Iranian and Turkish EFL instructors' Critical Openness, Reflective Skepticism, Innovative Thinking and Accountability: A comparative study

The increasing interest in Turkish educational programs has led to the increasing interest in educational migration among the younger Iranian generations. Instructors, as the executive part of educational programs, can also play a key role in satisfying students' expectations and educational goals. The aim of this study was to identify and compare the Critical Openness (CO), Reflective Skepticism (RS), innovative thinking (IT), external accountability (EA), and internal accountability (IA) for the Iranian and Turkish English as Foreign Language (EFL) instructors through an online survey with Critical Thinking Theory and the 21st century skill consideration lens. To this end, a convenient sample of Iranian (N = 286) and Turkish (N = 281) EFL instructors were invited to take part in the online survey voluntarily. The scales consisted of the Likert scales of Rosenblatt (2017) [1], Semerci (2007) [2], and Sosu (2013) [3], because the conceptual frameworks were also taken from these studies. In the analysis stage, MANOVA was conducted to compare the results of the online survey between Iranian and Turkish EFL instructors in terms of their level of CO, RS, IT, EA, and IA. The analysis of the collected data uncovered that Turkish EFL instructors got higher scores in CO, RS, EA, and IA than Iranian EFL instructors while Iranian EFL instructors received higher scores in the employment of IT. The implications of the results would suggest collaborations between educational policymakers and teacher training course designers.

New Facts on the Antimicrobial Essential Oil of Satureja kitaibelii

The objective of the present study was to assess the difference in antimicrobial activity of Satureja kitaibelii Wierzb. ex Heuff. essential oil at three growth stages. In addition, using statistical and chemometric tools, we tried to explain why the essential oil from a certain vegetation stage shows the highest antimicrobial activity. S. kitaibelii essential oils demonstrated minimum inhibitory concentration values from 160 to 10000 μg mL-1 , and minimum microbicidal concentration values from 630 to 20000 μg mL-1 . Geraniol, borneol, limonene and p-cymene are the dominant compounds of S. kitaibelii essential oil. The most abundant compound, geraniol, possesses antimicrobial activity in a range of MIC values from 40 to 5000 μg mL-1 and MMC values from 80 to 10000 μg mL-1 . The highest activity of essential oil for all tested strains of microorganisms was recorded in November. Results of statistical analysis indicate that the percentage of dominant compounds of essential oils does not affect the antibacterial activity of essential oils. Chemometric analyses leads to the conclusion that borneol, spathulenol, caryophyllene oxide and limonene can be the main contributors to the antibacterial activity of essential oil from November and that their mutual ratio is important. These results may represent a new methodological approach for future research on essential oils.

TestDimorph: An R package for analysis of interpopulation sexual dimorphism differences using summary statistics

OBJECTIVE

The degree of sexual dimorphism in certain traits between males and females differ from one sample to another. Although trait differences by sex are often reported in bioanthropological research, few studies test for statistical significance or make raw data available. TestDimorph is the first R package dedicated to testing and comparing the degree of sexual dimorphism among different samples by leveraging summary statistics.

MATERIALS AND METHODS

We provide two approaches of analysis of inter-sample differences in degree of sexual dimorphism: univariate and multivariate for two or more samples. The methods follow upon publications primarily from the AJBA. Within-sex size variability between samples is compared using one-way ANOVA followed by control for multiple pairwise comparisons. In addition, we compute the overlapping area between the density functions of two normal distributions from the mixture intersection index or the non-overlapping area using the dissimilarity index as well as Hedges' g with inferential support using the 95% confidence interval. Finally, we use a multivariate analysis of differences in patterning of sexual dimorphism between samples.

RESULTS

We demonstrate various results from applying TestDimorph functions to data supplied with the package.

DISCUSSION

The package has many features including functionality for working with summary statistics, simulating data from summary statistics, and the extraction of summary statistics from raw data, so that the entire analysis can be performed through the package.

UPLC-ESI-QTOF-MS assisted targeted metabolomics to study the enrichment of vinca alkaloids and related metabolites in Catharanthus roseus plants grown under controlled LED environment

Enrichment of pharmaceutically important vinca alkaloids, vinblastine and vincristine, in the leaves of Madagascar periwinkle (Catharanthus roseus) plants through different pre- or postharvest treatments or cultivation conditions, e.g., exposing the plants to UV-irradiation, has been in focus for decades. Controlled LED environment in the visible light range offers the possibility of monitoring the changes in the concentration of metabolites in the vinca alkaloid-related pathway without involving UV-related abiotic stress. In the frame of our targeted metabolomics approach, 64 vinca alkaloids and metabolites were screened with the help of a UPLC-ESI-QTOF-MS instrumental setup from the leaf extracts of C. roseus plants grown in chambers under control (medium light), low light, and high blue / high red/ high far-red conditions. Out of the 14 metabolites that could be assigned either unambiguously with authentic standards or tentatively with high resolution mass spectrometry-based methods, all three dimer vinca alkaloids, that is, 3',4'-anhydrovinblastine, vinblastine and vincristine showed an at least nine-fold enrichment under high blue irradiation when compared with the control conditions: final concentrations of 961 mg kg-1 dry weight, 33.8 mg kg-1 dry weight, and 11.7 mg kg-1 dry weight could be achieved, respectively. As supported by multivariate statistical analysis, the key metabolites of the vinca alkaloid pathway were highly represented among the metabolites that were specifically stimulated by high blue light application.

Can we achieve biomimetic electrospun scaffolds with gelatin alone?

Introduction: Gelatin is a natural polymer commonly used in biomedical applications in combination with other materials due to its high biocompatibility, biodegradability, and similarity to collagen, principal protein of the extracellular matrix (ECM). The aim of this study was to evaluate the suitability of gelatin as the sole material to manufacture tissue engineering scaffolds by electrospinning. Methods: Gelatin was electrospun in nine different concentrations onto a rotating collector and the resulting scaffold's mechanical properties, morphology and topography were assessed using mechanical testing, scanning electron microscopy and white light interferometry, respectively. After characterizing the scaffolds, the effects of the concentration of the solvents and crosslinking agent were statistically evaluated with multivariate analysis of variance and linear regressions. Results: Fiber diameter and inter-fiber separation increased significantly when the concentration of the solvents, acetic acid (HAc) and dimethyl sulfoxide (DMSO), increased. The roughness of the scaffolds decreased as the concentration of dimethyl sulfoxide increased. The mechanical properties were significantly affected by the DMSO concentration. Immersed crosslinked scaffolds did not degrade until day 28. The manufactured gelatin-based electrospun scaffolds presented comparable mechanical properties to many human tissues such as trabecular bone, gingiva, nasal periosteum, oesophagus and liver tissue. Discussion: This study revealed for the first time that biomimetic electrospun scaffolds with gelatin alone can be produced for a significant number of human tissues by appropriately setting up the levels of factors and their interactions. These findings also extend statistical relationships to a form that would be an excellent starting point for future research that could optimize factors and interactions using both traditional statistics and machine learning techniques to further develop specific human tissue.

Data showing that post-traumatic stress symptoms and defense mechanisms change based on perceived stress levels: A multivariate analysis of variance approach during the COVID-19 pandemic

The present research article provides data about the differences in post-traumatic stress symptoms, and defense mechanisms based on the levels of perceived stress (low, average, high), using a large national sample of 1100 Italian individuals, collected during the first wave of the COVID-19 pandemic. Participants completed an online survey through the Google Form platform, where the Ten-Item Perceived Stress Scale, Impact of Event Scale - Revised, and Forty Item Defense Style Questionnaire were included. First, the cut-offs of the perceived stress scale were calculated by determining the 25^th and 75^th percentile scores for the sample of this survey. Then, MANOVA analyses were performed, together with ANOVAs and the Bonferroni Post hoc analyses as a follow-up. The dataset (.xlsx) includes the survey scores, while the tables and figures provide the analysed data, where the differences are shown. This data article may provide useful bases for future research on perceived stress and for suggesting associated factors on which focus clinical intervention and preventive programs.

