The association between childhood obesity and tooth eruption

Obesity is a growth-promoting process as evidenced by its effect on the timing of puberty. Although studies are limited, obesity has been shown to affect the timing of tooth eruption. Both the timing and sequence of tooth eruption are important to overall oral health. The purpose of this study was to examine the association between obesity and tooth eruption. Data were combined from three consecutive cycles (2001-2006) of the National Health and Nutrition Examination Survey (NHANES) and analyzed to examine associations between the number of teeth erupted (NET) and obesity status (BMI z-score >95th percentile BMI relative to the Centers for Disease Control and Prevention (CDC) growth reference) among children 5 up to 14 years of age, controlling for potential confounding by age, gender, race, and socioeconomic status (SES). Obesity is significantly associated with having a higher average NET during the mixed dentition period. On average, teeth of obese children erupted earlier than nonobese children with obese children having on average 1.44 more teeth erupted than nonobese children, after adjusting for age, gender, and race/ethnicity (P < 0.0001). SES was not a confounder of the observed associations. Obese children, on average, have significantly more teeth erupted than nonobese children after adjusting for gender, age, and race. These findings may have clinical importance in the area of dental and orthodontic medicine both in terms of risk for dental caries due to extended length of time exposed in the oral cavity and sequencing which may increase the likelihood of malocclusions.     

Dirichlet multinomial mixtures: generative models for microbial metagenomics

We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving the number of times each taxa is observed in each sample. The samples have different size, and the matrix is sparse, as communities are diverse and skewed to rare taxa. Most methods used previously to classify or cluster samples have ignored these features. We describe each community by a vector of taxa probabilities. These vectors are generated from one of a finite number of Dirichlet mixture components each with different hyperparameters. Observed samples are generated through multinomial sampling. The mixture components cluster communities into distinct 'metacommunities', and, hence, determine envirotypes or enterotypes, groups of communities with a similar composition. The model can also deduce the impact of a treatment and be used for classification. We wrote software for the fitting of DMM models using the 'evidence framework' (http://code.google.com/p/microbedmm/). This includes the Laplace approximation of the model evidence. We applied the DMM model to human gut microbe genera frequencies from Obese and Lean twins. From the model evidence four clusters fit this data best. Two clusters were dominated by Bacteroides and were homogenous; two had a more variable community composition. We could not find a significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the 'Anna Karenina principle (AKP)' applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohn's disease (ICD) is associated with a more variable community.     

Using Stochastic modelling to identify unusual continuous glucose monitor measurements and behaviour, in newborn infants

ABSTRACT: BACKGROUND: : Abnormal blood glucose (BG) concentrations have been associated with increased morbidity and mortality in both critically ill adults and infants. Furthermore, hypoglycaemia and glycaemic variability have both been independently linked to mortality in these patients. Continuous Glucose Monitoring (CGM) devices have the potential to improve detection and diagnosis of these glycaemic abnormalities. However, sensor noise is a trade-off of the high measurement rate and must be managed effectively if CGMs are going to be used to monitor, diagnose and potentially help treat glycaemic abnormalities. AIM: To develop a tool that will aid clinicians in identifying unusual CGM behaviour and highlight CGM data that potentially need to be interpreted with care. METHOD: S: CGM data and BG measurements from 50 infants at risk of hypoglycaemia were used. Unusual CGM measurements were classified using a stochastic model based on the kernel density method and historical CGM measurements from the cohort. CGM traces were colour coded with very unusual measurements coloured red, highlighting areas to be interpreted with care. A 5-fold validation of the model was Monte Carlo simulated 25 times to ensure an adequate model fit. RESULTS: : The stochastic model was generated using ~67,000 CGM measurements, spread across the glycaemic range ~2-10mmol/L. A 5-fold validation showed a good model fit: the model 80% confidence interval (CI) captured 83% of clinical CGM data, the model 90% CI captured 91% of clinical CGM data, and the model 99% CI captured 99% of clinical CGM data. Three patient examples show the stochastic classification method in use with 1) A stable, low variability patient which shows no unusual CGM measurements, 2) A patient with a very sudden, short hypoglycaemic event (classified as unusual), and, 3) A patient with very high, potentially un-physiological, glycaemic variability after day 3 of monitoring (classified as very unusual). CONCLUSIONS: : This study has produced a stochastic model and classification method capable of highlighting unusual CGM behaviour. This method has the potential to classify important glycaemic events (e.g. hypoglycaemia) as true clinical events or sensor noise, and to help identify possible sensor degradation. Colour coded CGM traces convey the information quickly and efficiently, while remaining computationally light enough to be used retrospectively or in real-time.     

