Pregnancy lipidomic profiles and DNA methylation in newborns from the CHAMACOS cohort.

Lipids play a role in many biological functions and the newly emerging field of lipidomics aims to characterize the varying classes of lipid molecules present in biological specimens. Animal models have shown associations between maternal dietary supplementation with fatty acids during pregnancy and epigenetic changes in their offspring, demonstrating a mechanism through which prenatal environment can affect outcomes in children; however, data on maternal lipid metabolite levels during pregnancy and newborn DNA methylation in humans are sparse. In this study, we assessed the relationship of maternal lipid metabolites measured in the blood from pregnant women with newborn DNA methylation profiles in the Center for the Health Assessment of Mothers and Children of Salinas cohort. Targeted metabolomics was performed by selected reaction monitoring liquid chromatography and triple quadrupole mass spectrometry to measure 92 metabolites in plasma samples of pregnant women at ∼26 weeks gestation. DNA methylation was assessed using the Infinium HumanMethylation 450K BeadChip adjusting for cord blood cell composition. We uncovered numerous false discovery rate significant associations between maternal metabolite levels, particularly phospholipid and lysolipid metabolites, and newborn methylation. The majority of the observed relationships were negative, suggesting that higher lipid metabolites during pregnancy are associated with lower methylation levels at genes related to fetal development. These results further elucidate the complex relationship between early life exposures, maternal lipid metabolites, and infant epigenetic status

Development and Maintenance of the Gut-Associated Lymphoid Tissue (Galt): the Roles of Enteric Bacteria and Viruses

GALT can be subdivided into several compartments: (a) Peyer's patches (PP); (b) lamina propria (LP); and (c) intraepithelial leukocyte (IEL) spaces. The B-cell follicles of PP are quiescent in neonatal and germ-free (GF) adult mice. Germinal centers (GC), including sIgA(+) blasts, appear in the B follicles of formerly GF adult mice about 10-14 days after monoassociation with various gut commensal bacteria. The GC wax and wane over about a 3-week period, although the bacterial colonizers remain in the gut at high density. Neonatal mice, born of conventionally reared (CV), immunocompetent mothers, display GC reactions in PP postweaning, although pups of SCID mothers display precocious GC reactions at about 14 days of life. Normally, gut colonization of neonates with segmented filamentous bacteria (SFB) leads to explosive development of IgA plasmablasts in LP shortly after weaning. Commensal gut bacteria and the immunocompetency of mothers also appears to control the rate of accumulation of primary B cells from “virgin” B cells in neonates. Enteric reovirus infection by the oral route can cause the activation of CD8(+) T cells in the interfollicular regions of PP and the appearance of virus-specific precursor cytotoxic T lymphocytes (pCTL) in the IEL spaces. Such oral stimulation can also lead to “activation” of both CTL and natural killer (NK) cells in the IEL spaces. More normally, colonization of the gut with SFB also leads to similar activations of NK cells and “constitutively” cytotoxic T cells

Structural Learning of Attack Vectors for Generating Mutated XSS Attacks

Web applications suffer from cross-site scripting (XSS) attacks that resulting from incomplete or incorrect input sanitization. Learning the structure of attack vectors could enrich the variety of manifestations in generated XSS attacks. In this study, we focus on generating more threatening XSS attacks for the state-of-the-art detection approaches that can find potential XSS vulnerabilities in Web applications, and propose a mechanism for structural learning of attack vectors with the aim of generating mutated XSS attacks in a fully automatic way. Mutated XSS attack generation depends on the analysis of attack vectors and the structural learning mechanism. For the kernel of the learning mechanism, we use a Hidden Markov model (HMM) as the structure of the attack vector model to capture the implicit manner of the attack vector, and this manner is benefited from the syntax meanings that are labeled by the proposed tokenizing mechanism. Bayes theorem is used to determine the number of hidden states in the model for generalizing the structure model. The paper has the contributions as following: (1) automatically learn the structure of attack vectors from practical data analysis to modeling a structure model of attack vectors, (2) mimic the manners and the elements of attack vectors to extend the ability of testing tool for identifying XSS vulnerabilities, (3) be helpful to verify the flaws of blacklist sanitization procedures of Web applications. We evaluated the proposed mechanism by Burp Intruder with a dataset collected from public XSS archives. The results show that mutated XSS attack generation can identify potential vulnerabilities.Comment: In Proceedings TAV-WEB 2010, arXiv:1009.330

Dysbiosis of bifidobacteria and Clostridium cluster XIVa in the cystic fibrosis fecal microbiota