The Relationship among Anxiety, Worry, Perceived Stress, Defense Mechanisms, and High Levels of Post-Traumatic Stress Symptoms: A Discriminant Analytic Approach

Post-traumatic stress disorder (PTSD) is a pathological condition that may lead to a significant deterioration in the quality of life over time. Therefore, the study of the elements that can characterize the disorder could be considered of great clinical interest and relevance. The aim of the present research was to empirically discriminate the influence of perceived stress, state anxiety, worry, and defense mechanisms (mature, neurotic, and immature) at different levels of post-traumatic stress symptoms. A sample of 1250 participants (69.5% women, 30.5% men; M_age = 34.52, SD = 11.857) completed an online survey including the Impact of Event Scale-Revised, Ten-Item Perceived Stress Scale, Penn State Worry Questionnaire, Forty Item Defense Style Questionnaire, and State-Trait Anxiety Inventory-Form X3. Data were analysed by implementing MANOVA and discriminant analysis. Results showed significant differences in the levels of perceived stress, state anxiety, and worry, as well as neurotic and immature defenses based on the levels of post-traumatic stress symptoms: F(12,2484) = 85.682, p < 0.001; Wilk's Λ = 0.430. Furthermore, these variables discriminate significant accuracy between participants who reported a mild psychological impact and those with a probable presence of PTSD, with perceived stress, which was found to be the best predictor. Classification results indicated that the original grouped cases were classified with 86.3% overall accuracy. Such findings may provide useful insight for clinical practice.

Isotopologue pattern based data mining for selenium species from HILIC-ESI-Orbitrap-MS-derived spectra

Automated and specific picking of selenium-containing molecular entities has not been an obvious option for software tools associated with electrospray high-resolution mass spectrometry (MS). In our study, a comprehensive pattern matching approach based on intra-isotopologue distance and isotopologue ratio data was critically evaluated in terms of reproducibility and selenium isotope selection on three samples, including selenized Torula yeast and the selenium hyperaccumulator plant Cardamine violifolia. Hydrophilic interaction liquid chromatography was applied to provide a one-step separation for water soluble metabolites to put an end to the need for either orthogonal setups or poor retention on reversed phase chromatography. Assistance from inductively coupled plasma-MS was taken only for chromatographic verification purposes, and the involvement of absolute mass defect (MD) data in selenometabolite-specific screening was assessed by multivariate statistical tools. High focus was placed on screening efficiency and on the validation of discovered selenized molecules to avoid reporting of artefacts. From the >1000 molecular entries detected, selenium-containing molecules were picked up with a recovery rate of >88% and a false positive rate of <10%. Isotop(ologu)e pairs of 78Se-80Se and 80Se-82Se proved to be the most performant in the detection. On the basis of accurate mass information and hypothetical deamination processes, elemental composition could be proposed for 72 species out of the 75 selenium species encountered without taking into account selenocompound databases. Absolute MD data were used to significantly differentiate a potentially sample-specific subgroup of false positive molecular entities from non-selenized and selenized entities.

Long-Term Effects of Inspiratory Muscle Training in Institutionalized Elderly Women: A Double-Blind Randomized Controlled Trial

BACKGROUND

Inspiratory muscle training (IMT) has been applied to different populations, but evidence in elderly women and maintenance of improvements achieved during training is scarce. The objective of the study was to evaluate the effectiveness of IMT 6 months after finishing the training period in elderly women.

METHODS

A controlled, randomized, double-blind trial was conducted, with allocation concealment performed on 26 institutionalized elderly women distributed into 2 groups: high loads and low loads. Over an 8-week period, an IMT protocol was followed; later, elderly women continued for 6 months with their daily normal activities. Inspiratory strength, expiratory strength, and functional capacity were measured pre-IMT, post-IMT, and 6 months after intervention.

RESULTS

After an 8-week training, respiratory strength and functional capacity increased in both groups. Six months after finishing the intervention, these benefits were lost. Inspiratory strength decreased in the high-loads group but was 32% higher than initial values (p = 0.007); in the low-loads group, patients lost 41% (p = 0.015). Improvements were higher in the high-loads group after training (p = 0.000) and follow-up (p = 0.002). Functional capacity was similar to initial values in both groups.

CONCLUSION

The improvements achieved with IMT in elderly women disappeared 6 months after the end of training. High loads were more effective to keep inspiratory strength than low loads.

Within- and between-person changes in work practice and experiences due to COVID-19: Lessons learned from employees working from home, hybrid working, and working at the office

Introduction

In response to the requirement of keeping social distance during the COVID-19 outbreak a lot of employees needed to change from a regular office to a home-office at short notice. The aim of the present study is to explore these employees' experiences and evaluate changes in their work situation during the pandemic.

Method

A mixed-method design was used with panel data collected twice in an insurance company in Norway. The first dataset was collected in December 2020 (Time 1; N = 558), with a follow up in March 2021 (Time 2; N = 601).

Results

Our study indicated that employees' main reasons for working from home were to keep social distance, avoid contagion and protect their loved ones. Flexibility, timesaving and more time with family and friends were also motivators. Most employees reported that they had the necessary technical equipment to work from home and wanted more opportunity to use their home office in the future. General Linear Models (GLM) indicated that work-family balance and workload were the same across age, gender, and worksites. Women and employees working from home reported more fear of being infected by COVID-19 at work. Younger employees reported experiencing less social contact with colleagues than normal during the pandemic, compared to the older employees. Overall, employees working at home were more positive toward digital solutions and digital meetings than those at the office. Repeated measures MANOVA showed that the work motivation and digital competence decreased over time for all worksites. Productivity increased for home-office employees but decreased for the hybrid and work-office employees.

Discussion

This paper contributes to knowledge of employees' experiences with different worksite solutions, which will be useful for anticipating employees experience in the future with more hybrid work.

Beyond ANOVA and MANOVA for repeated measures: Advantages of generalized estimated equations and generalized linear mixed models and its use in neuroscience research

In neuroscience research, longitudinal data are often analysed using analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) for repeated measures (rmANOVA/rmMANOVA). However, these analyses have special requirements: The variances of the differences between all possible pairs of within-subject conditions (i.e., levels of the independent variable) must be equal. They are also limited to fixed repeated time intervals and are sensitive to missing data. In contrast, other models, such as the generalized estimating equations (GEE) and the generalized linear mixed models (GLMM), suggest another way to think about the data and the studied phenomenon. Instead of forcing the data into the ANOVAs assumptions, it is possible to design a flexible/personalized model according to the nature of the dependent variable. We discuss some advantages of GEE and GLMM as alternatives to rmANOVA and rmMANOVA in neuroscience research, including the possibility of using different distributions for the parameters of the dependent variable, a better approach for different time length points, and better adjustment to missing data. We illustrate these advantages by showing a comparison between rmANOVA and GEE in a real example and providing the data and a tutorial code to reproduce these analyses in R. We conclude that GEE and GLMM may provide more reliable results when compared to rmANOVA and rmMANOVA in neuroscience research, especially in small sample sizes with unbalanced longitudinal designs with or without missing data.

A comparison of the university mathematics learning environment with its high school equivalent

In light of rising college student debt, many states now offer multiple options for students to earn college credit while still in high school. Concurrent enrollment programs, which allow qualified high school teachers to teach college credit-bearing classes in the high school, are one such option. Because concurrent enrollment classes teach college-level material at college-level rigor, they offer an ideal way to compare the secondary and tertiary learning environments across identical academic expectations. This study sought to compare the university mathematics environment with its concurrent enrollment counterpart. The WIHIC was found to be valid and reliable for the university population. The comparison of 242 students in university classrooms with 260 students in concurrent enrollment classrooms revealed a statistical difference, with the concurrent enrollment setting scoring higher in Involvement, Teacher Support, and Student Cohesion and the university setting scoring higher in Task Orientation. This implies that earning college credit in a secondary setting is a viable, and possibly even preferable, alternative to earning it in a university setting. We examine the discrepancy in scores-particularly the large discrepancy in Task Orientation-and discuss the benefit of the flipped classroom as one path to improving the university learning environment.

