Non-parametric Multivariate Analysis Of Variance Research Articles

Assessing coherence through linguistic connectives: Analysis of speech in patients with schizophrenia-spectrum disorders.

Incoherent speech is a core diagnostic symptom of schizophrenia-spectrum disorders (SSD) that can be studied using semantic space models. Since linguistic connectives signal relations between words, they and their surrounding words might represent linguistic loci to detect unusual coherence in speech. Therefore, we investigated whether connectives' measures are useful to assess incoherent speech in SSD. Connectives and their surrounding words were extracted from transcripts of spontaneous speech of 50 SSD-patients and 50 control participants. Using word2vec, two different cosine similarities were calculated: those of connectives and their surrounding words (connectives-related similarity), and those of free-of-connectives words-chunks (non-connectives similarity). Differences between groups in proportion of five types of connectives were assessed using generalized logistic models, and connectives-related similarity was analyzed through non-parametric multivariate analysis of variance. These features were evaluated in classification tasks to differentiate between groups. SSD-patients used less contingency (e.g., because) (p=.008) and multiclass connectives (e.g., as) (p<.001) than control participants. SSD-patients had higher minimum similarity of multiclass (adj-p=.04) and temporality connectives (e.g., after) (adj-p<.001), narrower similarity-range of expansion (e.g., and) (adj-p=.002) and multiclass connectives (adj-p=.04), and lower maximum similarity of expansion connectives (adj-p=.005). Using connectives' features alone, SSD-patients and controls could be distinguished with 85% accuracy. Our results show that SSD-speech can be distinguished from speech of control participants with high accuracy, based solely on connectives' features. We conclude that including connectives could strengthen computational models to categorize SSD.

Kids Surfing the Web: A Comparative Study in Portugal

The conditions for safe Internet access and the development of skills enabling full participation in online environments are recognized in the Council of Europe’s strategy for child rights, from 2022. The guarantee of this right has implications for experiences inside and outside the school context. Therefore, this study aims to compare the perceptions of students from different educational levels, who participated in a digital storytelling workshop, regarding online safety, searching habits, and digital competences. Data were collected through a questionnaire survey completed by 84 Portuguese students from elementary and secondary schools. A non-parametric multivariate analysis of variance was used to identify differences as children advanced across educational stages. The results revealed that secondary students tended to spend more time online and demonstrated more advanced search skills. Interestingly, the youngest children exhibited higher competences in creating games and practicing safety measures regarding online postings. These findings emphasize the importance of schools, in a joint action with the educational community, including parents, teachers and students, in developing a coordinated and vertically integrated approach to digital education that considers the children’s current knowledge, attitudes, and skills as a starting point for pedagogical intervention.

Anxiety and Induction of Labor: Measuring Scales and Associated Factors

BACKGROUND:Anxiety scores are higher in women being induced compared with women who are in spontaneous labor. There are limited published reports addressing anxiety associated with the induction of labor (IOL).OBJECTIVES:The aim is to measure anxiety scores that are associated with IOL using two measuring scales and identify women’s characteristics and obstetrics variables that are associated with higher anxiety scores.METHODS:A cross-sectional study was conducted between October 20, 2019, and October 20, 2020. Inclusion criteria required women to be 18 years or older, have a viable pregnancy, and have been admitted for IOL. Anxiety scores were measured using the visual analog scale for anxiety (VASA) first and then the State-Trait Anxiety Inventory (STAI). The factor analysis for mixed data was used to identify the independent variables that contributed the most to the variation among participants. These variables were then tested for association with the different anxiety variables using multivariate analysis of variance (MANOVA).RESULTS:In this study, 300 women with a mean age of 28 years and a mean fetal gestational age of 39.4 weeks were recruited. A factor analysis with a principal component analysis method on the STAI state subscale revealed two components with high internal consistency, designated Component 1 and Component 2. Women had anxiety scores above the mean as measured on VASA (48.7%) and Component 1 (50.3%) and Component 2 (70.7%) of the STAI state subscale. There were statistically significant correlations between the two components of the STAI state subscale and VASA (Spearman’s rank correlation: VASA with STAI State Component 1 = −.239,p-value = 2.8 × 10−5; VASA with STAI State Component 2 = .296,p-value = 1.7 × 10−7). The three outcome anxiety variables (VASA and Components 1 and 2 of the STAI state subscale) in nonparametric MANOVA analysis showed significant associations with a prior history of subfertility (Wilk’s lambda test statistic = 3.668,df1 = 3,df2 = 296,p-value = .014) and higher parity (Wilk’s lambda test statistic = 2.100,df1 = 6,df2 = 590,p-value = .043).CONCLUSION:High anxiety scores are prevalent among women who are being induced. The VASA and STAI state subscales are correlated, and VASA may be an alternative to the lengthy STAI. Antenatal identification of women at high risk may help in implementing strategies to reduce anxiety scores.