BACKGROUND: Recurrent antimicrobial interventions and disease-related intestinal dysfunction are suspected to contribute to the dysbiosis of the gastrointestinal microbial ecosystem in patients with cystic fibrosis (CF). The present study set out to detect and identify microbial discriminants in the gut microbiota composition that are associated with CF-related intestinal dysbiosis. METHODS: An in-depth description of CF-associated gut dysbiosis was obtained by screening denaturing gradient gel electrophoresis (DGGE) fingerprints for potentially discriminating bacterial species, and quantification by means of real-time PCR analyses using group-specific primers. RESULTS: A total of 8 DGGE band-classes assigned to the genus Bifidobacterium (n=3), and members of Clostridium clusters XIVa (n=3) and IV (n=2), were significantly (p<0.05) underrepresented in samples of patients with CF. Real-time PCR analyses confirmed a significantly lower abundance and temporal stability of bifidobacteria and Clostridium cluster XIVa in the faecal microbiota of patients with CF. CONCLUSION: This study is the first to report specific microbial determinants of dysbiosis in patients with CF

A Spectroscopic Search for Leaking Lyman Continuum at Zeta Approximately 0.7

We present the results of rest-frame, UV slitless spectroscopic observations of a sample of 32 z approx. 0.7 Lyman Break Galaxy (LBG) analogs in the COSMOS field. The spectroscopic search was performed with the Solar Blind Channel (SBC) on HST. While we find no direct detections of the Lyman Continuum we achieve individual limits (3sigma) of the observed non-ionizing UV to Lyman continuum flux density ratios, f(sub nu)(1500A)/f(sub nu)(830A) of 20 to 204 (median of 73.5) and 378.7 for the stack. Assuming an intrinsic Lyman Break of 3.4 and an optical depth of Lyman continuum photons along the line of sight to the galaxy of 85% we report an upper limit for the relative escape fraction in individual galaxies of 0.02 - 0.19 and a stacked 3sigma upper limit of 0.01. We find no indication of a relative escape fraction near unity as seen in some LBGs at z approx. 3. Our UV spectra achieve the deepest limits to date at any redshift on the escape fraction in individual sources. The contrast between these z approx. 0.7 low escape fraction LBG analogs with z approx. 3 LBGs suggests that either the processes conducive to high f(sub esc) are not being selected for in the z less than or approx.1 samples or the average escape fraction is decreasing from z approx. 3 to z approx. 1. We discuss possible mechanisms which could affect the escape of Lyman continuum photon

Policies and Opportunities for Physical Activity in Middle School Environments

This study examined physical activity opportunities and barriers at 36 geographically diverse middle schools participating in the Trial of Activity for Adolescent Girls

Metabolomic Markers of Phthalate Exposure in Plasma and Urine of Pregnant Women

Phthalates are known endocrine disruptors and found in almost all people with several associated adverse health outcomes reported in humans and animal models. Limited data are available on the relationship between exposure to endocrine disrupting chemicals and the human metabolome. We examined the relationship of metabolomic profiles in plasma and urine of 115 pregnant women with eleven urine phthalate metabolites measured at 26 weeks of gestation to identify potential biomarkers and relevant pathways. Targeted metabolomics was performed by selected reaction monitoring liquid chromatography and triple quadrupole mass spectrometry to measure 415 metabolites in plasma and 151 metabolites in urine samples. We have chosen metabolites with the best defined peaks for more detailed analysis (138 in plasma and 40 in urine). Relationship between urine phthalate metabolites and concurrent metabolomic markers in plasma and urine suggested potential involvement of diverse pathways including lipid, steroid, and nucleic acid metabolism and enhanced inflammatory response. Most of the correlations were positive for both urine and plasma, and further confirmed by regression and PCA analysis. However, after the FDR adjustment for multiple comparisons, only 9 urine associations remained statistically significant (q-values 0.0001–0.0451), including Nicotinamide mononucleotide, Cysteine T2, Cystine, and L-Aspartic acid. Additionally, we found negative associations of maternal pre-pregnancy body mass index (BMI) with more than 20 metabolomic markers related to lipid and amino-acid metabolism and inflammation pathways in plasma (p = 0.01–0.0004), while Mevalonic acid was positively associated (p = 0.009). Nicotinic acid, the only significant metabolite in urine, had a positive association with maternal BMI (p = 0.002). In summary, when evaluated in the context of metabolic pathways, the findings suggest enhanced lipid biogenesis, inflammation and altered nucleic acid metabolism in association with higher phthalate levels. These results provide new insights into the relationship between phthalates, common in most human populations, and metabolomics, a novel approach to exposure and health biomonitoring