Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens (
This content will become publicly available on December 1, 2024
Antimicrobial resistance (AMR) is one of the major challenges of the century and should be addressed with a One Health approach. This study aimed to develop a tool that can provide a better understanding of AMR patterns and improve management practices in swine production systems to reduce its spread between farms. We generated similarity networks based on the phenotypic AMR pattern for each farm with information on important bacterial pathogens for swine farming based on the Euclidean distance. We included seven pathogens:
- Award ID(s):
- 1838207
- NSF-PAR ID:
- 10494906
- Publisher / Repository:
- Nature Portfolio
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Escherichia coli, Streptococcus suis, Salmonella sp., Pasteurella multocida , andBordetella bronchiseptica ). We found that Seasonal Auto-Regressive Integrated Moving Average (SARIMA) outperformed five baselines, including Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA). We hope this study provides valuable tools to predict the AMR burden not only of the pathogens assessed in this study but also of other bacterial pathogens. -
Antimicrobial resistance (AMR) can develop in deep-pit swine manure storage when bacteria are selectively pressured by unmetabolized antibiotics. Subsequent manure application on row crops is then a source of AMR into soil and downstream runoff water. Therefore, understanding the patterns of diverse antibiotic resistance genes (ARGs) in manure among different farms is important for both interpreting the results of the detection of these genes from previous studies and for the use of these genes as bioindicators of manure borne antibiotic resistance in the environment. Previous studies of manure-associated ARGs are based on limited samples of manures. To better understand the distribution of ARGs between manures, we characterized manures from 48 geographically independent swine farms across Iowa. The objectives of this study were to characterize the distribution of ARGs among these manures and to evaluate what factors in manure management may influence the presence of ARGs in manures. Our analysis included quantification of two commonly found ARGs in swine manure, ermB and tetM . Additionally, we characterized a broader suite of 31 ARGs which allowed for simultaneous assays of the presence or absence of multiple genes. We found the company integrator had a significant effect on both ermB ( P=0.0007 ) and tetM gene concentrations ( P=0.0425 ). Our broad analysis on ARG profiles found that the tet(36) gene was broadly present in swine manures, followed by the detection of tetT , tetM , erm(35) , ermF , ermB , str , aadD , and intl3 in samples from 14 farms. Finally, we provide a comparison of methods to detect ARGs in manures, specifically comparing conventional and high-throughput qPCR and discuss their role in ARG environmental monitoring efforts. Results of this study provide insight into commonalities of ARG presence in manure holding pits and provide supporting evidence that company integrator decisions may impact ARG concentrations.more » « less
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Abstract Antimicrobial resistance (AMR) is a growing threat to public health and farming at large. In clinical and veterinary practice, timely characterization of the antibiotic susceptibility profile of bacterial infections is a crucial step in optimizing treatment. High-throughput sequencing is a promising option for clinical point-of-care and ecological surveillance, opening the opportunity to develop genotyping-based AMR determination as a possibly faster alternative to phenotypic testing. In the present work, we compare the performance of state-of-the-art methods for detection of AMR using high-throughput sequencing data from clinical settings. We consider five computational approaches based on alignment (AMRPlusPlus), deep learning (DeepARG), k-mer genomic signatures (KARGA, ResFinder) or hidden Markov models (Meta-MARC). We use an extensive collection of 585 isolates with available AMR resistance profiles determined by phenotypic tests across nine antibiotic classes. We show how the prediction landscape of AMR classifiers is highly heterogeneous, with balanced accuracy varying from 0.40 to 0.92. Although some algorithms—ResFinder, KARGA and AMRPlusPlus—exhibit overall better balanced accuracy than others, the high per-AMR-class variance and related findings suggest that: (1) all algorithms might be subject to sampling bias both in data repositories used for training and experimental/clinical settings; and (2) a portion of clinical samples might contain uncharacterized AMR genes that the algorithms—mostly trained on known AMR genes—fail to generalize upon. These results lead us to formulate practical advice for software configuration and application, and give suggestions for future study designs to further develop AMR prediction tools from proof-of-concept to bedside.
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Abstract Background As the sustainability of the agricultural workforce has been threatened by the high prevalence of back pain, developing effective interventions to reduce its burden within farming will contribute to the long‐term health and productivity of workers. Passive back‐support exoskeletons are being explored as an intervention to reduce the physical demands on the back muscles, and consequently mitigate the risk of back pain, in many industrial sectors.
Methods This study investigated whether exoskeleton use could reduce farmers' low back muscle load. Electromyography was used to evaluate exoskeleton use in field and laboratory settings. A total of 14 farmers (13 males and 1 female) with a mean age of 49 (
SD = 12) years and 6 female nonfarmers (mean age 28,SD = 5 years) performed a standardized set of tasks that included symmetric and asymmetric lifting and sustained trunk flexion. Following the standardized tasks, 14 farmers also performed regular, real‐world, farm tasks with and without use of the exoskeleton at their farms.Results Exoskeleton use decreased back muscular load during farming activities up to 65%, 56%, and 48% in static, median, and peak muscle activity, respectively. This indicates potential benefits of exoskeleton use to help farmers work under less muscular load. Paradoxically, exoskeleton use during standardized tasks increased muscle activity for some participants.
Conclusions This study demonstrates the potential effects of using passive exoskeletons in agriculture through observational and experimental research, and is among the first that explores the potential for using exoskeletons during actual work tasks in farm settings.