Measuring repeatability of compositional diet estimates: Anexample using quantitative fatty acid signature analysis.

By measuring the temporal consistency, or repeatability, in the diets of predators, we can gain a better understanding of the degree of individual specialization in resource utilization and implications for predator–prey interactions, population dynamics, and food web structure. To measure repeatability, we require repeated diet estimates of individuals over time, such as those derived from quantitative fatty acid signature analysis (QFASA), a popular diet estimation technique. However, diet estimates are often lengthy compositional vectors with many zeros, as some prey will not be consumed by all individuals, precluding the use of previously proposed measures of repeatability. In this paper, we propose a novel approach for inferring repeatability for multivariate data and, in particular, compositional diet estimates. We extend the commonly used measure of repeatability for univariate data to the multivariate compositional setting by utilizing the mean squares obtained from a nonparametric multivariate analysis of variance, and an appropriate choice of statistical distance. Our measure and its extension are compatible with both balanced and unbalanced data sets. Associated confidence intervals via nonparametric bootstrapping are also developed for the case of QFASA diet estimates that incorporate both sampling error and measurement error, where the latter error arises because the diets of predators are estimated. Simulation study results suggest that for practical levels of repeatability, our methods yield confidence intervals with the desired coverage probability even when the sample size relative to the dimension of the data (i.e., number of prey species eaten) is small. We tested our methods using QFASA diet estimates for free‐ranging Northwest Atlantic grey seals. Given the importance of understanding how predator diets vary over time and space, our method may find broad application to other compositional diet estimates, including those derived from the stomach or fecal contents, and stable isotope analyses.

Fertilization practices affect biological nitrogen fixation by modulating diazotrophic communities in an acidic soil in southern China

Biological nitrogen (N) fixation (BNF) driven by diazotrophs is an important pathway for N input in agricultural ecosystems. However, free-living BNF and its associated diazotrophic communities under different fertilization practices in acidic soils are poorly studied. Here, we conducted a long-term fertilization experiment (29 years), including an unfertilized control (CK), chemical N, P and K fertilizer (NPK), NPK plus lime (NPKL), NPK plus straw (NPKS), NPK plus straw and lime (NPKSL) and NPK plus manure (NPKM), and explored how fertilization practices affect free-living BNF by changing biotic and abiotic variables. Compared with CK (1.51 nmol C2H2 d−1 g−1), BNF rates were significantly increased in NPKM (1.99 nmol C2H2 d−1 g−1) but reduced in NPK (0.55 nmol C2H2 d−1 g−1), NPKL (0.61 nmol C2H2 d−1 g−1) and NPKS (0.69 nmol C2H2 d−1 g−1). Similarly, chemical fertilization, regardless of the addition of lime or straw, reduced the abundance (0.71-1.18 × 108 gene copies g−1) and α-diversity (1.11-2.43) of the diazotrophic community, while manure application had a positive effect on diazotrophic abundance (3.23 × 108 gene copies g−1) and α-diversity (3.36). Non-parametric multivariate analysis of variance (PERMANOVA) suggested that manure application (R2 = 0.212, p = 0.001) showed the stronger influence on diazotrophic community composition than additions of lime (R2 = 0.115, p = 0.019) and straw (R2 = 0.064, p = 0.161). Random forest model analysis revealed that BNF rates can be significantly (p < 0.05) explained by soil pH (9.9%), diazotrophic community attributes (composition, 8.5%; Chao1, 8.1%; abundance, 6.0%; Shannon, 5.7%), and soil total carbon (5.1%). Partial least squares path modeling (PLS-PM) suggested that diazotrophic community and soil properties mainly provided direct and indirect contributions to the variations of BNF rates, respectively. The dominant genera Pelomonas, Azospirillum and Dechloromonas were positively associated with BNF rates, with their members observed as keystone species in the community network. Together, chemical fertilizer combined with manure is an effective practice for improving BNF in acidic soil by affecting soil diazotrophic community.

