Epidemiology and microbiota of early childhood caries

Hintergrund: Die frühkindliche Karies (Milchzahnkaries) betrifft mehr als ein Drittel der Kinder in Deutschland mit Milchzähnen. Verlässliche Daten hinsichtlich der Verteilung in Deutschland oder auf kleinräumiger Stadtebene sind nicht verfügbar. Als Auslöser für die Karies sind mehrere Bakterien bereits identifiziert worden (zum Beispiel Streptococcus, Veillonella oder auch Prevotella). Karies zeigt somit einen polymikrobiellen Charakter, bei dem kein bestimmter Keim als Auslöser beschrieben werden kann. Ziele: (1) Analyse der räumliche Autokorrelation der Milchzahnkaries in den Kindergärten in Braunschweig, sowie einer zeitlichen Verlaufsanalyse der Daten von 2009-2014, (2-5) die Analyse des Mikrobioms kariöser und nicht-kariöser Milchzähne von Kindern aus drei verschiedenen Studien und (6) eine vergleichende Analyse der Mikrobiomzusammensetzung vor und nach dem Zähneputzen. Methoden:Die Daten der epidemiologischen Querschnittsstudie wurden mit einem spatial lag Modell analysiert. Die Analyse der mikrobiellen Zusammensetzung wurde nach der DNA-Extraktion mit Next Generation Sequencing durchgeführt. Ergebnisse: Wir konnten mit der epidemiologischen Studie Cluster von hohen dmft (decayed missing filled teeth)-Werten in der Stadt identifizieren und zeigen, dass diese Cluster durch soziodemographische Ungleichheiten erklärt werden können. Der dmft Index hat in Braunschweig von 2009-2014 abgenommen (1). Die „KraKi“-Studie zeigte, dass 5% der Teilnehmer mit Karies befallen waren (2). Hauptsächlich wurden in den Abstrichen Bakterien der Gattung Streptococcus und Neisseria identifiziert.. Die mikrobielle Plaquezusammensetzung bei Kindern aus Indonesien stellte sich ähnlich dar (3): Neisseria war die die häufigste Gattung gefolgt von Streptococcus. Der Vergleich beider Studien zeigte signifikante Unterschiede (4). Die klinischen Proben lieferten keine signifikanten Unterschiede zwischen Abstrichen von Probanden mit ECC und gesunden Probanden (5). Dennoch konnten zwei Gruppen identifiziert werden. Die erste Gruppe wurde charakterisiert durch die Gattungen Brevibacterium und Haemophilus. Die zweite Gruppe zeigte eine Dominanz von Actinomyces und Tannerella. Im abschließenden Teil (6) wurden Biomarker, die mit der Zahnplaque nach dem Zähneputzen waren, identifiziert. Streptococcus sobrinus war in der gereiften Plaque zu finden.Background: Early childhood caries (ECC) affects more than one third of the children in Germany with primary dentition. Reliable data on the distribution in Germany or on a small-scale city level are not available. As a trigger for caries, several bacteria have already been identified (for example Streptococcus, Veillonella or Prevotella). Caries is a polymicrobial disease, in which no specific bacteria can be described as infectious. Objectives: (1) Analysis of the spatial autocorrelation of ECC in daycare centers in Braunschweig, and a temporal analysis of these data from 2009-2014, (2-5) the analysis of the microbiome of carious and non-carious children from three different studies, and (6) a comparative analysis of microbial composition before and after tooth brushing. Methods: Data from the epidemiological cross-sectional study were analyzed using a spatial lag model. The analysis of the microbial composition was carried out after DNA extraction with Next Generation Sequencing. Results: With the epidemiologic study, we identified clusters of high dmft (decayed missing filled teeth) indices in city and were able to show that these clusters can be explained by sociodemographic disparities. The dmft index has decreased from 2009 to 2014 in Braunschweig (1). The “KraKi”-study showed at least 5% of the participants with decayed teeth (2). The main genera identified in these samples belonged to Streptococcus followed by Neisseria. The microbiota of children from Indonesia showed similar results with Neisseria followed by Streptococcus (3). The comparison of these two projects showed significant differences of the dental plaque composition (4). The clinical samples showed no differences comparing ECC and healthy samples (5). However, two different groups could be identified. Finally (6), biomarker identification showed Neisseria elongata and Streptococcus oralis within the dental plaque after tooth brushing, while Streptococcus sobrinus can be found in the maturated plaque of healthy adults

Information-Theoretic Inference of Large Transcriptional Regulatory Networks

The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR), an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes) network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Tcf7l2 plays pleiotropic roles in the control of glucose homeostasis, pancreas morphology, vascularization and regeneration

