Determination of vertical dimension of occlusion in dentate patients by cephalometric analysis--pilot study

doi: 10.1111/j.1741‐2358.2011.00469.x
Determination of vertical dimension of occlusion in dentate patients by cephalometric analysis – pilot study Objectives: The concept of vertical dimension of occlusion (VDO) refers to a measure in the vertical plane that establishes the relation between the maxilla and the mandible when the posterior teeth, both from the maxillary and from the mandibular arches, are occluded, regardless of whether they are natural or prosthetic, healthy or restored. This measure is subject to change, and when this occurs, it can compromise both the function and the facial aesthetics. This study proposed to develop a methodology based on cephalometric analysis by studying the 31 lateral teleradiographs of adult, dentate individuals to determine the VDO, based on bone structures that are not dependent on the presence or absence of posterior teeth. The final goal was to make this application accessible to individuals who have undergone alterations of the lower portion of the face. Materials and methods: The cephalometric analysis of this study, called Seraidarian‐Tavano, was verified through facial angles (upper and middle angles) that, when correlated, determine the lower position of the face. Results: The analysis of results showed that no statistically significant difference between the angles studied could be observed (superior angle 50.29 ± 3.35 e median angle 49.95 ± 3.37). In the same manner, no variation in the results regarding gender in the measure of these angles could be observed. Conclusion: This cephalometric analysis can be applied to determine the VDO, regardless of the presence or absence of posterior teeth.     

What's on the horizon for macroecology?

Over the last two decades, macroecology – the analysis of large‐scale, multi‐species ecological patterns and processes – has established itself as a major line of biological research. Analyses of statistical links between environmental variables and biotic responses have long and successfully been employed as a main approach, but new developments are due to be utilized. Scanning the horizon of macroecology, we identified four challenges that will probably play a major role in the future. We support our claims by examples and bibliographic analyses. 1) Integrating the past into macroecological analyses, e.g. by using paleontological or phylogenetic information or by applying methods from historical biogeography, will sharpen our understanding of the underlying reasons for contemporary patterns. 2) Explicit consideration of the local processes that lead to the observed larger‐scale patterns is necessary to understand the fine‐grain variability found in nature, and will enable better prediction of future patterns (e.g. under environmental change conditions). 3) Macroecology is dependent on large‐scale, high quality data from a broad spectrum of taxa and regions. More available data sources need to be tapped and new, small‐grain large‐extent data need to be collected. 4) Although macroecology already lead to mainstreaming cutting‐edge statistical analysis techniques, we find that more sophisticated methods are needed to account for the biases inherent to sampling at large scale. Bayesian methods may be particularly suitable to address these challenges. To continue the vigorous development of the macroecological research agenda, it is time to address these challenges and to avoid becoming too complacent with current achievements.     

The phytohormone auxin is a component of the regulatory system that controls UV-mediated accumulation of flavonoids and UV-induced morphogenesis

In plants, ultraviolet (UV)-B acclimation is a complex, dynamic process that plays an essential role in preventing UV-B damage to targets such as DNA and the photosynthetic machinery. In this study we tested the hypothesis that the phytohormone auxin is a component of the regulatory system that controls both UV-mediated accumulation of flavonoids and UV-induced morphogenesis. We found that the leaf area of Arabidopsis thaliana Col-0 plants raised under a low dose of UV radiation (0.56 kJ m(-2) daily dose) was, on average, decreased by 23% relative to plants raised in the absence of UV-B, and this was accompanied by a decrease (P = 0.063) in free auxin in young leaf tissues. Compared to Col-0, both the auxin influx mutant axr4-1 and the auxin biosynthesis mutant nit1-3 displayed significantly stronger morphogenic responses, i.e. relative decreases in leaf area were greater for these two mutants. UV exposure also induced accumulation of flavonoids. In Col-0, increases in the concentrations of specific kaempferol derivatives ranged from 2.1- to 19-fold. Thus, UV induces complex changes in flavonoid-glycosylation patterns. Compared to Col-0, three auxin mutants displayed significantly different flavonoid profiles. Thus, based on mutant analysis, it is concluded that the phytohormone auxin plays a role in UV acclimation by regulating flavonoid concentration, flavonoid-glycosylation pattern and by controlling UV-induced morphogenic responses.     

