (8.8%) had a positive test result. Of the 124 (11.4%) who received an antibody test, 45 (36.3%) had antibodies. Rates of PCR testing were higher among participants who were non–US-born (25.4%) versus US-born (16.3%) and employed full-time or part-time (18.5%) versus unemployed (10.8%). Antibody testing rates were higher among gay cisgender men (17.2%) versus other SOGI groups, non–US-born (25.4%) versus US-born participants, employed (12.6%) versus unemployed participants, and participants residing in the Northeast (20.0%) versus other regions. Among SOGI groups with sufficient cell sizes (n > 10), positive PCR results were highest among cisgender gay men (16.1%). Conclusions The differential patterns of testing and positivity, particularly among gay men in our sample, confirm the need to create COVID-19 public health messaging and programming that attend to the LGBTQ+ population.

scholarly journals SARS-CoV-2 RNA Detection with Duplex-Specific Nuclease Signal Amplification

Micromachines ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 197
Author(s):  
Meiqing Liu ◽  
Haoran Li ◽  
Yanwei Jia ◽  
Pui-In Mak ◽  
Rui P. Martins
Keyword(s):  
Signal Amplification ◽  
Incubation Temperature ◽  
Dna Probes ◽  
Detection Sensitivity ◽  
N Gene ◽  
Rna Detection ◽  
Gold Standard Method ◽  
Complementary Method ◽  
Amplification Method ◽  
Virus Rna

The emergence of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a zoonotic pathogen, has led to the outbreak of coronavirus disease 2019 (COVID-19) pandemic and brought serious threats to public health worldwide. The gold standard method for SARS-CoV-2 detection requires both reverse transcription (RT) of the virus RNA to cDNA and then polymerase chain reaction (PCR) for the cDNA amplification, which involves multiple enzymes, multiple reactions and a complicated assay optimization process. Here, we developed a duplex-specific nuclease (DSN)-based signal amplification method for SARS-CoV-2 detection directly from the virus RNA utilizing two specific DNA probes. These specific DNA probes can hybridize to the target RNA at different locations in the nucleocapsid protein gene (N gene) of SARS-CoV-2 to form a DNA/RNA heteroduplex. DSN cleaves the DNA probe to release fluorescence, while leaving the RNA strand intact to be bound to another available probe molecule for further cleavage and fluorescent signal amplification. The optimized DSN amount, incubation temperature and incubation time were investigated in this work. Proof-of-principle SARS-CoV-2 detection was demonstrated with a detection sensitivity of 500 pM virus RNA. This simple, rapid, and direct RNA detection method is expected to provide a complementary method for the detection of viruses mutated at the PCR primer-binding regions for a more precise detection.

scholarly journals Traveling Wave Solutions of a Four Dimensional Reaction-Diffusion Model for Porcine Reproductive and Respiratory Syndrome with Time Dependent Infection Rate

Computation ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 30
Author(s):  
Jeerawan Suksamran ◽  
Yongwimon Lenbury ◽  
Sanoe Koonprasert
Keyword(s):  
Traveling Wave ◽  
Traveling Wave Solutions ◽  
Reaction Diffusion ◽  
Growing Pigs ◽  
Respiratory Syndrome Virus ◽  
Swine Industry ◽  
Reaction Diffusion Model ◽  
Spatial Dimensions ◽  
Tangent Method ◽  
Spatial Transmission

Porcine reproductive and respiratory syndrome virus (PRRSV) causes reproductive failure in sows and respiratory disease in piglets and growing pigs. The disease rapidly spreads in swine populations, making it a serious problem causing great financial losses to the swine industry. However, past mathematical models used to describe the spread of the disease have not yielded sufficient understanding of its spatial transmission. This work has been designed to investigate a mathematical model for the spread of PRRSV considering both time and spatial dimensions as well as the observed decline in infectiousness as time progresses. Moreover, our model incorporates into the dynamics the assumption that some members of the infected population may recover from the disease and become immune. Analytical solutions are derived by using the modified extended hyperbolic tangent method with the introduction of traveling wave coordinate. We also carry out a stability and phase analysis in order to obtain a clearer understanding of how PRRSV spreads spatially through time.

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