A New Aculodes Species (Prostigmata: Eriophyidae) Described from an Invasive Weed by Morphological, Morphometric and DNA Barcode Analyses

A new species of eriophyoid mite, Aculodes marcelli sp. nov., was discovered on cheatgrass, Anisantha tectorum (L.) Nevski (syn. Bromus tectorum L.), an annual grass that is native to Eurasia and Northern Africa. This grass was introduced to North America near the end of the 19th century and now is widespread and associated with the observed increases in the size, frequency, and intensity of wildfires in western N. America. In this paper, A. marcelli sp. nov., is morphologically described and illustrated. Compared with other Aculodes spp., it differs based on morphology and the sequence of the mitochondrial cytochrome oxidase gene, subunit I (MT-CO1). Results of morphometric analysis showed clear differentiation between A. marcelli sp. nov., and the most similar congener, A. altamurgiensis from Taeniatherum caput-medusae. Analysis of MT-CO1 sequence divergence revealed significant levels of genetic variation (17.7%) and supported the results from the morphometric analysis; therefore, it is determined that they are two different species. Aculodes marcelli sp. nov., is a new candidate agent for classical biological control of A. tectorum.

The multivariate analysis of variance as a powerful approach for circular data

BACKGROUND

A broad range of scientific studies involve taking measurements on a circular, rather than linear, scale (often variables related to times or orientations). For linear measures there is a well-established statistical toolkit based on linear modelling to explore the associations between this focal variable and potentially several explanatory factors and covariates. In contrast, statistical testing of circular data is much simpler, often involving either testing whether variation in the focal measurements departs from circular uniformity, or whether a single explanatory factor with two levels is supported.

METHODS

We use simulations and example data sets to investigate the usefulness of a MANOVA approach for circular data in comparison to commonly used statistical tests.

RESULTS

Here we demonstrate that a MANOVA approach based on the sines and cosines of the circular data is as powerful as the most-commonly used tests when testing deviation from a uniform distribution, while additionally offering extension to multi-factorial modelling that these conventional circular statistical tests do not.

CONCLUSIONS

The herein presented MANOVA approach offers a substantial broadening of the scientific questions that can be addressed statistically using circular data.

Forensic examination of textile fibres using Raman imaging and multivariate analysis

Vibrational spectroscopic techniques have shown to be highly suitable for the identification and comparison of textile fibres and clothing fabrics. On the other hand, new chemical imaging modes based on these spectroscopic techniques are becoming useful in multiple fields. This is particularly important to, for instance, chemically visualize and screen different samples including forensic evidence (crime scene investigation), chemical and food products (quality control), biological tissues and living beings (medical imaging), among others. This study explores the forensic examination and selective chemical visualization of textile fibres and clothing fabrics using Raman imaging. Four experiments were performed, which were focused on the screening of (i) white different materials made of 100 % cotton (gauze, cotton wool, t-shirt, and swab), (ii) polyester and cotton fabrics evidence of the same colour, (iii) five different coloured cotton fabrics, and (iv) textile fibres of different materials (acrylic, cotton, nylon, polyester, and silk). Several methods of multivariate chemometric analysis including principal component analysis (PCA), multivariate analysis of variance (MANOVA), and multivariate curve resolution (MCR) were applied to enhance the limited visual comparison of the spectra accomplished with the unaided eye. The results evidenced the suitability of Raman imaging to statistically discriminate textile fibres and fabrics due to the chemical composition of both the clothing material and the dyestuff.

Uncovering the shared lipidomic markers of subclinical osteoporosis-atherosclerosis comorbidity: The Young Finns Study

BACKGROUND

Osteoporosis and atherosclerosis are complex multifactorial diseases sharing common risk factors and pathophysiological mechanisms suggesting that these are comorbidities. Omics studies identifying joint molecular markers associated with these diseases are sparse.

SUBJECTS AND METHODS

Using liquid chromatography-tandem mass spectrometry, we quantified 437 molecular lipid species from the Young Finns Study cohort (aged 30-45 years and 57% women) and performed lipidome-wide multivariate analysis of variance (MANOVA) with early markers for both diseases. Carotid intima-media thickness for atherosclerosis measured with ultrasound and bone mineral density from distal radius and tibia for osteoporosis measured with peripheral quantitative computed tomography were used as early markers of the diseases.

RESULTS

MANOVA adjusted with age, sex and body mass index, identified eight statistically significant (adjusted p-value (p_adj) < 0.05) and 15 suggestively significant (p_adj < 0.25) molecular lipid species associated with the studied markers. Similar analysis adjusted additionally for smoking habit, physical activity and alcohol consumption identified four significant and six suggestively significant molecular lipid species. These most significant lipid classes/species jointly associated with the studied markers were glycerolipid/TAG(18:0/18:0/18:1), glycerophospholipid/PC(40:3), sphingolipid/Gb3(d18:1/22:0), and sphingolipid/Gb3(d18:1/24:0).

CONCLUSION

Our results support the osteoporosis-atherosclerosis comorbidity hypothesis and present potential new joint lipid biomarkers for these diseases.

Content and distribution of major and trace elements as a tool to assess the genotypes, harvesting time, and cultivation systems of potato

The effects of organic versus integral and conventional crop management on content and distribution of major and trace elements in different varieties of potato were investigated in a three year field trial. A set of 48 potato tubers of four varieties with different harvesting times, cultivated in three types of agricultural systems: conventional (C), integral (I), and organic (O) were characterized based on the composition of the elements of their bulk and peel. A total of 16 elements were quantified. In order to determine the source of variation among the types of production, parts of potato and varieties, multivariate analysis of variance (MANOVA) was conducted. The results indicate that Ca, Mg, and K may be considered as important indicators of the type of production, genotypes of potato, and harvesting time. Additionally, the analyses show that ten microelements are able to distinguish between production types and genotypes of potato.

13C NMR-Based Chemical Fingerprint for the Varietal and Geographical Discrimination of Wines

A fast, economic, and eco-friendly methodology for the wine variety and geographical origin differentiation using ¹³C nuclear magnetic resonance (NMR) data in combination with machine learning was developed. Wine samples of different grape varieties cultivated in different regions in Greece were subjected to ¹³C NMR analysis. The relative integrals of the ¹³C spectral window were processed and extracted to build a chemical fingerprint for the characterization of each specific wine variety, and then subjected to factor analysis, multivariate analysis of variance, and k-nearest neighbors analysis. The statistical analysis results showed that the ¹³C NMR fingerprint could be used as a rapid and accurate indicator of the wine variety differentiation. An almost perfect classification rate based on training (99.8%) and holdout methods (99.9%) was obtained. Results were further tested on the basis of Cronbach's alpha reliability analysis, where a very low random error (0.30) was estimated, indicating the accuracy and strength of the aforementioned methodology for the discrimination of the wine variety. The obtained data were grouped according to the geographical origin of wine samples and further subjected to principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA and variable importance in projection (VIP) allowed the determination of a chemical fingerprint characteristic of each geographical group. The statistical analysis revealed the possibility of acquiring useful information on wines, by simply processing the ¹³C NMR raw data, without the need to determine any specific metabolomic profile. In total, the obtained fingerprint can be used for the development of rapid quality-control methodologies concerning wine.

Spatial variability in orchards after land transformation: Consequences for precision agriculture practices

The change from traditional to a more mechanized and technical agriculture has involved, in many cases, land transformations. This has supposed alteration of landforms and soils, with significant consequences. The effects of induced soil variability and the subsequent implications in site-specific crop management have not been sufficiently studied. The present work investigated the application of a resistivity soil sensor (Veris 3100), to map the apparent electrical conductivity (ECa), and detailed multispectral airborne images to analyse soil and crop spatial variability to assist in site-specific orchard management. The study was carried out in a peach orchard (Prunus persica (L.) Stokes), in an area transformed in the 1980 decade to change from rainfed arable crops to irrigated orchards. A total of 40 soil samples at two depths (0-30cm and 30-60cm) were analysed and compared to ECa and the normalised difference vegetation index (NDVI). Two types of statistical analysis were performed between ECa or NDVI classes with soil properties: a linear correlation analysis and multivariate analysis of variance (MANOVA). The results showed that the land transformation altered the spatial distribution and continuity of soil properties. Although a relationship between ECa and peach tree vigour could be expected, it was not found, even in the case of trees planted in soils with salts content above the tolerance threshold. Two types of management zones were proposed: a) zones delineated according to ECa classes to leach salts in the high ECa zones, and b) zones delineated according to NDVI classes to regulate tree vigour and yield. These strategies respond to the alteration of the original soil functions due to the land transformation carried out in previous years.

