Multivariate Procedures Research Articles

Determination of landslide susceptibility in linear infrastructure. Case: aqueduct network in Palacé, Popayan (Colombia)

This research aimed to predict the occurrence of mass movements in the aqueduct network of Palacé, in the municipality of Popayan (Colombia). We evaluated the quality of SRTM and ASTER digital terrain models by comparing them with contour lines using a map scale of 1: 25000. The landscape parameters derived from the SRTM-DEM were analyzed with a multivariate procedure using algorithms implemented in free software, along with thematic information of the study area (coverage, distance to faults, rivers and precipitation). We selected non-redundant variables with the non-parametric ACP technique, and obtained a susceptibility prediction model using logistic regression, with two types of variables: dependent (landslides inventory from field observation) and independent (slope, slope length factor, topographic wetness index, flow path length, soil units and rate of convergence) resulting in a susceptibility map, reclassified into categories according to the values of probability. The prediction model could not be quantitatively assessed because of the absence of studies with a semi-detailed scale, but the estimation of the mean square error of elevation, from which the terrain parameters were derived, the level of detail and the performance of the classifier with ROC curve, yielded a zoning consistent with the findings of the field visits.

Chemometric assisted sonochemical dyes adsorption in ternary solutions onto Cu nanowires loaded on activated carbon

Abstract The aim of this research is the evaluation of ultrasonic assisted adsorption of basic dyes in ternary mixtures onto Cu nanowires loaded on activated carbon (Cu-NWs-AC) via the normal and derivative spectrophotometric methods. These are considered as simple, inexpensive, rapid, and accurate methods for the dyes determination in multi-components mixtures. The shape, size, and specific surface area of Cu-NWs-AC was characterized by FESEM, XRD, and BET techniques. The main parameters affecting the simultaneous dyes removal were studied, and subsequently optimized through central composite design (CCD), one of the most widely used multivariate procedures. Because of high R 2 values and being close to adj- R 2 values, the predicted and experimental responses were in the reasonable agreement with each other. In optimal conditions consisted of 20 mg of adsorbent dosage, 4 min of sonication time, pH 6, 26 mg/L of BB41dye, 18 mg/L of BR46 dye, and 15 mg/L of BY13 dye; high removal percentages (higher than 95%) with logical desirability of 0.95 were obtained. Among the conventional isotherms and kinetic models, extended Freundlich isotherm and pseudo-second-order model showed reasonable fit to the adsorption equilibrium data, respectively.

Diagnostic Accuracy of Multivariate Universal Screening Procedures for Reading in Upper Elementary Grades

We examined the diagnostic accuracy and efficiency of three approaches to universal screening for reading difficulties using retrospective data from 1,307 students in Grades 3 through 5. School staff collected screening data using the Measures of Academic Progress (MAP), a curriculum-based measure (CBM), and running records (RR). The criterion measure was a high-stakes state accountability test aligned with the Common Core State Standards. We examined the diagnostic accuracy of the tests in isolation, as multivariate batteries, and via a simulated gated-screening approach. CBM and RR data resulted in unacceptable diagnostic accuracy across all three grades. In the fourth grade, the MAP alone resulted in the best balance of sensitivity and specificity. Among third- and fifth-grade students, the multivariate combination of MAP and CBM demonstrated the best balance between diagnostic accuracy and efficiency. Gated-screening increased specificity but lowered sensitivity. Results highlight the need for population-specific considerations in universal screening.

Predictors of Templestay Satisfaction: A Comparison Between Korean and International Participants

ABSTRACTThe present study aims to determine the underlying factors critical to the overall satisfaction (Templestay satisfaction) of local and foreign participants in South Korean Buddhist temple stay programs (hereafter, Templestay). A two-step multivariate procedure was performed where the data were first subjected to a factor analysis followed by structural equation model. The findings are congruent with three out of five research hypotheses. Moreover, the empirical evidence showed that there are no differences in reporting of Templestay satisfaction between Korean and international participants. Nevertheless, being with nature, self-growth, and relaxation are underlying factors that define satisfaction for Koreans. For international participants, being with nature and relaxation are significant. The study reveals that learning experience is a poor predictor of Templestay satisfaction. The practical implication of this research is that Templestay managers should focus on the factors that have the highest importance for obtaining Templestay satisfaction for both Korean and international visitors.

