Regional Seismic Loss Assessment by Deep-Learning-based Prediction of Structural Responses

As urban systems become more complex and sophisticated, the vulnerability of densely populated areas under earthquake hazard also increases. To establish risk-based strategies for hazard mitigation and recovery at the urban community level, many research efforts have been made for probabilistic seismic risk assessment (PSRA). When performing PSRA, structural responses are usually estimated by fragility functions or nonlinear static procedures. It is, however, noted that developing fragilities of each of numerous structures in a large area may require huge computational cost whereas nonlinear static procedures may not incorporate variabilities of the structural responses given a seismic intensity. Recently, the authors developed a deep-learning-based approach for probabilistic evaluation of the structural responses for a wide class of hysteretic behavior and ground motions. To reduce the computational cost of a regional seismic loss estimation and improve its accuracy, this paper proposes a new PSRA using the deep-learning-based method. To demonstrate the applicability of the proposed method and its merits, a hypothetical example of PSRA is investigated. In addition, this paper proposes a procedure to determine the optimal number of sensors, in which the deep-learning-based method is used to evaluate the seismic loss. Furthermore, the trained deep neural network model is employed as a surrogate model for a real-time PSRA. The deep-learning-based PSRA and the procedure to determine the sensors for installation are expected to improve PSRA at community level in terms of efficiency and applicability, and provide new insights into the seismic risk assessment and management of urban systems.The first and the second author are supported by the project Development of Life-cycle Engineering Technique and Construction Method for Global Competitiveness Upgrade of Cable Bridges of the Ministry of Land, Infrastructure and Transport (MOLIT) of the Korean Government (Grant No. 16SCIP-B119960-01), and the third author is supported by the Korean Federation of Science and Technology Societies (KOFST) grant funded by the Korean government (MSIP: Ministry of Science, ICT and Future Planning). Finally, this research was enabled in part by support provided by WestGrid (www.westgrid.ca) and Compute Canada (www.computecanada.ca)

Persistence of African Swine Fever Virus in Feed and Feed Mill Environment over Time after Manufacture of Experimentally Inoculated Feed

To reduce the risk of disease from harmful feed-based pathogens, some feed manufacturers quarantine high-risk ingredients prior to their inclusion in feed. Data exist that confirms this practice is effective, but to our knowledge there is no information about porcine pathogen survival in mill environments. The objective of this study was to determine survival of African swine fever virus (ASFV) in swine feed and on mill surfaces after manufacture of experimentally inoculated swine feed. A pilot-scale feed mill was placed within a biosecurity level (BSL) 3 facility to manufacture batches of feed. The priming batch, Batch 1, was ASFV-free feed and was followed with Batch 2 which was experimentally inoculated with ASFV (5.6 × 104 TCID50/gram). Four subsequent ASFV-free batches were then manufactured (Batch 3-6). After each batch of feed, 10 feed samples were aseptically collected in a double ‘X’ pattern. During feed manufacturing, 24 steel coupons were placed on the floor of the manufacturing area and feed dust was allowed to settle onto them overnight. Once feed manufacturing was completed, feed samples and steel coupons were stored at room temperature. On the day of (day 0) and d 3, 7, 14, 28, 60, 90, and 180 after feed manufacturing, feed samples and 3 steel coupons were randomly selected, taken out of storage, and analyzed for ASFV DNA. For feed samples there was a statistically significant (P = 0.023) batch × day interaction for log10 genomic copies per gram of feed, and a marginal statistical significance (P = 0.072) for batch × day interaction for cycle threshold (Ct) values. This indicates that the batch of feed and days held at room temperature impacted the amount of the detectable ASFV DNA in feed samples. There was no evidence (P = 0.433) of ASFV degradation on environmental coupons over the 180-d storage period. This study found that quarantine time can help reduce, but not eliminate ASFV DNA in feed over time. Surprisingly, ASFV DNA is detectable on feed manufacturing surfaces for at least 180 days

Effect of mixing and feed batch sequencing on the prevalence and distribution of African swine fever virus in swine feed

