scholarly journals THE INFLUENCE OF CHECK DAMS ON FLUVIAL PROCESSES AND RIPARIAN VEGETATION IN MOUNTAIN REACHES OF TORRENTS

2010 ◽  
Vol 41 (3) ◽  
pp. 37 ◽  
Author(s):  
Giuseppe Bombino ◽  
Vincenzo Tamburino ◽  
Demetrio Antonio Zema ◽  
Santo Marcello Zimbone
Keyword(s):  
Riparian Vegetation ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Vegetation Development ◽  
Multivariate Statistic ◽  
Check Dams ◽  
Multivariate Statistic Analysis ◽  
Multivariate Statistical Approach ◽  
Specific Discharge ◽  
Active Channel

The complex hydrogeomorphological processes within the active channel of rivers strongly influence riparian vegetation development and organization, particularly in mountain streams where such processes can be remarkably impacted by engineering control works. In four mountain reaches of Calabrian fiumaras we analyze, through previously arranged methods (integrated by a multivariate statistic analysis), the relationships among hydrogeomorphological river characteristics and structure and the development of riparian vegetation within the active channel in transects located in proximity of check dams and in less disturbed sites. The results of this study demonstrate clear and relevant contrasts, due to the presence of check dams, in the physical and vegetation properties of upstream, downstream and intermediate sites around check dams. The multivariate statistical approach through the Principal Component Analysis (PCA) highlighted evident relationships in all transects between groups of physical and vegetation properties. The regression analysis performed between the vegetation properties and the width:depth ratio or the specific discharge showed very different relationships between groups of transects, due to evident changes in channel morphology and in flow regime locally induced by check dams. Overall we have shown that check dams have far reaching effects in the extent and development of riparian vegetation of mountain torrent reaches, which extend far beyond physical adjustments to changed morphological, hydraulic and sedimentary conditions.

scholarly journals DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2136 ◽  
Author(s):  
Patrycja Garbacz ◽  
Marek Wesolowski
Keyword(s):  
Principal Component ◽  
Water Soluble ◽  
Crystal Formation ◽  
Multivariate Statistical ◽  
Molar Ratios ◽  
Pharmaceutical Ingredients ◽  
Multivariate Statistical Approach ◽  
And Cluster Analysis ◽  
Dsc Curves ◽  
Physical Mixtures

Co-crystals have garnered increasing interest in recent years as a beneficial approach to improving the solubility of poorly water soluble active pharmaceutical ingredients (APIs). However, their preparation is a challenge that requires a simple approach towards co-crystal detection. The objective of this work was, therefore, to verify to what extent a multivariate statistical approach such as principal component analysis (PCA) and cluster analysis (CA) can be used as a supporting tool for detecting co-crystal formation. As model samples, physical mixtures and co-crystals of indomethacin with saccharin and furosemide with p-aminobenzoic acid were prepared at API/co-former molar ratios 1:1, 2:1 and 1:2. Data acquired from DSC curves and FTIR and Raman spectroscopies were used for CA and PCA calculations. The results obtained revealed that the application of physical mixtures as reference samples allows a deeper insight into co-crystallization than is possible with the use of API and co-former or API and co-former with physical mixtures. Thus, multivariate matrix for PCA and CA calculations consisting of physical mixtures and potential co-crystals could be considered as the most profitable and reliable way to reflect changes in samples after co-crystallization. Moreover, complementary interpretation of results obtained using DSC, FTIR and Raman techniques is most beneficial.

scholarly journals Reconstructing Herbivore Diets: A Multivariate Statistical Approach To Interpreting Compound-Specific Isotope Values

2021 ◽  
Author(s):  
Mickey Hong Yi Chen ◽  
Iain P. Kendall ◽  
Richard P. Evershed ◽  
Amy Bogaard ◽  
Amy K. Styring
Keyword(s):  
Case Studies ◽  
Isotope Analysis ◽  
Statistical Treatment ◽  
Principal Component ◽  
Nitrogen Isotope ◽  
Multivariate Statistical ◽  
Sharp Separation ◽  
Statistical Framework ◽  
Multivariate Statistical Approach ◽  
Isotope Analyses

