scholarly journals Classification and prediction of Mycobacterium Avium subsp. Paratuberculosis (MAP) shedding severity in cattle based on young stock heifer faecal microbiota composition using random forest algorithms

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Alexander Umanets ◽  
Annemieke Dinkla ◽  
Stephanie Vastenhouw ◽  
Lars Ravesloot ◽  
Ad P. Koets
Keyword(s):  
Random Forest ◽  
Mycobacterium Avium ◽  
Clinical Symptoms ◽  
Classification Model ◽  
Economic Losses ◽  
Diagnostic Tools ◽  
Microbiota Composition ◽  
Faecal Microbiota ◽  
Microbial Composition ◽  
Model Based

Abstract Background Bovine paratuberculosis is a devastating infectious disease caused by Mycobacterium avium subsp. paratuberculosis (MAP). The development of the paratuberculosis in cattle can take up to a few years and vastly differs between individuals in severity of the clinical symptoms and shedding of the pathogen. Timely identification of high shedding animals is essential for paratuberculosis control and minimization of economic losses. Widely used methods for detection and quantification of MAP, such as culturing and PCR based techniques rely on direct presence of the pathogen in a sample and have little to no predictive value concerning the disease development. In the current study, we investigated the possibility of predicting MAP shedding severity in cattle based on the faecal microbiota composition. Twenty calves were experimentally infected with MAP and faecal samples were collected biweekly up to four years of age. All collected samples were subjected to culturing on selective media to obtain data about shedding severity. Faecal microbiota was profiled in a subset of samples (n = 264). Using faecal microbiota composition and shedding intensity data a random forest classifier was built for prediction of the shedding status of the individual animals. Results The results indicate that machine learning approaches applied to microbial composition can be used to classify cows into groups by severity of MAP shedding. The classification accuracy correlates with the age of the animals and use of samples from older individuals resulted in a higher classification precision. The classification model based on samples from the first 12 months of life showed an AUC between 0.78 and 0.79 (95% CI), while the model based on samples from animals older than 24 months showed an AUC between 0.91 and 0.92 (95% CI). Prediction for samples from animals between 12 and 24 month of age showed intermediate accuracy [AUC between 0.86 and 0.87 (95% CI)]. In addition, the results indicate that a limited number of microbial taxa were important for classification and could be considered as biomarkers. Conclusions The study provides evidence for the link between microbiota composition and severity of MAP infection and shedding, as well as lays ground for the development of predictive diagnostic tools based on the faecal microbiota composition.

scholarly journals Interactions between Teladorsagia circumcincta Infections and Microbial Composition of Sheep with or without Successful Monepantel Treatment—A Preliminary Study

Ruminants ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 31-45
Author(s):  
Craig A. Watkins ◽  
Dave J. Bartley ◽  
Burcu Gündüz Ergün ◽  
Büşra Yıldızhan ◽  
Tracy Ross-Watt ◽  
...  
Keyword(s):  
Intestinal Microbiota ◽  
Terminal Ileum ◽  
Anthelmintic Resistance ◽  
Economic Losses ◽  
Faecal Microbiota ◽  
Microbial Composition ◽  
Teladorsagia Circumcincta ◽  
Faecal Samples ◽  
Resistant Strains ◽  
Preliminary Study

Nematodes are one of the main impactors on the health, welfare and productivity of farmed animals. Teladorsagia circumcincta are endemic throughout many sheep-producing countries, particularly in the northern hemisphere, and contribute to the pathology and economic losses seen on many farms. Control of these nematode infections is essential and heavily reliant on chemotherapy (anthelmintics), but this has been compromised by the development of anthelmintic resistance. In mammals, the composition of the intestinal microbiota has been shown to have a significant effect on overall health. The interactions between host, microbiota and pathogens are complex and influenced by numerous factors. In this study, comparisons between intestinal and faecal microbiota of sheep infected with sensitive or resistant strains of T. circumcincta, with or without monepantel administration were assessed. The findings from both faecal samples and terminal ileum mucosal scrapings showed clear differences between successfully treated animals and those sheep that were left untreated and/or those carrying resistant nematodes. Specifically, the potentially beneficial genus Bifidobacterium was identified as elevated in successfully treated animals. The detection of these and other biomarkers will provide the basis for new therapeutic reagents particularly relevant to the problems of emerging multidrug anthelmintic resistance.

scholarly journals Differences in the Microbial Composition of Hemodialysis Patients Treated with and without β-Blockers