It is critical to have methods that can detect and mitigate the risk of African swine fever virus (ASFV) in potentially contaminated feed or ingredients bound for the United States. The purpose of this work was to evaluate feed batch sequencing as a mitigation technique for ASFV contamination in a feed mill, and to determine if a feed sampling method could identify ASFV following experimental inoculation. Batches of feed were manufactured in a BSL-3Ag room at Kansas State University's Biosafety Research Institute in Manhattan, Kansas. First, the pilot feed manufacturing system mixed, conveyed, and discharged an ASFV-free diet. Next, a diet was manufactured using the same equipment, but contained feed inoculated with ASFV for final concentration of 5.6 × 104 TCID50/g. Then, four subsequent ASFV-free batches of feed were manufactured. After discharging each batch into a collection container, 10 samples were collected in a double ‘X’ pattern. Samples were analysed using a qPCR assay for ASFV p72 gene then the cycle threshold (Ct) and Log10 genomic copy number (CN)/g of feed were determined. The qPCR Ct values (p < .0001) and the Log10 genomic CN/g (p < .0001) content of feed samples were impacted based on the batch of feed. Feed samples obtained after manufacturing the ASFV-contaminated diet contained the greatest amounts of ASFV p72 DNA across all criteria (p < .05). Quantity of ASFV p72 DNA decreased sequentially as additional batches of feed were manufactured, but was still detectable after batch sequence 4. This subsampling method was able to identify ASFV genetic material in feed samples using p72 qPCR. In summary, sequencing batches of feed decreases concentration of ASFV contamination in feed, but does not eliminate it. Bulk ingredients can be accurately evaluated for ASFV contamination by collecting 10 subsamples using the sampling method described herein. Future research is needed to evaluate if different mitigation techniques can reduce ASFV feed contamination

Evaluating the distribution of African swine fever virus within a feed mill environment following manufacture of inoculated feed

11 Pág. Centro de Investigación en Sanidad Animal (CISA)It is critical to understand the role feed manufacturing may have regarding potential African swine fever virus (ASFV) transmission, especially given the evidence that feed and/or ingredients may be potential vectors. The objective of the study was to evaluate the distribution of ASFV in a feed mill following manufacture of contaminated feed. To accomplish this, a pilot-scale feed mill consisting of a mixer, bucket elevator, and spouting was constructed in a BSL-3Ag facility. First, a batch of ASFV-free feed was manufactured, followed by a batch of feed that had an ASFV-contaminated ingredient added to feed, which was then mixed and discharged from the equipment. Subsequently, four additional ASFV-free batches of feed were manufactured using the same equipment. Environmental swabs from 18 locations within the BSL-3Ag room were collected after each batch of feed was discharged. The locations of the swabs were categorized into four zones: 1) feed contact surface, 2) non-feed contact surface 1 meter from feed, and 4) transient surfaces. Environmental swabs were analyzed using a qPCR specific for the ASFV p72 gene and reported as genomic copy number (CN)/mL of environmental swab processing buffer. Genomic copies were transformed with a log10 function for statistical analysis. There was no evidence of a zone × batch interaction for log10 genomic CN/mL (P = 0.625) or cycle threshold (Ct) value (P = 0.608). Sampling zone impacted the log10 p72 genomic CN/mL (P < 0.0001) and Ct values (P < 0.0001), with a greater amount of viral genome detected on transient surfaces compared to other surfaces (P < 0.05). This study illustrates that once ASFV enters the feed mill environment it becomes widespread and movement of people can significantly contribute to the spread of ASFV in a feed mill environment.Funding for this work was obtained from the NBAF Transition Funds from the state of Kansas (JAR), the National Pork Board under award number 20-018 (CKJ), the Department of Homeland Security Center of Excellence for Emerging and Zoonotic Animal Diseases under grant number HSHQDC 16-A-B0006 (JAR), and the AMP Core of the NIGMS COBRE Center on Emerging and Zoonotic Infectious Diseases (CEZID) under award number P20GM13044 (JAR)Peer reviewe

Prevalence and Distribution of African Swine Fever Virus in Swine Feed After Mixing and Feed Batch Sequencing

