The Cyprus Institute

About: The Cyprus Institute is a other organization based out in Nicosia, Cyprus. It is known for research contribution in the topics: Aerosol & Environmental science. The organization has 418 authors who have published 1252 publications receiving 32586 citations.

Human Emissions of Size-Resolved Fluorescent Aerosol Particles: Influence of Personal and Environmental Factors.

Abstract: Human emissions of fluorescent aerosol particles (FAPs) can influence the biological burden of indoor air. Yet, quantification of FAP emissions from human beings remains limited, along with a poor understanding of the underlying emission mechanisms. To reduce the knowledge gap, we characterized human emissions of size-segregated FAPs (1-10 μm) and total particles in a climate chamber with low-background particle levels. We probed the influence of several personal factors (clothing coverage and age) and environmental parameters (level of ozone, air temperature, and relative humidity) on particle emissions from human volunteers. A material-balance model showed that the mean emission rate ranged 5.3-16 × 106 fluorescent particles per person-h (0.30-1.2 mg per person-h), with a dominant size mode within 3-5 μm. Volunteers wearing long-sleeve shirts and pants produced 40% more FAPs relative to those wearing t-shirts and shorts. Particle emissions varied across the age groups: seniors (average age 70.5 years) generated 50% fewer FAPs compared to young adults (25.0 years) and teenagers (13.8 years). While we did not observe a measurable influence of ozone (0 vs 40 ppb) on human FAP emissions, there was a strong influence of relative humidity (34 vs 62%), with FAP emissions decreasing by 30-60% at higher humidity.

SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes

Abstract: Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits. We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements “reverse regression” methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10−8), which has not been reported in previous large-scale GWAS of lipid traits. The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach.

Sex estimation: a comparison of techniques based on binary logistic, probit and cumulative probit regression, linear and quadratic discriminant analysis, neural networks, and naïve Bayes classification using ordinal variables

Abstract: The performance of seven classification methods, binary logistic (BLR), probit (PR) and cumulative probit (CPR) regression, linear (LDA) and quadratic (QDA) discriminant analysis, artificial neural networks (ANN), and naive Bayes classification (NBC), is examined in skeletal sex estimation. These methods were tested using cranial and pelvic sexually dimorphic traits recorded on a modern documented collection, the Athens Collection. For their implementation, an R package has been written to perform cross-validated (CV) sex classification and give the discriminant function of each of the methods studied. A simple algorithm that combines two discriminant functions is also proposed. It was found that the differences in the classification performance between BLR, PR, CPR, LDA, QDA, ANN, and NBC are overall small. However, LDA is simpler and more flexible than CPR, QDA, and ANN and has a small but clear advantage over BLR, NBC, and PR. Consequently, LDA may be preferred in skeletal sex estimation. Finally, it is striking that the combination of pelvic and cranial traits via their discriminant functions, determined either by BLR or LDA, removes practically any population-specificity and yields much better predictions than the individual functions; in fact, the prediction accuracy increases above 97%.

Neutron electric dipole moment using lattice QCD simulations at the physical point

Abstract: We extract the neutron electric dipole moment $|{\stackrel{\ensuremath{\rightarrow}}{d}}_{N}|$ within the lattice QCD formalism. We analyze one ensemble of ${N}_{f}=2+1+1$ twisted mass clover-improved fermions with lattice spacing of $a\ensuremath{\simeq}0.08\text{ }\text{ }\mathrm{fm}$ and physical values of the quark masses corresponding to a pion mass ${m}_{\ensuremath{\pi}}\ensuremath{\simeq}139\text{ }\text{ }\mathrm{MeV}$. The neutron electric dipole moment is extracted by computing the $CP$-odd electromagnetic form factor ${F}_{3}({Q}^{2}\ensuremath{\rightarrow}0)$ through small $\ensuremath{\theta}$-expansion of the action. This approach requires the calculation of the topological charge for which we employ a fermionic definition by means of spectral projectors while we also provide a comparison with the gluonic definition accompanied by the gradient flow. We show that using the topological charge from spectral projectors leads to absolute errors that are more than two times smaller than those provided when the field theoretic definition is employed. We find a value of $|{\stackrel{\ensuremath{\rightarrow}}{d}}_{N}|=0.0009(24)\ensuremath{\theta}\text{ }\text{ }e\ifmmode\cdot\else\textperiodcentered\fi{}\mathrm{fm}$ when using the fermionic definition, which is statistically consistent with zero.