Synthetic microbiomes offer new possibilities for modulating microbiota, to address the barriers in multidtug resistance (MDR) research. We present a Bayesian optimization approach to enable efficient searching over the space of synthetic microbiome variants to identify candidates predictive of reduced MDR. Microbiome datasets were encoded into a low-dimensional latent space using autoencoders. Sampling from this space allowed generation of synthetic microbiome signatures. Bayesian optimization was then implemented to select variants for biological screening to maximize identification of designs with restricted MDR pathogens based on minimal samples. Four acquisition functions were evaluated: expected improvement, upper confidence bound, Thompson sampling, and probability of improvement. Based on each strategy, synthetic samples were prioritized according to their MDR detection. Expected improvement, upper confidence bound, and probability of improvement consistently produced synthetic microbiome candidates with significantly fewer searches than Thompson sampling. By combining deep latent space mapping and Bayesian learning for efficient guided screening, this study demonstrated the feasibility of creating bespoke synthetic microbiomes with customized MDR profiles.

Pathogen control is a critical aspect of modern poultry farming, providing important benefits for both public health and productivity. Effective poultry management measures to reduce pathogen levels in poultry flocks promote food safety by lowering risks of food-borne illnesses. They also support animal health and welfare by preventing infectious diseases that can rapidly spread and impact flock growth, egg production, and overall health. This study frames the search for optimal management practices that minimize the presence of multiple pathogens as a combinatorial optimization problem. Specifically, we model the various possible combinations of management settings as a solution space that can be efficiently explored to identify configurations that optimally reduce pathogen levels. This design incorporates a neural network feedback-based method that combines feature explanations with global sensitivity analysis to ensure combinatorial optimization in multiobjective settings. Our preliminary experiments have promising results when applied to two real-world agricultural datasets. While further validation is still needed, these early experimental findings demonstrate the potential of the model to derive targeted feature interactions that adaptively optimize pathogen control under varying real-world constraints.

Coccidiosis, Salmonella, and Newcastle are the common poultry diseases that curtail poultry production if they are not detected early. In Tanzania, these diseases are not detected early due to limited access to agricultural support services by poultry farmers. Deep learning techniques have the potential for early diagnosis of these poultry diseases. In this study, a deep Convolutional Neural Network (CNN) model was developed to diagnose poultry diseases by classifying healthy and unhealthy fecal images. Unhealthy fecal images may be symptomatic of Coccidiosis, Salmonella, and Newcastle diseases. We collected 1,255 laboratory-labeled fecal images and fecal samples used in Polymerase Chain Reaction diagnostics to annotate the laboratory-labeled fecal images. We took 6,812 poultry fecal photos using an Open Data Kit. Agricultural support experts annotated the farm-labeled fecal images. Then we used a baseline CNN model, VGG16, InceptionV3, MobileNetV2, and Xception models. We trained models using farm and laboratory-labeled fecal images and then fine-tuned them. The test set used farm-labeled images. The test accuracies results without fine-tuning were 83.06% for the baseline CNN, 85.85% for VGG16, 94.79% for InceptionV3, 87.46% for MobileNetV2, and 88.27% for Xception. Finetuning while freezing the batch normalization layer improved model accuracies, resulting in 95.01% for VGG16, 95.45% for InceptionV3, 98.02% for MobileNetV2, and 98.24% for Xception, with F1 scores for all...

A team of scientists led by researchers at the University of Georgia Center for Food Safety in Griffin has developed a machine-learning approach that could lead to quicker identification of the animal source of certain Salmonella outbreaks. In the research, published in the January 2019 issue of Emerging Infectious Diseases, Xiangyu Deng and his colleagues used more than a thousand genomes to predict the animal sources, especially livestock, of Salmonella Typhimurium. Deng, an assistant professor of food microbiology at the center, and Shaokang Zhang, a postdoctoral associate with the center, led the project, which also included experts from the Centers for Disease Control and Prevention, the U.S. Food and Drug Administration, the Minnesota Department of Health and the Translational Genomics Research Institute. According to the Foodborne Disease Outbreak Surveillance System, close to 3,000 outbreaks of foodborne illness were reported in the U.S. from 2009 to 2015. Of those, 900 -- or 30 percent -- were caused by different serotypes of Salmonella, including Typhimurium, Deng said.

Scientists at the University of Georgia Center for Food Safety has developed a new approach to identify the animal source of some types of Salmonella outbreaks. The researchers have developed a machine learning approach. The study is published in the January 2019 issue of Emerging Infectious Diseases. Researchers Xiangyu Deng and Shaokang Zhang, along with a team of colleagues, used more than a thousand genomes to predict the animal sources of Salmonella Typhimurium. The project used experts from the Centers for Disease Control and Prevention, the Food & Drug Administration, the Minnesota Department of Health, and the Translational Genomics Research Institute.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection. The group of bacteria known as Salmonella includes many different types that vary in the severity of the disease they cause. Some types cause food poisoning, known as gastrointestinal Salmonella, whereas others cause severe disease by spreading beyond the gut, for example Salmonella Typhi which causes typhoid fever.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.