Intrauterine growth restriction affects bone mineral density of the mandible and the condyle in growing rats

Objectives:To investigate in growing rats the effect of intrauterine growth restriction (IUGR) on the bone mineral density of the mandible and tibia, as well as the quality of the mandibular and condylar bone.Methods:Twelve male rats were born IUGR by mothers sustaining 50% food restriction during pregnancy. Twelve control male rats were born by mothers fed ad libitum. Dual-energy X-ray absorptiometry (DEXA) of the tibia, proximal tibial metaphysis and the mandible, biochemical markers, histology and histomorphometrical analysis on the mandibular and subchondral bone of the condyle were performed.Results:IUGR significantly affected bone mineral density (BMD) of both tibial and mandibular bones. IUGR rats had significantly lower osteocalcin values (p=0.021) and phosphorus (p=0.028), but not 25-OH vitamin D (p=0.352). Bone area percentage in the mandible was significantly lower (51.21±5.54) in IUGR compared to controls (66.00±15.49), and for subchondral bone of the condyle for IUGR (47.01±6.82) compared to controls (68.27±13.37). IUGR had a significant reduction in the fibrous layer, but not the proliferating layer, with the hypertrophic layer significantly increased.Conclusion:Maternal restricted nutrition during gestation can affect BMD of the mandible and the tibia of the offspring animals.     

Flow Measurements in a Blood-Perfused Collagen Vessel Using X-Ray Micro-Particle Image Velocimetry

Blood-perfused tissue models are joining the emerging field of tumor engineering because they provide new avenues for modulation of the tumor microenvironment and preclinical evaluation of the therapeutic potential of new treatments. The characterization of fluid flow parameters in such in-vitro perfused tissue models is a critical step towards better understanding and manipulating the tumor microenvironment. However, traditional optical flow measurement methods are inapplicable because of the opacity of blood and the thickness of the tissue sample. In order to overcome the limitations of optical method we demonstrate the feasibility of using phase-contrast x-ray imaging to perform microscale particle image velocimetry (PIV) measurements of flow in blood perfused hydrated tissue-representative microvessels. However, phase contrast x-ray images significantly depart from the traditional PIV image paradigm, as they have high intensity background, very low signal-to-noise ratio, and volume integration effects. Hence, in order to achieve accurate measurements special attention must be paid to the image processing and PIV cross-correlation methodologies. Therefore we develop and demonstrate a methodology that incorporates image preprocessing as well as advanced PIV cross-correlation methods to result in measured velocities within experimental uncertainty.     

Non-equilibrium trajectory dynamics and the kinematics of gliding in a flying snake

Given sufficient space, it is possible for gliding animals to reach an equilibrium state with no net forces acting on the body. In contrast, every gliding trajectory must begin with a non-steady component, and the relative importance of this phase is not well understood. Of any terrestrial animal glider, snakes exhibit the greatest active movements, which may affect their trajectory dynamics. Our primary aim was to determine the characteristics of snake gliding during the transition to equilibrium, quantifying changes in velocity, acceleration, and body orientation in the late phase of a glide sequence. We launched 'flying' snakes (Chrysopelea paradisi) from a 15 m tower and recorded the mid-to-end portion of trajectories with four videocameras to reconstruct the snake's body position with mm to cm accuracy. Additionally, we developed a simple analytical model of gliding assuming only steady-state forces of lift, drag and weight acting on the body and used it to explore effects of wing loading, lift-to-drag ratio, and initial velocity on trajectory dynamics. Despite the vertical space provided to transition to steady-state gliding, snakes did not exhibit equilibrium gliding and in fact displayed a net positive acceleration in the vertical axis, an effect also predicted by the analytical model.     

Searching for Footprints of Positive Selection in Whole-Genome SNP Data From Nonequilibrium Populations