2021 ◽  
Vol 11 (3) ◽  
pp. 198
Author(s):  
Yi-Ting Lin ◽  
Ting-Yun Lin ◽  
Szu-Chun Hung ◽  
Po-Yu Liu ◽  
Wei-Chun Hung ◽  
...  
Keyword(s):  
Random Forest ◽  
Gut Microbiota ◽  
Hemodialysis Patients ◽  
Microbiota Composition ◽  
Microbial Composition ◽  
Α Diversity ◽  
Β Blocker ◽  
Β Blockers ◽  
The Impact ◽  
Gut Microbiota Composition

β-blockers are commonly prescribed to treat cardiovascular disease in hemodialysis patients. Beyond the pharmacological effects, β-blockers have potential impacts on gut microbiota, but no study has investigated the effect in hemodialysis patients. Hence, we aim to investigate the gut microbiota composition difference between β-blocker users and nonusers in hemodialysis patients. Fecal samples collected from hemodialysis patients (83 β-blocker users and 110 nonusers) were determined by 16S ribosomal RNA amplification sequencing. Propensity score (PS) matching was performed to control confounders. The microbial composition differences were analyzed by the linear discriminant analysis effect size, random forest, and zero-inflated Gaussian fit model. The α-diversity (Simpson index) was greater in β-blocker users with a distinct β-diversity (Bray–Curtis Index) compared to nonusers in both full and PS-matched cohorts. There was a significant enrichment in the genus Flavonifractor in β-blocker users compared to nonusers in full and PS-matched cohorts. A similar finding was demonstrated in random forest analysis. In conclusion, hemodialysis patients using β-blockers had a different gut microbiota composition compared to nonusers. In particular, the Flavonifractor genus was increased with β-blocker treatment. Our findings highlight the impact of β-blockers on the gut microbiota in hemodialysis patients.

scholarly journals Investigating the Interaction Between Prosody and Pragmatics Quantitatively: A Case Study of the Chinese Discourse Marker ni zhidao (“You Know”)

2021 ◽  
Vol 12 ◽  
Author(s):  
Yi Shan
Keyword(s):  
Statistical Analysis ◽  
Random Forest ◽  
Classification Model ◽  
Prosodic Features ◽  
Discourse Marker ◽  
Model Based ◽  
Communicative Functions ◽  
Pragmatic Awareness ◽  
Instrumental Methods

This study briefly describes the prosodic and pragmatic characteristics of the discourse marker ni zhidao (“you know”) in spoken Chinese. It mainly explores the interaction between its prosody and pragmatics using instrumental methods. It is the first attempt to use acoustic and statistical analysis to examine the prosodic parameters and prosody-pragmatics interaction of a Chinese discourse marker. The corpus includes 71 interview conversations totaling more than 30 h, in which 490 discourse marker tokens of ni zhidao were found. Ni zhidao mainly fulfilled four broad pragmatic functions of initiating a topic when occurring sentence-initially, of holding the floor when appearing within clauses, of marking coherence when making its presence between clauses, and of projecting attitudes and feelings when showing up sentence-finally. Drawing on the algorithm of random forest in R, the acoustic and statistical analysis of the performance of ni zhidao in these four functions showed that its prosodic features, including duration, tempo, pre-pause, post-pause, F0, and intensity, significantly relate to and thus imply its pragmatic functions, that the interaction between its prosody and pragmatics can be modeled statistically, and that the established pragmatics classification model based on prosody can be utilized to predict the pragmatics of ni zhidao. These findings seem to strengthen the hypothesis that prosodic variables play a role in deciphering the different pragmatic functions of ni zhidao. This study uses prosodic evidence to more objectively reveal not only the part of ni zhidao in dynamically constructing and embodying specific contexts but also its communicative functions and the underlying meta-pragmatic awareness behind it. This study breaks through the limitations of traditional discourse marker research, which mainly relies on context and discourse characteristics for subjective reasoning.

scholarly journals Anti-Acid Drug Treatment Induces Changes in the Gut Microbiome Composition of Hemodialysis Patients

2021 ◽  
Vol 9 (2) ◽  
pp. 286
Author(s):  
Yi-Ting Lin ◽  
Ting-Yun Lin ◽  
Szu-Chun Hung ◽  
Po-Yu Liu ◽  
Ping-Hsun Wu ◽  
...  
Keyword(s):  
Random Forest ◽  
Gut Microbiota ◽  
Gut Microbiome ◽  
Hemodialysis Patients ◽  
Microbiota Composition ◽  
Microbial Composition ◽  
Linear Discriminant ◽  
H2 Blocker ◽  
Α Diversity ◽  
16S Rna

