Genetic variation in early fitness traits across European populations of silver birch (Betula pendula)

Given that the ecological niche of tree species is typically narrower for earlier life stages, intraspecific genetic variation at early fitness traits may greatly influence the adaptive response of tree populations to changing environmental conditions. In this study, we evaluated genetic variation in early fitness traits among 12 populations of Betula pendula from a wide latitudinal range in Europe (41-55 degrees N). We first conducted a chamber experiment to test for population differences in germination and the effect of pre-chilling treatment on seed dormancy release. We then established three common gardens spread across the species latitudinal range in order to evaluate levels of quantitative genetic variation and genotype-by-environment interaction at different early life traits. Our results showed significant variation in chamber germination rates among populations (0-60 %), with southern populations exhibiting lower germination. Pre-chilling treatments did not generally improve germination success. Population seedling emergence rates in the field were correlated with chamber germination rates, though being an order of magnitude lower, with an average ranging from 0 to 1.3 % across gardens. Highly significant variation was found in field emergence rates among populations, and between seed-crop years within populations, but not among families within populations. Populations differed in seedling height, diameter, slenderness and budburst date, with significant among-family variation. Population latitude was positively associated with chamber germination rate and with seedling emergence rate in one of the central field sites. Overall, genetic, environmental and demographic factors seem to influence the observed high levels of variation in early fitness traits among B. pendula populations. Our results suggest limited regeneration capacity for the study species under drier conditions, but further field trials with sufficient replication over environments and seed crops will improve our understanding of its vulnerability to climate change

Oxidative Damage to DNA and Lipids as Biomarkers of Exposure to Air Pollution

Ba c k g r o u n d: Air pollution is thought to exert health effects through oxidative stress, which causes damage to DNA and lipids. Obj e c t i v e: We determined whether levels of oxidatively damaged DNA and lipid peroxidation products in cells or bodily fluids from humans are useful biomarkers of biologically effective dose in studies of the health effects of exposure to particulate matter (PM) from combustion processes. Data s o u r c e s: We identified publications that reported estimated associations between environmental exposure to PM and oxidative damage to DNA and lipids in PubMed and EMBASE. We also identified publications from reference lists and articles cited in the Web of Science. Data extraction: For each study, we obtained information on the estimated effect size to calculate the standardized mean difference (unitless) and determined the potential for errors in exposure assessment and analysis of each of the biomarkers, for total and stratified formal meta-analyses. Data synthesis: In the meta-analysis, the standardized mean differences (95 % confidence interval) between exposed and unexposed subjects for oxidized DNA and lipids were 0.53 (0.29–0.76) and 0.73 (0.18–1.28) in blood and 0.52 (0.22–0.82) and 0.49 (0.01–0.97) in urine, respectively. The standardized mean difference for oxidized lipids was 0.64 (0.07–1.21) in the airways. Restricting analyses to studies unlikely to have substantial biomarker or exposure measurement error, studies likely to have biomarker and/or exposure error, or studies likely to have both sources of error resulted in standardized mean differences of 0.55 (0.19–0.90), 0.66 (0.37–0.95), and 0.65 (0.34–0.96), respectively. Co n c l u s i o n s: Exposure to combustion particles is consistenly associated with oxidatively damaged DNA and lipids in humans, suggesting that it is possible to use these measurements as biomarkers of biologically effective dose. Key w o r d s: biomarker, DNA damage, lipid peroxidation products, oxidative stress, particulate matter. Environ Health Perspect 118:1126–1136 (2010). doi:10.1289/ehp.0901725 [Onlin

Salivary Parameters (Salivary Flow, pH and Buffering Capacity) in Stimulated Saliva of Mexican Elders 60 Years Old and Older