Modeling and MANOVA studies on toxicity and endocrine potential of packaging materials exposed to different extraction schemes

The stability of the linings of packaging that is in contact with the goods stored has been of major concern during decades of the development of packaging materials. In this work, an attempt was undertaken to assess the applicability of using two bioassays (Microtox^® and XenoScreen YES/YAS) in estimating the stability of packaging (cans, caps, multilayer material) and the impact of their degradation on the toxicity of some simulated media. The assessment of the impact of packaging storage conditions (temperature, disinfection, preservation, extracting and washing solvents) was planned and performed with i) regression modeling of the experimental effects on the ecotoxicity readings, ii) ANOVA and MANOVA estimation of the experimental conditions as significant factors affecting the toxicity results and iii) FTIR analysis of the packages. It is shown that the effects of temperature and extraction solvents could be quantitatively assessed by the agreement between all methods applied. It can be stated that temperature and acidity as well as the alcohol content in the sensitive media have the greatest impact on the toxicity of the extract and thus on the stability of the internal lining and the extractability of xenobiotics.

Power and Sample Size for Fixed-Effects Inference in Reversible Linear Mixed Models

Despite the popularity of the general linear mixed model for data analysis, power and sample size methods and software are not generally available for commonly used test statistics and reference distributions. Statisticians resort to simulations with homegrown and uncertified programs or rough approximations which are misaligned with the data analysis. For a wide range of designs with longitudinal and clustering features, we provide accurate power and sample size approximations for inference about fixed effects in linear models we call reversible. We show that under widely applicable conditions, the general linear mixed-model Wald test has non-central distributions equivalent to well-studied multivariate tests. In turn, exact and approximate power and sample size results for the multivariate Hotelling-Lawley test provide exact and approximate power and sample size results for the mixed-model Wald test. The calculations are easily computed with a free, open-source product that requires only a web browser to use. Commercial software can be used for a smaller range of reversible models. Simple approximations allow accounting for modest amounts of missing data. A real-world example illustrates the methods. Sample size results are presented for a multicenter study on pregnancy. The proposed study, an extension of a funded project, has clustering within clinic. Exchangeability among participants allows averaging across them to remove the clustering structure. The resulting simplified design is a single level longitudinal study. Multivariate methods for power provide an approximate sample size. All proofs and inputs for the example are in the Supplementary Materials (available online).

Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions

To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer's disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved.

Variable criteria sequential stopping rule: Validity and power with repeated measures ANOVA, multiple correlation, MANOVA and relation to Chi-square distribution

The variable criteria sequential stopping rule (vcSSR) is an efficient way to add sample size to planned ANOVA tests while holding the observed rate of Type I errors, α_o, constant. The only difference from regular null hypothesis testing is that criteria for stopping the experiment are obtained from a table based on the desired power, rate of Type I errors, and beginning sample size. The vcSSR was developed using between-subjects ANOVAs, but it should work with p values from any type of F test. In the present study, the α_o remained constant at the nominal level when using the previously published table of criteria with repeated measures designs with various numbers of treatments per subject, Type I error rates, values of ρ, and four different sample size models. New power curves allow researchers to select the optimal sample size model for a repeated measures experiment. The criteria held α_o constant either when used with a multiple correlation that varied the sample size model and the number of predictor variables, or when used with MANOVA with multiple groups and two levels of a within-subject variable at various levels of ρ. Although not recommended for use with χ² tests such as the Friedman rank ANOVA test, the vcSSR produces predictable results based on the relation between F and χ². Together, the data confirm the view that the vcSSR can be used to control Type I errors during sequential sampling with any t- or F-statistic rather than being restricted to certain ANOVA designs.

A multivariate distance-based analytic framework for microbial interdependence association test in longitudinal study

Human microbiome is the collection of microbes living in and on the various parts of our body. The microbes living on our body in nature do not live alone. They act as integrated microbial community with massive competing and cooperating and contribute to our human health in a very important way. Most current analyses focus on examining microbial differences at a single time point, which do not adequately capture the dynamic nature of the microbiome data. With the advent of high-throughput sequencing and analytical tools, we are able to probe the interdependent relationship among microbial species through longitudinal study. Here, we propose a multivariate distance-based test to evaluate the association between key phenotypic variables and microbial interdependence utilizing the repeatedly measured microbiome data. Extensive simulations were performed to evaluate the validity and efficiency of the proposed method. We also demonstrate the utility of the proposed test using a well-designed longitudinal murine experiment and a longitudinal human study. The proposed methodology has been implemented in the freely distributed open-source R package and Python code.

Random subsets of structured deterministic frames have MANOVA spectra

We draw a random subset of [Formula: see text] rows from a frame with [Formula: see text] rows (vectors) and [Formula: see text] columns (dimensions), where [Formula: see text] and [Formula: see text] are proportional to [Formula: see text] For a variety of important deterministic equiangular tight frames (ETFs) and tight non-ETFs, we consider the distribution of singular values of the [Formula: see text]-subset matrix. We observe that, for large [Formula: see text], they can be precisely described by a known probability distribution-Wachter's MANOVA (multivariate ANOVA) spectral distribution, a phenomenon that was previously known only for two types of random frames. In terms of convergence to this limit, the [Formula: see text]-subset matrix from all of these frames is shown to be empirically indistinguishable from the classical MANOVA (Jacobi) random matrix ensemble. Thus, empirically, the MANOVA ensemble offers a universal description of the spectra of randomly selected [Formula: see text] subframes, even those taken from deterministic frames. The same universality phenomena is shown to hold for notable random frames as well. This description enables exact calculations of properties of solutions for systems of linear equations based on a random choice of [Formula: see text] frame vectors of [Formula: see text] possible vectors and has a variety of implications for erasure coding, compressed sensing, and sparse recovery. When the aspect ratio [Formula: see text] is small, the MANOVA spectrum tends to the well-known Marčenko-Pastur distribution of the singular values of a Gaussian matrix, in agreement with previous work on highly redundant frames. Our results are empirical, but they are exhaustive, precise, and fully reproducible.

Protein network construction using reverse phase protein array data

In this paper, we introduce a novel computational method for constructing protein networks based on reverse phase protein array (RPPA) data to identify complex patterns in protein signaling. The method is applied to phosphoproteomic profiles of basal expression and activation/phosphorylation of 76 key signaling proteins in three breast cancer cell lines (MCF7, LCC1, and LCC9). Temporal RPPA data are acquired at 48h, 96h, and 144h after knocking down four genes in separate experiments. These genes are selected from a previous study as important determinants for breast cancer survival. Interaction networks are constructed by analyzing the expression levels of protein pairs using a multivariate analysis of variance model. A new scoring criterion is introduced to determine relevant protein pairs. Through a network topology based analysis, we search for wiring patterns to identify key proteins that are associated with significant changes in expression levels across various experimental conditions.

Moving Beyond Univariate Post-Hoc Testing in Exercise Science: A Primer on Descriptive Discriminate Analysis

There has been a recent call to improve data reporting in kinesiology journals, including the appropriate use of univariate and multivariate analysis techniques. For example, a multivariate analysis of variance (MANOVA) with univariate post hocs and a Bonferroni correction is frequently used to investigate group differences on multiple dependent variables. However, this univariate approach decreases power, increases the risk for Type 1 error, and contradicts the rationale for conducting multivariate tests in the first place.

PURPOSE

The purpose of this study was to provide a user-friendly primer on conducting descriptive discriminant analysis (DDA), which is a post-hoc strategy to MANOVA that takes into account the complex relationships among multiple dependent variables.

METHOD

A real-world example using the Statistical Package for the Social Sciences syntax and data from 1,095 middle school students on their body composition and body image are provided to explain and interpret the results from DDA.

