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Porcine reproductive and respiratory syndrome virus (PRRSV) remains widely distributed across the U.S. swine industry. Between-farm movements of animals and transportation vehicles, along with local transmission are the primary routes by which PRRSV is spread. Given the farm-to-farm proximity in high pig production areas, local transmission is an important pathway in the spread of PRRSV; however, there is limited understanding of the role local transmission plays in the dissemination of PRRSV, specifically, the distance at which there is increased risk for transmission from infected to susceptible farms. We used a spatial and spatiotemporal kernel density approach to estimate PRRSV relative risk and utilized a Bayesian spatiotemporal hierarchical model to assess the effects of environmental variables, between-farm movement data and on-farm biosecurity features on PRRSV outbreaks. The maximum spatial distance calculated through the kernel density approach was 15.3 km in 2018, 17.6 km in 2019, and 18 km in 2020. Spatiotemporal analysis revealed greater variability throughout the study period, with significant differences between the different farm types. We found that downstream farms (i.e., finisher and nursery farms) were located in areas of significant-high relative risk of PRRSV. Factors associated with PRRSV outbreaks were farms with higher number of access points to barns, higher numbers of outgoing movements of pigs, and higher number of days where temperatures were between 4°C and 10°C. Results obtained from this study may be used to guide the reinforcement of biosecurity and surveillance strategies to farms and areas within the distance threshold of PRRSV positive farms. 

Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using Cα atoms in protein chains were developed: (1) a mathematical approach, (2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were compared against each other and with a state-of-the-art approach, PCASSO. 

Samples from potato fields with late blight-like symptoms were collected from eastern North Carolina in 2017 and the causal agent was identified as Phytophthora nicotianae. We have identified P. nicotianae in potato and tomato from North Carolina, Virginia, Maryland, Pennsylvania, and New York. Ninety-two field samples were collected from 46 fields and characterized for mefenoxam sensitivity, mating type, and SSR genotype using microsatellites. Thirty two percent of isolates were the A1 mating type, while 53% were A2 mating type. In six cases, both A1 and A2 mating type were detected in the same field in the same year. All isolates tested were sensitive to mefenoxam. Two genetic groups were discerned based on STRUCTURE analysis: one included samples from North Carolina and Maryland, and one included samples from all five states. The data suggest two different sources of inoculum from the field sites sampled. Multiple haplotypes within a field and the detection of both mating types in close proximity suggests that P. nicotianae may be reproducing sexually in North Carolina. There was a decrease in the average number of days with weather suitable for late blight, from 2012-2016 to 2017-2021 in all of the NC counties where P. nicotianae was reported. Phytophthora nicotianae is more thermotolerant than P. infestans and grows at higher temperatures (25-35°C) than P. infestans (18-22°C). Late blight outbreaks have decreased in recent years and first reports of disease are later, suggesting that the thermotolerant P. nicotianae may cause more disease as temperatures rise due to climate change.