scholarly journals NMR-based metabonomic studies reveal changes in the biochemical profile of plasma and urine from pigs fed high-fibre rye bread

2006 ◽  
Vol 95 (5) ◽  
pp. 955-962 ◽  
Author(s):  
Hanne C. Bertram ◽  
Knud E. Bach Knudsen ◽  
Anja Serena ◽  
Anders Malmendal ◽  
Niels Chr. Nielsen ◽  
...  
Keyword(s):  
Nmr Spectra ◽  
Partial Least Squares Regression ◽  
Urine Samples ◽  
Plasma Samples ◽  
Least Squares Regression ◽  
Spectral Difference ◽  
H Nmr ◽  
Rye Bread ◽  
Biochemical Effects ◽  
High Fibre

This study presents an NMR-based metabonomic approach to elucidate the overall endogenous biochemical effects of a wholegrain diet. Two diets with similar levels of dietary fibre and macronutrients, but with contrasting levels of wholegrain ingredients, were prepared from wholegrain rye (wholegrain diet (WGD)) and non-wholegrain wheat (non-wholegrain diet (NWD)) and fed to four pigs in a crossover design. Plasma samples were collected after 7 d on each diet, and 1H NMR spectra were acquired on these. Partial least squares regression discriminant analysis (PLSDA) on spectra obtained for plasma samples revealed that the spectral region at 3·25 parts per million dominates the differentiation between the two diets, as the WGD is associated with higher spectral intensity in this region. Spiking experiments and LC–MS analyses of the plasma verified that this spectral difference could be ascribed to a significantly higher content of betaine in WGD plasma samples compared with NWD samples. In an identical study with the same diets, urine samples were collected, and1H NMR spectra were acquired on these. PLS-DA on spectra obtained for urine samples revealed changes in the intensities of spectral regions, which could be ascribed to differences in the content of betaine and creatine/creatinine between the two diets, and LC–MS analyses verified a significantly lower content of creatinine in WGD urine samples compared with NWD urine samples. In conclusion, using an explorative approach, the present studies disclosed biochemical effects of a wholegrain diet on plasma betaine content and excretion of betaine and creatinine.

scholarly journals Human blood lipoprotein predictions from 1H NMR spectra: protocol, model performances and cage of covariance

2021 ◽  
Author(s):  
Bekzod Khakimov ◽  
Huub C.J. Hoefsloot ◽  
Nabiollah Mobaraki ◽  
Violetta Aru ◽  
Mette Kristensen ◽  
...  
Keyword(s):  
Human Blood ◽  
Regression Models ◽  
Nmr Spectra ◽  
Prediction Models ◽  
Reference Method ◽  
Least Squares Regression ◽  
Lipoprotein Subfractions ◽  
H Nmr ◽  
Lipid Species ◽  
Independent Cohort

AbstractLipoprotein subfractions are biomarkers for early diagnosis of cardiovascular diseases. The reference method, ultracentrifugation, for measuring lipoproteins is time consuming and there is a need to develop a rapid method for cohort screenings. Here we present partial least squares regression models developed using 1H-NMR spectra and concentrations of lipoproteins as measured by ultracentrifugation on 316 healthy Danes. Different regions of the 1H-NMR spectra representing signals of the lipoproteins and different lipid species were investigated to develop parsimonious, reliable and best performing prediction models. 65 LP main and subfractions were predictable with an accuracy Q2 of > 0.6 on test set samples. The models were tested on an independent cohort of 290 healthy Swedes with predicted and reference values matching by up to 85-95%. The software was developed to predict lipoproteins in human blood using 1H-NMR spectra and made freely available to be applied for future cohort screenings.