Objective: To compare a limited array of chewing-stimulated saliva features (salivary flow, pH and buffer capacity) in a sample of elderly Mexicans with clinical, sociodemographic and socio-economic variables. Subjects and Methods: A cross-sectional study was carried out in 139 adults, 60 years old and older, from two retirement homes and a senior day care centre in the city of Pachuca, Mexico. Sociodemographic, socio-economic and behavioural variables were collected through a questionnaire. A trained and standardized examiner obtained the oral clinical variables. Chewing-stimulated saliva (paraffin method) was collected and the salivary flow rate, pH and buffer capacity were measured. The analysis was performed using non-parametric tests in Stata 9.0. Results: Mean age was 79.1 ± 9.8 years. Most of the subjects included were women (69.1%). Mean chewing-stimulated salivary flow was 0.75 ± 0.80 mL/minute, and the pH and buffer capacity were 7.88 ± 0.83 and 4.20 ± 1.24, respectively. Mean chewing-stimulated salivary flow varied (p < 0.05) across type of retirement home, tooth brushing frequency, number of missing teeth and use of dental prostheses. pH varied across the type of retirement home (p < 0.05) and marginally by age (p = 0.087); buffer capacity (p < 0.05) varied across type of retirement home, tobacco consumption and the number of missing teeth. Conclusions: These exploratory data add to the body of knowledge with regard to chewing-stimulated salivary features (salivary flow rate, pH and buffer capacity) and outline the variability of those features across selected sociodemographic, socio-economic and behavioural variables in a group of Mexican elders

An accurate test for homogeneity of odds ratios based on Cochran's Q-statistic

Background: A frequently used statistic for testing homogeneity in a meta-analysis of K independent studies is Cochran's Q. For a standard test of homogeneity the Q statistic is referred to a chi-square distribution with K - 1 degrees of freedom. For the situation in which the effects of the studies are logarithms of odds ratios, the chi-square distribution is much too conservative for moderate size studies, although it may be asymptotically correct as the individual studies become large. Methods: Using a mixture of theoretical results and simulations, we provide formulas to estimate the shape and scale parameters of a gamma distribution to t the distribution of Q. Results: Simulation studies show that the gamma distribution is a good approximation to the distribution for Q. Conclusions: : Use of the gamma distribution instead of the chi-square distribution for Q should eliminate inaccurate inferences in assessing homogeneity in a meta-analysis. (A computer program for implementing this test is provided.) This hypothesis test is competitive with the Breslow-Day test both in accuracy of level and in power

Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control

It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for two main reasons: nonlinear recurrent networks often exhibit chaotic behavior and most known learning rules do not work in robust fashion in recurrent networks. Here we address both these problems by demonstrating how random recurrent networks (RRN) that initially exhibit chaotic dynamics can be tuned through a supervised learning rule to generate locally stable neural patterns of activity that are both complex and robust to noise. The outcome is a novel neural network regime that exhibits both transiently stable and chaotic trajectories. We further show that the recurrent learning rule dramatically increases the ability of RRNs to generate complex spatiotemporal motor patterns, and accounts for recent experimental data showing a decrease in neural variability in response to stimulus onset

Risk score for first-screening of prevalent undiagnosed chronic kidney disease in Peru: the CRONICAS-CKD risk score.

BACKGROUND: Chronic Kidney Disease (CKD) represents a great burden for the patient and the health system, particularly if diagnosed at late stages. Consequently, tools to identify patients at high risk of having CKD are needed, particularly in limited-resources settings where laboratory facilities are scarce. This study aimed to develop a risk score for prevalent undiagnosed CKD using data from four settings in Peru: a complete risk score including all associated risk factors and another excluding laboratory-based variables. METHODS: Cross-sectional study. We used two population-based studies: one for developing and internal validation (CRONICAS), and another (PREVENCION) for external validation. Risk factors included clinical- and laboratory-based variables, among others: sex, age, hypertension and obesity; and lipid profile, anemia and glucose metabolism. The outcome was undiagnosed CKD: eGFR < 60 ml/min/1.73m2. We tested the performance of the risk scores using the area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive/negative predictive values and positive/negative likelihood ratios. RESULTS: Participants in both studies averaged 57.7 years old, and over 50% were females. Age, hypertension and anemia were strongly associated with undiagnosed CKD. In the external validation, at a cut-off point of 2, the complete and laboratory-free risk scores performed similarly well with a ROC area of 76.2% and 76.0%, respectively (P = 0.784). The best assessment parameter of these risk scores was their negative predictive value: 99.1% and 99.0% for the complete and laboratory-free, respectively. CONCLUSIONS: The developed risk scores showed a moderate performance as a screening test. People with a score of ≥ 2 points should undergo further testing to rule out CKD. Using the laboratory-free risk score is a practical approach in developing countries where laboratories are not readily available and undiagnosed CKD has significant morbidity and mortality