Distinct Compartmentalization of Microbial Community and Potential Metabolic Function in the Fruiting Body of Tricholoma matsutake.

The uniquely compartmentalized fruiting body structure of the ectomycorrhizal fungus (EMF) Tricholoma matsutake, is a hotspot of microbial habitation and interaction. However, microbial diversity within this microniche structure of the EMF is rarely investigated. Furthermore, there is limited information concerning microbiomes associated with sporomes belonging to the ubiquitous fungal phylum Basidiomycota, particularly with respect to fungus-EMF interactions. In this study, we conducted high throughput sequencing, using ITS (fungal) and 16S rRNA (bacterial) marker genes to characterize and compare fruiting body microbiomes in the outer (pileipellis and stipitipellis) and inner layers (pileum context, stipe context, and lamellae) of the fruiting body of T. matsutake. Our results show the number of unique bacterial operational taxonomic units (OTUs) among the different compartments ranged from 410 to 499 and was more than double that of the shared/common OTUs (235). Micrococcales, Bacillales, Caulobacter, and Sphingomonas were the primary significant bacterial taxa within the different compartments of the dissected T. matsutake fruiting body. Non-parametric multivariate analysis of variance showed significant compartmental differences for both the bacterial and the fungal community structure within the T. matsutake fruiting body. The metabolic profiling revealed putative metabolisms (of amino acids, carbohydrates, and nucleotides) and the biosynthesis of secondary metabolites to be highly enriched in outer layers; in the inner parts, the metabolisms of energy, cofactors, vitamins, and lipids were significantly higher. This study demonstrates for the first time the distinct compartmentalization of microbial communities and potential metabolic function profiles in the fruiting body of an economically important EMF T. matsutake.

Combined hormonal contraceptives are associated with minor changes in composition and diversity in gut microbiota of healthy women

Recent human and animal studies have found associations between gut microbiota composition and serum levels of sex hormones, indicating that they could be an important factor in shaping the microbiota. However, little is known about the effect of regular hormonal fluctuations over the menstrual cycle or CHC-related changes of hormone levels on gut microbiota structure, diversity and dynamics. The aim of this study was to investigate the effect of CHCs on human gut microbiota composition. The effect of CHC pill intake on gut microbiota composition was studied in a group of 7 healthy pre-menopausal women using the CHC pill, compared to the control group of 9 age-matched healthy women that have not used hormonal contraceptives in the six months prior the start of the study. By analyzing the gut microbiota composition in both groups during one menstrual cycle, we found that CHC usage is associated with a minor decrease in gut microbiota diversity and differences in the abundance of several bacterial taxa. These results call for further investigation of the mechanisms underlying hormonal and hormonal contraceptive-related changes of the gut microbiota and the potential implications of these changes for women's health. This article is protected by copyright. All rights reserved.

The composition of the gut microbiome differs among community dwelling older people with good and poor appetite.

