scholarly journals Exploratory data inference for detecting mastitis in dairy cattle

2020 ◽  
Vol 42 ◽  
pp. e46394
Author(s):  
Rodes Angelo Batista da Silva ◽  
Héliton Pandorfi ◽  
Gledson Luiz Pontes de Almeida ◽  
Marcos Vinícius da Silva
Keyword(s):  
Dairy Cattle ◽  
Principal Component ◽  
Subclinical Mastitis ◽  
Statistical Technique ◽  
Clinical Mastitis ◽  
Physiological Data ◽  
Multivariate Statistical ◽  
Component Technique ◽  
Physiological Variables ◽  
Exploratory Data

The aim of this study was to employ the principal component technique to physiological data and environmental thermohygrometric variables correlated with detection of clinical and subclinical mastitis in dairy cattle. A total of 24 lactating Girolando cows with different clinical conditions were selected (healthy, and with clinical or subclinical mastitis). The following physiological variables were recorded: udder surface temperature, ST (°C); eyeball temperature, ET (°C); rectum temperature, RT (°C); respiratory frequency, RF (mov. min-1). Thermohygrometric variables included air temperature, AirT (°C), and relative humidity, RU (%). ST was determined by means of thermal images, with four images per animal, on these quarters: front left side (FL), front right side (FR), rear right side (RR) and rear left side (RL), totaling 96 images. Exploratory data analysis was run through multivariate statistical technique with the employment of principal components, comprehending nine variables: ST on the FL, FR, RL and RR quarters; ET, RT; RF, AirT and RU. The representative quarters of the animals with clinical and subclinical mastitis showed udder temperatures 8.55 and 2.46° C higher than those of healthy animals, respectively. The ETs of the animals with subclinical and clinical mastitis were, respectively, 7.9 and 8.0% higher than those of healthy animals. Rectum temperatures were 2.9% (subclinical mastitis) and 5.5% (clinical mastitis) higher compared to those of healthy animals. Respiratory frequencies were 40.3% (subclinical mastitis) and 61.6% (clinical mastitis) higher compared to those of healthy animals. The first component explained 91% of the total variance for the variables analyzed. The principal component technique allowed verifying the variables correlated with the animals' clinical condition and the degree of dependence between the study variables.

Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

2021 ◽  
pp. 141-146
Author(s):  
Carlo Cusatelli ◽  
Massimiliano Giacalone ◽  
Eugenia Nissi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Well Being ◽  
Component Analysis ◽  
Measurement Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Spatial Principal Component Analysis ◽  
Italian Provinces ◽  
Reflective Measurement

Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces.

scholarly journals Detection of Circulating Tumor Cells Using Membrane-Based SERS Platform: A New Diagnostic Approach for ‘Liquid Biopsy’

Nanomaterials ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 366 ◽  
Author(s):  
Agnieszka Kamińska ◽  
Tomasz Szymborski ◽  
Evelin Witkowska ◽  
Ewa Kijeńska-Gawrońska ◽  
Wojciech Świeszkowski ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Principal Component ◽  
Cell Types ◽  
Surface Enhanced Raman Spectroscopy ◽  
Statistical Technique ◽  
Diagnostic Efficiency ◽  
Multivariate Statistical ◽  
Multi Stage ◽  
Filtration Enrichment

The detection and monitoring of circulating tumor cells (CTCs) in blood is an important strategy for early cancer evidence, analysis, monitoring of therapeutic response, and optimization of cancer therapy treatments. In this work, tailor-made membranes (MBSP) for surface-enhanced Raman spectroscopy (SERS)-based analysis, which permitted the separation and enrichment of CTCs from blood samples, were developed. A thin layer of SERS-active metals deposited on polymer mat enhanced the Raman signals of CTCs and provided further insight into CTCs molecular and biochemical composition. The SERS spectra of all studied cells—prostate cancer (PC3), cervical carcinoma (HeLa), and leucocytes as an example of healthy (normal) cell—revealed significant differences in both the band positions and/or their relative intensities. The multivariate statistical technique based on principal component analysis (PCA) was applied to identify the most significant differences (marker bands) in SERS data among the analyzed cells and to perform quantitative analysis of SERS data. Based on a developed PCA algorithm, the studied cell types were classified with an accuracy of 95% in 2D PCA to 98% in 3D PCA. These results clearly indicate the diagnostic efficiency for the discrimination between cancer and normal cells. In our approach, we exploited the one-step technology that exceeds most of the multi-stage CTCs analysis methods used and enables simultaneous filtration, enrichment, and identification of the tumor cells from blood specimens.