GC‐MS Analysis of Various Phytoestrogens in Health Functional Foods

This study aimed to measure phytoestrogen contents in health functional foods (HFFs) to alleviate menopausal symptoms that are commercially available in South Korea. A sensitive and selective method using gas chromatography–mass spectrometry (GC‐MS) for the monitoring of 21 phytoestrogens was developed and fully validated. Phytoestrogens were extracted by liquid–liquid extraction using methyl tert‐butylether as the solvent. Food samples were enzymatically hydrolyzed with β‐glucuronidase/arylsulfatase. Before GC‐MS, the analytes were derivatized to their trimethylsilyl derivatives by incubation with N,O‐bis(trimethylsilyl)trifluoroacetamide with 1% trimethylchlorosilane for 30 min at 80°C. The calibration curves showed good linearity (r 2 > 0.99) in the range of interest for each analyte. The limits of detection and quantification of the 21 phytoestrogens were in the ranges of 0.5–20 and 1–50 ng/mL, respectively. The intra‐ and inter‐day precisions were all below 15.7%. These multivariate procedures were efficient in determining the optimal conditions using peak heights as responses.

Application of cluster analysis for spatial classification

The present study was undertaken in South Konkan region of Maharashtra state. Cluster analysis is a multivariate statistical procedure that starts with data set containing information about a sample of entities into attempts to reorganize these entities into relatively homogeneous groups or groups of highly similar entities, called as The variables were based on the set of 23 agroclimatic characteristics. The analysis has classified in to all 17 Tahsils of South Konkan region into two distinct clusters. It was concluded from the study that cluster analysis technique is useful in spatial classification, which can be further used for making development policy in general agriculture in particular for deciding the region specific cropping as well fanning systems.

Environmental and spatial drivers of spider diversity at contrasting microhabitats

AbstractThe relative importance of environmental and spatial drivers of animal diversity varies across scales, but identifying these scales can be difficult if a sampling design does not match the scale of the target organisms' interaction with their habitat. In this study, we quantify and compare the effects of environmental variation and spatial proximity on ground‐dwelling spider assemblages sampled from three distinct microhabitat types (open grassland, logs, trees) that recur across structurally heterogeneous grassy woodlands. We used model selection and multivariate procedures to compare the effects of different environmental attributes and spatial proximity on spider assemblages at each microhabitat type. We found that species richness and assemblage composition differed among microhabitat types. Bare ground cover had a negative effect on spider richness under trees, but a positive effect on spider richness in open grassland. Turnover in spider assemblages from open grassland was correlated with environmental distance, but not geographic distance. By contrast, turnover in spiders at logs and trees was correlated with geographic distance, but not environmental distance. Our study suggests that spider assemblages from widespread and connected open grassland habitat were more affected by environmental than spatial gradients, whereas spiders at log and tree habitats were more affected by spatial distance among these discrete but recurring microhabitats. Deliberate selection and sampling of small‐scale habitat features can provide robust information about the drivers of arthropod diversity and turnover in landscapes.

M61. Data-Driven Approach Identified Functionally and Physiologically Distinct Psychosis Subtypes

