scholarly journals Validating the Use of Bovine Buccal Sampling as a Proxy for the Rumen Microbiota by Using a Time Course and Random Forest Classification Approach

2020 ◽  
Vol 86 (17) ◽  
Author(s):  
Juliana Young ◽  
Joseph H. Skarlupka ◽  
Madison S. Cox ◽  
Rafael Tassinari Resende ◽  
Amelie Fischer ◽  
...  
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Time Course ◽  
Optimal Time ◽  
Buccal Swab ◽  
Noninvasive Methods ◽  
Representative Sampling ◽  
Rumen Microbiota ◽  
Rumen Microbes ◽  
Development And Validation

ABSTRACT Analysis of the cow microbiome, as well as host genetic influences on the establishment and colonization of the rumen microbiota, is critical for development of strategies to manipulate ruminal function toward more efficient and environmentally friendly milk production. To this end, the development and validation of noninvasive methods to sample the rumen microbiota at a large scale are required. In this study, we further optimized the analysis of buccal swab samples as a proxy for direct bacterial samples of the rumen of dairy cows. To identify an optimal time for sampling, we collected buccal swab and rumen samples at six different time points relative to animal feeding. We then evaluated several biases in these samples using a machine learning classifier (random forest) to select taxa that discriminate between buccal swab and rumen samples. Differences in the inverse Simpson’s diversity, Shannon’s evenness, and Bray-Curtis dissimilarities between methods were significantly less apparent when sampling was performed prior to morning feeding (P < 0.05), suggesting that this time point was optimal for representative sampling. In addition, the random forest classifier was able to accurately identify nonrumen taxa, including 10 oral and putative feed-associated taxa. Two highly prevalent (>60%) taxa in buccal and rumen samples had significant variance in relative abundances between sampling methods but could be qualitatively assessed via regular buccal swab sampling. This work not only provides new insights into the oral community of ruminants but also further validates and refines buccal swabbing as a method to assess the rumen bacterial in large herds. IMPORTANCE The gastrointestinal tracts of ruminants harbor a diverse microbial community that coevolved symbiotically with the host, influencing its nutrition, health, and performance. While the influence of environmental factors on rumen microbes is well documented, the process by which host genetics influences the establishment and colonization of the rumen microbiota still needs to be elucidated. This knowledge gap is due largely to our inability to easily sample the rumen microbiota. There are three common methods for rumen sampling but all of them present at least one disadvantage, including animal welfare, sample quality, labor, and scalability. The development and validation of noninvasive methods, such as buccal swabbing, for large-scale rumen sampling is needed to support studies that require large sample sizes to generate reliable results. The validation of buccal swabbing will also support the development of molecular tools for the early diagnosis of metabolic disorders associated with microbial changes in large herds.

scholarly journals Validating the use of bovine buccal sampling as a proxy for the rumen microbiota using a time course and random forest classification approach

2020 ◽  
Author(s):  
Juliana Young ◽  
Joseph H. Skarlupka ◽  
Rafael Tassinari Resende ◽  
Amelie Fischer ◽  
Kenneth F. Kalscheur ◽  
...  
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Time Course ◽  
Optimal Time ◽  
Buccal Swab ◽  
Noninvasive Methods ◽  
Representative Sampling ◽  
Rumen Microbiota ◽  
Rumen Microbes ◽  
Development And Validation

ABSTRACTAnalysis of the cow microbiome, as well as host genetic influences on the establishment and colonization of the rumen microbiota, is critical for development of strategies to manipulate ruminal function toward more efficient and environmentally friendly milk production. To this end, the development and validation of noninvasive methods to sample the rumen microbiota at a large-scale is required. Here, we further optimized the analysis of buccal swab samples as a proxy for direct microbial samples of the rumen of dairy cows. To identify an optimal time for sampling, we collected buccal swab and rumen samples at six different time points relative to animal feeding. We then evaluated several biases in these samples using a machine learning classifier (random forest) to select taxa that discriminate between buccal swab and rumen samples. Differences in the Simpson’s diversity, Shannon’s evenness and Bray-Curtis dissimilarities between methods were significantly less apparent when sampling was performed prior to morning feeding (P<0.05), suggesting that this time point was optimal for representative sampling. In addition, the random forest classifier was able to accurately identify non-rumen taxa, including 10 oral and feed-associated taxa. Two highly prevalent (> 60%) taxa in buccal and rumen samples had significant variance in absolute abundance between sampling methods, but could be qualitatively assessed via regular buccal swab sampling. This work not only provides new insights into the oral community of ruminants, but further validates and refines buccal swabbing as a method to assess the rumen microbiota in large herds.IMPORTANCEThe gastrointestinal tract of ruminants harbors a diverse microbial community that coevolved symbiotically with the host, influencing its nutrition, health and performance. While the influence of environmental factors on rumen microbes is well-documented, the process by which host genetics influences the establishment and colonization of the rumen microbiota still needs to be elucidated. This knowledge gap is due largely to our inability to easily sample the rumen microbiota. There are three common methods for rumen sampling but all of them present at least one disadvantage, including animal welfare, sample quality, labor, and scalability. The development and validation of non-invasive methods, such as buccal swabbing, for large-scale rumen sampling is needed to support studies that require large sample sizes to generate reliable results. The validation of buccal swabbing will also support the development of molecular tools for the early diagnosis of metabolic disorders associated with microbial changes in large herds.

