scholarly journals Metabolic Profiling to Identify the Latent Infection of Strawberry by Botrytis cinerea

2019 ◽  
Vol 15 ◽  
pp. 117693431983851 ◽  
Author(s):  
Zhihong Hu ◽  
Xunian Chang ◽  
Tan Dai ◽  
Lei Li ◽  
Panqing Liu ◽  
...  
Keyword(s):  
Least Squares ◽  
Botrytis Cinerea ◽  
Partial Least Squares ◽  
Metabolic Profiling ◽  
Latent Infection ◽  
Early Stage ◽  
Interaction Mechanism ◽  
Gray Mold ◽  
Plant Pathogen Interaction ◽  
Metabolome Profiling

In plant-pathogen interaction systems, plant metabolism is usually agitated in the early stages of infection and much before visible symptoms appear. To identify the latent infection of strawberry by Botrytis cinerea by metabolome profiling, a metabolomics method based on gas chromatography and mass spectrometry was applied to identify the affected metabolites and discriminate diseased plants from healthy ones. An orthogonal partial least squares (OPLS) score plot showed that the metabolic profiling well separated B. cinerea-infected strawberry plants at 2, 5, and 7 days after infection from non-infected healthy plants. Combined analysis of variance (ANOVA) and OPLS analysis revealed candidate biomarkers of plant resistance and of infection and expansion of the pathogen in the plants. Among them, hexadecanoic acid, octadecanoic acid, sucrose, β-lyxopyranose, melibiose, and 1,1,4a-Trimethyl-5,6-dimethylenedecahydronaphthalene were closely related to the early stage of disease development when symptoms were not visible. A discrimination method that could distinguish Botrytis gray mold diseased strawberry plants from healthy ones was established based on the partial least squares discriminant analysis (PLS-DA) model with a correct recognition accuracy of 100%. This research offers a good application of metabolome profiling for early diagnosis of plant disease and interaction mechanism exploration.

scholarly journals 1H-NMR spectroscopy identifies potential biomarkers in serum metabolomic signatures for early stage esophageal squamous cell carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8151 ◽  
Author(s):  
Yan-Yan Liu ◽  
Zhong-Xian Yang ◽  
Li-Min Ma ◽  
Xu-Qing Wen ◽  
Huan-Lin Ji ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Discriminant Analysis ◽  
Nmr Spectroscopy ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Least Squares ◽  
Partial Least Squares ◽  
Early Stage ◽  
H Nmr ◽  
Potential Biomarkers

Background Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent types of upper gastrointestinal malignancies. Here, we used 1H nuclear magnetic resonance spectroscopy (1H-NMR) to identify potential serum biomarkers in patients with early stage ESCC. Methods Sixty-five serum samples from early stage ESCC patients (n = 25) and healthy controls (n = 40) were analysed using 1H-NMR spectroscopy. We distinguished between different metabolites through principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis (OPLS-DA) using SIMCA-P+ version 14.0 software. Receiver operating characteristic (ROC) analysis was conducted to verify potential biomarkers. Results Using OPLS-DA, 31 altered serum metabolites were successfully identified between the groups. Based on the area under the ROC curve (AUROC), and the biomarker panel with AUROC of 0.969, six serum metabolites (α-glucose, choline, glutamine, glutamate, valine, and dihydrothymine) were selected as potential biomarkers for early stage ESCC. Dihydrothymine particularly was selected as a new feasible biomarker associated with tumor occurrence. Conclusions 1H-NMR spectroscopy may be a useful tumour detection approach in identifying useful metabolic ESCC biomarkers for early diagnosis and in the exploration of the molecular pathogenesis of ESCC.

An fMRI study examining spatial navigation through a virtual environment: Partial least squares analysis

2005 ◽  
Author(s):  
Richard Mraz ◽  
Nancy J. Lobaugh ◽  
Genevieve Quintin ◽  
Konstantine K. Kakzanis ◽  
Simon J. Graham
Keyword(s):  
Least Squares ◽  
Virtual Environment ◽  
Partial Least Squares ◽  
Spatial Navigation ◽  
Partial Least Squares Analysis ◽  
Fmri Study

Controlling in der Produktentwicklung

Controlling ◽  
2020 ◽  
Vol 32 (3) ◽  
pp. 45-50
Author(s):  
Marc Janka
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Finite Mixture

Gemeinhin gilt die Annahme, dass das Controlling für viele deutsche Unternehmen auch oder besonders in der Produktentwicklung von großer Bedeutung ist und vor allem unter Umfeldunsicherheit ein wesentlicher Erfolgsfaktor sein kann. Der vorliegende Beitrag zeigt unter Anwendung einer für die Controlling-Forschung neuartigen Methode zur Schätzung von Mischverteilungen mittels partieller Regressionen (englisch finite mixture partial least squares [FIMIX-PLS]), ob diese Annahme für alle Unternehmen gleichermaßen gilt.

Partial Least Squares Strukturgleichungsmodellierung

2017 ◽  
Author(s):  
Joseph F. Hair ◽  
Sven Hauff ◽  
G. Tomas M. Hult ◽  
Nicole F. Richter ◽  
Christian M. Ringle ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares

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.

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