scholarly journals Application of the Support Vector Machine to Predict Subclinical Mastitis in Dairy Cattle

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Nazira Mammadova ◽  
İsmail Keskin
Keyword(s):  
Support Vector Machine ◽  
Dairy Cattle ◽  
Somatic Cell ◽  
Control Period ◽  
Subclinical Mastitis ◽  
Clinical Mastitis ◽  
Support Vector ◽  
Cross Sectional ◽  
Somatic Cell Counts ◽  
Cell Counts

This study presented a potentially useful alternative approach to ascertain the presence of subclinical and clinical mastitis in dairy cows using support vector machine (SVM) techniques. The proposed method detected mastitis in a cross-sectional representative sample of Holstein dairy cattle milked using an automatic milking system. The study used such suspected indicators of mastitis as lactation rank, milk yield, electrical conductivity, average milking duration, and control season as input data. The output variable was somatic cell counts obtained from milk samples collected monthly throughout the 15 months of the control period. Cattle were judged to be healthy or infected based on those somatic cell counts. This study undertook a detailed scrutiny of the SVM methodology, constructing and examining a model which showed 89% sensitivity, 92% specificity, and 50% error in mastitis detection.

Correlation of Heavy Metal Concentrations with Various Factors in Canned Liver Paste Products Using Multivariate Statistical Strategies

2004 ◽  
Vol 67 (9) ◽  
pp. 1927-1932 ◽  
Author(s):  
G. BRITO ◽  
K. NOVOTNÁ ◽  
E. M. PEÑA-MÉNDEZ ◽  
C. DÍAZ ◽  
F. J. GARCÍA
Keyword(s):  
Control Charts ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Absorption Spectrometry ◽  
Statistical Techniques ◽  
Multivariate Statistical ◽  
Heavy Metal Concentrations ◽  
Multivariate Quality Control ◽  
Heat Treated ◽  
Exploratory Data

The content of Cu, Fe, Mn, Zn, Co, Cr, Ni, and Pb were determined in 496 samples of heat-treated canned liver pastes by atomic absorption spectrometry. Canned samples were classified according to the presence or absence of coated varnish on the inner side of the can. For each sample, two subsamples were taken: one from the area in contact with the side of the can, the other from the center of the container. Univariate (correlation, box and whisker) and multivariate (quality control charts, principal component analysis, and factor analysis) statistical techniques were applied to detect the presence of outliers and for exploratory data analysis. No significant differences (P < 0.05) were found between the subsamples considered, presence or absence of coated varnish, the sampling areas, or countries of origin. The multivariate analysis allows for the interpretation of grouping tendencies in samples. Cr, Ni, and Pb were associated with presence or absence of oxide in the can, and the essential metals (Fe, Cu, Zn, and Co) were associated with the kind of can. The samples tended to differentiate according to the type of container.

Instant Insights: Mastitis in dairy cattle

2020 ◽  
Keyword(s):  
Dairy Cattle ◽  
Subclinical Mastitis ◽  
Milk Composition ◽  
Clinical Mastitis ◽  
Dry Cow Therapy ◽  
Dairy Cattle Breeding ◽  
Environmental Pathogens ◽  
And Control ◽  
The Impact

This specially curated collection features four reviews of current and key research on mastitis in dairy cattle. The first chapter reviews the indicators of mastitis and the contagious and environmental pathogens which cause it. It then discusses how mastitis can be managed and controlled on dairy farms, including consideration of dry cow therapy and the use of antibiotics. The second chapter examines the impact of clinical and subclinical mastitis in cows on milk quality, and provides a detailed account of indicators of mastitis. It describes the impact of mastitis on milk composition and quality, addressing its effect on the protein, fat, lactose and iron content of milk. The third chapter reviews advances in dairy cattle breeding to improve resistance to mastitis. It includes sections on both conventional and new phenotypes for improving resistance to clinical mastitis and concludes with a section on increasing rates of genetic gain through genomic selection. The final chapter considers recent research on the prevalence and development of antimicrobial resistance in mastitis pathogens. It shows how consistent diagnostic protocols and recording systems, attention to medical history, appropriate choice of antibiotics and control of treatment duration can all contribute to minimizing unnecessary use of antimicrobials and promoting effective treatment of mastitis.