As the United States maintains trade with countries where African swine fever virus (ASFV) is endemic, it is critical to have methods that can detect and mitigate the risk of ASFV in potentially contaminated feed or ingredients. Therefore, the objectives of this study were to 1) evaluate feed batch sequencing as a mitigation technique for ASFV contamination in a feed mill, and 2) determine if a feed sampling method could identify ASFV following experimental inoculation. Batches of feed were manufactured in a BSL-3Ag room at Kansas State University’s Biosafety Research Institute in Manhattan, KS. First, the pilot feed manufacturing system mixed, conveyed, and discharged an ASFV-free diet. Next, a diet was manufactured using the same equipment, but contained feed inoculated with ASFV for a final concentration of 5.6 × 104 TCID50/g. Then, four subsequent ASFV-free batches of feed were manufactured. After discharging each batch into a biohazard tote, 10 samples were collected in a double ‘X’ pattern. Samples were analyzed using a qPCR assay specific for the ASFV p72 gene to determine the cycle threshold (Ct) and log10 genomic copy number (CN)/g of feed. Batch of feed affected the qPCR Ct values (P \u3c 0.0001) and the log10 genomic CN/g (P \u3c 0.0001) content of feed. Feed samples obtained after manufacturing the ASFV-contaminated diet contained the greatest (P \u3c 0.05) amounts of ASFV p72 DNA across all criteria. Quantity of ASFV p72 DNA decreased sequentially as additional batches of initially ASFV-free feed were manufactured, but it was still detectable after batch sequence 4, suggesting cross contamination between batches. This subsampling method was able to identify ASFV genetic material in feed samples using the PCR assay specific for the ASFV p72 gene. In summary, sequencing batches of feed decreases concentration of ASFV contamination in feed, but does not eliminate it. Bulk ingredients or feed can be accurately evaluated for ASFV contamination by collecting 10 evenly distributed subsamples, representing 0.05% of the volume of the container, using the sampling method described herein

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC00100

Environmental Stability of SARS-CoV-2 on Different Types of Surfaces under Indoor and Seasonal Climate Conditions

Transmission of severe acute respiratory coronavirus 2 (SARS-CoV-2) mainly occurs through direct contact with an infected person via droplets. A potential role of contaminated surfaces in SARS-CoV-2 transmission has been suggested since the virus has been extensively detected on environmental surfaces. These findings have driven the investigation of virus stability on surfaces under several conditions. However, it remains unclear how long the infectious virus survives on surfaces under different climate conditions, which could play a role in predicting the seasonality of SARS-CoV-2. Therefore, the aim of this study was to estimate the virus stability and its biological half-life on various types of surfaces under indoor and seasonal climate conditions. This study revealed that SARS-CoV-2 survived the longest on surfaces under winter conditions, with a survival post-contamination on most surfaces up to 21 days, followed by spring/fall conditions, with a survival up to 7 days. Infectious virus was isolated up to 4 days post-contamination under indoor conditions, whereas no infectious virus was found at 3 days post-contamination under summer conditions. Our study demonstrates the remarkable persistence of SARS-CoV-2 on many different common surfaces, especially under winter conditions, and raises awareness to the potential risk of contaminated surfaces to spread the virus

Comparison of Entropy Methods for an Optimal Rain Gauge Network: A Case Study of Daegu and Gyeongbuk Area in South Korea

To reduce hydrological disasters, it is necessary to operate rain gauge stations at locations where the spatio-temporal characteristics of rainfall can be reflected. Entropy has been widely used to evaluate the designs and uncertainties associated with rain gauge networks. In this study, the optimal rain gauge network in the Daegu and Gyeongbuk area, which requires the efficient use of water resources due to low annual precipitation and severe drought damage, was determined using conditional and joint entropy, and the selected network was quantitatively evaluated using the root mean square error (RMSE). To consider spatial distribution, prediction errors were generated using kriging. Four estimators used in entropy calculations were compared, and weighted entropy was calculated by weighting the precipitation. The optimal number of rain gauge stations was determined by calculating the RMSE reduction and the reduction ratio according to the number of selected rain gauge stations. Our findings show that the results of conditional entropy were better than those of joint entropy. The optimal rain gauge stations showed a tendency wherein peripheral rain gauge stations were selected first, with central stations being added afterward