Anti-acid drugs, proton pump inhibitor (PPI) and histamine-2 blocker (H2-blocker), are commonly prescribed to treat gastrointestinal disorders. These anti-acid drugs alter gut microbiota in the general population, but their effects are not known in hemodialysis patients. Hence, we investigated the microbiota composition in hemodialysis patients treated with PPIs or H2-blocker. Among 193 hemodialysis patients, we identified 32 H2-blocker users, 23 PPI users, and 138 no anti-acid drug subjects. Fecal samples were obtained to analyze the gut microbiome using 16S RNA amplicon sequencing. Differences in the microbial composition of the H2-blocker users, PPI users, and controls were assessed using linear discriminant analysis effect size and the random forest algorithm. The species richness or evenness (α-diversity) was similar among the three groups, whereas the inter-individual diversity (β-diversity) was different between H2-blocker users, PPI users, and controls. Hemodialysis patients treated with H2-blocker and PPIs had a higher microbial dysbiosis index than the controls, with a significant increase in the genera Provetella 2, Phascolarctobacterium, Christensenellaceae R-7 group, and Eubacterium oxidoreducens group in H2-blocker users, and Streptococcus and Veillonella in PPI users. In addition, compared to the H2-blocker users, there was a significant enrichment of the genera Streptococcus in PPI users, as confirmed by the random forest analysis and the confounder-adjusted regression model. In conclusion, PPIs significantly changed the gut microbiota composition in hemodialysis patients compared to H2-blocker users or controls. Importantly, the Streptococcus genus was significantly increased in PPI treatment. These findings caution against the overuse of PPIs.

scholarly journals Identification of Mycobacterium avium subspecies paratuberculosis in sheep farms in Bayannaoer, Inner Mongolia, China

2021 ◽  
Author(s):  
yuandi yu ◽  
Suhui Zhang ◽  
Guoyang Xu ◽  
Dengfeng Xu ◽  
Hua Zheng ◽  
...  
Keyword(s):  
Mycobacterium Avium ◽  
Inner Mongolia ◽  
Clinical Symptoms ◽  
Mycobacterium Avium Subspecies Paratuberculosis ◽  
Elisa Test ◽  
Economic Losses ◽  
Serum Samples ◽  
Polymerase Chain ◽  
Mycobacterium Avium Subsp Paratuberculosis

Abstract Background Paratuberculosis, is a widespread chronic infection of Mycobacterium avium subsp. paratuberculosis (Map), causes significant economic losses to the sheep industry. The current study investigated this disease, which causes diarrhea in sheep, particularly, in Bayannaoer, Inner Mongolia. Diagnosis was based on clinical symptoms, pathological autopsy, histopathological inspection, and serological and molecular methods. Results Paratuberculosis was confirmed via a polymerase chain reaction using DNA extracted from tissue and fecal samples. Serum samples from 472 individual sheep were obtained to detect antibodies against MAP by ELISA test and MAP antibodies were separately detected in 17.86% (35/196) and 18.48% (51/276) of sheep herds at 6 months and 1 years of age respectively. The results of tissue lesion and pathological section were consistent with paratuberculosis infection. Conclusions This is the first report of Mycobacterium avium subspecies paratuberculosis seroprevalence in Bayannaoer sheep in Inner Mongolia. Our findings show that MAP was prevalent and potentially threaten in this region and more further investigations on long-term epidemiological

scholarly journals Application of the Actiphage® Assay to Detect Viable Mycobacterium avium subsp. paratuberculosis Cells in Fresh Sheep and Goat Milk and Previously Frozen Milk and In-Line Milk Filters

2021 ◽  
Vol 8 ◽  
Author(s):  
Monika Beinhauerova ◽  
Iva Slana
Keyword(s):  
Mycobacterium Avium ◽  
Limit Of Detection ◽  
Goat Milk ◽  
Time Lapse ◽  
Cow Milk ◽  
Economic Losses ◽  
Diagnostic Tools ◽  
Lytic Bacteriophage ◽  
Milk Samples ◽  
Detection And Identification

Mycobacterium avium subsp. paratuberculosis (MAP) is a well-known causative agent of paratuberculosis, a chronic infectious granulomatous enteritis of ruminants contributing to significant economic losses worldwide. Current conventional diagnostic tools are far from being sufficient to manage and control this disease. Therefore, increased attention has been paid to alternative approaches including phage-based assays employing lytic bacteriophage D29 to detect MAP cells. The aim of the present study was to assess the applicability and efficiency of the recently developed phage-based kit termed Actiphage® combined with IS900 real-time PCR (qPCR) for rapid detection and quantification of viable MAP in milk samples. We demonstrated that Actiphage® in combination with IS900 qPCR allows for rapid and sensitive detection and identification of viable MAP in milk samples with a limit of detection of 1 MAP per 50 ml milk. Using this method, the presence of viable MAP cells was successfully determined in 30.77% of fresh goat, sheep and cow milk samples originating from paratuberculosis-affected herds. We further used Actiphage assay to define the time-lapse aspect of testing naturally contaminated milk and milk filters frozen for various lengths of time by phage-based techniques. Viable MAP was detected in 13.04% of frozen milk samples and 28.57% of frozen milk filters using Actiphage-qPCR. The results suggest the ability to detect viable MAP in these samples following freezing for more than 1 year. The obtained results support the views of the beneficial role of this technology in the control or monitoring of paratuberculosis.