A major goal of population genomics is to reconstruct the history of natural populations and to infer the neutral and selective scenarios that can explain the present-day polymorphism patterns. However, the separation between neutral and selective hypotheses has proven hard, mainly because both may predict similar patterns in the genome. This study focuses on the development of methods that can be used to distinguish neutral from selective hypotheses in equilibrium and nonequilibrium populations. These methods utilize a combination of statistics on the basis of the site frequency spectrum (SFS) and linkage disequilibrium (LD). We investigate the patterns of genetic variation along recombining chromosomes using a multitude of comparisons between neutral and selective hypotheses, such as selection or neutrality in equilibrium and nonequilibrium populations and recurrent selection models. We perform hypothesis testing using the classical P-value approach, but we also introduce methods from the machine-learning field. We demonstrate that the combination of SFS- and LD-based statistics increases the power to detect recent positive selection in populations that have experienced past demographic changes.GENOMES contain information related to the history of natural populations. Past neutral and selective processes may have left footprints in the genome. Recent advances in population genetics aim to understand the patterns of genetic diversity and identify events that have led to genetic adaptations. Among them, positive selection has been a focus of many recent studies (Harr et al. 2002; Kim and Stephan 2002; Glinka et al. 2003; Akey et al. 2004; Orengo and Aguadé 2004). Their goal is to (i) provide evidence of positive selection, (ii) estimate the strength and the rate of selection, and (iii) localize the targets of selection. These objectives form the basis of a long-term pursuit, which is the understanding of the molecular basis of adaptation of populations in a changing environment.Positive selection can cause genetic hitchhiking when a beneficial mutation spreads in the population (Maynard Smith and Haigh 1974). When a strongly beneficial mutation occurs and spreads in a population, linked neutral or slightly deleterious variants hitchhike with it, and their frequency increases. According to Maynard Smith and Haigh''s model, three patterns are generated locally around the position of the beneficial mutation. First, the level of variability will be reduced since standing variation of the population that is not linked to the beneficial allele vanishes, and tightly linked polymorphisms may fix (Kaplan et al. 1989; Stephan et al. 1992). Second, the site frequency spectrum (SFS), which describes the frequency of allelic variants, shifts from its neutral expectation toward rare and high-frequency derived variants (Braverman et al. 1995; Fay and Wu 2000). The third signature describes the emergence of specific linkage disequilibrium (LD) patterns around the target of positive selection, such as an elevated level of LD in the early phase of the fixation process of the beneficial mutation and a decay of LD across the selected site at the end of the selective phase (Kim and Nielsen 2004; Stephan et al. 2006).The availability of genome-wide SNP data has made possible the scanning of genomes and the identification of loci that may have been targets of recent selective events. Several approaches have been developed within the last years that can detect the molecular signatures of positive selection (Kim and Stephan 2002; Jensen et al. 2005; Nielsen et al. 2005). While the methods of Kim and Stephan (2002) and Jensen et al. (2005) are designed to analyze subgenomic SNP data, the approach of Nielsen et al. (2005) can be applied to both subgenomic and whole-genome data (reviewed in Pavlidis et al. 2008). For this reason we concentrate here on the latter procedure. This method, called SweepFinder, calculates the probability P(x) that a polymorphism of multiplicity x is linked to a beneficial mutation using a simple selective model and the SFS prior to the selective event. Then, for each location in the genome it compares a selective with a neutral model assuming independence between the SNPs, therefore calculating the composite likelihood ratio Λ. Thus, it identifies regions where the likelihood of the selective sweep is greater than that of the neutral model using the maximum value Λ_MAX of Λ.The ω-statistic, developed by Kim and Nielsen (2004), detects specific LD patterns caused by genetic hitchhiking (described above). In the study by Kim and Nielsen (2004) the maximum value of the ω-statistic was used to identify the targets of selective sweeps. Later, Jensen et al. (2007) studied its performance in separating demographic from selective scenarios. An important result by Jensen et al. (2007) is the demonstration that for demographic parameters relevant to nonequilibrium populations (such as the cosmopolitan populations of Drosophila melanogaster) the ω-statistic can distinguish between neutral and selective scenarios. This article further develops SweepFinder and the ω-statistic such that they can eventually be applied to whole-genome SNP data sets that have been collected from nonequilibrium populations. In particular, populations undergoing population-size bottlenecks are of interest as these size changes may confound the patterns of selective sweeps (Barton 1998). For this reason we use the following approach: first, we theoretically analyze the genealogies of bottlenecked populations under neutrality and show to what extent they resemble the genealogies of single hitchhiking (SHH) events. We also point out the importance of high-frequency-derived variants in the identification of selective sweeps. Second, we study the statistical properties of SweepFinder and the ω-statistic separately and in combination. As the main result, we demonstrate that the combination of these two methods (that include both SFS and LD information) increases the power for detecting recent SHH events in nonequilibrium populations, in particular when machine-learning techniques are employed. Third we analyze the performance of SweepFinder and the ω-statistic in the detection of recurrent hitchhiking (RHH) events.     

Eleven‐year Prevalence Trends of Obesity in Greek Children: First Evidence that Prevalence of Obesity Is Leveling Off

We examined 11‐year (1997–2007) trends in underweight, overweight, and obesity in Greek children. Population data derived from a yearly, school‐based health survey carried out between 1997 and 2007 in >80% of all Greek schools. Height and weight measurements from 651,582 children, aged 8–9 years (boys: 51.2%) were analyzed. The gender‐ and age‐specific BMI cutoff points by the International Obesity Task Force (IOTF) were used in order to define underweight, normal weight, overweight, and obesity. Trend analysis showed an increase in the prevalence of obesity from 7.2 ± 0.2% in 1997 to 11.3 ± 0.2% in 2004 for girls (P < 0.001) and from 8.1 ± 0.2% in 1997 to 12.3 ± 0.2% in 2004 for boys (P < 0.001). An apparent leveling off in obesity rates was observed during 2004–2007 for both boys and girls. The prevalence of overweight rose between 1997 and 2007 from 20.2 ± 0.2% to 26.7 ± 0.2% for girls (P < 0.001) and from 19.6 ± 0.2% to 26.5 ± 0.2% for boys (P < 0.001). The overall prevalence of thinness in the same period remained constant in both sexes. The presented population‐based data revealed that the prevalence of overweight and obesity among 8‐ to 9‐year‐old Greek children is alarmingly elevated, with the overweight rates rising continuously. However, an apparent leveling off in obesity rates for the past 4 consecutive years was documented for the first time in both genders.     

Tunable Collagen I Hydrogels for Engineered Physiological Tissue Micro-Environments

Collagen I hydrogels are commonly used to mimic the extracellular matrix (ECM) for tissue engineering applications. However, the ability to design collagen I hydrogels similar to the properties of physiological tissues has been elusive. This is primarily due to the lack of quantitative correlations between multiple fabrication parameters and resulting material properties. This study aims to enable informed design and fabrication of collagen hydrogels in order to reliably and reproducibly mimic a variety of soft tissues. We developed empirical predictive models relating fabrication parameters with material and transport properties. These models were obtained through extensive experimental characterization of these properties, which include compression modulus, pore and fiber diameter, and diffusivity. Fabrication parameters were varied within biologically relevant ranges and included collagen concentration, polymerization pH, and polymerization temperature. The data obtained from this study elucidates previously unknown fabrication-property relationships, while the resulting equations facilitate informed a priori design of collagen hydrogels with prescribed properties. By enabling hydrogel fabrication by design, this study has the potential to greatly enhance the utility and relevance of collagen hydrogels in order to develop physiological tissue microenvironments for a wide range of tissue engineering applications.