scholarly journals PSXII-1 High-throughput Phenotyping of Rumen Microbial Contents Using Buccal Swabs

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 444-445
Author(s):  
Juliana Young ◽  
Joseph H Skarlupka ◽  
Rafael Tassinari ◽  
Amelie Fischer ◽  
Kenneth Kalscheur ◽  
...  
Keyword(s):  
Random Forest ◽  
Cross Validation ◽  
Principal Component ◽  
Buccal Swab ◽  
Random Forest Regression ◽  
Rumen Microbes ◽  
Microbial Counts ◽  
Buccal Swabs ◽  
High Throughput Phenotyping ◽  
Direct Regression

Abstract The rumen microbial community is the agent that allows cattle and other ruminants to process complex plant polymers into digestible fatty acids. Traditional methods to sample rumen microbes often involve labor-intensive stomach tubing, or invasive surgeries to access the rumen lumen via cannula ports, thereby limiting the number of animals that could be sampled in a specific study. In this study, we tested the viability of using buccal swabs as a proxy of the rumen microbial contents in a timecourse experiment on eight cannulated cows. Rumen contents and buccal swabs were collected at six equally spaced timepoints, with the first timepoint being 2 hours prior to feeding. Simpson diversity and Shannon evenness estimates of the microbial counts of each sample revealed that the first timepoint had the lowest diversity and highest evenness (Tukey HSD &lt; 0.05) out of all other timepoints. Principal component analysis confirmed that the buccal swab samples from the first timepoint were the most similar to paired rumen samples taken at the same times. Using a Random Forest Classifier analysis, we estimated the Gini importance scores for individual microbial taxa as a proxy of their uniqueness to the rumen or oral environments of the cows. We identified 18 oral-only microbial taxa that are contaminants and could be removed from future comparisons using this method. Finally, we attempted to estimate the exact relative abundance of rumen microbial taxa from buccal swab samples using paired rumen-swab data in a Random Forest Regression model. The model was found to have moderate (~38%) accuracy in cross-validation studies. Our data suggests that buccal swabs can serve as fast and suitable proxies for rumen microbial contents of dairy cattle, but that additional factors must be measured to improve direct regression of results to those of the rumen.

scholarly journals Usefulness of Machine Learning for Identification of Referable Diabetic Retinopathy in a Large-Scale Population-Based Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Cheng Yang ◽  
Qingyang Liu ◽  
Haike Guo ◽  
Min Zhang ◽  
Lixin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Diabetic Retinopathy ◽  
Random Forest ◽  
Large Scale ◽  
Naive Bayes ◽  
Population Based ◽  
Naïve Bayes ◽  
Extreme Gradient Boosting ◽  
Development And Validation

Purpose: To development and validation of machine learning-based classifiers based on simple non-ocular metrics for detecting referable diabetic retinopathy (RDR) in a large-scale Chinese population–based survey.Methods: The 1,418 patients with diabetes mellitus from 8,952 rural residents screened in the population-based Dongguan Eye Study were used for model development and validation. Eight algorithms [extreme gradient boosting (XGBoost), random forest, naïve Bayes, k-nearest neighbor (KNN), AdaBoost, Light GBM, artificial neural network (ANN), and logistic regression] were used for modeling to detect RDR in individuals with diabetes. The area under the receiver operating characteristic curve (AUC) and their 95% confidential interval (95% CI) were estimated using five-fold cross-validation as well as an 80:20 ratio of training and validation.Results: The 10 most important features in machine learning models were duration of diabetes, HbA1c, systolic blood pressure, triglyceride, body mass index, serum creatine, age, educational level, duration of hypertension, and income level. Based on these top 10 variables, the XGBoost model achieved the best discriminative performance, with an AUC of 0.816 (95%CI: 0.812, 0.820). The AUCs for logistic regression, AdaBoost, naïve Bayes, and Random forest were 0.766 (95%CI: 0.756, 0.776), 0.754 (95%CI: 0.744, 0.764), 0.753 (95%CI: 0.743, 0.763), and 0.705 (95%CI: 0.697, 0.713), respectively.Conclusions: A machine learning–based classifier that used 10 easily obtained non-ocular variables was able to effectively detect RDR patients. The importance scores of the variables provide insight to prevent the occurrence of RDR. Screening RDR with machine learning provides a useful complementary tool for clinical practice in resource-poor areas with limited ophthalmic infrastructure.