Towards the potential use of1H NMR spectroscopy in urine samples for prostate cancer detection

The Analyst ◽  
2014 ◽  
Vol 139 (16) ◽  
pp. 3875-3878 ◽  
Author(s):  
Patricia Zaragozá ◽  
Jose Luis Ruiz-Cerdá ◽  
Guillermo Quintás ◽  
Salvador Gil ◽  
Ana M. Costero ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Nmr Spectroscopy ◽  
Cancer Detection ◽  
Nmr Spectra ◽  
Urine Samples ◽  
Multivariate Approach ◽  
Prostate Cancer Detection ◽  
H Nmr ◽  
Potential Use

An multivariate approach based on1H NMR spectra profiles of urine samples to detect patients with prostate cancer.

scholarly journals Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites

2019 ◽  
Author(s):  
Bita Khalili ◽  
Mattia Tomasoni ◽  
Mirjam Mattei ◽  
Roger Mallol Parera ◽  
Reyhan Sonmez ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Human Urine ◽  
Nmr Spectra ◽  
Large Scale ◽  
Principal Component ◽  
Automated Analysis ◽  
Urine Samples ◽  
H Nmr ◽  
Nmr Data

AbstractIdentification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we test the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of co-varying features derived from Principal Component Analysis (PCA), the Iterative Signature Algorithm (ISA) and Averaged Correlation Profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. Based on the number and quality of the matches we concluded that both ISA and ACP can robustly identify about a dozen metabolites, half of which were shared, while PCA did not produce any signatures with robust matches.

scholarly journals Integrated miRNA/cytokine/chemokine profiling reveals immunopathological step changes associated with COVID-19 severity

2021 ◽  
Author(s):  
Julie C. WIlson ◽  
David Kealy ◽  
Sally R. James ◽  
Katherine Newling ◽  
Christopher Jagger ◽  
...  
Keyword(s):  
Cell Death ◽  
Correlation Analysis ◽  
Partial Least Squares Regression ◽  
Clinical Parameters ◽  
Plasma Samples ◽  
Least Squares Regression ◽  
Circulating Micrornas ◽  
Circulating Mirna ◽  
Novel Approach ◽  
Insight Into

Circulating microRNAs (miRNAs) are exceptional mechanism-based correlates of disease, yet their potential remains largely untapped in COVID-19. Here, we determined circulating miRNA and cytokine and chemokine (CC) profiles in 171 blood plasma samples from 58 hospitalised COVID-19 patients. Thirty-two miRNAs were differentially expressed in severe cases when compared to moderate and mild cases. These miRNAs and their predicted targets reflected key COVID-19 features including cell death and hypoxia. Compared to mild cases, moderate and severe cases were characterised by a global decrease in circulating miRNA levels. Partial least squares regression using miRNA and CC measurements allowed for discrimination of severe cases with greater accuracy (87%) than using miRNA or CC levels alone. Correlation analysis revealed severity group-specific associations between CC and miRNA levels. Importantly, the miRNAs that correlated with IL6 and CXCL10, two cardinal COVID-19-associated cytokines, were distinct between severity groups, providing a novel qualitative way to stratify patients with similar levels of proinflammatory cytokines but different disease severity. Integration of miRNA and CC levels with clinical parameters revealed severity-specific signatures associated with clinical hallmarks of COVID-19. Our study highlights the existence of severity-specific circulating CC/miRNA networks, providing insight into COVID-19 pathogenesis and a novel approach for monitoring COVID-19 progression.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

Multivariate micropollutants analysis in marine waters

1995 ◽  
Vol 32 (9-10) ◽  
pp. 341-348
Author(s):  
V. Librando ◽  
G. Magazzù ◽  
A. Puglisi
Keyword(s):  
Multivariate Data Analysis ◽  
Partial Least Squares Regression ◽  
Environmental Chemistry ◽  
Sampling Site ◽  
Principal Component ◽  
Least Squares Regression ◽  
Organic Micropollutants ◽  
Suspended Material ◽  
The Difference

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a function of time. In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of organic micropollutants, particularly since some are known to be carcinogenic. As the number of measured parameters increases examining the data and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Multivariate Data Analysis (MVDA), Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). The use of the first technique in this work has been applied to the interpretation of the quality of Augusta bay, by measuring the concentration of numerous organic micropollutants, together with the classical water pollution parameters, in different sites and at different times. The MVDA has highlighted the difference between various sampling sites whose data were initially thought to be similar. Furthermore, it has allowed a choice of more significant parameters for future monitoring and more suitable sampling site locations.

Sign in / Sign up

Export Citation Format

Share Document