Type 2 diabetes (T2D) is a disease characterized by impaired insulin secretion. The Wnt signaling transcription factor Tcf7l2 is to date the T2D-associated gene with the largest effect on disease susceptibility. However, the mechanisms by which TCF7L2 variants affect insulin release from \u3b2-cells are not yet fully understood. By taking advantage of a tcf7l2 zebrafish mutant line, we first show that these animals are characterized by hyperglycemia and impaired islet development. Moreover, we demonstrate that the zebrafish tcf7l2 gene is highly expressed in the exocrine pancreas, suggesting potential bystander effects on \u3b2-cell growth, differentiation and regeneration. Finally, we describe a peculiar vascular phenotype in tcf7l2 mutant larvae, characterized by significant reduction in the average number and diameter of pancreatic islet capillaries. Overall, the zebrafish Tcf7l2 mutant, characterized by hyperglycemia, pancreatic and vascular defects, and reduced regeneration proves to be a suitable model to study the mechanism of action and the pleiotropic effects of Tcf7l2, the most relevant T2D GWAS hit in human populations

Validation of temporal parameters within the skating sub-techniques when roller skiing on a treadmill, using inertial measurement units

The aim of this study was to develop and validate a method using inertial measurements units (IMUs) to determine inner-cycle parameters (e.g., cycle, poles and skis contact, and swing time) and the main sub-techniques (i.e., G2, G3 and G4) in cross-country roller ski skating on a treadmill. The developed method is based on the detection of poles and skis initial and final contacts with the ground during the cyclic movements. Thirteen well-trained athletes skied at different combinations of speed (6–24 km∙h-1) and incline (2–14%) on a treadmill using the three different sub-techniques. They were equipped with IMUs attached to their wrists and skis. Their movements were tracked using reflective markers and a multiple camera infrared system. The IMU-based method was able to detect more than 99% of the temporal events. It calculated the inner-cycle temporal parameters with a precision ranging from 19 to 66 ms, corresponding to 3.0% to 7.8% of the corresponding inner-cycle duration. The obtained precision would likely allow differentiation of skiers on different performance levels and detection of technique changes due to fatigue. Overall, this laboratory validation provides interesting possibilities also for outdoor applications.publishedVersio

Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski Skating

Objective: The aim of this study was to provide a new machine learning method to determine temporal events and inner-cycle parameters (e.g., cycle, pole and ski contact and swing time) in cross-country roller-ski skating on the field, using a single inertial measurement unit (IMU). Methods: The developed method is based on long short-term memory neural networks to detect the initial and final contact of the poles and skis with the ground during the cyclic movements. Eleven athletes skied four laps of 2.5 km at a low and high intensity using skis with two different rolling coefficients. They were equipped with IMUs attached to the upper back, lower back and to the sternum. Data from force insoles and force poles were used as the reference system. Results: The IMU placed on the upper back provided the best results, as the LSTM network was able to determine the temporal events with a mean error ranging from −1 to 11 ms and had a standard deviation (SD) of the error between 64 and 70 ms. The corresponding inner-cycle parameters were calculated with a mean error ranging from −11 to 12 ms and an SD between 66 and 74 ms. The method detected 95% of the events for the poles and 87% of the events for the skis. Conclusion: The proposed LSTM method provides a promising tool for assessing temporal events and inner-cycle phases in roller-ski skating, showing the potential of using a single IMU to estimate different spatiotemporal parameters of human locomotion.publishedVersio

Plasma Cleaning of Steam Ingressed ITER First Mirrors

In ITER, the first mirrors (FMs) are vulnerable to an in-vessel coolant leak which could severely diminish their optical properties. To understand the scope of this potential impact, several FM samples were exposed to a steam and humidity test simulating the event in ITER. Both rhodium and molybdenum mirrors, observed a loss in specular reflectivity as a result (the loss being greater for the Mo mirror). Their surfaces were tarnished with the development a thin Rh oxide and a thick Mo oxide (120–170 nm). This study focusses on capacitively coupled radio frequency (CCRF) plasma cleaning of steam ingressed (SI) FM samples and follow their optical recovery. Plasma cleaning experiments were performed with 13.56 MHz CCRF plasma using argon and/or hydrogen as process gas (with 230 eV ion energy). Initial and final reflectivity measurements, chemical surface analysis using in vaccuo X-ray photoelectron spectroscopy, scanning electron microscopy, focused ion beam and roughness measurements, were carried out for each sample to evaluate the cleaning efficiency. Using the plasma cleaning technique, it was possible to remove the SI induced contamination from the mirror surfaces and recover their optical properties to the pristine levels. Several ‘voids/inclusions’ were seen to arise along the grain boundaries as a result of the SI procedure. The concentration of these ‘voids/inclusions’ was observed to increase till a certain point followed by a decrease with increasing cleaning time

Non-invasive laminar inference with MEG: comparison of methods and source inversion algorithms

Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency-specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings