scholarly journals Enabling technology for microbial source tracking based on transfer learning: From ontology-aware general knowledge to context-aware expert systems

2021 ◽  
Author(s):  
Hui Chong ◽  
Qingyang Yu ◽  
Yuguo Zha ◽  
Guangzhou Xiong ◽  
Nan Wang ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Transfer Learning ◽  
Microbial Source Tracking ◽  
Source Tracking ◽  
Context Aware ◽  
Tracking Accuracy ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Context Dependent ◽  
Disease Stages

AbstractHabitat specific patterns reflected by microbial communities, as well as complex interactions between the community and their environments or hosts’ characteristics, have created obstacles for microbial source tracking: diverse and context-dependent applications are asking for quantification of the contributions of different niches (biomes), which have already overwhelmed existing methods. Moreover, existing source tracking methods could not extend well for source tracking samples from understudied biomes, as well as samples from longitudinal studies.Here, we introduce EXPERT (https://github.com/HUST-NingKang-Lab/EXPERT), an exact and pervasive expert model for source tracking microbial communities based on transfer learning. Built upon the biome ontology information and transfer learning techniques, EXPERT has acquired the context-aware flexibility and could easily expand the supervised model’s search scope to include the context-dependent community samples and understudied biomes. While at the same time, it is superior to current approaches in source tracking accuracy and speed. EXPERT’s superiority has been demonstrated on multiple source tracking tasks, including source tracking samples collected at different disease stages and longitudinal samples. For example, when dealing with 635 samples from a recent study of colorectal cancer, EXPERT could achieve an AUROC of 0.977 when predicting the host’s phenotypical status. In summary, EXPERT has unleashed the potential of model-based source tracking approaches, enabling source tracking in versatile context-dependent settings, accomplishing pervasive and in-depth knowledge discovery from microbiome.

scholarly journals Enterococcal Concentrations in a Coastal Ecosystem Are a Function of Fecal Source Input, Environmental Conditions, and Environmental Sources

2018 ◽  
Vol 84 (17) ◽  
Author(s):  
Derek Rothenheber ◽  
Stephen Jones
Keyword(s):  
Public Health ◽  
Water Quality ◽  
Microbial Communities ◽  
Environmental Conditions ◽  
Suspended Solids ◽  
High Tide ◽  
Microbial Source Tracking ◽  
Source Tracking ◽  
Management Problem ◽  
Recreational Waters

ABSTRACTFecal pollution at coastal beaches requires management efforts to address public health and economic concerns. Feces-borne bacterial concentrations are influenced by different fecal sources, environmental conditions, and ecosystem reservoirs, making their public health significance convoluted. In this study, we sought to delineate the influences of these factors on enterococcal concentrations in southern Maine coastal recreational waters. Weekly water samples and water quality measurements were conducted at freshwater, estuarine, and marine beach sites from June through September 2016. The samples were analyzed for total and particle-associated enterococcal concentrations, total suspended solids, and microbial source tracking markers (PCR: Bac32, HF183, CF128, DF475, and Gull2; quantitative PCR [qPCR]: AllBac, HF183, and GFD). Water, soil, sediment, and marine sediment samples were also subjected to 16S rRNA sequencing and SourceTracker analysis to determine the influence from these environmental reservoirs on water sample microbial communities. Enterococcal and particle-associated enterococcal concentrations were elevated in freshwater, but the concentrations of suspended solids were relatively similar. Mammal fecal contamination was significantly elevated in the estuary, with human and bird fecal contaminant levels similar between sites. A partial least-squares regression model indicated particle-associated enterococcal and mammal marker concentrations had the most significant positive relationships with enterococcal concentrations across marine, estuary, and freshwater environments. Freshwater microbial communities were significantly influenced by underlying sediment, while estuarine/marine beach communities were influenced by freshwater, high tide height, and estuarine sediment. Elevated enterococcal levels were reflective of a combination of increased fecal source input, environmental sources, and environmental conditions, highlighting the need for encompassing microbial source tracking (MST) approaches for managing water quality issues.IMPORTANCEEnterococci have long been the federal standard in determining water quality at estuarine and marine environments. Although enterococci are highly abundant in the intestines of many animals, they are not exclusive to that environment and can persist and grow outside fecal tracts. This presents a management problem for areas that are largely impaired by nonpoint source contamination, as fecal sources might not be the root cause of contamination. This study employed different microbial source tracking methods for delineating the influences from fecal source input, environmental sources, and environmental conditions to determine which combination of variables are influencing enterococcal concentrations in recreational waters at a historically impaired coastal town. The results showed that fecal source input, environmental sources, and conditions all play roles in influencing enterococcal concentrations. This highlights the need to include an encompassing microbial source tracking approach to assess the effects of all important variables on enterococcal concentrations.

scholarly journals Effects of Fecal Source Input, Environmental Conditions, and Environmental Sources on Enterococci Concentrations in a Coastal Ecosystem

2018 ◽  
Author(s):  
Derek Rothenheber ◽  
Stephen Jones
Keyword(s):  
Public Health ◽  
Water Quality ◽  
Microbial Communities ◽  
Environmental Conditions ◽  
Suspended Solids ◽  
High Tide ◽  
Microbial Source Tracking ◽  
Source Tracking ◽  
Management Problem ◽  
Recreational Waters