RESULTS

While univariate post hocs increased the risk for Type 1 error to 76%, the DDA identified which dependent variables contributed to group differences and which groups were different from each other. For example, students in the very lean and Healthy Fitness Zone categories for body mass index experienced less pressure to lose weight, more satisfaction with their body, and higher physical self-concept than the Needs Improvement Zone groups. However, perceived pressure to gain weight did not contribute to group differences because it was a suppressor variable.

CONCLUSION

Researchers are encouraged to use DDA when investigating group differences on multiple correlated dependent variables to determine which variables contributed to group differences.

Cerebrovascular pattern improved by ozone autohemotherapy: an entropy-based study on multiple sclerosis patients

Ozone major autohemotherapy is effective in reducing the symptoms of multiple sclerosis (MS) patients, but its effects on brain are still not clear. In this work, we have monitored the changes in the cerebrovascular pattern of MS patients and normal subjects during major ozone autohemotherapy by using near-infrared spectroscopy (NIRS) as functional and vascular technique. NIRS signals are analyzed using a combination of time, time-frequency analysis and nonlinear analysis of intrinsic mode function signals obtained from empirical mode decomposition technique. Our results show that there is an improvement in the cerebrovascular pattern of all subjects indicated by increasing the entropy of the NIRS signals. Hence, we can conclude that the ozone therapy increases the brain metabolism and helps to recover from the lower activity levels which is predominant in MS patients.

Categorizing the severity of pain using questions from the 2012 National Health Interview Survey

Background

Based on qualitative and mixed-method approaches, Miller and Loeb have proposed a coding system that combines questions on pain persistence and bothersomeness to create discrete categories of increasing pain severity for use in large population-based surveys. In the current analyses, using data from the 2012 National Health Interview Survey, we quantitatively assess the pain category definitions proposed by Miller and Loeb and compare this original definition to ten alternative definitions.

Methods

Using multivariate analysis of variance, each definition was related simultaneously to four dependent measures – the Kessler 6 score for measuring psychological distress, the number of health-related bed-disability days, the number of visits to a health professional, and the number of emergency room visits. Following the protocol of Serlin et al, the definition yielding the largest F score was considered the optimal definition.

Results

The Miller and Loeb definition produced the largest F value (185.87), followed consecutively by several alternative definitions #5 (184.17), #10 (180.95), and #9 (179.5). A nearly identical ordering was found when looking at the mean F value generated from 100 random samples. We also examined the frequencies with which each alternative definition achieved the optimal F value over the 100 random samples. Only two definitions had achieved the optimal F value >5% of the time: the Miller and Loeb definition was optimal 46% of the time, while alternative definition #5 was optimal 41% of the time. Similar results were seen in subpopulations with back pain and joint pain.

Conclusion

Additional support was provided for the Miller and Loeb coding of pain persistence and bothersomeness to produce discrete categories of increasing pain severity. This two-question coding scheme may prove to be a viable option for assessing pain severity in clinical settings where clinician time and patient burden are limiting factors.

Effects of land use and land cover on selected soil quality indicators in the headwater area of the Blue Nile basin of Ethiopia

Understanding changes in soil quality resulting from land use and land management changes is important to design sustainable land management plans or interventions. This study evaluated the influence of land use and land cover (LULC) on key soil quality indicators (SQIs) within a small watershed (Jedeb) in the Blue Nile Basin of Ethiopia. Factor analysis based on principal component analysis (PCA) was used to determine different SQIs. Surface (0-15 cm) soil samples with four replications were collected from five main LULC types in the watershed (i.e., natural woody vegetation, plantation forest, grassland, cultivated land, and barren land) and at two elevation classes (upland and midland), and 13 soil properties were measured for each replicate. A factorial (2 × 5) multivariate analysis of variance (MANOVA) showed that LULC and altitude together significantly affected organic matter (OM) levels. However, LULC alone significantly affected bulk density and altitude alone significantly affected bulk density, soil acidity, and silt content. Afforestation of barren land with eucalypt trees can significantly increase the soil OM in the midland part but not in the upland part. Soils under grassland had a significantly higher bulk density than did soils under natural woody vegetation indicating that de-vegetation and conversion to grassland could lead to soil compaction. Thus, the historical LULC change in the Jedeb watershed has resulted in the loss of soil OM and increased soil compaction. The study shows that a land use and management system can be monitored if it degrades or maintains or improves the soil using key soil quality indicators.

Taxonomic implications from morphological and anatomical studies in the section Stenodiptera from the genus Grammosciadium (Apiaceae)

Grammosciadium pterocarpum subsp. bilgilii and Grammosciadium pterocarpum subsp. sivasicum from Turkey are herein described as two new subspecies, and the species Grammosciadium schischkinii is synonymied under Grammosciadium pterocarpum subsp. pterocarpum. Quantitative variation of morphological and anatomical characters have been analysed to provide discriminative characters between the taxa of section Stenodiptera and to provide a key to the species. The taxonomic status of the taxa has been discussed in light of these morphological and fruit anatomical data using multivariate statistics such as MANOVA and Principal Component Analysis. The results are also used to present a critical discussion of characters used to distinguish and determine different taxa within Grammosciadium. MANOVA showed that ten characters, except stylopodium and style length, differed significantly among the taxa, and the results were confirmed by Tukey tests and PCA analysis (except the character of fruit number). However, only ranges of the characters of sepal length, fruit length, fruit width, fruit width/wing width ratio, and width of fruit wing are not overlapped. Qualitative characters of petiolate stipular segments of lower leaves and presence of funicular oil ducts in transvers section of mericarps were found as diagnostic characters.

USAT: A Unified Score-Based Association Test for Multiple Phenotype-Genotype Analysis

Genome-wide association studies (GWASs) for complex diseases often collect data on multiple correlated endo-phenotypes. Multivariate analysis of these correlated phenotypes can improve the power to detect genetic variants. Multivariate analysis of variance (MANOVA) can perform such association analysis at a GWAS level, but the behavior of MANOVA under different trait models has not been carefully investigated. In this paper, we show that MANOVA is generally very powerful for detecting association but there are situations, such as when a genetic variant is associated with all the traits, where MANOVA may not have any detection power. In these situations, marginal model based methods, however, perform much better than multivariate methods. We investigate the behavior of MANOVA, both theoretically and using simulations, and derive the conditions where MANOVA loses power. Based on our findings, we propose a unified score-based test statistic USAT that can perform better than MANOVA in such situations and nearly as well as MANOVA elsewhere. Our proposed test reports an approximate asymptotic P-value for association and is computationally very efficient to implement at a GWAS level. We have studied through extensive simulations the performance of USAT, MANOVA, and other existing approaches and demonstrated the advantage of using the USAT approach to detect association between a genetic variant and multivariate phenotypes. We applied USAT to data from three correlated traits collected on 5, 816 Caucasian individuals from the Atherosclerosis Risk in Communities (ARIC, The ARIC Investigators []) Study and detected some interesting associations.

Iron binding efficiency of polyphenols: Comparison of effect of ascorbic acid and ethylenediaminetetraacetic acid on catechol and galloyl groups

Dietary polyphenols are markedly studied for their antioxidant activity. They also have a negative impact on nutrition whereby they interfere with iron absorption. Common dietary polyphenols include: catechins, flavonols, flavanols, flavones, anthocyanins, proanthocyanidins and phenolic acids. Ascorbic acid (AA) and Ethylenediaminetetraacetic acid (EDTA) are commonly used to counter act this reaction and increase iron bioavailability. This study was aimed at determining the effect of AA and EDTA on the catechol or galloyl iron binding ability of pure phenolics, coffee and tea. Phenolic concentrations of 40, 80, 610, 240, 320, 400, 520 and 900 μg/ml were tested against six levels of AA and EDTA. These effects were studied in detail using Multivariate Analysis of Variance (MANOVA) with the hypothesis that there would be one or more mean differences between the ratio of enhancer and the different concentrations of samples tested. AA was found to be more efficient than EDTA in a way that lesser quantity is required for completely overcoming negative iron binding effects of polyphenols and similar samples.