INCIDENCE OF SUBCLINICAL AND CLINICAL MASTITIS IN PURELINE AND CROSSLINE DAIRY CATTLE

1983 ◽  
Vol 63 (4) ◽  
pp. 773-780 ◽  
Author(s):  
T. R. BATRA ◽  
A. J. McALLISTER
Keyword(s):  
Dairy Cattle ◽  
Key Words ◽  
Mixed Model ◽  
Subclinical Mastitis ◽  
Clinical Mastitis ◽  
Intensive Management ◽  
Milk Samples ◽  
Cost Of Drugs ◽  
The Cost ◽  
Mixed Model Methodology

Bi-monthly California Mastitis Tests (CMT) scores and the number of cases of clinical mastitis in 758 lactations of the Holstein-based H line, 376 lactations of the Ayrshire-based A line and 409 lactations of their reciprocal crossbreds housed under intensive management were studied to examine effects of sire line, dam line and sire line by dam line interaction using mixed model methodology. The incidence of subclinical mastitis as judged by CMT score, number of cases of clinical mastitis during the lactation, proportion of cows showing clinical mastitis at least once during the lactation and the cost of drugs for the treatment of clinical mastitis were lower in the crossline cows than pureline cows. The heterosis for these traits ranged from 2.5 to 7.8%. Sire line effect was significant for CMT score, cost of drugs for clinical mastitis and most of the traits measuring clinical mastitis. Dam line effect was small and nonsignificant for most of the traits studied. Significant sire line and dam line interaction was found for number of clinical cases in right front and proportion infected in right front and left hindquarters. Pathogenic organisms were isolated from 10.4, 34.2, 56.3, 77.7, and 84.8% of the composite milk samples, showing a CMT score of negative, trace, 1, 2, and 3, respectively. Staphylococcus was the most frequently isolated organism from the composite milk samples. Key words: Subclinical, clinical, mastitis, dairy cattle

scholarly journals A Review on Sub Clinical Mastitis in Dairy Cattle

2019 ◽  
Author(s):  
Champak Bhakat
Keyword(s):  
Risk Factors ◽  
Early Detection ◽  
Dairy Cattle ◽  
Milk Production ◽  
Management Practices ◽  
Complex Disease ◽  
Subclinical Mastitis ◽  
Clinical Mastitis ◽  
Dairy Herds ◽  
Potential Risk Factors

Subclinical mastitis is the most prevalent and economically destructive disease in dairy cattle throughout the country. It is 3–40 times more common than clinical mastitis and causes the greatest overall losses in most dairy herds. It is a multi etiological complex disease which consists infectious and noninfectious agents as potential risk factors. The prevalence of subclinical mastitis in cows increases with increased milk production, unhygienic management practices and with increasing number of lactation. There are no visible changes in the udder or milk but it reduces milk production and adversely affects milk quality. Early detection of sub clinical mastitis can be done by various indirect and direct tests.

Comparison of Methods to Display Principal Component Analysis, Focusing on Biplots and the Selection of Biplot Axes

2016 ◽  
pp. 289-332
Author(s):  
Carla Barbosa ◽  
M. Rui Alves ◽  
Beatriz Oliveira
Keyword(s):  
Principal Component ◽  
Statistical Technique ◽  
Practical Case ◽  
Comparison Of Methods ◽  
Multivariate Statistical ◽  
New Developments ◽  
Model Complex ◽  
Components Analysis ◽  
Almost All ◽  
Selection Of

Principal components analysis (PCA) is probably the most important multivariate statistical technique, being used to model complex problems or just for data mining, in almost all areas of science. Although being well known by researchers and available in most statistical packages, it is often misunderstood and poses problems when applied by inexperienced users. A biplot is a way of concentrating all information related to sample units and variables in a single display, in an attempt to help interpretations and avoid overestimations. This chapter covers the main mathematical aspects of PCA, as well as the form and covariance biplots developed by Gabriel and the predictive and interpolative biplots devised by Gower and coworkers. New developments are also presented, involving techniques to automate the production of biplots, with a controlled output in terms of axes predictivities and interpolative accuracies, supported by the AutoBiplot.PCA function developed in R. A practical case is used for illustrations and discussions.