Visualizing the genetic landscape of Arabidopsis seed performance

Perfect timing of germination is required to encounter optimal conditions for plant survival and is the result of a complex interaction between molecular processes, seed characteristics, and environmental cues. To detangle these processes, we made use of natural genetic variation present in an Arabidopsis (Arabidopsis thaliana) Bayreuth × Shahdara recombinant inbred line population. For a detailed analysis of the germination response, we characterized rate, uniformity, and maximum germination and discuss the added value of such precise measurements. The effects of after-ripening, stratification, and controlled deterioration as well as the effects of salt, mannitol, heat, cold, and abscisic acid (ABA) with and without cold stratification were analyzed for these germination characteristics. Seed morphology (size and length) of both dry and imbibed seeds was quantified by using image analysis. For the overwhelming amount of data produced in this study, we developed new approaches to perform and visualize high-throughput quantitative trait locus (QTL) analysis. We show correlation of trait data, (shared) QTL positions, and epistatic interactions. The detection of similar loci for different stresses indicates that, often, the molecular processes regulating environmental responses converge into similar pathways. Seven major QTL hotspots were confirmed using a heterogeneous inbred family approach. QTLs colocating with previously reported QTLs and well-characterized mutants are discussed. A new connection between dormancy, ABA, and a cripple mucilage formation due to a naturally occurring mutation in the MUCILAGE-MODIFIED2 gene is proposed, and this is an interesting lead for further research on the regulatory role of ABA in mucilage production and its multiple effects on germination parameters.     

Brain-Derived Neurotrophic Factor in Post-Partum Depressive Mothers

Postpartum depression (PPD) is the most common psychiatric complication observed in women after they give birth. Some women are particularly sensitive to hormonal changes, starting in early menarche, thus increasing their vulnerability to psychological stressing agents that are triggered by environmental and physiological factors throughout their lives. Decreased serum brain-derived neurotrophic factor (BDNF) levels have been associated to different neuropsychiatric conditions and BDNF has been considered as a candidate marker for such dysfunctions. The goal of this study was to compare the levels of BDNF between mothers who suffer from PPD and healthy control mothers as well as to searching for associations between BDNF levels and the severity of PPD. This is a case-control study including 36 mothers with PPD and 36 healthy control mothers. PPD was defined according to the Beck Depression Inventory (BDI). Serum BDNF was assayed with the sandwich ELISA method. Results: Serum levels of BDNF were significantly lower in women with PPD than in control mothers (P?≤?0.03). A significant negative correlation between BDI score and serum BDNF levels was observed (P?≤?0.02 and r?=?-0.29). Our study demonstrated that low BDNF levels are associated with PPD. This result point out to the potential usage of BDNF in the screening of PPD, which could promote early treatment and, therefore, reduce the burden to the PPD women and to the health system.     

Individual differences in metabolomics: individualised responses and between-metabolite relationships

Many metabolomics studies aim to find ‘biomarkers’: sets of molecules that are consistently elevated or decreased upon experimental manipulation. Biological effects, however, often manifest themselves along a continuum of individual differences between the biological replicates in the experiment. Such differences are overlooked or even diminished by methods in standard use for metabolomics, although they may contain a wealth of information on the experiment. Properly understanding individual differences is crucial for generating knowledge in fields like personalised medicine, evolution and ecology. We propose to use simultaneous component analysis with individual differences constraints (SCA-IND), a data analysis method from psychology that focuses on these differences. This method constructs axes along the natural biochemical differences between biological replicates, comparable to principal components. The model may shed light on changes in the individual differences between experimental groups, but also on whether these differences correspond to, e.g., responders and non-responders or to distinct chemotypes. Moreover, SCA-IND reveals the individuals that respond most to a manipulation and are best suited for further experimentation. The method is illustrated by the analysis of individual differences in the metabolic response of cabbage plants to herbivory. The model reveals individual differences in the response to shoot herbivory, where two ‘response chemotypes’ may be identified. In the response to root herbivory the model shows that individual plants differ strongly in response dynamics. Thereby SCA-IND provides a hitherto unavailable view on the chemical diversity of the induced plant response, that greatly increases understanding of the system.     

Generic framework for high-dimensional fixed-effects ANOVA

In functional genomics it is more rule than exception that experimental designs are used to generate the data. The samples of the resulting data sets are thus organized according to this design and for each sample many biochemical compounds are measured, e.g. typically thousands of gene-expressions or hundreds of metabolites. This results in high-dimensional data sets with an underlying experimental design. Several methods have recently become available for analyzing such data while utilizing the underlying design. We review these methods by putting them in a unifying and general framework to facilitate understanding the (dis-)similarities between the methods. The biological question dictates which method to use and the framework allows for building new methods to accommodate a range of such biological questions. The framework is built on well known fixed-effect ANOVA models and subsequent dimension reduction. We present the framework both in matrix algebra as well as in more insightful geometrical terms. We show the workings of the different special cases of our framework with a real-life metabolomics example from nutritional research and a gene-expression example from the field of virology.     