Background: Unlike diagnoses in other branches of medicine, current classification of psychiatric disorders relies on phenomenological observations, despite the increasing evidence pointing to superior characterization by physiological-based markers. Common risk genes, symptoms, and medications had been found between patients traditionally diagnosed with schizophrenia, schizoaffective, and bipolar disorder with psychosis. To address this issue, and to ultimately achieve customizable treatment plans for illnesses with psychotic features, identification of biomarkers for psychosis and their criteria is essential. Methods: Part of Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP), the present study was conducted in continuation of previous work that identified psychosis Biotypes (Clementz et al., 2016), and it included 709 probands with psychotic features and 342 control subjects. In addition to the biomarker panel used in Clementz et al. (2016), this iteration included additional biomarkers capturing variance in ocular motor performance, inhibition, and cognitive control. Similar multivariate analysis procedures were used. Birchwood Social Functioning Scale and structural MRI characteristics from Freesurfer analyses were used as validators of cluster membership. Results: On average, probands showed lower cognitive performance, lower accuracy to motor tasks, and lower EEG power to auditory stimuli than controls. Interestingly, these anomalies were not ubiquitous among the probands. From within-task principle component analysis (PCA) and k-means clustering, 4 (rather than 3 as previously reported by Clementz et al.) phenotypically distinct proband groups emerged: Group 4 had hyperactive motor response and low cognitive control; Group 3 showed normal cognition and motor control but abnormally low EEG amplitude in response to auditory stimuli; Group 2 was slow in motor tasks combined with poor cognitive control, and Group 1 performed more similarly to controls across tasks. Social functioning scale confirmed with these findings: a significant difference in total score was found between groups, with Group 1 scoring similarly to control subjects, higher than the other 3 (F(3, 560) = 3.882, P = .009). Various cortical regions involved in motor and executive functions, including middle temporal and superior frontal areas, showed drastic differences in volume and thickness across groups (all Ps < .001). Conclusion: Four clusters within probands showing non-unified structural differences, increments in cognitive deficits, and low reactivity to auditory inputs in only one group suggested a high degree of heterogeneity among psychotic populations that does not necessarily conform to traditional diagnoses. Investigation to psychosis phenotypes at network and circuit level will help further discern disease subtypes and refine diagnoses and prescription guidelines.

Numerical analysis of the influence of the impregnation ratio on the microporous structure formation of activated carbons, prepared by chemical activation of waste biomass with phosphoric(V) acid

This paper presents the results of the application of new mathematical adsorption models with the unique numerical fast multivariate identification procedure as the tool for analysing the influence of the impregnation ratio i.e. activator to the raw material mass ratio on the microporous structure development of the activated carbons, obtained from sugarcane bagasse and rice husk by chemical activation with phosphoric(V) acid.

An analysis of the effect of the additional activation process on the formation of the porous structure and pore size distribution of the commercial activated carbon WG-12

The paper presents the results of research into the effects of the additional activation process of the commercial activated carbon WG-12 with KOH, ZnCl2, KOH/ZnCl2 and K2CO3 as activating agents on the formation of the porous structure and the adsorptive properties of that material. The numerical analyses were carried out on the basis of the isotherms of nitrogen adsorption with the use of the method based on the Brunauer-Emmett-Teller, the Dubinin-Radushkevich equations, the non-local and the quenched solid density functional theories as well as the LBET method with the unique fast multivariate procedure of porous structure identification and the new LBET class adsorption models. Also, the research in question yielded information regarding the usefulness of the said methods of carbonaceous adsorbent porous structure description for practical technological applications and scientific research, as well as the possibilities to make practical use of the research results.

Genetic diversity among 16 genotypes of Coffea arabica in the Brazilian cerrado.

For the selection of coffee plants that have favorable characteristics, it is necessary to evaluate variables related to production. Knowledge of the genetic divergence of arabica coffee is of extreme importance, as this knowledge can be associated with plant breeding programs in order to combine genetic divergence with good productive performance. The objective of this study was to evaluate the genetic divergence among 16 genotypes of Coffea arabica with the purpose of identifying the most dissimilar genotypes for the establishment of breeding programs and adaptation to the Brazilian cerrado. The genetic divergence was evaluated using multivariate procedures, the analysis of the average grouping unweighted pair group method with arithmetic mean (UPGMA) and main components in 2013 and 2014. Eight characters were evaluated in an experiment conducted in Morrinhos, Goiás. The presence of genetic divergence among the 16 C. arabica genotypes under cerrado conditions was recorded. The formation of UPGMA groups for the evaluated characteristics was pertinent due to the number of genotypes. The first three major components accounted for 81.77% of the total variance. The genotype H-419-3-4-4-13(C-241) of low size was the most divergent, followed by Catucaí 2 SL and Catiguá MG2, according to the main components.

Eficiência do nitrogênio e divergência genética em milho visando à produção de proteína

The genetic diversity of maize populations, coupled with the efficiency and use of nutrients, allows the identification of populations that may be used as parents in future breeding programs. Thus, were realized four trials of maize populations in the year 2010, be two installate in inter-crop period and two crops period, in Center Agrotechnological - University Federal of Tocantins, in Palmas city of Tocantins State. At each period, populations were conducted under high and low N (150 kg ha-1 N and 0 kg ha-1 N) respectively in the coverage. The experimental design used in each trial was randomized blocks with three replications and 10 treatments. We studied the characteristic percentage of protein and determined the EUN by populations, using the methodology from Fischer. The genetic diversity was performed by multivariate procedures: Mahalanobis distance and grouping method of Tocher in the study of genetic diversity, each trial representing a distinct variable in the multivariate model. The lower content of protein were obtained under low N the crop period. The Populations POP 3 and POP 13 were efficiency and promising for protein production.