BackgroundAnorexia of ageing is common and important in the development of sarcopenia in older individuals. Links have been proposed between the gut microbiota and sarcopenia. Disordered gut function is also recognized in anorexia of ageing, but how this may relate to resident gut microbiota is unexplored. Understanding this relationship may provide a basis for novel interventions for anorexia of ageing and sarcopenia. This study explores compositional differences of the gut microbiota between community dwelling healthy older adults with good or poor appetite, and associated differences in sarcopenia.MethodsWe assessed appetite by the Simplified Nutritional Appetite Questionnaire (SNAQ) in members of the TwinsUK cohort aged ≥65 years. Using a pool of 776 individuals with existing microbiome data estimated from 16S rRNA sequencing data, we identified 102 cases (SNAQ score < 14) (95% female, mean age 68 years) matched to controls (SNAQ > 14) on body mass index, gender, age, diet, calorie consumption, frailty, antibiotic use, socio‐economic status, and technical variables to minimize confounding microbiota associations. Species abundance and diversity, compositional differences, and paired differences in taxa abundance were compared between cases and controls. Additionally, we compared case and controls for sarcopenia as measured by muscle mass (appendicular lean mass/height2) and strength (chair stand time in seconds).ResultsCases with poor appetite had reduced species richness and diversity of their gut microbiome (adjusted OBSERVED: beta = −0.2, P < 0.001; adjusted SHANNON: beta = −0.17, P = 0.0135), significant compositional differences (adjusted non‐parametric multivariate analysis of variance, P = 0.0095), and significant differences in taxa abundance including reduction of genus Lachnospira (logFC = −1.015, q = 0.023). In all‐female subgroup analysis, cases with poor appetite demonstrated reduction in muscle strength (11.03 s vs. 9.26 s, P = 0.02).ConclusionsThis study is the first to observe differences in the composition of gut microbiota between healthy community dwelling older individuals with good and poor appetite. We found female individuals with reduced muscle strength had poor appetite compared with those with normal strength. These associations require further examination to understand causality and mechanisms of interaction, to inform potential strategies targeting the gut microbiota as a novel intervention for anorexia of ageing and sarcopenia.

Alteration in the Skin Microbiome in Cutaneous Graft Versus Host Disease

Graft-versus-host disease (GVHD) is a common complication of haematopoietic stem cell transplantation. This study examined the cutaneous microbiome in relation to the pathogenesis of cutaneous GVHD. Bacterial swabs were taken from several sites on 12 patients with cutaneous GVHD. Microbiotas were characterized by sequencing 16S rRNA bacterial genes on the MiSeq platform. Microbiome diversity in patients with cutaneous GVHD was reduced compared with healthy controls. GVHD was related to an increased abundance of Firmicutes and a reduction in Actinobacteria, especially in lesions. Non-parametric multivariate analysis of variance revealed that the skin microbial community disorders in patients with GVHD correlated with several clinical features of cutaneous GVHD. This study indicates that changes in the cutaneous microbiota in lesions could play a key role in the pathogenesis of cutaneous GVHD. Further studies are needed to explore the mechanistic relevance of these microbial dynamics, which may provide new clues to therapeutic interventions.

Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data

The study was focused on a plant native to Poland, the European dewberry Rubus caesius L., which is a species with the ability to become excessively abundant within its original range, potentially causing significant changes in ecosystems, including biodiversity loss. Monitoring plant distributions over large areas requires mapping that is fast, reliable, and repeatable. For Rubus, different types of data were successfully used for classification, but most of the studies used data with a very high spectral resolution. The aim of this study was to indicate, using hyperspectral and Light Detection and Ranging (LiDAR) data, the main functional trait crucial for R. caesius differentiation from non-Rubus. This analysis was carried out with consideration of the seasonal variability and different percentages of R. caesius in the vegetation patches. The analysis was based on hyperspectral HySpex images and Airborne Laser Scanning (ALS) products. Data were acquired during three campaigns: early summer, summer, and autumn. Differentiation based on Linear Discriminate Analysis (LDA) and Non-Parametric Multivariate Analysis of Variance (NPMANOVA) analysis was successful for each of the analysed campaigns using optical data, but the ALS data were less useful for identification. The analysis indicated that selected spectral ranges (VIS, red-edge, and parts of the NIR and possibly SWIR ranges) can be useful for differentiating R. caesius from non-Rubus. The most useful indices were ARI1, CRI1, ARVI, GDVI, CAI, NDNI, and MRESR. The obtained results indicate that it is possible to classify R. caesius using images with lower spectral resolution than hyperspectral data.

