Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies.

Whole-grain foods are touted for multiple health benefits, including enhancing insulin sensitivity and reducing type 2 diabetes risk. Recent genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) associated with fasting glucose and insulin concentrations in individuals free of diabetes. We tested the hypothesis that whole-grain food intake and genetic variation interact to influence concentrations of fasting glucose and insulin. Via meta-analysis of data from 14 cohorts comprising ∼ 48,000 participants of European descent, we studied interactions of whole-grain intake with loci previously associated in GWAS with fasting glucose (16 loci) and/or insulin (2 loci) concentrations. For tests of interaction, we considered a P value <0.0028 (0.05 of 18 tests) as statistically significant. Greater whole-grain food intake was associated with lower fasting glucose and insulin concentrations independent of demographics, other dietary and lifestyle factors, and BMI (β [95% CI] per 1-serving-greater whole-grain intake: -0.009 mmol/l glucose [-0.013 to -0.005], P < 0.0001 and -0.011 pmol/l [ln] insulin [-0.015 to -0.007], P = 0.0003). No interactions met our multiple testing-adjusted statistical significance threshold. The strongest SNP interaction with whole-grain intake was rs780094 (GCKR) for fasting insulin (P = 0.006), where greater whole-grain intake was associated with a smaller reduction in fasting insulin concentrations in those with the insulin-raising allele. Our results support the favorable association of whole-grain intake with fasting glucose and insulin and suggest a potential interaction between variation in GCKR and whole-grain intake in influencing fasting insulin concentrations.

Flow cytometry in the analysis of cellular populations

Flow cytometry has become a valuable tool in cell biology. By analyzing large number of cells individually using light-scatter and fluorescence measurements, this technique reveals both cellular characteristics and the levels of cellular components. Flow cytometry has been developed to rapidly enumerate cells and to distinguish among different cell stages and structures using multiple staining. In addition to high-speed multiparametric data acquisition, analysis and cell sorting, which allow other characteristics of individual cells to be studied, have increased the interest of researchers in this technique. This chapter gives an overview of the principles of flow cytometry and examples of the application ofthe technique.

Heterogeneity and event dependence in the analysis of sickness absence

Sickness absence (SA) is an important social, economic and public health issue. Identifying and understanding the determinants, whether biological, regulatory or, health services-related, of variability in SA duration is essential for better management of SA. The conditional frailty model (CFM) is useful when repeated SA events occur within the same individual, as it allows simultaneous analysis of event dependence and heterogeneity due to unknown, unmeasured, or unmeasurable factors. However, its use may encounter computational limitations when applied to very large data sets, as may frequently occur in the analysis of SA duration. To overcome the computational issue, we propose a Poisson-based conditional frailty model (CFPM) for repeated SA events that accounts for both event dependence and heterogeneity. To demonstrate the usefulness of the model proposed in the SA duration context, we used data from all non-work-related SA episodes that occurred in Catalonia (Spain) in 2007, initiated by either a diagnosis of neoplasm or mental and behavioral disorders. As expected, the CFPM results were very similar to those of the CFM for both diagnosis groups. The CPU time for the CFPM was substantially shorter than the CFM. The CFPM is an suitable alternative to the CFM in survival analysis with recurrent events,especially with large databases.

Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations.

Chronic kidney disease (CKD), impairment of kidney function, is a serious public health problem, and the assessment of genetic factors influencing kidney function has substantial clinical relevance. Here, we report a meta-analysis of genome-wide association studies for kidney function-related traits, including 71,149 east Asian individuals from 18 studies in 11 population-, hospital- or family-based cohorts, conducted as part of the Asian Genetic Epidemiology Network (AGEN). Our meta-analysis identified 17 loci newly associated with kidney function-related traits, including the concentrations of blood urea nitrogen, uric acid and serum creatinine and estimated glomerular filtration rate based on serum creatinine levels (eGFRcrea) (P < 5.0 × 10(-8)). We further examined these loci with in silico replication in individuals of European ancestry from the KidneyGen, CKDGen and GUGC consortia, including a combined total of ∼110,347 individuals. We identify pleiotropic associations among these loci with kidney function-related traits and risk of CKD. These findings provide new insights into the genetics of kidney function.

Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

An allele-specific, stochastic gene expression process controls the expression of multiple Ly49 family genes and generates a diverse, MHC-specific NK cell receptor repertoire.