A projective Dirac operator on CP^2 within fuzzy geometry

We propose an ansatz for the commutative canonical spin_c Dirac operator on CP^2 in a global geometric approach using the right invariant (left action-) induced vector fields from SU(3). This ansatz is suitable for noncommutative generalisation within the framework of fuzzy geometry. Along the way we identify the physical spinors and construct the canonical spin_c bundle in this formulation. The chirality operator is also given in two equivalent forms. Finally, using representation theory we obtain the eigenspinors and calculate the full spectrum. We use an argument from the fuzzy complex projective space CP^2_F based on the fuzzy analogue of the unprojected spin_c bundle to show that our commutative projected spin_c bundle has the correct SU(3)-representation content.Comment: reduced to 27 pages, minor corrections, minor improvements, typos correcte

A Randomized Double-Blind Study Comparing the Efficacy and Safety of Orlistat Versus Placebo in Obese Patients with Mild to Moderate Hypercholesterolemia

INTRODUCTION: Obesity is a chronic disease and a serious health problem that leads to increased prevalence of diabetes, hypertension, dyslipidemia and gallbladder disease. OBJECTIVE: To evaluate the efficacy of orlistat for weight loss and improved lipid profile compared to placebo in obese patients with hypercholesterolemia, treated over a period of 6 months. METHODOLOGY: In a 6-month, multicenter (10 centers in Portugal), double-blind, parallel, placebo-controlled study, 166 patients, aged 18-65 years, body mass index (BMI) > or = 27 kg/m2, LDL cholesterol > 155 mg/dl, were randomized to a reduced calorie diet (600 kcal/day deficit) plus orlistat three times a day or placebo. Exclusion criteria included triglycerides > 400 mg/dl, severe cardiovascular disease, uncontrolled hypertension, type 1 or 2 diabetes under pharmacological treatment, and gastrointestinal or pancreatic disease. RESULTS: The mean difference in weight from baseline was 5.9% (5.6 kg) in the orlistat group vs. 2.3% (2.2 kg) in the placebo group. In the orlistat group 49% of patients achieved 5-10% weight loss and 8.8% achieved > 10%. The orlistat group showed a significant reduction in total and LDL cholesterol, with similar changes for HDL in both treatment groups. The frequency of gastrointestinal adverse events was slightly higher in the orlistat group than in the placebo group, leading to discontinuation in 7 patients. CONCLUSION: Treatment with orlistat plus a reduced calorie diet for 6 months achieved significant reductions in weight, BMI and lipid parameters

Treatment Needs for Dental Caries, Restorative Care Index, and Index of Extractions in Adolescents 12 and 15 Years Old

Objective: To determine the Treatment Needs Index (TNI) for dental caries, the restorative Care Index (CI), and to introduce a Tooth Extractions Index (TEI) to estimate past and current treatment needs among Mexican adolescents. Subjects and methods: A descriptive cross-sectional study was carried out on 1538 adolescents aged 12 and 15 years in the state of Hidalgo, Mexico, to collect decayed, missing, filled teeth (DMFT) data to describe TNI, CI and TEI indices. Results: Higher TNI was identified in younger male teenagers who had always lived in the same community, without dental visits in the last year, and who had poorer socio-economic markers. Higher CI was found in older, female subjects who had moved in their lifetimes to a new community in the area, with dental visits in the last year and who had better socio-economic markers. Higher TEI was found in older, female teenagers who had moved in their lifetimes to a new community in the area, without dental visits in the last year, and who had worse socio-economic markers. Conclusions: We observed high rates of treatment needs for dental caries and little experience of restorative treatment. While dental extractions due to advanced caries should ideally be zero, relatively few adolescents had this treatment experience. Despite the fact that the overall background of these adolescents is rather homogeneous, it was still possible to find that treatment needs' indices (past and present needs) appeared to be modified by sociodemographic and socio-economic variables

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample