scholarly journals Welfare Assessment in Shelter Dogs by Using Physiological and Immunological Parameters

Animals ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 340 ◽  
Author(s):  
Cecilia Righi ◽  
Laura Menchetti ◽  
Riccardo Orlandi ◽  
Livia Moscati ◽  
Stefania Mancini ◽  
...  
Keyword(s):  
Principal Component ◽  
Multivariate Statistical Analyses ◽  
Multivariate Statistical ◽  
Immunological Parameters ◽  
Welfare Assessment ◽  
Fecal Cortisol ◽  
Shelter Dogs ◽  
Changes Over Time ◽  
Time Of Admission ◽  
Over Time

This study aimed to evaluate the state of welfare of a group of dogs during the first month after entering the shelter by using different stress parameters. Blood and fecal samples were collected from a group of 71 dogs at the time of admission to the shelter. In 46 of these dogs, sampling was repeated after four weeks. Well-recognized welfare biomarkers, such as fecal cortisol and leukocytes, as well as some innovative parameters (β-endorphin and lysozyme) were determined. Uni- and multivariate statistical analyses were used to evaluate their interactions and changes over time. Neutrophils (p < 0.01), lysozyme (p < 0.05), and fecal cortisol (p < 0.05) decreased, while lymphocytes (p < 0.05) increased after four weeks compared to the first days of being in the shelter, suggesting an improvement in the dogs’ welfare over time. A principal component analysis extracted three bipolar components (PCs), explaining 75% of the variance and indicating negative associations between neutrophil and lymphocyte (PC1), lysozyme and β-endorphin (PC2), cortisol and lysozyme (PC3). The associations between these variables within each PC also confirmed the intricate relationships between the hypothalamic-pituitary-adrenal (HPA) axis and the immune system as well as the importance of a multiparametric approach in evaluating welfare.

Landsat Views of Sudbury (Canada) Area Acidic and Nonacidic Lakes

1992 ◽  
Vol 49 (S1) ◽  
pp. 33-39 ◽  
Author(s):  
J. Roger Pitblado
Keyword(s):  
Thematic Mapper ◽  
Landsat Data ◽  
Multivariate Statistical ◽  
Temporal Dimension ◽  
Acidic Lake ◽  
Water Colour ◽  
Statistical Procedures ◽  
Changes Over Time ◽  
Radiance Data ◽  
Over Time

Multivariate statistical procedures are used to establish empirical associations between acidity, visual lake water colour, dissolved organic carbon (DOC) concentrations, and Landsat-5 Thematic Mapper (TM) radiance data. Acidic lakes in an area northeast of Sudbury (Canada) are characterized by their clear, blue colours and very low DOC. With a subjective, three-class water colour grouping, 92% of the study lakes were correctly classified using TM data. Further, it is shown that DOC, the major component of water colour in this area, can be predicted within 1 mg/L of observed concentrations using TM data (multiple r = 0.93, P < 0.01). By deriving interrelationships between pH levels, water colour, and DOC, Landsat data provide a means to discriminate and map the acidic and nonacidic lakes of the study area. Examination of the reflectance characteristics of a single acidic lake (Bowland Lake) that has undergone neutralization suggests that Landsat data may be used to detect optical changes over time. However, the capability for monitoring the temporal dimension of lake acidification using satellite data has yet to be established.

Wood anatomical variability in Early Permian ‘araucarioids’

IAWA Journal ◽  
2014 ◽  
Vol 35 (3) ◽  
pp. 307-331 ◽  
Author(s):  
Menno Booi ◽  
Isabel M. van Waveren ◽  
Johanna H.A. van Konijnenburg-van Cittert
Keyword(s):  
Principal Component Analysis ◽  
Wood Species ◽  
Principal Component ◽  
Statistical Analyses ◽  
Late Paleozoic ◽  
Individual Species ◽  
Early Permian ◽  
Multivariate Statistical Analyses ◽  
Multivariate Statistical ◽  
Anatomical Analysis

Although araucarioid wood is poor in diagnostic characters, well in excess of 200 Late Paleozoic species have been described. This study presents a largescale anatomical analysis of this wood type based on the fossil wood collections from the Early Permian Mengkarang Formation of Sumatra, Indonesia. Principal Component Analysis visualisation, in conjunction with uni- and multivariate statistical analyses clearly show the wood from the Mengkarang Formation to be a contiguous micromorphological unit in which no individual species can be distinguished. Pycnoxylic wood species described previously from this collection or other collections from the Mengkarang Formation fall within the larger variability described here. Based on comparison with wood from modern-day Araucariaceae, the Early Permian specimens can be differentiated from extant (but unrelated) “araucarioids” by a few (continuous) characters.

scholarly journals Water quality assessment and geochemical processes in the unconfined coastal Boa Viagem Aquifer, Recife, NE Brazil

2021 ◽  
pp. 56-77
Author(s):  
Thyego Silva ◽  
Mariucha Lima ◽  
Teresa Leitão ◽  
Tiago Martins ◽  
Mateus Albuquerque
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Water Quality Assessment ◽  
Multivariate Statistical Analyses ◽  
Multivariate Statistical ◽  
Rock Interaction ◽  
Groundwater Samples ◽  
Water Rock Interaction

A hydrochemical study was conducted on the Quaternary Aquifer, in Recife, Brazil. Groundwater samples were collected in March–April 2015, at the beginning of the rainy season. Conventional graphics, ionic ratios, saturation indices, GIS mapping, and geostatistical and multivariate statistical analyses were used to water quality assessment and to characterize the main hydrochemical processes controlling groundwater’s chemistry. Q-mode hierarchical cluster analysis separated the samples into three clusters and five sub-clusters according to their hydrochemical similarities and facies. Principal Component Analysis (PCA) was employed to the studied groundwater samples where a three-factor model explains 80% of the total variation within the dataset. The PCA results revealed the influence of seawater intrusion, water-rock interaction, and nitrate contamination. The physico-chemical parameters of ~30% groundwaters exceed the World Health Organization (WHO) guidelines for drinking water quality. Nitrate was found at a concentration >10 mg NO3−/L in ~21% of the wells and exceeded WHO reference values in one. The integrated approach indicates the occurrence of the main major hydrogeochemical processes occurring in the shallow marine to alluvial aquifer as follow: 1) progressive freshening of remaining paleo-seawater accompanying cation exchange on fine sediments, 2) water-rock interaction (i.e., dissolution of silicates), and 3) point and diffuse wastewater contamination, and sulfate dissolution. This study successfully highlights the use of classical geochemical methods, GIS techniques, and multivariate statistical analyses (hierarchical cluster and principal component analyses) as complementary tools to understand hydrogeochemical processes and their influence on groundwater quality status to management actions, which could be used in similar alluvial coastal aquifers.

scholarly journals A multivariate statistical analysis to determine catch intention in the pink cusk-eel (Genypterus blacodes) longline fishery in southern Chile

2020 ◽  
Vol 84 (1) ◽  
pp. 17
Author(s):  
Rodrigo Wiff ◽  
Guillermo Martin Gonzalez ◽  
Francisco Contreras ◽  
Marcelo A. San Martín ◽  
T. Mariella Canales
Keyword(s):  
Stock Assessment ◽  
Principal Component ◽  
Sensitivity Analyses ◽  
Commercial Fishing ◽  
Southern Chile ◽  
Multivariate Statistical ◽  
By Catch ◽  
Catch Rates ◽  
Catch Composition ◽  
Over Time

The definition of catch intention in multispecies fisheries is a key step toward building abundance indexes based on commercial fishing data. Previous analysis to determine catch intention in the pink cusk-eel (Genypterus blacodes) has been based on the idea that fishing tactics remain constant over time (static fishing tactics). We propose a statistical procedure to determine the catch intention of each haul in the industrial longline fisheries in southern Chile, where fishing tactics may vary over time. This procedure is based on principal component analysis and agglomerative hierarchical analysis of the catch composition, and relaxes the assumption of static fishing tactics by selecting a subset of data that is informative for fishing intention (target versus by-catch) every year. Sensitivity analyses were conducted to assess the robustness of variable fishing tactics on the nominal catch rates in pink cusk-eel. Targeted and by-catch time series of nominal catch rates showed a different trend, so determining the catch intention became relevant. Sensitivity analyses showed that trends in targeted catch rates are robust to the variations of fishing tactic per year. We recommend the use of variable fishing tactics for further use in effort standardization and stock assessment of the pink cusk-eel fishery in southern Chile.

scholarly journals Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data

2020 ◽  
Author(s):  
Torfinn S. Madssen ◽  
Guro F. Giskeødegård ◽  
Age K. Smilde ◽  
Johan A. Westerhuis
Keyword(s):  
Clinical Trials ◽  
Repeated Measures ◽  
Intervention Studies ◽  
Linear Models ◽  
Principal Component ◽  
Component Analysis ◽  
Outcome Data ◽  
Biological Data ◽  
Changes Over Time ◽  
Over Time

AbstractLongitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of “omics”-sciences (genomics, transcriptomics, proteomics, metabolomics), longitudinal studies generate increasingly multivariate outcome data. Analysis of such data must take both the longitudinal intervention structure and multivariate nature of the data into account. The ASCA+-framework combines general linear models with principal component analysis, and can be used to separate and visualize the multivariate effect of different experimental factors. However, this methodology has not yet been developed for the more complex designs often found in longitudinal intervention studies, which may be unbalanced, involve randomized interventions, and have substantial missing data. Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed. This approach can facilitate easier interpretation of data from longitudinal clinical trials with multivariate outcomes.Author summaryClinical trials are increasingly generating large amounts of complex biological data. Examples can include measuring metabolism or gene expression in tissue or blood sampled repeatedly over the course of a treatment. In such cases, one might wish to compare changes in not one, but hundreds, or thousands of variables simultaneously. In order to effectively analyze such data, both the study design and the multivariate nature of the data should be considered during data analysis. ANOVA simultaneous component analysis+ (ASCA+) is a statistical method which combines general linear models with principal component analysis, and provides a way to separate and visualize the effects of different factors on complex biological data. In this work, we describe how repeated measures linear mixed models, a class of models commonly used when analyzing changes over time and treatment effects in longitudinal studies, can be used together with ASCA+ for analyzing clinical trials in a novel method called repeated measures-ASCA+ (RM-ASCA+).