Mouse NK cells express MHC class I-specific inhibitory Ly49 receptors. Since these receptors display distinct ligand specificities and are clonally distributed, their expression generates a diverse NK cell receptor repertoire specific for MHC class I molecules. We have previously found that the Dd (or Dk)-specific Ly49A receptor is usually expressed from a single allele. However, a small fraction of short-term NK cell clones expressed both Ly49A alleles, suggesting that the two Ly49A alleles are independently and randomly expressed. Here we show that the genes for two additional Ly49 receptors (Ly49C and Ly49G2) are also expressed in a (predominantly) mono-allelic fashion. Since single NK cells can co-express multiple Ly49 receptors, we also investigated whether mono-allelic expression from within the tightly linked Ly49 gene cluster is coordinate or independent. Our clonal analysis suggests that the expression of alleles of distinct Ly49 genes is not coordinate. Thus Ly49 alleles are apparently independently and randomly chosen for stable expression, a process that directly restricts the number of Ly49 receptors expressed per single NK cell. We propose that the Ly49 receptor repertoire specific for MHC class I is generated by an allele-specific, stochastic gene expression process that acts on the entire Ly49 gene cluster.

Case-control analysis of paternal age and trisomic anomalies.

OBJECTIVES: To determine whether older paternal age increases the risk of fathering a pregnancy with Patau (trisomy 13), Edwards (trisomy 18), Klinefelter (XXY) or XYY syndrome. DESIGN: Case-control: cases with each of these syndromes were matched to four controls with Down syndrome from within the same congenital anomaly register and with maternal age within 6 months. SETTING: Data from 22 EUROCAT congenital anomaly registers in 12 European countries. PARTICIPANTS: Diagnoses with observed or (for terminations) predicted year of birth from 1980 to 2005, comprising live births, fetal deaths with gestational age ≥ 20 weeks and terminations after prenatal diagnosis of the anomaly. Data include 374 cases of Patau syndrome, 929 of Edwards syndrome, 295 of Klinefelter syndrome, 28 of XYY syndrome and 5627 controls with Down syndrome. MAIN OUTCOME MEASURES: Odds ratio (OR) associated with a 10-year increase in paternal age for each anomaly was estimated using conditional logistic regression. Results were adjusted to take account of the estimated association of paternal age with Down syndrome (1.11; 95% CI 1.01 to 1.23). RESULTS: The OR for Patau syndrome was 1.10 (95% CI 0.83 to 1.45); for Edwards syndrome, 1.15 (0.96 to 1.38); for Klinefelter syndrome, 1.35 (1.02 to 1.79); and for XYY syndrome, 1.99 (0.75 to 5.26). CONCLUSIONS: There was a statistically significant increase in the odds of Klinefelter syndrome with increasing paternal age. The larger positive associations of Klinefelter and XYY syndromes with paternal age compared with Patau and Edwards syndromes are consistent with the greater percentage of these sex chromosome anomalies being of paternal origin.

Graphical probabilistic analysis of the combination of items of evidence

Unlike the evaluation of single items of scientific evidence, the formal study and analysis of the jointevaluation of several distinct items of forensic evidence has to date received some punctual, ratherthan systematic, attention. Questions about the (i) relationships among a set of (usually unobservable)propositions and a set of (observable) items of scientific evidence, (ii) the joint probative valueof a collection of distinct items of evidence as well as (iii) the contribution of each individual itemwithin a given group of pieces of evidence still represent fundamental areas of research. To somedegree, this is remarkable since both, forensic science theory and practice, yet many daily inferencetasks, require the consideration of multiple items if not masses of evidence. A recurrent and particularcomplication that arises in such settings is that the application of probability theory, i.e. the referencemethod for reasoning under uncertainty, becomes increasingly demanding. The present paper takesthis as a starting point and discusses graphical probability models, i.e. Bayesian networks, as frameworkwithin which the joint evaluation of scientific evidence can be approached in some viable way.Based on a review of existing main contributions in this area, the article here aims at presentinginstances of real case studies from the author's institution in order to point out the usefulness andcapacities of Bayesian networks for the probabilistic assessment of the probative value of multipleand interrelated items of evidence. A main emphasis is placed on underlying general patterns of inference,their representation as well as their graphical probabilistic analysis. Attention is also drawnto inferential interactions, such as redundancy, synergy and directional change. These distinguish thejoint evaluation of evidence from assessments of isolated items of evidence. Together, these topicspresent aspects of interest to both, domain experts and recipients of expert information, because theyhave bearing on how multiple items of evidence are meaningfully and appropriately set into context.