Parameter Space Analysis Suggests Multi-Site Plasticity Contributes to Motor Pattern Initiation in Tritonia

2007 ◽  
Vol 98 (4) ◽  
pp. 2382-2398 ◽  
Author(s):  
Robert J. Calin-Jageman ◽  
Mark J. Tunstall ◽  
Brett D. Mensh ◽  
Paul S. Katz ◽  
William N. Frost
Keyword(s):  
Parameter Space ◽  
Large Scale ◽  
Time Course ◽  
Scale Parameter ◽  
Motor Pattern ◽  
Rhythmic Activity ◽  
Excitatory Input ◽  
Space Analysis ◽  
Gastropod Mollusk ◽  
Detailed Simulation

This research examines the mechanisms that initiate rhythmic activity in the episodic central pattern generator (CPG) underlying escape swimming in the gastropod mollusk Tritonia diomedea. Activation of the network is triggered by extrinsic excitatory input but also accompanied by intrinsic neuromodulation and the recruitment of additional excitation into the circuit. To examine how these factors influence circuit activation, a detailed simulation of the unmodulated CPG network was constructed from an extensive set of physiological measurements. In this model, extrinsic input alone is insufficient to initiate rhythmic activity, confirming that additional processes are involved in circuit activation. However, incorporating known neuromodulatory and polysynaptic effects into the model still failed to enable rhythmic activity, suggesting that additional circuit features are also required. To delineate the additional activation requirements, a large-scale parameter-space analysis was conducted (∼2 × 106 configurations). The results suggest that initiation of the swim motor pattern requires substantial reconfiguration at multiple sites within the network, especially to recruit ventral swim interneuron-B (VSI) activity and increase coupling between the dorsal swim interneurons (DSIs) and cerebral neuron 2 (C2) coupling. Within the parameter space examined, we observed a tendency for rhythmic activity to be spontaneous and self-sustaining. This suggests that initiation of episodic rhythmic activity may involve temporarily restructuring a nonrhythmic network into a persistent oscillator. In particular, the time course of neuromodulatory effects may control both activation and termination of rhythmic bursting.

scholarly journals Large-scale Dynamics of Perceptual Decision Information across Human Cortex

2020 ◽  
Author(s):  
Niklas Wilming ◽  
Peter R Murphy ◽  
Florent Meyniel ◽  
Tobias H Donner
Keyword(s):  
Large Scale ◽  
Time Course ◽  
Action Plan ◽  
Frequency Channel ◽  
Human Cortex ◽  
Cortical Hierarchy ◽  
Specific Frequency ◽  
Sensory Evidence ◽  
Visual Cortical ◽  
Endogenous Component

AbstractPerceptual decisions entail the accumulation of sensory evidence for a particular choice towards an action plan. An influential framework holds that sensory cortical areas encode the instantaneous sensory evidence and downstream, action-related regions accumulate this evidence. The large-scale distribution of this computation across the cerebral cortex has remained largely elusive. We developed a regionally-specific magnetoencephalography decoding approach to exhaustively map the dynamics of stimulus- and choice-specific signals across the human cortical surface during a visual decision. Comparison with the evidence accumulation dynamics inferred from behavior enabled us to disentangle stimulus-dependent and endogenous components of choice-predictive activity across the visual cortical hierarchy. The endogenous component was present in primary visual cortex, expressed in a low (< 20 Hz) frequency-band, and its time course tracked, with delay, the build-up of choice-predictive activity in (pre-)motor regions. Our results are consistent with choice-specific cortical feedback signaling in a specific frequency channel during decision formation.

Short-term emersion affects cardiac function and regional haemolymph distribution in the crab Cancer magister

1996 ◽  
Vol 199 (3) ◽  
pp. 569-578
Author(s):  
C Airriess ◽  
B Mcmahon
Keyword(s):  
Stroke Volume ◽  
Cardiac Function ◽  
Large Scale ◽  
Time Course ◽  
Beat Frequency ◽  
Heart Beat ◽  
Experimental Chamber ◽  
Cancer Magister ◽  
Flow Through ◽  
Cardiac Stroke Volume

Changes in cardiac function and arterial haemolymph flow associated with 6 h of emersion were investigated in the crab Cancer magister using an ultrasonic flowmeter. This species is usually found sublittorally but, owing to the large-scale horizontal water movements associated with extreme tides, C. magister may occasionally become stranded on the beach. Laboratory experiments were designed such that the emersion period was typical of those that might be experienced by this crab in its natural environment. The frequency of the heart beat began to decline sharply almost immediately after the start of the experimental emersion period. Cardiac stroke volume fell more gradually. The combined reduction in these two variables led to a maximum decrease in cardiac output of more than 70 % from the control rate. Haemolymph flow through all the arteries originating at the heart, with the exception of the anterior aorta, also declined markedly during emersion. As the water level in the experimental chamber fell below the inhalant branchial openings, a stereotypical, dramatic increase in haemolymph flow through the anterior aorta began and this continued for the duration of the emersion period. The rapid time course of the decline in heart-beat frequency and the increase in haemolymph flow through the anterior aorta suggest a neural mechanism responding to the absence of ventilatory water in the branchial chambers. These responses may be adaptations, respectively, to conserve energy by reducing the minute volume of haemolymph pumped by the heart and to protect the supply of haemolymph to cephalic elements of the central nervous system. The decline in cardiac stroke volume, which occurs more slowly over the emersion period, may be a passive result of the failure to supply sufficient O2 to meet the aerobic demands of the cardiac ganglion.

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