Anti-plasmodial activity of Dicoma tomentosa (Asteraceae) and identification of urospermal A-15- O-acetate as the main active compound

ABSTRACT: BACKGROUND: Natural products could play an important role in the challenge to discover new anti-malarial drugs. In a previous study, Dicoma tomentosa (Asteraceae) was selected for its promising anti-plasmodial activity after a preliminary screening of several plants traditionally used in Burkina Faso to treat malaria. The aim of the present study was to further investigate the antiplasmodial properties of this plant and to isolate the active anti-plasmodial compounds. METHODS: Eight crude extracts obtained from D. tomentosa whole plant were tested in vitro against two Plasmodium falciparum strains (3D7 and W2) using the p-LDH assay (colorimetric method). The Peters' four-days suppressive test model (Plasmodium berghei-infected mice) was used to evaluate the in vivo anti-plasmodial activity. An in vitro bioguided fractionation was undertaken on a dichloromethane extract, using preparative HPLC and TLC techniques. The identity of the pure compound was assessed using UV, MS and NMR spectroscopic analysis. In vitro cytotoxicity against WI38 human fibroblasts (WST-1 assay) and haemolytic activity were also evaluated for extracts and pure compounds in order to check selectivity. RESULTS: The best in vitro anti-plasmodial results were obtained with the dichloromethane, diethylether, ethylacetate and methanol extracts, which exhibited a high activity (IC50 [less than or equal to] 5 mug/ml). Hot water and hydroethanolic extracts also showed a good activity (IC50 [less than or equal to] 15 mug/ml), which confirmed the traditional use and the promising anti-malarial potential of the plant. The activity was also confirmed in vivo for all tested extracts. However, most of the active extracts also exhibited cytotoxic activity, but no extract was found to display any haemolytic activity. The bioguided fractionation process allowed to isolate and identify a sesquiterpene lactone (urospermal A-15-O-acetate) as the major anti-plasmodial compound of the plant (IC50 < 1 mug/ml against both 3D7 and W2 strains). This was also found to be the main cytotoxic compound (SI =3.3). While this melampolide has already been described in the plant, this paper is the first report on the biological properties of this compound. CONCLUSIONS: The present study highlighted the very promising anti-plasmodial activity of D. tomentosa and enabled to identify its main active compound, urospermal A-15-O-acetate. The high antiplasmodial activity of this compound merits further study about its anti-plasmodial mechanism of action. The active extracts of D. tomentosa, as well as urospermal A 15-Oacetate, displayed only a moderate selectivity, and further studies are needed to assess the safety of the use of the plant by the local population.     

Genome Analysis of Legionella pneumophila Strains Using a Mixed-Genome Microarray

Background

Legionella, the causative agent for Legionnaires’ disease, is ubiquitous in both natural and man-made aquatic environments. The distribution of Legionella genotypes within clinical strains is significantly different from that found in environmental strains. Developing novel genotypic methods that offer the ability to distinguish clinical from environmental strains could help to focus on more relevant (virulent) Legionella species in control efforts. Mixed-genome microarray data can be used to perform a comparative-genome analysis of strain collections, and advanced statistical approaches, such as the Random Forest algorithm are available to process these data.

Methods

Microarray analysis was performed on a collection of 222 Legionella pneumophila strains, which included patient-derived strains from notified cases in the Netherlands in the period 2002–2006 and the environmental strains that were collected during the source investigation for those patients within the Dutch National Legionella Outbreak Detection Programme. The Random Forest algorithm combined with a logistic regression model was used to select predictive markers and to construct a predictive model that could discriminate between strains from different origin: clinical or environmental.

Results

Four genetic markers were selected that correctly predicted 96% of the clinical strains and 66% of the environmental strains collected within the Dutch National Legionella Outbreak Detection Programme.

Conclusions

The Random Forest algorithm is well suited for the development of prediction models that use mixed-genome microarray data to discriminate between Legionella strains from different origin. The identification of these predictive genetic markers could offer the possibility to identify virulence factors within the Legionella genome, which in the future may be implemented in the daily practice of controlling Legionella in the public health environment.