Monitoring the Transition Period in Dairy Cows through 1H NMR-Based Untargeted Metabolomics

The metabolic alterations associated with the increase in milk production make the transition period critical to the health of dairy cows, usually leading to a higher incidence of disease in periparturient animals. In this manuscript, we describe the use of NMR-based untargeted metabolomics to follow how these changes impact the serum metabolome in a group of 28 transition dairy cows with no initial clinical diseases. Principal component analysis (PCA) of serum 1H NMR data from four weeks before calving to 8 weeks after parturition allowed us to clearly identify four stages during the transition period. Pairwise comparisons using orthogonal partial least square discriminant analysis (OPLS-DA) and univariate data analysis led to the identification of 18 metabolites that varied significantly through these stages. Species such as acetate, betaine, and creatine are observed early after calving, while other markers of metabolic stress, including acetone, β-hydroxybutyrate (BHB), and choline, accumulate significantly at the height of milk production. Furthermore, marked variations in the levels of lactate, allantoin, alanine, and other amino acids reveal the activation of different gluconeogenic pathways following parturition. Concomitant with a return to homeostasis, a gradual normalization of the serum metabolome occurs 8 weeks after calving. Correlations of metabolite levels with dietary and metabolic adaptations based on animal parity could also be identified. Overall, these results show that NMR-based chemometric methods are ideally suited to monitor manifestations of metabolic diseases throughout the transition period and to assess the impact of nutritional management schemes on the metabolism of dairy cows.

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Metabotypes of flavan-3-ol colonic metabolites after cranberry intake: elucidation and statistical approaches

European Journal of Nutrition ◽

10.1007/s00394-021-02692-z ◽

2021 ◽

Author(s):

Pedro Mena ◽

Claudia Favari ◽

Animesh Acharjee ◽

Saisakul Chernbumroong ◽

Letizia Bresciani ◽

...

Keyword(s):

Cluster Analysis ◽

Methodological Approach ◽

Principal Component ◽

Individual Variability ◽

Partial Least Square ◽

Least Square ◽

Consensus Algorithm ◽

Clustering Methods ◽

Pre Treatment ◽

The Impact

Abstract Purpose Extensive inter-individual variability exists in the production of flavan-3-ol metabolites. Preliminary metabolic phenotypes (metabotypes) have been defined, but there is no consensus on the existence of metabotypes associated with the catabolism of catechins and proanthocyanidins. This study aims at elucidating the presence of different metabotypes in the urinary excretion of main flavan-3-ol colonic metabolites after consumption of cranberry products and at assessing the impact of the statistical technique used for metabotyping. Methods Data on urinary concentrations of phenyl-γ-valerolactones and 3-(hydroxyphenyl)propanoic acid derivatives from two human interventions has been used. Different multivariate statistics, principal component analysis (PCA), cluster analysis, and partial least square-discriminant analysis (PLS-DA), have been considered. Results Data pre-treatment plays a major role on resulting PCA models. Cluster analysis based on k-means and a final consensus algorithm lead to quantitative-based models, while the expectation–maximization algorithm and clustering according to principal component scores yield metabotypes characterized by quali-quantitative differences in the excretion of colonic metabolites. PLS-DA, together with univariate analyses, has served to validate the urinary metabotypes in the production of flavan-3-ol metabolites and to confirm the robustness of the methodological approach. Conclusions This work proposes a methodological workflow for metabotype definition and highlights the importance of data pre-treatment and clustering methods on the final outcomes for a given dataset. It represents an additional step toward the understanding of the inter-individual variability in flavan-3-ol metabolism. Trial registration The acute study was registered at clinicaltrials.gov as NCT02517775, August 7, 2015; the chronic study was registered at clinicaltrials.gov as NCT02764749, May 6, 2016.

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Untargeted Metabolomic Characteristics of Skeletal Muscle Dysfunction in Rabbits Induced by a High Fat Diet

10.21203/rs.3.rs-159084/v1 ◽

2021 ◽

Author(s):

Huimei Fan ◽

YanHong Li ◽

Jie Wang ◽

Jiahao Shao ◽

Tao Tang ◽

...