Investigating the effects of community-based interventions on mathematics achievement of girls from low-income households in Kenya

AbstractThis paper uses difference-in-difference and multivariate analyses procedures to examine the effects of two community-based intervention packages on mathematics achievement of primary school girls from low-income urban households in Kenya. The data involved in this study were collected between 2013 and 2015 from 748 12–19 years old primary school girls residing in two major Nairobi slums of Viwandani and Korogocho. These data were part of a larger intervention study that sought to improve schooling outcomes among girls from disadvantaged home backgrounds by providing them with after-school homework support, life skill mentoring and parental counselling. Results reveal intervention effects of between 23 and 26 mathematics score points on a Rasch scale with a mean of 400 a standard deviation of 100. These effects translate to at least one school term of learning for girls in the comparison group. Effect size for one of the intervention packages was small (0.24), while that of the second package was ...

A multivariate analytical method to characterize sediment attributes from high-frequency acoustic backscatter and ground-truthing data (Jade Bay, German North Sea coast)

One of the burning issues on the topic of acoustic seabed classification is the lack of solid, repeatable, statistical procedures that can support the verification of acoustic variability in relation to seabed properties. Acoustic sediment classification schemes often lead to biased and subjective interpretation, as they ultimately aim at an oversimplified categorization of the seabed based on conventionally defined sediment types. However, grain size variability alone cannot be accounted for acoustic diversity, which will be ultimately affected by multiple physical processes, scale of heterogeneity, instrument settings, data quality, image processing and segmentation performances. Understanding and assessing the weight of all of these factors on backscatter is a difficult task, due to the spatially limited and fragmentary knowledge of the seabed from of direct observations (e.g. grab samples, cores, videos). In particular, large-scale mapping requires an enormous availability of ground-truthing data that is often obtained from heterogeneous and multidisciplinary sources, resulting into a further chance of misclassification. Independently from all of these limitations, acoustic segments still contain signals for seabed changes that, if appropriate procedures are established, can be translated into meaningful knowledge. In this study we design a simple, repeatable method, based on multivariate procedures, with the scope to classify a 100km2, high-frequency (450kHz) sidescan sonar mosaic acquired in the year 2012 in the shallow upper-mesotidal inlet of the Jade Bay (German North Sea coast). The tool used for the automated classification of the backscatter mosaic is the QTC SWATHVIEWTMsoftware. The ground-truthing database included grab sample data from multiple sources (2009–2011). The method was designed to extrapolate quantitative descriptors for acoustic backscatter and model their spatial changes in relation to grain size distribution and morphology. The modelled relationships were used to: 1) asses the automated segmentation performance, 2) obtain a ranking of most discriminant seabed attributes responsible for acoustic diversity, 3) select the best-fit ground-truthing information to characterize each acoustic class. Using a supervised Linear Discriminant Analysis (LDA), relationships between seabed parameters and acoustic classes discrimination were modelled, and acoustic classes for each data point were predicted. The model predicted a success rate of 63.5%. An unsupervised LDA was used to model relationships between acoustic variables and clustered seabed categories with the scope of identifying misrepresentative ground-truthing data points. The model prediction scored a success rate of 50.8%. Misclassified data points were disregarded for final classification. Analyses led to clearer, more accurate appreciation of relationship patterns and improved understanding of site-specific processes affecting the acoustic signal. Value to the qualitative classification output was added by comparing the latter with a more recent set of acoustic and ground-truthing information (2014). Classification resulted in the first acoustic sediment map ever produced in the area and offered valuable knowledge for detailed sediment variability. The method proved to be a simple, repeatable strategy that may be applied to similar work and environments.

