Degeneracy of non-abelian quantum Hall states on the torus: domain walls and conformal field theory

We analyze the non-abelian Read-Rezayi quantum Hall states on the torus, where it is natural to employ a mapping of the many-body problem onto a one-dimensional lattice model. On the thin torus--the Tao-Thouless (TT) limit--the interacting many-body problem is exactly solvable. The Read-Rezayi states at filling $\nu=\frac k {kM+2}$ are known to be exact ground states of a local repulsive $k+1$-body interaction, and in the TT limit this is manifested in that all states in the ground state manifold have exactly $k$ particles on any $kM+2$ consecutive sites. For $M\neq 0$ the two-body correlations of these states also imply that there is no more than one particle on $M$ adjacent sites. The fractionally charged quasiparticles and quasiholes appear as domain walls between the ground states, and we show that the number of distinct domain wall patterns gives rise to the nontrivial degeneracies, required by the non-abelian statistics of these states. In the second part of the paper we consider the quasihole degeneracies from a conformal field theory (CFT) perspective, and show that the counting of the domain wall patterns maps one to one on the CFT counting via the fusion rules. Moreover we extend the CFT analysis to topologies of higher genus.Comment: 15 page

An interaction map of circulating metabolites, immune gene networks, and their genetic regulation

Background: Immunometabolism plays a central role in many cardiometabolic diseases. However, a robust map of immune-related gene networks in circulating human cells, their interactions with metabolites, and their genetic control is still lacking. Here, we integrate blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts (total N = 2168), including a subset of individuals with matched multi-omic data at 7-year follow-up. Results: We identify topologically replicable gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules show complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, small molecules, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) reveal five modules with mQTLs that have both cis and trans effects. The strongest mQTL is in ARHGEF3 (rs1354034) and affects a module enriched for platelet function, independent of platelet counts. Modules of mast cell/basophil and neutrophil function show temporally stable metabolite associations over 7-year follow-up, providing evidence that these modules and their constituent gene products may play central roles in metabolic inflammation. Furthermore, the strongest mQTL in ARHGEF3 also displays clear temporal stability, supporting widespread trans effects at this locus. Conclusions: This study provides a detailed map of natural variation at the blood immunometabolic interface and its genetic basis, and may facilitate subsequent studies to explain inter-individual variation in cardiometabolic disease.Peer reviewe

RD-Connect: An Integrated Platform Connecting Databases, Registries, Biobanks and Clinical Bioinformatics for Rare Disease Research

Research into rare diseases is typically fragmented by data type and disease. Individual efforts often have poor interoperability and do not systematically connect data across clinical phenotype, genomic data, biomaterial availability, and research/trial data sets. Such data must be linked at both an individual-patient and whole-cohort level to enable researchers to gain a complete view of their disease and patient population of interest. Data access and authorization procedures are required to allow researchers in multiple institutions to securely compare results and gain new insights. Funded by the European Union’s Seventh Framework Programme under the International Rare Diseases Research Consortium (IRDiRC), RD-Connect is a global infrastructure project initiated in November 2012 that links genomic data with registries, biobanks, and clinical bioinformatics tools to produce a central research resource for rare diseases

A Central Role for GRB10 in Regulation of Islet Function in Man

Peer reviewe

Pharmacometric Models for Biomarkers, Side Effects and Efficacy in Anticancer Drug Therapy

New approaches quantifying the effect of treatment are needed in oncology to improve the drug development process and to enable treatment optimization for existing therapies. This thesis focuses on the development of pharmacometric models for biomarkers, side effects and efficacy in order to identify predictors of clinical response in anti-cancer drug therapy. The variability in myelosuppression was characterized in six different cytotoxic anticancer treatments to evaluate a model-based dose individualization approach utilizing neutrophil counts as a biomarker. The estimated impact of inter-occasion variability was relatively low in relation to the inter-individual variability, indicating that myelosuppression is predictable from one treatment course to another. The approach may thereby be useful for dose optimization within an individual. To further study and to identify predictors for the severe side effect febrile neutropenia (FN), the relationship between the shape of the myelosuppression time-course and the probability of FN was characterized. Patients with a rapid decline in neutrophil counts and high drug sensitivity were identified to have a higher probability of developing FN compared with other patients who experience grade 4 neutropenia. Predictors of clinical response in patients receiving sunitinib for the treatment of gastro-intestinal stromal tumor (GIST) were identified by the development of an integrated modeling framework. Drug exposure, biomarkers, tumor dynamics, side effects and overall survival (OS) were linked in a unified structure, and univariate and multivariate exposure variables were tested for their predictive capacities. The soluble biomarker, sVEGFR-3 and tumor size at start of treatment were found to be promising predictors of overall survival, with decreased sVEGFR-3 levels and smaller baseline tumor size being predictive of longer OS. Also hypertension and neutropenia was identified as predictors of OS. The developed modeling framework may be useful to monitor clinical response, optimize dosing in sunitinib and to facilitate dose individualization