The Status of Surface Water in West Tripura District, India: An Approach by Using Water Quality Index and Multivariate Statistical Technique

2021 ◽  
Vol 18 (2) ◽  
pp. 27-36
Author(s):  
Biplab Roy ◽  
Ajay Kumar Manna
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Principal Component ◽  
Statistical Technique ◽  
Total Hardness ◽  
Multivariate Statistical ◽  
And Cluster Analysis ◽  
Entropy Weighted

The present investigation provides a better interpretation of surface water (rivers, ponds, bills, lakes, etc.) quality utilising entropy weighted water quality index (EWWQI) and different multivariate statistical techniques. Eleven physicochemical parameters including alkalinity, dissolved oxygen (DO), pH, total dissolved solids (TDS), electrical conductivity (EC), calcium (Ca), turbidity, magnesium (Mg), total hardness (TH), chloride (Cl-), and iron (Fe) were analysed and monitored at 23 sampling sites (in December 2018) of West Tripura district. Experimental outcomes of turbidity followed by Fe contamination exceeded recommended WHO standard limit. The maximum values of Fe and turbidity were estimated as 8.745 mg/L and 797.7 NTU, respectively. WQI values confirmed that most of the monitoring locations had poor water quality except three reported areas (S7, S14, and S15) but without Fe and turbidity, estimated WQI confirmed drinkable water condition for entire samples. Multivariate statistical approaches like correlation analysis, principal component analysis (PCA) and cluster analysis (CA) were applied to explore water quality. PCA outcomes recognised three principal factors explaining almost 85% of the total variance. CA investigated three major clusters of 23 sampling sites namely less polluted, highly polluted and moderately polluted zone. Confirming all above, the surface water at the monitoring locations is a major concern which may lead to serious health issues in local people.

scholarly journals Hydro-Geochemical Assessment of Groundwater Quality in Aseer Region, Saudi Arabia

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1847 ◽  
Author(s):  
Javed Mallick ◽  
Chander Singh ◽  
Mohammed AlMesfer ◽  
Anand Kumar ◽  
Roohul Khan ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Groundwater Quality ◽  
Structural Equation ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Statistical Technique ◽  
Climatic Conditions ◽  
Geochemical Modelling ◽  
Multivariate Statistical ◽  
Study Region

Saudi Arabia is an arid country with very limited water resources. The absence of surface water bodies along with erratic rainfall renders groundwater as the most reliable source of potable water in arid and semi-arid regions globally. Groundwater quality is determined by aquifer characteristics regional geology and it is extensively influenced by both natural and anthropogenic activities. In the recent past, several methodologies have been adopted to analyze the quality of groundwater and associated hydro-geochemical process i.e., multivariate statistical analysis, geochemical modelling, stable isotopes, a redox indicator, structural equation modelling. In the current study, statistical methods combined with geochemical modelling and conventional plots have been used to investigate groundwater and related geochemical processes in the Aseer region of Saudi Arabia. A total of 62 groundwater samples has been collected and analyzed in laboratory for major cations and anions. Groundwater in the study region is mostly alkaline with electrical conductivity ranging from 285–3796 μS/cm. The hydro-geochemical characteristics of groundwater are highly influenced by extreme evaporation. Climatic conditions combined with low rainfall and high temperature have resulted in a highly alkaline aquifer environment. Principal component analysis (PCA) yielded principal components explaining 79.9% of the variance in the dataset. PCA indicates ion exchange, soil mineralization, dissolution of carbonates and halite are the major processes governing the groundwater geochemistry. Groundwater in this region is oversaturated with calcite and dolomite while undersaturated with gypsum and halite which suggests dissolution of gypsum and halite as major process resulting into high chloride in groundwater. The study concludes that the combined approach of a multivariate statistical technique, conventional plots and geochemical modelling is effective in determining the factors controlling the groundwater quality.

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