Abstract Stable nitrogen (N) isotope analysis of bulk tissues is a technique for reconstructing the diets of organisms. However, bulk nitrogen isotope (δ15N) values can be influenced by a variety of metabolic and environmental factors that can confound accurate dietary reconstruction. Compound-specific isotope analyses of amino acids (CSIA-AA) have demonstrated the power of the approach in understanding how the δ15N values of bulk collagen are assembled from the constituent AAs. Furthermore, by connecting these AA δ15N values within a robust biochemical framework interpretation of diet and environment are greatly enhanced. Several new proxies have emerged, built around selected AAs; however, the interconnectedness of AA biosynthetic pathways means that patterning of δ15N values across a wider suite of collagen AAs will occur under different environmental or dietary influences. This work seeks to test this idea by situating CSIA-AA within a robust statistical framework using principal component analysis (PCA) and Bayesian statistics to increase the interpretability of a wider range of AA δ15N values in terms of reconstructing herbivore diet. The model was tested using wild and domestic herbivores from the Neolithic settlements of Çatalhöyük (Turkey), Makriyalos (Greece), and Vaihingen (Germany) as case studies. It was found that at Makriyalos there was a sharp separation between domesticated and wild herbivores, which was present to a lesser extent at Çatalhöyük and not observed at Vaihingen. The case studies presented in this work demonstrate that multivariate statistical treatment of CSIA-AA data can deliver new insights into herbivore diet, exceeding those achievable with the Bayesian model.

scholarly journals Monitoring the Modifications of the Vitreous Humor Metabolite Profile after Death: An Animal Model

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Maria Francesca Rosa ◽  
Paola Scano ◽  
Antonio Noto ◽  
Matteo Nioi ◽  
Roberta Sanna ◽  
...  
Keyword(s):  
Principal Component ◽  
Metabolite Profile ◽  
Vitreous Humor ◽  
Multivariate Statistical ◽  
H Nmr ◽  
Multivariate Statistical Approach ◽  
Compositional Changes ◽  
Latent Structures ◽  
Metabolomic Approach

We applied a metabolomic approach to monitor the modifications occurring in goat vitreous humor (VH) metabolite composition at different times (0, 6, 12, 18, and 24 hours) after death. The1H-NMR analysis of the VH samples was performed for the simultaneous determination of several metabolites (i.e., the metabolite profile) representative of the VHstatusat different times. Spectral data were analyzed by Principal Component Analysis (PCA) and by Orthogonal Projection to Latent Structures (OPLS) regression technique. PCA and OPLS suggested that different spectral regions were involved in time-related changes. The major time-related compositional changes, here detected, were the increase of lactate, hypoxanthine, alanine, total glutathione, choline/phosphocholine, creatine, andmyo-inositol and the decrease of glucose and 3-hydroxybutyrate. We attempted a speculative interpretation of the biological mechanisms underlying these changes. These results show that multivariate statistical approach, based on1H NMR metabolite profiling, is a powerful tool for detecting ongoing differences in VH composition and may be applied to investigate several physiological and pathological conditions.

scholarly journals The 3Rs: Reduction and refinement through a multivariate statistical analysis approach in a behavioural study to unveil anxiolytic effects of natural extracts of Tilia tomentosa

2020 ◽  
Author(s):  
Guendalina Olivero ◽  
Federica Turrini ◽  
Matteo Vergassola ◽  
Raffaella Boggia ◽  
Paola Zunin ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Principal Component ◽  
Experimental Protocol ◽  
Multivariate Statistical ◽  
Data Set ◽  
Natural Extracts ◽  
Multivariate Statistical Approach ◽  
Behavioural Tests ◽  
Tilia Tomentosa

We propose a multivariate statistical approach based on Principal Component Analysis (PCA) as an useful instrument to improve the Rules of Refinement and Reduction in in vivo animal experimentation. We analysed with PCA the preliminary data from a study on the effects of the oral administration of Tilia tomentosa bud extracts (TTBEs) on the behavioural skills of adult and aged male and female mice. PCA allows to rationalize the data set information and to dissect the results, showing connections among variables under study (behavioural parameters) and different trends in the experimental groups (control and TTBEs-administered animals). Our results show that PCA can give some important information that can be useful for the refinement of the experimental protocol, in order to reduce the number of the animals used in the experiments and/or the behavioural tests to get reliable information.