Advances in quantitative muscle ultrasonography using texture analysis of ultrasound images

Musculoskeletal ultrasound imaging can be used to investigate the skeletal muscle structure in terms of architecture (thickness, cross-sectional area, fascicle length and fascicle pennation angle) and texture. Gray-scale analysis is commonly used to characterize transverse scans of the muscle. Gray mean value is used to distinguish between normal and pathologic muscles, but it depends on the image acquisition system and its settings. In this study, quantitative ultrasonography was performed on five muscles (biceps brachii, vastus lateralis, rectus femoris, medial gastrocnemius and tibialis anterior) of 20 healthy patients (10 women, 10 men) to assess the characterization performance of higher-order texture descriptors to differentiate genders and muscle types. A total of 53 features (7 first-order descriptors, 24 Haralick features, 20 Galloway features and 2 local binary pattern features) were extracted from each muscle region of interest (ROI) and were used to perform the multivariate linear regression analysis (MANOVA). Our results show that first-order descriptors, Haralick features (energy, entropy and correlation measured along different angles) and local binary pattern (LBP) energy and entropy were highly linked to the gender, whereas Haralick entropy and symmetry, Galloway texture descriptors and LBP entropy helped to distinguish muscle types. Hence, the combination of first-order and higher-order texture descriptors (Haralick, Galloway and LBP) can be used to discriminate gender and muscle types. Therefore, multi-texture analysis may be useful to investigate muscle damage and myopathic disorders.

Enhancing metabolomics research through data mining

Metabolomics research, like other disciplines utilizing high-throughput technologies, generates a large amount of data for every sample. Although handling this data is a challenge and one of the biggest bottlenecks of the metabolomics workflow, it is also the clue to accomplish valuable results. This work has been designed to supply methodological data mining guidelines, describing systematically the steps to be followed in metabolomics data exploration. Instrumental raw data refinement in the pre-processing step and assessment of the statistical assumptions in pre-treatment directly affect the results of subsequent univariate and multivariate analyses. A study of aging in a healthy population was selected to represent this data mining process. Multivariate analysis of variance and linear regression methods were used to analyze the metabolic changes underlying aging. Selection of both multivariate methods aims to illustrate the treatment of age from two rather different perspectives, as a categorical variable and a continuous variable.

BIOLOGICAL SIGNIFICANCE

Metabolomics is a discipline involving the analysis of a large amount of data to gather relevant information. Researchers in this field have to overcome the challenges of complex data processing and statistical analysis issues. A wide range of tasks has to be executed, from the minimization of batch-to-batch/systematic variations in pre-processing, to the application of common data analysis techniques relying on statistical assumptions. In this work, a real-data metabolic profiling research on aging was used to illustrate the proposed workflow and suggest a set of guidelines for analyzing metabolomics data. This article is part of a Special Issue entitled: HUPO 2014.

Empirical mode decomposition analysis of near-infrared spectroscopy muscular signals to assess the effect of physical activity in type 2 diabetic patients

Type 2 diabetes is a metabolic disorder that may cause major problems to several physiological systems. Exercise has proven to be very effective in the prevention, management and improvement of this pathology in patients. Muscle metabolism is often studied with near-infrared spectroscopy (NIRS), a noninvasive technique that can measure changes in the concentration of oxygenated (O2Hb) and reduced hemoglobin (HHb) of tissues. These NIRS signals are highly non-stationary, non-Gaussian and nonlinear in nature. The empirical mode decomposition (EMD) is used as a nonlinear adaptive model to extract information present in the NIRS signals. NIRS signals acquired from the tibialis anterior muscle of controls and type 2 diabetic patients are processed by EMD to yield three intrinsic mode functions (IMF). The sample entropy (SE), fractal dimension (FD), and Hurst exponent (HE) are computed from these IMFs. Subjects are monitored at the beginning of the study and after one year of a physical training programme. Following the exercise programme, we observed an increase in the SE and FD and a decrease in the HE in all diabetic subjects. Our results show the influence of physical exercise program in improving muscle performance and muscle drive by the central nervous system in the patients. A multivariate analysis of variance performed at the end of the training programme also indicated that the NIRS metabolic patterns of controls and diabetic subjects are more similar than at the beginning of the study. Hence, the proposed EMD technique applied to NIRS signals may be very useful to gain a non-invasive understanding of the neuromuscular and vascular impairment in diabetic subjects.

HIERARCHICAL ARRAY PRIORS FOR ANOVA DECOMPOSITIONS OF CROSS-CLASSIFIED DATA

ANOVA decompositions are a standard method for describing and estimating heterogeneity among the means of a response variable across levels of multiple categorical factors. In such a decomposition, the complete set of main effects and interaction terms can be viewed as a collection of vectors, matrices and arrays that share various index sets defined by the factor levels. For many types of categorical factors, it is plausible that an ANOVA decomposition exhibits some consistency across orders of effects, in that the levels of a factor that have similar main-effect coefficients may also have similar coefficients in higher-order interaction terms. In such a case, estimation of the higher-order interactions should be improved by borrowing information from the main effects and lower-order interactions. To take advantage of such patterns, this article introduces a class of hierarchical prior distributions for collections of interaction arrays that can adapt to the presence of such interactions. These prior distributions are based on a type of array-variate normal distribution, for which a covariance matrix for each factor is estimated. This prior is able to adapt to potential similarities among the levels of a factor, and incorporate any such information into the estimation of the effects in which the factor appears. In the presence of such similarities, this prior is able to borrow information from well-estimated main effects and lower-order interactions to assist in the estimation of higher-order terms for which data information is limited.

Power calculation for overall hypothesis testing with high-dimensional commensurate outcomes

The complexity of system biology means that any metabolic, genetic, or proteomic pathway typically includes so many components (e.g., molecules) that statistical methods specialized for overall testing of high-dimensional and commensurate outcomes are required. While many overall tests have been proposed, very few have power and sample size methods. We develop accurate power and sample size methods and software to facilitate study planning for high-dimensional pathway analysis. With an account of any complex correlation structure between high-dimensional outcomes, the new methods allow power calculation even when the sample size is less than the number of variables. We derive the exact (finite-sample) and approximate non-null distributions of the 'univariate' approach to repeated measures test statistic, as well as power-equivalent scenarios useful to generalize our numerical evaluations. Extensive simulations of group comparisons support the accuracy of the approximations even when the ratio of number of variables to sample size is large. We derive a minimum set of constants and parameters sufficient and practical for power calculation. Using the new methods and specifying the minimum set to determine power for a study of metabolic consequences of vitamin B6 deficiency helps illustrate the practical value of the new results. Free software implementing the power and sample size methods applies to a wide range of designs, including one group pre-intervention and post-intervention comparisons, multiple parallel group comparisons with one-way or factorial designs, and the adjustment and evaluation of covariate effects.

Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA

Multi-voxel pattern analysis (MVPA) is a fruitful and increasingly popular complement to traditional univariate methods of analyzing neuroimaging data. We propose to replace the standard 'decoding' approach to searchlight-based MVPA, measuring the performance of a classifier by its accuracy, with a method based on the multivariate form of the general linear model. Following the well-established methodology of multivariate analysis of variance (MANOVA), we define a measure that directly characterizes the structure of multi-voxel data, the pattern distinctness D. Our measure is related to standard multivariate statistics, but we apply cross-validation to obtain an unbiased estimate of its population value, independent of the amount of data or its partitioning into 'training' and 'test' sets. The estimate D^ can therefore serve not only as a test statistic, but also as an interpretable measure of multivariate effect size. The pattern distinctness generalizes the Mahalanobis distance to an arbitrary number of classes, but also the case where there are no classes of trials because the design is described by parametric regressors. It is defined for arbitrary estimable contrasts, including main effects (pattern differences) and interactions (pattern changes). In this way, our approach makes the full analytical power of complex factorial designs known from univariate fMRI analyses available to MVPA studies. Moreover, we show how the results of a factorial analysis can be used to obtain a measure of pattern stability, the equivalent of 'cross-decoding'.