Where and how family members spend time at home? A quantitative analysis of the observational tracking on the everyday lives of Italian families

This paper examines a dataset that derives from an observational tracking, in order to analyze where and how middle-class working families spend time at home. We use an ethnographic approach to study the everyday lives of Italian dual-income middle-class families, with the aim to analyze quantitatively the use of home spaces and the types of activities of family members on weekday afternoons and evenings. The different analyses (multiple correspondence analysis, agglomerative hierarchical cluster, discriminant analysis) show how particular spaces and activities in these spaces are dominated by certain family members. We suggest a combination of qualitative and quantitative methodologies as useful tools to explore in detail the everyday lives of families, and to understand how family members use the domestic spaces. In particular, we consider relevant the use of quantitative analyses to examine ethnographic data, especially in connection with the methodological reflexivity among researchers

Analysis of cannabinoids in oral fluid by liquid chromatography-tandem mass spectrometry

A sensitive method was developed for quantifying a wide range of cannabinoids in oral fluid (OF) by liquid chromatography-tandem mass spectrometry (LC-MS/MS). These cannabinoids include a dagger(9)-tetrahydrocannabinol (THC), 11-hydroxy-a dagger(9)-tetrahydrocannabinol (11-OH-THC), 11-nor-9-carboxy-a dagger(9)-tetrahydrocannabinol (THCCOOH), cannabinol (CBN), cannabidiol (CBD), a dagger(9)-tetrahydrocannabinolic acid A (THC-A), 11-nor-9-carboxy-a dagger(9)-tetrahydrocannabinol glucuronide (THCCOOH-gluc), and a dagger(9)-tetrahydrocannabinol glucuronide (THC-gluc). Samples were collected using a Quantisal (TM) device. The advantages of performing a liquid-liquid extraction (LLE) of KCl-saturated OF using heptane/ethyl acetate versus a solid-phase extraction (SPE) using HLB copolymer columns were determined. Chromatographic separation was achieved in 11.5 min on a Kinetex (TM) column packed with 2.6-mu m core-shell particles. Both positive (THC, 11-OH-THC, CBN, and CBD) and negative (THCCOOH, THC-gluc, THCCOOH-gluc, and THC-A) electrospray ionization modes were employed with multiple reaction monitoring using a high-end AB Sciex API 5000 (TM) triple quadrupole LC-MS/MS system. Unlike SPE, LLE failed to extract THC-gluc and THCCOOH-gluc. However, the LLE method was more sensitive for the detection of THCCOOH than the SPE method, wherein the limit of detection (LOD) and limit of quantification (LOQ) decreased from 100 to 50 pg/ml and from 500 to 80 pg/ml, respectively. The two extraction methods were successfully applied to OF samples collected from volunteers before and after they smoked a homemade cannabis joint. High levels of THC were measured soon after smoking, in addition to significant amounts of THC-A. Other cannabinoids were found in low concentrations. Glucuronide conjugate levels were lower than the method's LOD for most samples. Incubation studies suggest that glucuronides could be enzymatically degraded by glucuronidase prior to OF collection

New prognostic factors and calculators for outcome prediction in patients with recurrent glioblastoma: A pooled analysis of EORTC Brain Tumour Group phase I and II clinical trials.

BACKGROUND: Prognostic models have been developed to predict survival of patients with newly diagnosed glioblastoma (GBM). To improve predictions, models should be updated with information at the recurrence. We performed a pooled analysis of European Organization for Research and Treatment of Cancer (EORTC) trials on recurrent glioblastoma to validate existing clinical prognostic factors, identify new markers, and derive new predictions for overall survival (OS) and progression free survival (PFS).¦METHODS: Data from 300 patients with recurrent GBM recruited in eight phase I or II trials conducted by the EORTC Brain Tumour Group were used to evaluate patient's age, sex, World Health Organisation (WHO) performance status (PS), presence of neurological deficits, disease history, use of steroids or anti-epileptics and disease characteristics to predict PFS and OS. Prognostic calculators were developed in patients initially treated by chemoradiation with temozolomide.¦RESULTS: Poor PS and more than one target lesion had a significant negative prognostic impact for both PFS and OS. Patients with large tumours measured by the maximum diameter of the largest lesion (⩾42mm) and treated with steroids at baseline had shorter OS. Tumours with predominant frontal location had better survival. Age and sex did not show independent prognostic values for PFS or OS.¦CONCLUSIONS: This analysis confirms performance status but not age as a major prognostic factor for PFS and OS in recurrent GBM. Patients with multiple and large lesions have an increased risk of death. With these data prognostic calculators with confidence intervals for both medians and fixed time probabilities of survival were derived.

DnaSP v5: A software for comprehensive analysis of DNA polymorphism data

DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser.

Survival-time analysis of white spruce during spruce budworm defoliation

Abstract