Air Pollution Exposure Assessment for Epidemiologic Studies of Pregnant Women and Children: Lessons Learned from the Centers for Children’s Environmental Health and Disease Prevention Research

The National Children’s Study is considering a wide spectrum of airborne pollutants that are hypothesized to potentially influence pregnancy outcomes, neurodevelopment, asthma, atopy, immune development, obesity, and pubertal development. In this article we summarize six applicable exposure assessment lessons learned from the Centers for Children’s Environmental Health and Disease Prevention Research that may enhance the National Children’s Study: a) Selecting individual study subjects with a wide range of pollution exposure profiles maximizes spatial-scale exposure contrasts for key pollutants of study interest. b) In studies with large sample sizes, long duration, and diverse outcomes and exposures, exposure assessment efforts should rely on modeling to provide estimates for the entire cohort, supported by subject-derived questionnaire data. c) Assessment of some exposures of interest requires individual measurements of exposures using snapshots of personal and microenvironmental exposures over short periods and/or in selected microenvironments. d) Understanding issues of spatial–temporal correlations of air pollutants, the surrogacy of specific pollutants for components of the complex mixture, and the exposure misclassification inherent in exposure estimates is critical in analysis and interpretation. e) “Usual” temporal, spatial, and physical patterns of activity can be used as modifiers of the exposure/outcome relationships. f) Biomarkers of exposure are useful for evaluation of specific exposures that have multiple routes of exposure. If these lessons are applied, the National Children’s Study offers a unique opportunity to assess the adverse effects of air pollution on interrelated health outcomes during the critical early life period

Preventing Sybil attacks in P2P file sharing networks based on the evolutionary game model

In cooperative Peer-to-Peer (P2P) networks, a number of users, called Free-riders, try to receive service from others without cooperating with them. Some others, called Sybil nodes, break the rules of the system by colluding and showing fake identities. P2P networks are highly vulnerable to these attacks. In previous research, no method has been suggested to counter these two attacks simultaneously. In the proposed method, a new centrality relationship has been used in the incentive mechanism to deal with both problems at the same time. In this regard, the more varied the nodes receiving service from a peer are, the better the peer reputation will be. The results show that the longer the network life goes on, the more free-riders are detected, and the number of services delivered to the collusive nodes will also be reduced. © 201

A metabolic cage for the hindlimb suspended rat

Hindlimb suspension has been successfully used to simulate the effects of microgravity in rats. The cage and suspension system developed by E. R. Holton is designed to produce a headward shift of fluid and unload the hindlimbs in rodents, causing changes in bone and muscle similar to those in animals and humans exposed to spaceflight. While the Holton suspension system simulates many of the conditions observed in the spaceflight animal, it does not provide for the collection of urine and feces needed to monitor some metabolic activities. As a result, only limited information has been gathered on the nutritional status, and the gastrointestinal and renal function of animals using that model. Although commercial metabolic cages are available, they are usually cylindrical and require a centrally located suspension system and thus, do not readily permit movement of the rats. The limited floor space of commercial cages may affect comparisons with studies using the Holton model which has more than twice the living space of most commercially available cages. To take advantage of the extra living space and extensive data base that has been developed with the Holton model, Holton's cage was modified to make urine and fecal collections possible

Effects of epitaxial strain on the growth mechanism of YBa2Cu3O7-x thin films in [YBa2Cu3O7-x / PrBa2Cu3O7-x] superlattices

We report on the growth mechanism of YBa2Cu3O7-x (YBCO). Our study is based on the analysis of ultrathin, YBa2Cu3O7-x layers in c-axis oriented YBa2Cu3O7-x / PrBa2Cu3O7-x superlattices. We have found that the release of epitaxial strain in very thin YBCO layers triggers a change in the dimensionality of the growth mode. Ultrathin, epitaxially strained, YBCO layers with thickness below 3 unit cells grow in a block by block two dimensional mode coherent over large lateral distances. Meanwhile, when thickness increases, and the strain relaxes, layer growth turns into three dimensional, resulting in rougher layers and interfaces.Comment: 10 pages + 9 figures, accepted in Phys. Rev.