Effects of female bovine plasma collected at different days of the estrous cycle on epididymal spermatozoa motility

The aim of this study was to assess the effects of female bovine plasma collected at different days of the reproductive cycle on epididymal spermatozoa motility and to test hypothesis that the subpopulations pattern of motile spermatozoa is affected by this treatment. Blood plasma samples were collected from five Holstein Friesian cows at different stages of the estrous cycle (days 0, 5, 10, 12 and 18), one pregnant cow and one adult bull and were diluted 1:9 (V/V) with normal saline. Female charcoal-treated plasma, Bull plasma and saline were used as controls. Semen samples were obtained from cauda epididymidis through retrograde flushing and diluted in saline to approximately 60 × 106 sperm/ml. The extended semen was diluted 1:2 (V/V) with tested media and motility was evaluated at 15 min and then every hour for 6 h using a computer-assisted semen analysis. Multivariate clustering procedure was applied to identify and quantify specific subpopulations within the semen samples. The statistical analysis clustered all the motile spermatozoa into three separate subpopulations with defined patterns of movement: Subpopulation 1 poorly motile and non-progressive spermatozoa (39.3%), subpopulation 2 including the fastest and the most vigorous spermatozoa (46.4%) and subpopulation 3 represented by slow, non-vigorous but linear spermatozoa (14.3%). Initially, sperm samples supplemented with female, male or female charcoal-treated plasma stimulated equally total motility and spermatozoa belonging to subpopulation 2 regardless of the estrous cycle stage. After 1-h incubation, the motility of these both categories of spermatozoa (total motile and those assigned to subpopulation 2) is enhanced and maintained more in day 12, 18 and pregnant cow plasma than in female plasma from earlier stage of the estrous cycle (day 0, 5 and 10), male plasma and female-charcoal treated plasma. In conclusion, the overall results showed that female plasma stimulated significantly sperm motility, especially at the late stage of the estrous cycle. Additionally, to the diverse compounds contained in blood plasma, progesterone may play a key role in such motility activation.

Social Inequalities and Rehabilitation Among Patients with Mental Illness

Objectives: The concept of social position ("soziale Lagen") includes horizontal (gender, age) and vertical (occupation, education) indicators of social inequalities. For the first time, this concept is used to identify health and health care inequalities among rehabilitation patients with mental illness. Methods: Analyses are based on a survey of 327 patients with mental illness in rehabilitation clinics in northern Germany. Social inequalities are analyzed according to psychosocial health at baseline, utilization of therapies during the stay and psychosocial health 4 month after discharge by applying bi- and multivariate statistical procedures. Results: Vertical inequalities were found in psychosocial health at baseline and 4 months later. In contrast, no inequalities were found in the utilization of therapies during the stay. Conclusion: Health inequalities can be identified by vertical indicators, whereas horizontal indicators are not significantly associated with health outcomes in this study.

AMMI VERSUS NONPARAMETRIC ANALYSIS FOR INVESTIGATION OF GE INTERACTION OF PLANT DISEASE EVALUATION

In breeding for plant disease resistance programs, a large number of new improvedgenotypes are tested over a range of test pathogens or environments and theunderlying statistics used to model this system may be rather complicated. Due toordinal nature of most measured traits of disease responses, some nonparametricmethods used for analyzing genotype × environment (GE) interaction in twodatasets for disease severity of gray leaf spot of maize (with ten genotypes plantedin 10 and 11 environments). Usually, the presence of the GE interaction effectcomplicates the selection of the most favorable genotypes and there are severalstatistical procedures available to analyze these dataset including a range ofunivariate, nonparametric and multivariate procedures. Present analysis separatednonparametric methods based on dynamic concept from those which are based onthe static type indicated that RS statistic following to S6, NP2, NP3 and RSstatistics were found to be useful in detecting the non-complicated phenotypicstability in disease severity dataset. In complicated GE interaction, the ability ofAMMI stability parameters especially SPC1, SPCF, D1, DF, EV1, EVF and ASVstatistics were high in the detection of stability in complicated GE interaction. Ingeneral, nonparametric methods are useful alternatives to parametric methods andallow drawing valid conclusions with considerably better chances of detecting theGE interaction in experiments of plant pathology. Also, in some cases the GEinteraction structure is too complex to be summarized by only one parameter andso, it is essential to use multivariate statistical methods like AMMI.