Thermal luminescence spectra of polyamides and their Maillard reaction with reducing sugars

Thermal luminescence (TL) spectra of polyamides were measured with a Fourier‐transform chemiluminescence spectrometer to elucidate the emission mechanism. A TL band of ε‐polylysine with a peak at 542 nm observed at 403 K was assigned to the emission due to the interaction of the –CO–NH– group with oxygen molecules by comparison with nylon‐6, polyglycine, and polyalanine. When the sample was kept at 453 K, the intensity of the TL band decreased and the wavelength of the peak shifted to 602 nm, which was assigned to the emission due to the interaction of the NH₂ group on the side chain with oxygen molecules by comparison with monomeric lysine. A weak emission with a peak at 668 nm was assigned to the advanced glycosylation end products (AGEs) yielded by the Maillard reaction with a catalytic amount of water. To understand this reaction and to examine the TL emission of AGEs, we measured TL spectra of mixtures of polylysine and reducing sugars such as glucose, maltose, lactose, and dextrin. The minimum temperature for TL emission, wavelength of the peak and the relative intensities of the TL emission were found to depend on the size of the sugars. Copyright © 2011 John Wiley & Sons, Ltd.     

Photoinitiated alkyne-azide click and radical cross-linking reactions for the patterning of PEG hydrogels

The photolithographical patterning of hydrogels based solely on the surface immobilization and cross-linking of alkyne-functionalized poly(ethylene glycol) (PEG-tetraalkyne) is described. Photogenerated radicals as well as UV absorption by a copper chelating ligand result in the photochemical redox reduction of Cu(II) to Cu(I). This catalyzes the alkyne-azide click reaction to graft the hydrogels onto an azide-functionalized plasma polymer (N(3)PP) film. The photogenerated radicals were also able to abstract hydrogen atoms from PEG-tetraalkyne to form poly(α-alkoxy) radicals. These radicals can initiate cross-linking by addition to the alkynes and intermolecular recombination to form the PEG hydrogels. Spatially controlling the two photoinitiated reactions by UV exposure through a photomask leads to surface patterned hydrogels, with thicknesses that were tunable from tens to several hundreds of nanometers. The patterned PEG hydrogels (ca. 60 μm wide lines) were capable of resisting the attachment of L929 mouse fibroblast cells, resulting in surfaces with spatially controlled cell attachment. The patterned hydrogel surface also demonstrated spatially resolved chemical functionality, as postsynthetic modification of the hydrogels was successfully carried out with azide-functionalized fluorescent dyes via subsequent alkyne-azide click reactions.     

Direct maximization of protein identifications from tandem mass spectra

The goal of many shotgun proteomics experiments is to determine the protein complement of a complex biological mixture. For many mixtures, most methodological approaches fall significantly short of this goal. Existing solutions to this problem typically subdivide the task into two stages: first identifying a collection of peptides with a low false discovery rate and then inferring from the peptides a corresponding set of proteins. In contrast, we formulate the protein identification problem as a single optimization problem, which we solve using machine learning methods. This approach is motivated by the observation that the peptide and protein level tasks are cooperative, and the solution to each can be improved by using information about the solution to the other. The resulting algorithm directly controls the relevant error rate, can incorporate a wide variety of evidence and, for complex samples, provides 18-34% more protein identifications than the current state of the art approaches.     

Isolation of immune cells from primary tumors

Tumors create a unique immunosuppressive microenvironment (tumor microenvironment, TME) whereby leukocytes are recruited into the tumor by various chemokines and growth factors. However, once in the TME, these cells lose the ability to promote anti-tumor immunity and begin to support tumor growth and down-regulate anti-tumor immune responses. Studies on tumor-associated leukocytes have mainly focused on cells isolated from tumor-draining lymph nodes or spleen due to the inherent difficulties in obtaining sufficient cell numbers and purity from the primary tumor. While identifying the mechanisms of cell activation and trafficking through the lymphatic system of tumor bearing mice is important and may give insight to the kinetics of immune responses to cancer, in our experience, many leukocytes, including dendritic cells (DCs), in tumor-draining lymph nodes have a different phenotype than those that infiltrate tumors. Furthermore, we have previously demonstrated that adoptively-transferred T cells isolated from the tumor-draining lymph nodes are not tolerized and are capable of responding to secondary stimulation in vitro unlike T cells isolated from the TME, which are tolerized and incapable of proliferation or cytokine production. Interestingly, we have shown that changing the tumor microenvironment, such as providing CD4(+) T helper cells via adoptive transfer, promotes CD8(+) T cells to maintain pro-inflammatory effector functions. The results from each of the previously mentioned studies demonstrate the importance of measuring cellular responses from TME-infiltrating immune cells as opposed to cells that remain in the periphery. To study the function of immune cells which infiltrate tumors using the Miltenyi Biotech isolation system, we have modified and optimized this antibody-based isolation procedure to obtain highly enriched populations of antigen presenting cells and tumor antigen-specific cytotoxic T lymphocytes. The protocol includes a detailed dissection of murine prostate tissue from a spontaneous prostate tumor model (TRansgenic Adenocarcinoma of the Mouse Prostate -TRAMP) and a subcutaneous melanoma (B16) tumor model followed by subsequent purification of various leukocyte populations.     

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,^5,12,20) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization.Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods^{3,4,9,10,13-15,17-19,22,23,25}. In this application, we demonstrate a program which follows Rabotyagov et al.''s approach and integrates a modern and commonly used SWAT water quality model⁷ with a multiobjective evolutionary algorithm SPEA2²⁶, and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.