Evaluation of the Agronomic Traits of 80 Accessions of Proso Millet (Panicum miliaceum L.) under Mediterranean Pedoclimatic Conditions

The continuous increase in the world population and the associated food demands in the wake of climate change are pushing for the development and cultivation of climate-resilient crops that are able to efficiently use natural resources. Proso millet (Panicum miliaceum L.) might be a promising candidate crop thanks to its heat stress resistance and its limited water demand. To date, one of the most important strategies to increase grain yield and to improve other agronomic important traits is through an efficient breeding program based on a wide genetic variability of parental germplasm. In this study, we evaluated the agronomical traits of a world collection of 80 P. miliaceum accessions. The entire collection was evaluated over a 2 year field experiment under Mediterranean pedoclimatic conditions, which exhibited a wide range of variability for plant height (25–111 cm), grain yield (842–3125 kg ha−1), total dry biomass (2767–10,627 kg ha−1), harvest index (HI; 0.25–0.35), Growing Degree Days (GDDs; 581–899), and days to maturity (80–111 d). A non-parametric multivariate analysis of variance (Np-MANOVA) analysis indicated that GDDs to flowering, grain yield, total dry biomass, days to maturity, plant height, and seed yield per plant were useful parameters to differentiate the germplasm accessions. High heritability (&gt;0.60) was observed in both years for plant height, leaf number, basal tiller, seed yield per plant, 100-seed weight, GDDs to flowering, and days to maturity. Grain yield, total dry biomass, and HI reported moderate heritability (0.30–0.60). The findings reported in the present study may provide valuable information that could support researchers in breeding programs to develop high grain-yielding accessions.

Analyzing Stress as a Multivariate Phenotype.

The stress phenotype is multivariate. Recent advances have broadened our understanding beyond characterizing the stress response in a single dimension. Simultaneously, the toolbox available to ecophysiologists has expanded greatly in recent years, allowing the measurement of multiple biomarkers from an individual at a single point in time. Yet these advances-in our conceptual understanding and available methodologies-have not yet been combined in a unifying multivariate statistical framework. Here, we offer a brief review of the multivariate stress phenotype and describe a general statistical approach for analysis using nonparametric multivariate analysis of variance with residual randomization in permutation procedures (RRPP) implemented using the "RRPP" package in R. We also provide an example illustrating the novel insights that can be gained from a holistic multivariate approach to stress and provide a tutorial for how we analyzed these data, including annotated R code and a guide to interpretation of outputs (Online Appendix 1). We hope that this statistical methodology will provide a quantitative framework facilitating the unification of our theoretical understanding and empirical observations into a quantitative, multivariate theory of stress.

Variations in the Compositions of Soil Bacterial and Fungal Communities Due to Microhabitat Effects Induced by Simulated Nitrogen Deposition of a Bamboo Forest in Wetland

Although numerous studies have been published on nitrogen (N) deposition, little is known about its impact on microbial communities in wetland forests. Here, we used simulated nitrogen deposition (SND) to analyze the importance of differences in soil microhabitats in promoting the diversity of soil bacteria and fungi. We compared various levels of SND (control (CK), low N (N30), medium N (N60), and high N (N90)) and found that these were associated with changes in soil microhabitats. Additionally, SND affected soil pH, clay and sand content of the soil, and specific surface area (SSA). Bacteria and fungi responded differently to increased SND levels. The alpha diversity of bacteria decreased with an increased SND level, while fungal abundance, diversity, and community evenness reached their maximum values at the N60 threshold. Principal coordinates analysis (PCoA), nonparametric multivariate analysis of variance (PERMANOVA), and linear discriminant analysis (LDA) coupled with effect size measurements (LefSe) also confirmed that the bacterial composition was different at N90 compared to other levels of SND while that of fungi was different at N60. A higher discriminant level (LDA score ≥4) may be a valuable index of selecting indicator microbial clades sensitive to SND for wetland management. Further, an increased pH was associated with a greater abundance of bacteria and fungi. In addition, the volume contents of clay and SSA were negatively correlated with bacteria but fungi are associated with soil specific gravity (SSG). Overall, in a neutral soil pH environment, pH fluctuation is the main influencing factor in terms of bacterial and fungal abundance and diversity. The diversity of fungi is more dependent on the type and relative content of solid phase components in soil than that of bacteria, implying the presence of species-specific niches for bacteria and fungi. These results demonstrate that changes in SND can induce short-term microbial and microhabitat changes.

