84 research outputs found
Particle Physics from Almost Commutative Spacetimes
Our aim in this review article is to present the applications of Connes'
noncommutative geometry to elementary particle physics. Whereas the existing
literature is mostly focused on a mathematical audience, in this article we
introduce the ideas and concepts from noncommutative geometry using physicists'
terminology, gearing towards the predictions that can be derived from the
noncommutative description. Focusing on a light package of noncommutative
geometry (so-called 'almost commutative manifolds'), we shall introduce in
steps: electrodynamics, the electroweak model, culminating in the full Standard
Model. We hope that our approach helps in understanding the role noncommutative
geometry could play in describing particle physics models, eventually unifying
them with Einstein's (geometrical) theory of gravity.Comment: 104 pages, 5 figures, version 2 (minor changes and some additional
references
The influence of population characteristics on variation in general practice based morbidity estimations
Contains fulltext :
96442.pdf (publisher's version ) (Open Access
Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty
Background: Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases. Methods. Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000. Results: Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank. Conclusion: Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences
A survey of spectral models of gravity coupled to matter
This is a survey of the historical development of the Spectral Standard Model
and beyond, starting with the ground breaking paper of Alain Connes in 1988
where he observed that there is a link between Higgs fields and finite
noncommutative spaces. We present the important contributions that helped in
the search and identification of the noncommutative space that characterizes
the fine structure of space-time. The nature and properties of the
noncommutative space are arrived at by independent routes and show the
uniqueness of the Spectral Standard Model at low energies and the Pati-Salam
unification model at high energies.Comment: An appendix is added to include scalar potential analysis for a
Pati-Salam model. 58 Page
Intersecting single-cell transcriptomics and genome-wide association studies identifies crucial cell populations and candidate genes for atherosclerosis
Genome-wide association studies (GWASs) have discovered hundreds of common genetic variants for atherosclerotic disease and cardiovascular risk factors. The translation of susceptibility loci into biological mechanisms and targets for drug discovery remains challenging. Intersecting genetic and gene expression data has led to the identification of candidate genes. However, previously studied tissues are often non-diseased and heterogeneous in cell composition, hindering accurate candidate prioritization. Therefore, we analysed single-cell transcriptomics from atherosclerotic plaques for cell-type-specific expression to identify atherosclerosis-associated candidate gene-cell pairs.\nWe applied gene-based analyses using GWAS summary statistics from 46 atherosclerotic and cardiovascular disease, risk factors, and other traits. We then intersected these candidates with single-cell RNA sequencing (scRNA-seq) data to identify genes specific for individual cell (sub)populations in atherosclerotic plaques. The coronary artery disease (CAD) loci demonstrated a prominent signal in plaque smooth muscle cells (SMCs) (SKI, KANK2, and SORT1) P-adj. = 0.0012, and endothelial cells (ECs) (SLC44A1, ATP2B1) P-adj. = 0.0011. Finally, we used liver-derived scRNA-seq data and showed hepatocyte-specific enrichment of genes involved in serum lipid levels.\nWe discovered novel and known gene-cell pairs pointing to new biological mechanisms of atherosclerotic disease. We highlight that loci associated with CAD reveal prominent association levels in mainly plaque SMC and EC populations. We present an intuitive single-cell transcriptomics-driven workflow rooted in human large-scale genetic studies to identify putative candidate genes and affected cells associated with cardiovascular traits. Collectively, our workflow allows for the identification of cell-specific targets relevant for atherosclerosis and can be universally applied to other complex genetic diseases and traits.Biopharmaceutic
Transcriptomic-based clustering of human atherosclerotic plaques identifies subgroups with different underlying biology and clinical presentation
Histopathological studies have revealed key processes of atherosclerotic plaque thrombosis. However, the diversity and complexity of lesion types highlight the need for improved sub-phenotyping. Here we analyze the gene expression profiles of 654 advanced human carotid plaques. The unsupervised, transcriptome-driven clustering revealed five dominant plaque types. These plaque phenotypes were associated with clinical presentation and showed differences in cellular compositions. Validation in coronary segments showed that the molecular signature of these plaques was linked to coronary ischemia. One of the plaque types with the most severe clinical symptoms pointed to both inflammatory and fibrotic cell lineages. Further, we did a preliminary analysis of potential circulating biomarkers that mark the different plaques phenotypes. In conclusion, the definition of the plaque at risk for a thrombotic event can be fine-tuned by in-depth transcriptomic-based phenotyping. These differential plaque phenotypes prove clinically relevant for both carotid and coronary artery plaques and point to distinct underlying biology of symptomatic lesions
Clinical practice: neonatal resuscitation. A Dutch consensus
The updated Dutch guidelines on Neonatal Resuscitation assimilate the latest evidence in neonatal resuscitation. Important changes with regard to the 2004 guidelines and controversial issues concerning neonatal resuscitation are reviewed, and recommendations for daily practice are provided and argued in the context of the ILCOR 2005 consensus
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