scholarly journals Multivariate statistical approach to comparison of the nutrient status of Norway spruce (Picea abies [L.] Karst.) and top-soil properties in differently managed forest stands

2008 ◽  
Vol 53 (No. 3) ◽  
pp. 101-112 ◽  
Author(s):  
P. Samec ◽  
D. Vavříček ◽  
P. Šimková ◽  
J. Pňáček
Keyword(s):  
Picea Abies ◽  
Soil Properties ◽  
Norway Spruce ◽  
Principal Component ◽  
Nutrient Status ◽  
Element Cycling ◽  
Multivariate Statistical ◽  
Plant Soil ◽  
Multivariate Statistical Approach ◽  
Altitudinal Zone

The soil is an irreplaceable component of forest ecosystems. Soil-forming processes directly influence element cycling (EC). Plant-soil interaction is a specific part of EC. Plant-soil interactions were observed on an example of natural spruce stand (NSS), semi-natural spruce stand (SNSS) and allochthonous spruce stand (ASS) in conditions of the spruce forest altitudinal zone (1,140&minus;1,260 m a.s.l.; +3.0&deg;C; 1,200 mm) of the Hrubý Jeseník Mts. (Czech Republic, Central Europe), where Norway spruce (<i>Picea abies</i> [L.] Karst.) is the main edificator and stand-forming tree species. We evaluated the soil properties of H- and Ep-horizons at selected sites with Haplic and Skeletic Podzols and they were compared with the nutrient status of spruce. A method of the principal component analysis was used for definition of the basic hypotheses: (1) each forest stand is in specific and topically individual interactions with soil and these interactions influence its state, (2) the influence of forest management reflects in humification and in the nutrient status in plant assimilatory tissues. Cluster analysis calculated results comparable with the multivariate analysis of variance. The results show that the continuity of linear and multivariate statistical methods gives the approach to detection of the forest stage based on soil and plant tissue data.

Statistical Analsysis to Evaluate Heavy Metal Pollution in the Air Obatained by Moss Technique in Hanoi and its Surrounding Region

2019 ◽  
Vol 29 (3SI) ◽  
pp. 411
Author(s):  
N. H. Quyet ◽  
Le Hong Khiem ◽  
V. D. Quan ◽  
T. T. T. My ◽  
M. V. Frontasieva ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Five Factors ◽  
Heavy Metal Elements ◽  
Surrounding Areas ◽  
Potential Pollution

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed.

It is better an approximate answer to the right question than the exact answer to the wrong question : the case of the psychometric analysis of the ASQ:SE

2020 ◽  
Author(s):  
Luis Anunciacao ◽  
janet squires ◽  
J. Landeira-Fernandez
Keyword(s):  
Internal Structure ◽  
Statistical Methods ◽  
Principal Component ◽  
Psychological Theory ◽  
Published Data ◽  
Multivariate Statistical ◽  
Exact Answer ◽  
Wide Range ◽  
Ages And Stages Questionnaire ◽  
The Right

One of the main activities in psychometrics is to analyze the internal structure of a test. Multivariate statistical methods, including Exploratory Factor analysis (EFA) and Principal Component Analysis (PCA) are frequently used to do this, but the growth of Network Analysis (NA) places this method as a promising candidate. The results obtained by these methods are of valuable interest, as they not only produce evidence to explore if the test is measuring its intended construct, but also to deal with the substantive theory that motivated the test development. However, these different statistical methods come up with different answers, providing the basis for different analytical and theoretical strategies when one needs to choose a solution. In this study, we took advantage of a large volume of published data (n = 22,331) obtained by the Ages and Stages Questionnaire Social-Emotional (ASQ:SE), and formed a subset of 500 children to present and discuss alternative psychometric solutions to its internal structure, and also to its subjacent theory. The analyses were based on a polychoric matrix, the number of factors to retain followed several well-known rules of thumb, and a wide range of exploratory methods was fitted to the data, including EFA, PCA, and NA. The statistical outcomes were divergent, varying from 1 to 6 domains, allowing a flexible interpretation of the results. We argue that the use of statistical methods in the absence of a well-grounded psychological theory has limited applications, despite its appeal. All data and codes are available at https://osf.io/z6gwv/.

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