Leaf geometric morphometrics among populations of Dalbergia ecastaphyllum (L.) Taub.

Leaves are plant structures that express important traits of the environment where they live. Leaf description has allowed identification of plant species as well as investigation of abiotic factors effects on their development, such as gases, light, temperature, and herbivory. This study described populations of Dalbergia ecastaphyllum through leaf geometric morphometrics in Brazil. We evaluated 200 leaves from four populations. The principal component analysis (PCA) showed that the first four principal components were responsible for 97.81% of variation. The non-parametric multivariate analysis of variance (NPMANOVA) indicated significant difference between samples (p = 0.0001). The Mentel test showed no correlation between geographical distances and shape. The canonical variate analysis (CVA) indicated that the first two variables were responsible for 96.77 % of total variation, while the cross-validation test showed an average of 83.33%. D. ecastaphyllum leaves are elliptical and ovate.

Measuring electronic communication networks in virtual care teams using electronic health records access-log data

ObjectiveTo develop methods for measuring electronic communication networks in virtual care teams using electronic health records (EHR) access-log data. MethodsFor a convenient sample of 100 surgical colorectal cancer patients, we used time-stamped EHR access-log data extracted from an academic medical center’s EHR system to construct communication networks among healthcare professionals (HCPs) in each patient’s virtual care team. We measured communication linkages between HCPs using the inverse of the average time between access events in which the source HCPs sent information to and the destination HCPs retrieved information from the EHR system. Social network analysis was used to examine and visualize communication network structures, identify principal care teams, and detect meaningful structural differences across networks. We conducted a non-parametric multivariate analysis of variance (MANOVA) to test the association between care teams’ communication network structures and patients’ cancer stage and site. ResultsThe 100 communication networks showed substantial variations in size and structures. Principal care teams, the subset of HCPs who formed the core of the communication networks, had higher proportions of nurses, physicians, and pharmacists and a lower proportion of laboratory medical technologists than the overall networks. The distributions of conditional uniform graph quantiles suggested that our network-construction technique captured meaningful underlying structures that were different from random unstructured networks. MANOVA results found that the networks’ topologies were associated with patients’ cancer stage and site. ConclusionsThis study demonstrates that it is feasible to use EHR access-log data to measure and examine communication networks in virtual care teams. The proposed methods captured salient communication patterns in care teams that were associated with patients’ clinical differences.

Nonparametric multivariate analysis of variance for affecting factors on the extent of forest fire damage in Jilin Province, China

Forest fires are influenced by several factors, including forest location, species type, age and density, date of fire occurrence, temperatures, and wind speeds, among others. This study investigates the quantitative effects of these factors on the degree of forest fire disaster using nonparametric statistical methods to provide a theoretical basis and data support for forest fire management. Data on forest fire damage from 1969 to 2013 was analyzed. The results indicate that different forest locations and types, fire occurrence dates, temperatures, and wind speeds were statistically significant. The eastern regions of the study area experienced the highest fire occurrence, accounting for 85.0% of the total number of fires as well as the largest average forested area burned. April, May, and October had more frequent fires than other months, accounting for 78.9%, while September had the most extensive forested area burned (63.08 ha) and burnt area (106.34 ha). Hardwood mixed forest and oak forest had more frequent fires, accounting for 31.9% and 26.0%, respectively. Hardwood-conifer mixed forest had the most forested area burned (50.18 ha) and burnt area (65.09 ha). Temperatures, wind speeds, and their interaction had significant impacts on forested area burned and area burnt.