<i>Virgatasporites</i> and <i>Attritasporites</i>: the oldest land plant derived spores, cryptospores or acritarchs?

peer reviewe

Promoting the use of the PRECISE score for prostate MRI during active surveillance: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship

OBJECTIVES: The PRECISE criteria for serial multiparametric magnetic resonance imaging (MRI) of the prostate during active surveillance recommend the use of a dedicated scoring system (PRECISE score) to assess the likelihood of clinically significant radiological change. This pilot study assesses the effect of an interactive teaching course on prostate MRI during active surveillance in assessing radiological change in serial imaging. METHODS: Eleven radiology fellows and registrars with different experience in prostate MRI reading participated in a dedicated teaching course where they initially evaluated radiological change (based on their previous training in prostate MRI reading) independently in fifteen patients on active surveillance (baseline and follow-up scan), and then attended a lecture on the PRECISE score. The initial scans were reviewed for teaching purposes and afterwards the participants re-assessed the degree of radiological change in a new set of images (from fifteen different patients) applying the PRECISE score. Receiver operating characteristic analysis was performed. Confirmatory biopsies and PRECISE scores given in consensus by two radiologists (involved in the original draft of the PRECISE score) were the reference standard. RESULTS: There was a significant improvement in the average area under the curve (AUC) for the assessment of radiological change from baseline (AUC: 0.60 [Confidence Intervals: 0.51-0.69] to post-teaching (AUC: 0.77 [0.70-0.84]). This was an improvement of 0.17 [0.016-0.28] (p = 0.004). CONCLUSIONS: A dedicated teaching course on the use of the PRECISE score improves the accuracy in the assessment of radiological change in serial MRI of the prostate

Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis

Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence between brain regions are usually derived from the analysis of covariance between activation levels in the different areas. However, these covariance-based methods are not able to estimate conditional independence between variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence between regions by estimating the covariance between two variables given the rest as constant. This paper uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters between subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance between GM (Gray Matter) densities of different regions reveals different structural covariation patterns between the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative power for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the usefulness of the Sparse Gaussian Graphical models to reveal functional and structural connectivity patterns. This information provided by the sparse inverse covariance matrices is not only used in an exploratory way but we also propose a method to use it in a discriminative way. Regression coefficients are used to compute reconstruction errors for the different classes that are then introduced in a SVM for classification. Classification experiments performed using 68 Controls, 70 AD, and 111 MCI images and assessed by cross-validation show the effectiveness of the proposed method.This work was partly supported by the MICINN under the TEC2012-34306 project and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Projects P09-TIC-4530, P11-TIC-7103, and the Universidad de Málaga. Programa de fortalecimiento de las capacidades de I+D+I en las Universidades 2014–2015, de la Consejería de Economía, Innovación, Ciencia y Empleo, cofinanciado por el fondo europeo de desarrollo regional (FEDER) under the project FC14-SAF30.This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation

Rapid Sampling of Molecules via Skin for Diagnostic and Forensic Applications

Skin provides an excellent portal for diagnostic monitoring of a variety of entities; however, there is a dearth of reliable methods for patient-friendly sampling of skin constituents. This study describes the use of low-frequency ultrasound as a one-step methodology for rapid sampling of molecules from the skin. Sampling was performed using a brief exposure of 20 kHz ultrasound to skin in the presence of a sampling fluid. In vitro sampling from porcine skin was performed to assess the effectiveness of the method and its ability to sample drugs and endogenous epidermal biomolecules from the skin. Dermal presence of an antifungal drug—fluconazole and an abused substance, cocaine—was assessed in rats. Ultrasonic sampling captured the native profile of various naturally occurring moisturizing factors in skin. A high sampling efficiency (79 ± 13%) of topically delivered drug was achieved. Ultrasound consistently sampled greater amounts of drug from the skin compared to tape stripping. Ultrasonic sampling also detected sustained presence of cocaine in rat skin for up to 7 days as compared to its rapid disappearance from the urine. Ultrasonic sampling provides significant advantages including enhanced sampling from deeper layers of skin and high temporal sampling sensitivity