ABSTRACTFecal pollution at coastal beaches in the Northeast, USA requires management efforts to address public health and economic concerns. Concentrations of fecal-borne bacteria are influenced by different fecal sources, environmental conditions, and ecosystem reservoirs, making their public health significance convoluted. In this study, we sought to delineate the influences of these factors on enterococci concentrations in southern Maine coastal recreational waters. Weekly water samples and water quality measurements were conducted at freshwater, estuarine, and marine beach sites from June through September 2016. Samples were analyzed for total and particle-associated enterococci concentrations, total suspended solids, and microbial source tracking markers for multiple sources. Water, soil, sediment, and marine sediment samples were also subjected to 16S rRNA sequencing and SourceTracker analysis to determine the influence from these environmental reservoirs on water sample microbial communities. Enterococci and particle-associated enterococci concentrations were elevated in freshwater, but suspended solids concentrations were relatively similar. Mammal fecal contamination was significantly elevated in the estuary, with human and bird fecal contaminant levels similar between sites. A partial least squares regression model indicated particle-associated enterococci and mammal marker concentrations had the most significant positive relationships with enterococci concentrations across marine, estuary, and freshwater environments. Freshwater microbial communities were significantly influenced by underlying sediment while estuarine/marine beach communities were influenced by freshwater, high tide height, and estuarine sediment. We found elevated enterococci levels are reflective of a combination of increased fecal source input, environmental sources, and environmental conditions, highlighting the need for encompassing MST approaches for managing water quality issues.IMPORTANCEEnterococci have long been the federal standard in determining water quality at estuarine and marine environments. Although enterococci are highly abundant in the fecal tracts of many animals they are not exclusive to that environment and can persist and grow outside of fecal tracts. This presents a management problem for areas that are largely impaired by non-point source contamination, as fecal sources might not be the root cause of contamination. This study employed different microbial source tracking methods to delineate influences from fecal source input, environmental sources, and environmental conditions to determine which combination of variables are influencing enterococci concentrations in recreational waters at a historically impaired coastal town. Results showed that fecal source input, environmental sources and conditions all play a role in influencing enterococci concentrations. This highlights the need to include an encompassing microbial source tracking approach to assess the effects of all important variables on enterococci concentrations.

Response of microbial communities of karst river water to antibiotics and microbial source tracking for antibiotics

2020 ◽  
Vol 706 ◽  
pp. 135730 ◽  
Author(s):  
Shizheng Xiang ◽  
Xusheng Wang ◽  
Wen Ma ◽  
Xiaoping Liu ◽  
Biao Zhang ◽  
...  
Keyword(s):  
Microbial Communities ◽  
River Water ◽  
Microbial Source Tracking ◽  
Source Tracking ◽  
Karst River

scholarly journals Deep Learning Techniques for Grape Plant Species Identification in Natural Images

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4850 ◽  
Author(s):  
Carlos S. Pereira ◽  
Raul Morais ◽  
Manuel J. C. S. Reis
Keyword(s):  
Transfer Learning ◽  
Climatic Conditions ◽  
Fine Tuning ◽  
Variety Identification ◽  
Test Accuracy ◽  
Accuracy Score ◽  
Learning Techniques ◽  
Four Corners ◽  
Integrated Software ◽  
Grape Varieties

Frequently, the vineyards in the Douro Region present multiple grape varieties per parcel and even per row. An automatic algorithm for grape variety identification as an integrated software component was proposed that can be applied, for example, to a robotic harvesting system. However, some issues and constraints in its development were highlighted, namely, the images captured in natural environment, low volume of images, high similarity of the images among different grape varieties, leaf senescence, and significant changes on the grapevine leaf and bunch images in the harvest seasons, mainly due to adverse climatic conditions, diseases, and the presence of pesticides. In this paper, the performance of the transfer learning and fine-tuning techniques based on AlexNet architecture were evaluated when applied to the identification of grape varieties. Two natural vineyard image datasets were captured in different geographical locations and harvest seasons. To generate different datasets for training and classification, some image processing methods, including a proposed four-corners-in-one image warping algorithm, were used. The experimental results, obtained from the application of an AlexNet-based transfer learning scheme and trained on the image dataset pre-processed through the four-corners-in-one method, achieved a test accuracy score of 77.30%. Applying this classifier model, an accuracy of 89.75% on the popular Flavia leaf dataset was reached. The results obtained by the proposed approach are promising and encouraging in helping Douro wine growers in the automatic task of identifying grape varieties.

scholarly journals Predictive modeling for peri-implantitis by using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoaki Mameno ◽  
Masahiro Wada ◽  
Kazunori Nozaki ◽  
Toshihito Takahashi ◽  
Yoshitaka Tsujioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Increased Risk ◽  
Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

Microbial Source Tracking in Small Farms: Use of Different Methods for Adenovirus Detection

2021 ◽  
Vol 232 (2) ◽  
Author(s):  
Meriane Demoliner ◽  
Juliana Schons Gularte ◽  
Viviane Girardi ◽  
Ana Karolina Antunes Eisen ◽  
Fernanda Gil de Souza ◽  
...  
Keyword(s):  
Microbial Source Tracking ◽  
Source Tracking ◽  
Small Farms
Sign in / Sign up

Export Citation Format

Share Document