Nonparametric MANOVA in meaningful effects

Multivariate analysis of variance (MANOVA) is a powerful and versatile method to infer and quantify main and interaction effects in metric multivariate multi-factor data. It is, however, neither robust against change in units nor meaningful for ordinal data. Thus, we propose a novel nonparametric MANOVA. Contrary to existing rank-based procedures, we infer hypotheses formulated in terms of meaningful Mann–Whitney-type effects in lieu of distribution functions. The tests are based on a quadratic form in multivariate rank effect estimators, and critical values are obtained by bootstrap techniques. The newly developed procedures provide asymptotically exact and consistent inference for general models such as the nonparametric Behrens–Fisher problem and multivariate one-, two-, and higher-way crossed layouts. Computer simulations in small samples confirm the reliability of the developed method for ordinal and metric data with covariance heterogeneity. Finally, an analysis of a real data example illustrates the applicability and correct interpretation of the results.

The CONSTANCES job exposure matrix based on self-reported exposure to physical risk factors: development and evaluation

ObjectivesJob exposure matrices (JEMs) can be constructed from expert-rated assessments, direct measurement and self-reports. This paper describes the construction of a general population JEM based on self-reported physical exposures, its...

Temporal shifts in endophyte bacterial community composition of sessile oak (Quercus petraea) are linked to foliar nitrogen, stomatal length, and herbivory.

We studied the relationship between plant functional foliar traits and the endophytic bacterial communities associated in trees, taking the example of sessile oak (Quercus petraea (Matt.) Liebl). Forty-five samples with replicates of eight leaves per sample were collected in spring, summer and autumn. Bacterial community diversity was analyzed via Automated Ribosomal Intergenic Spacer Analysis (ARISA). The leaf traits specific leaf area, level of herbivory, stomatal number, stomatal length, carbon and nitrogen concentration were measured for the leaves of each sample. For statistical analysis, linear mixed effect models, the Canonical Correlation Analysis (CCA) and Non-Parametric Multivariate Analysis of Variance (NPMANOVA) were applied. Herbivory, nitrogen and carbon concentration were significantly different in autumn compared to spring and summer (p value < 0.05), while stomatal length was differentiated between spring and the other two seasons (p value < 0.01). The seasonal differentiation of the bacterial community structure was explained by the first and second axes (29.7% and 25.3%, respectively) in the CCA. The bacterial community structure significantly correlated with herbivory, nitrogen concentration and stomatal length. We conclude that herbivory, nitrogen content, and size of stomatal aperture at the leaf level are important for endophyte colonization in oaks growth in alpine forest environments.

Evaluation of natural mandibular shape asymmetry: an approach by using elliptical Fourier analysis.

The purpose of this study was to demonstrate that asymmetry is a natural occurring phenomenon in the mandibular shape by using elliptical Fourier analysis. 164 digital orthopantomographs from Colombian patients of both sexes aged 18 to 25 years were collected. Curves from left and right hemimandible were digitized. An elliptical Fourier analysis was performed with 20 harmonics. In the general sexual dimorphism a principal component analysis (PCA) and a hotelling T2 from the multivariate warp space were employed. Exploratory analysis of general asymmetry and sexual dimorphism by side was made with a Procrustes Fit. A non-parametricmultivariate analysis of variance (MANOVA) was applied to assess differentiation of skeletal classes of each hemimandible, and a Procrustes analysis of variance (ANOVA) was applied to search any relation between skeletal class and side in both sexes. Significant values were found in general asymmetry, general sexual dimorphism, in dimorphism by side (p <0.0001), asymmetry by sex, and differences between Class I, II, and III (p <0.005). However, a relation of skeletal classes and side was not found. The mandibular asymmetry by shape is present in all patients and should not be articulated exclusively to pathological processes, therefore, along with sexual dimorphism and differences between skeletal classes must be taken into account for improving mandibular prediction systems.