Poor sleep quality, antepartum depression and suicidal ideation among pregnant women

ObjectiveTo evaluate the independent and combined associations of maternal self-reported poor sleep quality and antepartum depression with suicidal ideation during the third trimester MethodsA cross-sectional study was conducted among 1298 pregnant women (between 24 and 28 gestational weeks) attending prenatal clinics in Lima, Peru. Antepartum depression and suicidal ideation were assessed using the Patient Health Questionnaire-9 (PHQ-9). The Pittsburgh Sleep Quality Index (PSQI) questionnaire was used to assess sleep quality. Multivariate logistical regression procedures were used to estimate odds ratios (OR) and 95% confidence intervals (95% CI) after adjusting for putative confounders. ResultsApproximately, 17% of women were classified as having poor sleep quality (defined using the recommended criteria of PSQI global score of >5 vs. ≤5). Further, the prevalence of antepartum depression and suicidal ideation were 10.3% and 8.5%, respectively in this cohort. After adjusting for confounders including depression, poor sleep quality was associated with a 2.81-fold increased odds of suicidal ideation (OR=2.81; 95% CI 1.78–4.45). When assessed as a continuous variable, each 1-unit increase in the global PSQI score resulted in a 28% increase in odds for suicidal ideation, even after adjusting for depression (OR=1.28; 95% CI 1.15–1.41). The odds of suicidal ideation was particularly high among depressed women with poor sleep quality (OR=13.56 95% CI 7.53–24.41) as compared with women without either risk factor. LimitationsThis cross-sectional study utilized self-reported data. Causality cannot be inferred, and results may not be fully generalizable. ConclusionPoor sleep quality, even after adjusting for depression, is associated with antepartum suicidal ideation. Our findings support the need to explore sleep-focused interventions for pregnant women.

Integration of multivariate empirical mode decomposition and independent component analysis for fetal ECG separation from abdominal signals.

The fetal electrocardiogram (FECG) signals are essential to monitor the health condition of the baby. Fetal heart rate (FHR) is commonly used for diagnosing certain abnormalities in the formation of the heart. Usually, non-invasive abdominal electrocardiogram (AbECG) signals are obtained by placing surface electrodes in the abdomen region of the pregnant woman. AbECG signals are often not suitable for the direct analysis of fetal heart activity. Moreover, the strength and magnitude of the FECG signals are low compared to the maternal electrocardiogram (MECG) signals. The MECG signals are often superimposed with the FECG signals that make the monitoring of FECG signals a difficult task. Primary goal of the paper is to separate the fetal electrocardiogram (FECG) signals from the unwanted maternal electrocardiogram (MECG) signals. A multivariate signal processing procedure is proposed here that combines the Multivariate Empirical Mode Decomposition (MEMD) and Independent Component Analysis (ICA). The proposed method is evaluated with clinical abdominal signals taken from three pregnant women (N= 3) recorded during the 38-41 weeks of the gestation period. The number of fetal R-wave detected (NEFQRS), the number of unwanted maternal peaks (NMQRS), the number of undetected fetal R-wave (NUFQRS) and the FHR detection accuracy quantifies the performance of our method. Clinical investigation with three test subjects shows an overall detection accuracy of 92.8%. Comparative analysis with benchmark signal processing method such as ICA suggests the noteworthy performance of our method.

Alternative approach to species identification of Actaea racemosa L. (syn. Cimicifuga racemosa (L.) Nutt., black cohosh) herbal starting material: UV spectroscopy coupled with LDA

The North American Actaea racemosa L. (syn. Cimicifuga racemosa (L.) Nutt., commonly known as black cohosh), considered to be a more “natural” alternative to conventional therapies, is used to relieve menopausal symptoms. The high demand for plant material has led to problems with substitution/adulteration of raw material of wholesale origin. The authenticity of the starting material is crucial for the herbal product’s efficacy and safety, and tests on identity and substitution are integral parts of cGMP guidelines. Consequently, there is a need for economical and easy-applicable test procedures. The aim of this study is to reveal the capability of the well-established UV spectroscopy coupled with a multivariate classification procedure to serve as a tool for the identification of A. racemosa. We built a classification model applying linear discriminant analysis (LDA) to distinguish between A. racemosa and its common substitutes. The model showed a high level of accuracy predicting 100% of the samples correctly. Our results indicate that UV spectroscopy shows potential for the development of possible additional authentication methods for this herbal starting material.