Effects of congenital blindness on the subcortical representation of speech cues

Human modalities play a vital role in the way the brain produces mental representations of the world around us. Although congenital blindness limits the understanding of the environment in some aspects, blind individuals may have other superior capabilities from long-term experience and neural plasticity. This study investigated the effects of congenital blindness on temporal and spectral neural encoding of speech at the subcortical level. The study included 26 congenitally blind individuals and 24 normal-sighted individuals with normal hearing. Auditory brainstem response (ABR) was recorded with both click and speech synthetic 40-ms /da/ stimuli. No significant difference was observed between the two groups in wave latencies or amplitudes of click ABR. Latencies of speech ABR D (p=0.012) and O (p=0.014) waves were significantly shorter in blind individuals than in normal-sighted individuals. Amplitudes of the A (p<0.001) and E (p=0.001) speech ABR (sABR) waves were also significantly higher in blind subjects. Blind individuals had significantly better results for duration (p<0.001) amplitude (p=0.015) and slope of the V-A complex (p=0.004), signal-to-noise ratio (p<0.001), and amplitude of the stimulus fundamental frequency (F0) (p=0.009), first formant (F1) (p<0.001) and higher-frequency region (HF) (p<0.001) ranges. Results indicate that congenitally blind subjects have improved hearing function in response to the /da/ syllable in both source and filter classes of sABR. It is possible that these subjects have enhanced neural representation of vocal cord vibrations and improved neural synchronization in temporal encoding of the onset and offset parts of speech stimuli at the brainstem level. This may result from the compensatory mechanism of neural reorganization in blind subjects influenced from top-down corticofugal connections with the auditory cortex.

Validity and clinical utility of functional assessments in children with cerebral palsy

OBJECTIVE

To examine the validity and clinical utility of functional assessments (1-minute walk test, 10-meter walk test, Timed Up & Go [TUG] test, Timed Up and Down Stairs [TUDS] test, sit-to-stand [STS] test, and lateral step-up [LSU] test).

DESIGN

Cross-sectional study.

SETTING

Four special schools for adolescents with physical disabilities.

PARTICIPANTS

Adolescents with spastic tetraplegia and diplegia (at levels I-III) were selected through convenience sampling (N=35; mean age, 14.97±2.03y).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

GMFM-88 (dimensions D and E), 1-minute walk, 10-meter walk, TUG, TUDS, STS, and LSU tests. Data were analyzed using Pearson intercorrelations, multiple regression analysis, and multivariate analysis of variance (MANOVA).

RESULTS

Significant moderate to high intercorrelations were found. Three significant positive predictors emerged (1-minute walk, 10-meter walk, and LSU) with the following regression equation: YGMFM-88 (dimensions D and E) = 5.708 + .402 × X1-minute walk + .920 × XLSU + .404 × X10-meter walk The MANOVA was significant (Λ=.163, F=14.732, P<.001, η(2)=.596), and post hoc comparisons revealed significant differences across Gross Motor Function Classification System Expanded and Revised levels in all paired comparisons for the 1-minute walk and LSU tests. For the 10-meter walk test, significant differences were evident in the level I versus level III and level II versus level III comparisons. No significant differences were found in the 10-meter walk test between levels I and II.

CONCLUSIONS

These functional assessments (1-minute walk, LSU, and 10-meter walk tests) are simple to administer, quick, low cost, and user-friendly. Although these assessments are not a substitute for the criterion standard (GMFM-88), they may be used for a quick assessment in adolescents with cerebral palsy (levels I-III) either at school or during rehabilitation, especially when time is limited.

A proposal for refining the forced swim test in Swiss mice

The forced swim test (FST) is a preclinical test to the screening of antidepressants based on rats or mice behaviours, which is also sensitive to stimulants of motor activity. This work standardised and validated a method to register the active and passive behaviours of Swiss mice during the FST in order to strength the specificity of the test. Adult male Swiss mice were subjected to the FST for 6 min without any treatment or after intraperitoneal injection of saline (0.1 ml/10 g), antidepressants (imipramine, desipramine, or fluoxetine, 30 mg/kg) or stimulants (caffeine, 30 mg/kg or apomorphine, 10mg/kg). The latency, frequency and duration of behaviours (immobility, swimming, and climbing) were scored and summarised in bins of 6, 4, 2 or 1 min. Parameters were first analysed using Principal Components Analysis generating components putatively related to antidepressant (first and second) or to stimulant effects (third). Antidepressants and stimulants affected similarly the parameters grouped into all components. Effects of stimulants on climbing were better distinguished of antidepressants when analysed during the last 4 min of the FST. Surprisingly, the effects of antidepressants on immobility were better distinguished from saline when parameters were scored in the first 2 min. The method proposed here is able to distinguish antidepressants from stimulants of motor activity using Swiss mice in the FST. This refinement should reduce the number of mice used in preclinical evaluation of antidepressants.

A neuregulin 1 transmembrane domain mutation causes imbalanced glutamatergic and dopaminergic receptor expression in mice

The neuregulin 1 gene has repeatedly been identified as a susceptibility gene for schizophrenia, thus mice with genetic mutations in this gene offer a valuable tool for studying the role of neuregulin 1 in schizophrenia-related neurotransmission. In this study, slide-based receptor autoradiography was used to quantify glutamatergic N-methyl-d-aspartate (NMDA), dopaminergic D2, cannabinoid CB1 and acetylcholine M1/4 receptor levels in the brains of male heterozygous transmembrane domain neuregulin 1 mutant (Nrg1(+/-)) mice at two ages. Mutant mice expressed small but significant increases in NMDA receptor levels in the cingulate cortex (7%, p=0.044), sensory cortex (8%, p=0.024), and motor cortex (8%, p=0.047), effects that were independent of age. In the nucleus accumbens and thalamus Nrg1(+/-) mice exhibited age-dependent alterations in NMDA receptors. Nrg1(+/-) mice showed a statistically significant increase in NMDA receptor levels in the nucleus accumbens of 14-week-old Nrg1(+/-) mice compared to control littermates of the same age (12%, p=0.026), an effect that was not seen in 20-week-old mice. In contrast, NMDA receptor levels in the thalamus, while initially unchanged in 14-week-old mice, were then decreased in the 20-week-old Nrg1(+/-) mice compared to control littermates of the same age (14%, p=0.011). Nrg1(+/-) mutant mice expressed a significant reduction in D2 receptor levels (13-16%) in the striatum compared to controls, independent of age. While there was a borderline significant increase (6%, p=0.058) in cannabinoid CB1 receptor levels in the substantia nigra of Nrg1(+/-) mice compared to controls, CB1 as well as acetylcholine M1/4 receptors showed no change in Nrg1(+/-) mice in any other brain region examined. These data indicate that a Nrg1 transmembrane mutation produces selective imbalances in glutamatergic and dopaminergic neurotransmission, which are two key systems believed to contribute to schizophrenia pathogenesis. While the effects on these systems are subtle, they may underlie the susceptibility of these mutants to further impacts.

Changes in hemodynamic responses in chronic stroke survivors do not affect fMRI signal detection in a block experimental design

The use of canonical functions to model BOLD-fMRI data in people post-stroke may lead to inaccurate descriptions of task-related brain activity. The purpose of this study was to determine whether the spatiotemporal profile of hemodynamic responses (HDRs) obtained from stroke survivors during an event-related experiment could be used to develop individualized HDR functions that would enhance BOLD-fMRI signal detection in block experiments. Our long term goal was to use this information to develop individualized HDR functions for stroke survivors that could be used to analyze brain activity associated with locomotor-like movements. We also aimed to examine the reproducibility of HDRs obtained across two scan sessions in order to determine whether data from a single event-related session could be used to analyze block data obtained in subsequent sessions. Results indicate that the spatiotemporal profile of HDRs measured with BOLD-fMRI in stroke survivors was not the same as that observed in individuals without stroke. We observed small between-group differences in the rates of rise and decline of HDRs that were more apparent in individuals with cortical as compared to subcortical stroke. There were no differences in the peak or time to peak of HDRs in people with and without stroke. Of interest, differences in HDRs were not as substantial as expected from previous reports and were not large enough to necessitate the use of individualized HDR functions to obtain valid measures of movement-related brain activity. We conclude that all strokes do not affect the spatiotemporal characteristics of HDRs in such a way as to produce inaccurate representations of brain activity as measured by BOLD-fMRI. However, care should be taken to identify individuals whose BOLD-fMRI data may not provide an accurate representation of underlying brain activation when canonical models are used. Examination of HDRs need not be done for each scan session, as our data suggest that the characteristics of HDRs in stroke survivors are reproducible across days.

