Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows

Bobbo, Tania; Biffani, Stefano; Taccioli, Cristian; Penasa, M.; Cassandro, Martino

doi:10.1038/s41598-021-93056-4

Cited by 36 publications

(28 citation statements)

References 39 publications

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“…This is obviously not a formal attempt at building a predictive model for IMI, and DSCC is likely to be better used in combination with SCC (and other parameters, e.g. linked to milk production and the lactation cycle) in one of several possible statistical models (see for instance [ 30 ]). The results shown in Fig 3 , though, provide further insights into the relationship between SCC and DSCC and offer a basis upon which DSCC can be used for its potential contribution to the predictive ability of subclinical mastitis in combination with other parameters.…”

Section: Discussionmentioning

confidence: 99%

Relationship between total and differential quarter somatic cell counts at dry-off and early lactation

Prà

Biscarini²,

Cavani³

et al. 2022

PLoS ONE

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Mastitis is a most common disease of dairy cows and causes tremendous economic loss to the dairy industry worldwide. Somatic cell counts (SCC) reflect the inflammatory response to infections and is a metric used as key indicator in mastitis screening programs, typically within the framework of national milk recording schemes. Besides the determination of total SCC, the differentiation of cell types has been described to be beneficial for a more definite description of the actual udder health status of dairy cows. Differential somatic cell count (DSCC) represents the combined proportion of polymorphonuclear leukocytes (PMN) and lymphocytes expressed as a percentage of the total. The aim of this study was to investigate the relationship between SCC and differential somatic cell count (DSCC) in individual quarter milk samples collected at different time points: at dry-off, after calving and at the lactation peak. We used individual quarter data from farms representing the specialized production system of Parmigiano Reggiano cheese in Northern Italy. Average DSCC values ranged between 44.9% and 56.3%, with higher values (60.4%-72.1%) in milk samples with ≥ 1 million SCC/ml (where the proportion of samples with DSCC > 70% can be as high as 0.73). Moderate overall correlations between DSCC and log(SCC) were estimated (Pearson = 0.42, Spearman = 0.38), with a clear increasing trend with parity and around the lactation peak (e.g. Pearson = 0.59 at 60 DIM in parity 4). Taking SCC values as indicators of subclinical mastitis, DSCC would diagnose mastitis with 0.75 accuracy. Data editing criteria do have an impact on results, with stricter filtering leading to lower correlations between log(SCC) and DSCC. In conclusion DSCC and SCC provide different descriptions of the udder health status of dairy cows which, at least to some extent, are independent. DSCC alone doesn’t provide more accurate information than SCC at quarter level but, used in combination with SCC, can be of potential interest within the framework of milk recording programs, especially in the context of selective dry-cow therapy (SDCT). However, this needs further investigation and updated threshold values need to be selected and validated.

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Section: Discussionmentioning

confidence: 99%

Relationship between total and differential quarter somatic cell counts at dry-off and early lactation

Prà

Biscarini²,

Cavani³

et al. 2022

PLoS ONE

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“…Data processing was carried out following Bobbo et al [ 21 ]. Models to predict the probiotic/non-probiotic status using bacterial genomic features were developed using four ML algorithms: Generalized Linear Model (GLM), Random Forest (RF), Support Vector Machines (SVM), and Neural Network (NN).…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Algorithms Highlight tRNA Information Content and Chargaff’s Second Parity Rule Score as Important Features in Discriminating Probiotics from Non-Probiotics

Bergamini

Bianchi

Giaccone

et al. 2022

Biology

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Probiotic bacteria are microorganisms with beneficial effects on human health and are currently used in numerous food supplements. However, no selection process is able to effectively distinguish probiotics from non-probiotic organisms on the basis of their genomic characteristics. In the current study, four Machine Learning algorithms were employed to accurately identify probiotic bacteria based on their DNA characteristics. Although the prediction accuracies of all algorithms were excellent, the Neural Network returned the highest scores in all the evaluation metrics, managing to discriminate probiotics from non-probiotics with an accuracy greater than 90%. Interestingly, our analysis also highlighted the information content of the tRNA sequences as the most important feature in distinguishing the two groups of organisms probably because tRNAs have regulatory functions and might have allowed probiotics to evolve faster in the human gut environment. Through the methodology presented here, it was also possible to identify seven promising new probiotics that have a higher information content in their tRNA sequences compared to non-probiotics. In conclusion, we prove for the first time that Machine Learning methods can discriminate human probiotic from non-probiotic organisms underlining information within tRNA sequences as the most important genomic feature in distinguishing them.

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“…Many tests have been evaluated for the diagnosis of subclinical mastitis, which is defined as the presence of inflammation with a normal appearance of the mammary gland and visibly normal milk ( 85 ). Reflecting the inflammatory status of the mammary gland, milk SCC is used extensively to monitor udder health and milk quality ( 85 , 86 ). It is associated with the risk of IMI, both at the level of the quarter and the cow ( 87 ), and a cow-composite SCC of >200,000 cells/mL is a strong indicator of mastitis ( 81 ).…”

Section: The Evidence Consideredmentioning

confidence: 99%

“…Compared with culture, PCR is both faster and more sensitive, but more costly and with the potential to detect DNA from dead bacteria ( 85 ). Machine learning algorithms, to aid analysis of the large amounts of farm data that are generated, offer a promising new tool to support farm-level decision-making ( 86 ), including predictive algorithms for intramammary infection status in late-lactation cows ( 90 ).…”

Section: The Evidence Consideredmentioning

confidence: 99%

Mastitis Control and Intramammary Antimicrobial Stewardship in Ireland: Challenges and Opportunities

McAloon

Boloña

et al. 2022

Front. Vet. Sci.

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The Veterinary Medicines Regulation (EU 2019/6) came into force in all EU member states on 28 January 2022. This regulation places particular emphasis on prudent and responsible antimicrobial use in food animal production. Key changes include restrictions on the prophylactic use of antimicrobials in animals, and the possibility to reserve certain antimicrobials for humans only. The Regulation presents challenges to the Irish dairy industry, particularly with respect to current approaches to dry cow therapy. In response, the CellCheck technical working group (TWG, a technical group working in support of CellCheck, the national mastitis control programme) have developed pragmatic national and farm-level recommendations in support of improved mastitis control and intramammary antimicrobial stewardship in the Irish dairy industry. This paper outlines these recommendations, and provides an overview of the evidence considered to inform the TWG during its work (including the Regulation, policy perspectives, international best-practice, international scientific reviews and specific Irish challenges). In many key areas of concern, the TWG recognises the challenges in seeking to shape recommendations in the absence of robust and practical scientific evidence. For this reason, some of the recommended actions are pragmatic in nature, informed by national and international experiences. Periodic programme review will be needed, informed by ongoing monitoring of key performance indicators, to identify those actions that are most effective in an Irish context.

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Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows

Cited by 36 publications

References 39 publications

Relationship between total and differential quarter somatic cell counts at dry-off and early lactation

Relationship between total and differential quarter somatic cell counts at dry-off and early lactation

Machine Learning Algorithms Highlight tRNA Information Content and Chargaff’s Second Parity Rule Score as Important Features in Discriminating Probiotics from Non-Probiotics

Mastitis Control and Intramammary Antimicrobial Stewardship in Ireland: Challenges and Opportunities

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