Keyword(s):

Skeletal Muscle ◽

High Fat Diet ◽

Metabolic Diseases ◽

Muscle Metabolism ◽

Principal Component ◽

Least Square ◽

Rabbit Skeletal Muscle ◽

Untargeted Metabolomics ◽

High Fat ◽

Skeletal Muscle Metabolism

Abstract Background: Type 2 diabetes and metabolic syndrome caused by a high fat diet (HFD) have become public health problems around the world. These diseases are characterized by disrupted mitochondrial oxidation and insulin resistance in skeletal muscle, but the mechanism is not clear. Therefore, this study aims to reveal how a high-fat diet induces skeletal muscle metabolism disorder.Methods：Sixteen weaned rabbits were randomly divided into two groups, one fed with a standard normal diet (SND) and another one fed a HFD for five weeks. Skeletal muscle tissue samples were extracted from each rabbit at the end of the 5-week trial. An untargeted metabolomics profiling was performed using ultraperformance liquid chromatography combined with mass spectrometry (UHPLC-MS/MS).Results: The HFD significantly altered the expression levels of phospholipids, LCACs, histidine, carnosine and tetrahydrocorticosterone in skeletal muscle. Principal component analysis (PCA) and least square discriminant analysis (PLS-DA) indicated that rabbit skeletal muscle metabolism in the HFD group was significantly up-regulated compared with that of the SND group. Among the 43 skeletal muscle metabolites in the HFD group, phospholipids, LCACs, histidine, carnosine and tetrahydrocorticosterone were identified as biomarkers for skeletal muscle metabolic diseases, and may also serve as potential physiological targets for related diseases in the future.Conclusion: The untargeted metabolomics analysis revealed that a HFD altered the rabbit skeletal muscle metabolism of phospholipids, carnitine, amino acids, and steroids. Notably, phospholipids, LCACs, histidine, carnosine and tetrahydrocorticosterone blocked the oxidative ability of mitochondria, and disturbed the oxidative ability of glucose and the fatty acid-glucose cycle in rabbit skeletal muscle.

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Intraday variation of metabolic key indicators in serum of dairy cows between week 2 antepartum and week 12 postpartum

Czech Journal of Animal Science ◽

10.17221/6900-cjas ◽

2013 ◽

Vol 58 (No. 8) ◽

pp. 343-350

Author(s):

S. Wiedemann ◽

K. Horstmann ◽

M. Piechotta ◽

U. Meyer ◽

G. Flachowsky ◽

...

Keyword(s):

Dairy Cows ◽

Metabolic Diseases ◽

Transition Period ◽

Interindividual Variation ◽

Early Lactation ◽

Herd Health ◽

Serum Samples ◽

Beta Hydroxybutyrate ◽

Key Indicators ◽

The Impact

Metabolic diseases during early lactation in dairy cows can be routinely diagnosed assessing key indicators in blood. The objectives of the present study were to characterize the impact of interindividual along with intraday variation on specific metabolites and to investigate the effect of the sampling time point relative to calving. Serum samples of four high-yielding, clinically healthy, multiparous dairy cows (body weight 589 ± 27 kg) were obtained in 3-h intervals during 24-h intervals throughout the transition period and early lactation (week –2 antepartum (ap), weeks 1, 2, 3, 5, 7, and 12 postpartum (pp)). The lowest intraday variation (less than 15%) as indicated by relative coefficients of variation (CV) was found for glucose, cholesterol, and aspartate aminotransferase (AST). Intraday variation characterized by a CV between 15 and 30% was typical of urea, β-hydroxybutyrate (BHB), total bilirubin, and non-esterified fatty acids (NEFA). The highest intraday variation (CV > 30%) was assessed for insulin. Week relative to calving had significant influence on interindividual means of BHB, NEFA, insulin, and cholesterol in blood, but did not affect the interindividual variation of all parameters investigated. No significant intraday variation patterns were found. It is concluded that the considerable intraday variation of especially BHB and NEFA has to be taken into account in herd health monitoring for estimating the proportional outcome in respect to animals exceeding thresholds for specific metabolic key parameters.  