Effects of intramuscular trunk stimulation on manual wheelchair propulsion mechanics in 6 subjects with spinal cord injury

OBJECTIVE

To quantify the effects of stabilizing the paralyzed trunk and pelvis with electrical stimulation on manual wheelchair propulsion.

DESIGN

Single-subject design case series with subjects acting as their own concurrent controls.

SETTING

Hospital-based clinical biomechanics laboratory.

PARTICIPANTS

Individuals (N=6; 4 men, 2 women; mean age ± SD, 46 ± 10.8y) who were long-time users (6.1 ± 3.9y) of implanted neuroprostheses for lower extremity function and had chronic (8.6 ± 2.8y) midcervical- or thoracic-level injuries (C6-T10).

INTERVENTIONS

Continuous low-level stimulation to the hip (gluteus maximus, posterior adductor, or hamstrings) and trunk extensor (lumbar erector spinae and/or quadratus lumborum) muscles with implanted intramuscular electrodes.

MAIN OUTCOME MEASURES

Pushrim kinetics (peak resultant force, fraction effective force), kinematics (cadence, stroke length, maximum forward lean), and peak shoulder moment at preferred speed over 10-m level surface; speed, pushrim kinetics, and subjective ratings of effort for level 100-m sprints and up a 30.5-m ramp of approximately 5% grade.

RESULTS

Three of 5 subjects demonstrated reduced peak resultant pushrim forces (P≤.014) and improved efficiency (P≤.048) with stimulation during self-paced level propulsion. Peak sagittal shoulder moment remained unchanged in 3 subjects and increased in 2 others (P<.001). Maximal forward trunk lean also increased by 19% to 26% (P<.001) with stimulation in these 3 subjects. Stroke lengths were unchanged by stimulation in all subjects, and 2 showed extremely small (5%) but statistically significant increases in cadence (P≤.021). Performance measures for sprints and inclines were generally unchanged with stimulation; however, subjects consistently rated propulsion with stimulation to be easier for both surfaces.

CONCLUSIONS

Stabilizing the pelvis and trunk with low levels of continuous electrical stimulation to the lumbar trunk and hip extensors can positively impact the mechanics of manual wheelchair propulsion and reduce both perceived and physical measures of effort.

Somatotopic finger mapping using MEG: toward an optimal stimulation paradigm

OBJECTIVE

In non-invasive somatotopic mapping based on neuromagnetic source analysis, the recording time can be shortened and accuracy improved by applying simultaneously vibrotactile stimuli at different frequencies to multiple body sites and recording multiple steady-state responses. This study compared the reliability of sensory evoked responses, source localization performance, and reproducibility of digit maps for three different stimulation paradigms.

METHODS

Vibrotactile stimuli were applied to the fingertip and neuromagnetic steady-state responses were recorded. Index and middle fingers were stimulated either sequentially in separate blocks, simultaneously at different frequencies, or in alternating temporal order within a block.

RESULTS

Response amplitudes were largest and source localization was most accurate between 21 and 23 Hz. Separation of adjacent digits was significant for all paradigms in all participants. Suppressive interactions occurred between simultaneously applied stimuli. However, when frequently alternating between stimulus sites, the higher stimulus novelty resulted in increased amplitudes and superior localization performance.

CONCLUSIONS

When receptive fields are strongly overlapping, the alternating stimulation is preferable over recording multiple steady state responses.

SIGNIFICANCE

The new paradigm improved the measurement of the distance of somatotopic finger representation in human primary somatosensory cortex, which is an important metric for neuroplastic reorganization after learning and rehabilitation training.

Effects of stimulating hip and trunk muscles on seated stability, posture, and reach after spinal cord injury

OBJECTIVE

To determine the stimulated strength of the paralyzed gluteal and paraspinal muscles and their effects on the seated function of individuals with paralysis.

DESIGN

Case series with subjects acting as their own concurrent controls.

SETTING

Hospital-based clinical biomechanics laboratory.

PARTICIPANTS

Users (N=8) of implanted neuroprostheses for lower extremity function with low-cervical or thoracic level injuries.

INTERVENTIONS

Dynamometry and digital motion capture both with and without stimulation to the hip and trunk muscles.

MAIN OUTCOME MEASURES

Isometric trunk extension moment at 0°, 15°, and 30° of flexion; seated stability in terms of simulated isokinetic rowing; pelvic tilt, shoulder height, loaded and unloaded bimanual reaching to different heights, and subjective ratings of difficulty during unsupported sitting.

RESULTS

Stimulation produced significant increases in mean trunk extension moment (9.2±9.5Nm, P<.001) and rowing force (27.4±23.1N, P<.012) over baseline volitional values. Similarly, stimulation induced positive changes in average pelvic tilt (16.7±15.7°) and shoulder height (2.2±2.5cm) during quiet sitting and bimanual reaching, and increased mean reach distance (5.5±6.6cm) over all subjects, target heights, and loading conditions. Subjects consistently rated tasks with stimulation easier than voluntary effort alone.

CONCLUSIONS

In spite of considerable intersubject variability, stabilizing the paralyzed trunk with electrical stimulation can positively impact seated posture, extend forward reach, and allow exertion of larger forces on objects in the environment.

Voxel-wise co-analysis of macro- and microstructural brain alteration in mild cognitive impairment and Alzheimer's disease using anatomical and diffusion MRI

BACKGROUND AND PURPOSE

To determine if a voxel-wise "co-analysis" of structural and diffusion tensor magnetic resonance imaging (MRI) together reveals additional brain regions affected in mild cognitive impairment (MCI) and Alzheimer's disease (AD) than voxel-wise analysis of the individual MRI modalities alone.

METHODS

Twenty-one patients with MCI, 21 patients with AD, and 21 cognitively normal healthy elderly were studied with MRI. Maps of deformation and fractional anisotropy (FA) were computed and used as dependent variables in univariate and multivariate statistical models.

RESULTS

Univariate voxel-wise analysis of macrostructural changes in MCI showed atrophy in the right anterior temporal lobe, left posterior parietal/precuneus region, WM adjacent to the cingulate gyrus, and dorsolateral prefrontal regions, consistent with prior research. Univariate voxel-wise analysis of microstructural changes in MCI showed reduced FA in the left posterior parietal region extending into the corpus callosum, consistent with previous work. The multivariate analysis, which provides more information than univariate tests when structural and FA measures are correlated, revealed additional MCI-related changes in corpus callosum and temporal lobe.

CONCLUSION

These results suggest that in corpus callosum and temporal regions macro- and microstructural variations in MCI can be congruent, providing potentially new insight into the mechanisms of brain tissue degeneration.

Two-Step Hypothesis Testing When the Number of Variables Exceeds the Sample Size

Medical images and genetic assays typically generate data with more variables than subjects. Scientists may use a two-step approach for testing hypotheses about Gaussian mean vectors. In the first step, principal components analysis (PCA) selects a set of sample components fewer in number than the sample size. In the second step, applying classical multivariate analysis of variance (MANOVA) methods to the reduced set of variables provides the desired hypothesis tests. Simulation results presented here indicate that success of the PCA in the first step requires nearly all variation to occur in population components far fewer in number than the number of subjects. In the second step, multivariate tests fail to attain reasonable power except in restrictive, favorable cases. The results encourage using other approaches discussed in the article to provide dependable hypothesis testing with high dimension, low sample size data (HDLSS).

Comparing physicochemical properties of banana pulp and peel flours prepared from green and ripe fruits

Banana pulp and peel flour prepared from green and ripe Cavendish banana were assessed for physicochemical properties such as pH, total soluble solids (TSS), water holding capacity (WHC) and oil holding capacity (OHC) at 40, 60 and 80°C, colour values L^∗, a^∗ and b^∗, back extrusion force (BEF) and viscosity. Data obtained were analysed by MANOVA, discriminant analysis and cluster analysis. All statistical analyses showed that physicochemical properties of flour prepared from pulp and peel, and green and ripe banana were different from each other. TSS, WHC40, WHC60 and BEF can be used to discriminate between peel and pulp flour, whilst TSS and viscosity can be used to discriminate between flour prepared from green and ripe banana.