scholarly journals Prospects of Using Microbiota Correction Methods in the Treatment of Inflammatory Bowel Disease

2019 ◽  
Vol 29 (2) ◽  
pp. 12-22 ◽  
Author(s):  
A. I. Akinshina ◽  
D. V. Smirnova ◽  
A. V. Zagainova ◽  
V. V. Makarov ◽  
S. M. Yudin
Keyword(s):  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Molecular Genetic ◽  
Intestinal Barrier ◽  
Microbiota Composition ◽  
Faecal Microbiota ◽  
Microbial Composition ◽  
Extensive Discussion ◽  
Faecal Microbiota Transplantation ◽  
Inflammatory Bowel

Aim. The present article examines key methods of microbiota correction (antibiotic therapy; pro-, pre- and metabiotic therapy; faecal microbiota transplantation) used in treating inflammatory bowel disease, as well as compares the clinical trial results of these methods.Key findings. Inflammatory bowel disease (IBD) is an umbrella term used to describe a group of chronic diseases of unknown aetiology. In the past, bacteriological methods based on the isolation of a pure bacterial culture were used to determine the microbiota composition. However, such methods did not provide complete information on the microbiota composition. In recent years, preference has been given to more accurate and faster molecular genetic methods allowing a more detailed study of the key mechanisms by which microbiota affects the intestine in Crohn’s disease (CD) and ulcerative colitis (UC), as well as of the effect of microbial metabolites on their pathogenesis. The article provides an overview of main microbiota metabolites and their role in regulating the intestinal barrier function. One of the current issues consists in the development of personalised approaches to therapy and remission maintenance in IBD, including via methods for correcting the microbial composition: probiotic, prebiotic and metabiotic therapy, as well as faecal microbiota transplantation.Conclusion. The use of probiotics, prebiotics, and metabiotics can enhance the effectiveness of therapeutic regimens and significantly improve the quality of life of patients with chronic IBD. The use of antibiotics and faecal microbiota transplantation in treating IBD is the subject of extensive discussion and debate. The safety of these methods has not been confirmed so far; therefore, it is vital to continue studying their influence on the clinical condition of patients.

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

scholarly journals Monitoring the variation in the gut microbiota of captive woolly monkeys related to changes in diet during a reintroduction process

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Camilo Quiroga-González ◽  
Luis Alberto Chica Cardenas ◽  
Mónica Ramírez ◽  
Alejandro Reyes ◽  
Camila González ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Site Conditions ◽  
Microbiota Composition ◽  
Microbial Composition ◽  
Bacterial Composition ◽  
Diet Diversity ◽  
Woolly Monkeys ◽  
The Moment ◽  
Positive Effect ◽  
Lagothrix Lagothricha

AbstractMicrobiome is known to play an important role in the health of organisms and different factors such as diet have been associated with modifications in microbial communities. Differences in the microbiota composition of wild and captive animals has been evaluated; however, variation during a reintroduction process in primates has never been reported. Our aim was to identify changes in the bacterial composition of three individuals of reintroduced woolly monkeys (Lagothrix lagothricha) and the variables associated with such changes. Fecal samples were collected and the V4 region of the 16S rRNA gene was sequenced to determine gut microbial composition and functionality. Individual samples from released individuals showed a higher microbial diversity after being released compared to before liberation, associated with changes in their diet. Beta diversity and functionality analysis showed separation of samples from released and captive conditions and the major factor of variation was the moment of liberation. This study shows that intestinal microbiota varies depending on site conditions and is mainly associated with diet diversity. The intake of food from wild origin by released primates may promote a positive effect on gut microbiota, improving health, and potentially increasing success in reintroduction processes.

scholarly journals Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 371
Author(s):  
Yu Jin ◽  
Jiawei Guo ◽  
Huichun Ye ◽  
Jinling Zhao ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Feature Space ◽  
Kappa Coefficient ◽  
Classification Model ◽  
Support Vector ◽  
Textural Feature ◽  
Monitoring Accuracy ◽  
Areca Catechu ◽  
High Level

The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries.

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