Pharmacometric Models for Biomarkers, Side Effects and Efficacy in Anticancer Drug Therapy

New approaches quantifying the effect of treatment are needed in oncology to improve the drug development process and to enable treatment optimization for existing therapies. This thesis focuses on the development of pharmacometric models for biomarkers, side effects and efficacy in order to identify predictors of clinical response in anti-cancer drug therapy. The variability in myelosuppression was characterized in six different cytotoxic anticancer treatments to evaluate a model-based dose individualization approach utilizing neutrophil counts as a biomarker. The estimated impact of inter-occasion variability was relatively low in relation to the inter-individual variability, indicating that myelosuppression is predictable from one treatment course to another. The approach may thereby be useful for dose optimization within an individual. To further study and to identify predictors for the severe side effect febrile neutropenia (FN), the relationship between the shape of the myelosuppression time-course and the probability of FN was characterized. Patients with a rapid decline in neutrophil counts and high drug sensitivity were identified to have a higher probability of developing FN compared with other patients who experience grade 4 neutropenia. Predictors of clinical response in patients receiving sunitinib for the treatment of gastro-intestinal stromal tumor (GIST) were identified by the development of an integrated modeling framework. Drug exposure, biomarkers, tumor dynamics, side effects and overall survival (OS) were linked in a unified structure, and univariate and multivariate exposure variables were tested for their predictive capacities. The soluble biomarker, sVEGFR-3 and tumor size at start of treatment were found to be promising predictors of overall survival, with decreased sVEGFR-3 levels and smaller baseline tumor size being predictive of longer OS. Also hypertension and neutropenia was identified as predictors of OS. The developed modeling framework may be useful to monitor clinical response, optimize dosing in sunitinib and to facilitate dose individualization

Pharmacometric Models for Biomarkers, Side Effects and Efficacy in Anticancer Drug Therapy

New approaches quantifying the effect of treatment are needed in oncology to improve the drug development process and to enable treatment optimization for existing therapies. This thesis focuses on the development of pharmacometric models for biomarkers, side effects and efficacy in order to identify predictors of clinical response in anti-cancer drug therapy. The variability in myelosuppression was characterized in six different cytotoxic anticancer treatments to evaluate a model-based dose individualization approach utilizing neutrophil counts as a biomarker. The estimated impact of inter-occasion variability was relatively low in relation to the inter-individual variability, indicating that myelosuppression is predictable from one treatment course to another. The approach may thereby be useful for dose optimization within an individual. To further study and to identify predictors for the severe side effect febrile neutropenia (FN), the relationship between the shape of the myelosuppression time-course and the probability of FN was characterized. Patients with a rapid decline in neutrophil counts and high drug sensitivity were identified to have a higher probability of developing FN compared with other patients who experience grade 4 neutropenia. Predictors of clinical response in patients receiving sunitinib for the treatment of gastro-intestinal stromal tumor (GIST) were identified by the development of an integrated modeling framework. Drug exposure, biomarkers, tumor dynamics, side effects and overall survival (OS) were linked in a unified structure, and univariate and multivariate exposure variables were tested for their predictive capacities. The soluble biomarker, sVEGFR-3 and tumor size at start of treatment were found to be promising predictors of overall survival, with decreased sVEGFR-3 levels and smaller baseline tumor size being predictive of longer OS. Also hypertension and neutropenia was identified as predictors of OS. The developed modeling framework may be useful to monitor clinical response, optimize dosing in sunitinib and to facilitate dose individualization

A core undergraduate curriculum in plastic surgery – a Delphi consensus study in Scandinavia

Background and aims: In recent years, undergraduate medical education has undergone a transition from a speciality-based to a more competence-based training system. Consequently, whilst medical knowledge is rapidly expanding, time for teaching of the surgical specialties is decreasing. Thus, there appears to be a need to define the core competences that are to be taught. The aim of this study was to establish a Scandinavian core undergraduate curriculum of competences in plastic surgery, using scientific methods. Methods: The Delphi technique for group consensus was employed. An expert panel was recruited from various plastic surgery subspecialties, institutions, and levels of clinical experience, in four Nordic countries (Denmark, Finland, Norway and Sweden). Questionnaires were sent out and answers collected electronically via Google Forms™. Following completion of three predefined rounds of anonymous questionnaires; a final core curriculum competency list was agreed upon based on a consensus agreement level of 80%. Results: Two hundred and ninety-five competences were suggested in the first round. In the second round, 76 competences (33 skills and 43 knowledge items) received a score ≥3.00 on a 1–4 Likert scale. Final agreement in the third round resulted in a list of 68 competences with agreement above 80% (31 skills and 37 knowledge items). Conclusions: This study proposes the first scientifically developed undergraduate core curriculum in plastic surgery. It comprises of a consensus of competences a recently graduated medical doctor should be expected to possess