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INDICATORS FOR COMMON CROSSING STRUCTURAL HEALTH MONITORING WITH TRACK-SIDE INERTIAL MEASUREMENTS

Acta Polytechnica ◽

10.14311/ap.2019.59.0170 ◽

2019 ◽

Vol 59 (2) ◽

pp. 170-181 ◽

Cited By ~ 7

Author(s):

Mykola Sysyn ◽

Ulf Gerber ◽

Olga Nabochenko ◽

Yangyang Li ◽

Vitalii Kovalchuk

Keyword(s):

Principal Component ◽

Remaining Useful Life ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Surface Degradation ◽

Structural Health ◽

Rolling Surface ◽

Railway Crossing ◽

The Impact

This paper focuses on the experimental study of an alteration in the railway crossing dynamic response due to the rolling surface degradation during a crossing’s lifecycle. The maximal acceleration measured with the track-side measurement system as well as the impact position monitoring show no significant statistical relation to the rolling surface degradation. The additional spectral features are extracted from the acceleration measurements with a wavelet transform to improve the information usage. The reliable prediction of the railway crossing remaining useful life (RUL) demands the trustworthy indicators of structural health that systematically change during the lifecycle. The popular simple machine learning methods like principal component analysis and partial least square regression are used to retrieve two indicators from the experimental information. The feature ranking and selection are used to remove the redundant information and increase the relation of indicators to the lifetime.

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1H-NMR-Based Metabolomics: An Integrated Approach for the Detection of the Adulteration in Chicken, Chevon, Beef and Donkey Meat

Molecules ◽

10.3390/molecules26154643 ◽

2021 ◽

Vol 26 (15) ◽

pp. 4643

Author(s):

Muhammad Tayyab Akhtar ◽

Muneeba Samar ◽

Anam Amin Shami ◽

Muhammad Waseem Mumtaz ◽

Hamid Mukhtar ◽

...

Keyword(s):

1H Nmr ◽

Principal Component ◽

Integrated Approach ◽

Partial Least Square ◽

Least Square ◽

High Price ◽

Resonance Spectroscopy ◽

Muscle Tissues ◽

Meat Species ◽

Meat Samples

Meat is a rich source of energy that provides high-value animal protein, fats, vitamins, minerals and trace amounts of carbohydrates. Globally, different types of meats are consumed to fulfill nutritional requirements. However, the increasing burden on the livestock industry has triggered the mixing of high-price meat species with low-quality/-price meat. This work aimed to differentiate different meat samples on the basis of metabolites. The metabolic difference between various meat samples was investigated through Nuclear Magnetic Resonance spectroscopy coupled with multivariate data analysis approaches like principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). In total, 37 metabolites were identified in the gluteal muscle tissues of cow, goat, donkey and chicken using 1H-NMR spectroscopy. PCA was found unable to completely differentiate between meat types, whereas OPLS-DA showed an apparent separation and successfully differentiated samples from all four types of meat. Lactate, creatine, choline, acetate, leucine, isoleucine, valine, formate, carnitine, glutamate, 3-hydroxybutyrate and α-mannose were found as the major discriminating metabolites between white (chicken) and red meat (chevon, beef and donkey). However, inosine, lactate, uracil, carnosine, format, pyruvate, carnitine, creatine and acetate were found responsible for differentiating chevon, beef and donkey meat. The relative quantification of differentiating metabolites was performed using one-way ANOVA and Tukey test. Our results showed that NMR-based metabolomics is a powerful tool for the identification of novel signatures (potential biomarkers) to characterize meats from different sources and could potentially be used for quality control purposes in order to differentiate different meat types.

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ANALYSIS THE IMPACT OF THE IMPLEMENTATION OF QUALITY MANAGEMENT SYSTEM (QMS) AND SAFETY AND HEALTHY AT WORK MANAGEMENT SYSTEM (SHWMS / SMK3) ON THE JOB PERFORMANCE OF EMPLOYEES

The Management Journal of Binaniaga ◽

10.33062/mjb.v3i1.240 ◽

2018 ◽

Vol 3 (01) ◽

pp. 45

Author(s):

Nur Hidayat ◽

Indah Kusuma Hayati

Keyword(s):