scholarly journals Skull morphometrics of adult male Antartic fur seal, Arctocephalus gazella, and South American fur seal A. australis

2005 ◽  
Vol 95 (3) ◽  
pp. 261-267 ◽  
Author(s):  
Gustavo A. Daneri ◽  
César M. García Esponda ◽  
Luciano J. M. De Santis ◽  
Laura Pla
Keyword(s):  
Adult Male ◽  
Multivariate Analyses ◽  
Principal Component ◽  
Distinct Species ◽  
South American ◽  
Arctocephalus Gazella ◽  
Multivariate Statistical Analyses ◽  
Multivariate Statistical ◽  
Antarctic Fur Seal ◽  
Fur Seal

The skull morphometrics of adult male Antarctic fur seal, Arctocephalus gazella (Peters, 1875) and South American fur seal, A. australis (Zimmermann, 1783) were investigated using a collection of 45 and 38 skulls, respectively. Eighteen measurements were taken for each specimen. Comparative univariate and multivariate statistical analyses included standard statistics, one-way analysis of variance, principal component analysis and discriminant analysis. Individual variation was relatively high for some variables, as expressed by the coefficient of variation. Skulls of A. gazella were larger than those of A. australis for all but two variables: squamosal jugal suture and rostral length. Both species differed significantly as shown by both univariate and multivariate analyses. The discriminant function correctly classified all specimens. The standardized canonical coefficients showed that the variables which most contribute to the differentiation between species were, in decreasing order, the rostral length, palatal length, palatal width at postcanine 5 and braincase width. The present study corroborates that A. gazella and A. australis are phenotipically distinct species.

scholarly journals Switching principal component analysis for modeling means and covariance changes over time.

2014 ◽  
Vol 19 (1) ◽  
pp. 113-132 ◽  
Author(s):  
Kim De Roover ◽  
Marieke E. Timmerman ◽  
Ilse Van Diest ◽  
Patrick Onghena ◽  
Eva Ceulemans
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Changes Over Time ◽  
Over Time

An Ambient-temperature Collection and Stabilization Device Performs Comparably to Flash-frozen Collection for Stool Metabolomics in Infants

2020 ◽  
Author(s):  
Sivapriya Ramamoorthy ◽  
Shira Levy ◽  
Masouma Mohamed ◽  
Alaa Abdelghani ◽  
Anne M Evans ◽  
...  
Keyword(s):  
Ambient Temperature ◽  
Principal Component ◽  
Strongly Correlated ◽  
Metabolomic Analysis ◽  
Time Points ◽  
Liquid Chromatography Tandem Mass ◽  
Stool Samples ◽  
Changes Over Time ◽  
And Storage ◽  
Over Time

Abstract Background: Stool metabolites provide essential insights into the function of the gut microbiome. The current gold standard for collection and storage of stool samples for metabolomics is flash-freezing at -80°C which can be inconvenient and expensive. Ambient temperature collection of stool is more practical, however no available methodologies adequately preserve the metabolomic profile of stool. A novel sampling kit (OMNImet.GUT; DNA Genotek, Inc.) was introduced for ambient temperature collection and stabilization of feces for metabolomics; we aimed to test the performance of this kit vs. flash-freezing.Methods: Stool collected from an infant’s diaper was divided into two aliquots: 1) flash-frozen and 2) stored in an OMNImet.GUT tube at ambient temperature for 3-4 days. Samples from the same infant were collected at 2 different time points to assess metabolite changes over time. Subsequently, all samples underwent metabolomic analysis by liquid chromatography – tandem mass spectrometry (LC-MS/MS). Results: Paired fecal samples (flash-frozen and ambient temperature) from 16 infants were collected at 2 time points (n= 64 samples). Similar numbers of metabolites were detected in both the frozen and ambient temperature samples (1126 in frozen, 1107 in ambient temperature, 1064 shared between sample types). Metabolite abundances were strongly correlated between collection methods (median Spearman correlation Rs=0.785 across metabolites). Hierarchical clustering analysis and principal component analysis showed that samples from the same individuals at a given time point clustered closely, regardless of the collection method. Repeat samples from the same individual were compared by paired t-test, separately for the frozen and OMNImet.GUT. The number of metabolites in each biochemical class that significantly changed (p<0.05) at timepoint 2 relative to timepoint 1 was similar in flash-frozen versus ambient temperature collection. Changes in microbiota modified metabolites over time were also consistent across both methodologies. Conclusion: Ambient temperature collection and stabilization of stool in the OMNImet.GUT device yielded comparable metabolomic results to flash freezing in terms of 1) the identity and abundance of detected biochemicals 2) the distinct metabolomic profiles of subjects and 3) the biochemical signature of microbiome development over time. This method potentially provides a more convenient, less expensive home collection option for stool metabolomic analysis.

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