Quality Management ◽

Job Performance ◽

Latent Variables ◽

Management System ◽

Quality Management System ◽

Partial Least Square ◽

Least Square ◽

Global Challenges ◽

The Impact ◽

Work Management

Recently, the evolvement of globalization era has been the global challenges that cannot be avoided either by private or government sectors, and they are requested to be survived encountering such the condition. The implementation of Quality Management System (QMS) in the operational company is the way how to guarantee the quality of products or services offered to the people. One of the purposes of QMS implementation is to provide a prime satisfaction to the customers. The impact of QMS implementation is expected to increase job performance of the employees. Besides the implementation of Quality Management System (QMS), the impact of global challenges has been increasing the competitive efforts to execute more effective production process. However, it has required manpower protection accordingly. This research aims to find out whether the implementation of quality management system and safety and healthy at work management system have impacted on the job performance of employees. Objects of this research are the employees in the production department at PT Guna Senaputra Sejahtera Plant 1 Bogor. Data analysis technique of this research has applied software Smart PLS (Partial Least Square). PLS has estimated a model of correlation among the latent variables and correlation between latent variables and its indicators. Result of data processing has indicated that the implementation of Quality Management System (QMS) and system of safety and healthy at work have positively and significantly impacted job performance of employees.Keywords : Quality Management System (QMS), Safety and Healthy at Work System ( SHWS / SMK3), and Job Performance of Employees

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Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

Xplore Journal of Statistics ◽

10.29244/xplore.v8i1.275 ◽

2019 ◽

Vol 8 (1) ◽

Author(s):

Khairunnisa Khairunnisa ◽

Rizka Pitri ◽

Victor P Butar-Butar ◽

Agus M Soleh

Keyword(s):

Statistical Downscaling ◽

General Circulation ◽

Principal Component Regression ◽

Circulation Model ◽

Principal Component ◽

Meteorological Station ◽

Grid Size ◽

Partial Least Square ◽

Least Square ◽

Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

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Application of Blockchain for Supply Chain Financing: Explaining the Drivers Using SEM

Journal of Open Innovation Technology Market and Complexity ◽

10.3390/joitmc7030167 ◽

2021 ◽

Vol 7 (3) ◽

pp. 167

Author(s):

Mohammad Rokibul Kabir ◽

Md. Aminul Islam ◽

Marniati ◽

Herawati

Keyword(s):

Supply Chain ◽

Technology Acceptance ◽

Structural Equation ◽

Research Work ◽

Partial Least Square ◽

Least Square ◽

Equation Modeling ◽

Innovation Research ◽

Supply Chain Financing ◽

The Impact

Owing to the lack of research in emerging Asian nations, this research aimed to unearth the determinants of blockchain acceptance for supply chain financing by a Bangladeshi financing company called IPDC. Centred on a technology acceptance framework called UTAUT (unified theory of acceptance and use of technology) and open innovation research, an expanded model with a mediating variable is developed for this study. This research work employs the deductive inference method in conjunction with the positivism paradigm. A structural questionnaire was used to gather data, which were then processed through Smart-PLS (partial least square) for SEM (structural equation modeling). The survey includes all the people who are directly or indirectly involved in the supply chain financing platform of IPDC. The study consists of seven direct hypotheses and one mediating hypothesis. The results show that all the direct hypotheses except the impact of social influence on the behavioural intention to use (BINTU) blockchain are significant. The mediating hypothesis indicating the role of BINTU in the relationship between facilitating conditions (FCON) and the actual use of blockchain is also supported. FCON and BINTU together explain 88.7% variation in blockchain use behaviour for supply chain financing. The research advances past findings by employing an expanded UTAUT framework and validating observations with the other relevant studies throughout the world.

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A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽

10.3390/molecules26061546 ◽

2021 ◽

Vol 26 (6) ◽

pp. 1546

Author(s):

Ioanna Dagla ◽

Anthony Tsarbopoulos ◽

Evagelos Gikas

Keyword(s):

Design Of Experiments ◽

High Performance ◽

Similarity Index ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Distance Measures ◽

Linear Regression Models ◽

Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

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The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

BMC Neuroscience ◽

10.1186/s12868-021-00608-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Mika Jönsson ◽

Björn Gerdle ◽

Bijar Ghafouri ◽

Emmanuel Bäckryd

Keyword(s):

Cerebrospinal Fluid ◽

Neuropathic Pain ◽

Pain Intensity ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Therapeutic Interventions ◽

Healthy Controls ◽

Inflammatory Profile

Abstract Background Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls. Methods The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant. Results We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1). Conclusions Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

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