Dimensional Regularization in Position Space, and a Forest Formula for Epstein-Glaser Renormalization

We reformulate dimensional regularization as a regularization method in position space and show that it can be used to give a closed expression for the renormalized time-ordered products as solutions to the induction scheme of Epstein-Glaser. For scalar fields the resulting renormalization method is always applicable, we compute several examples. We also analyze the Hopf algebraic aspects of the combinatorics. Our starting point is the Main Theorem of Renormalization of Stora and Popineau and the arising renormalization group as originally defined by Stueckelberg and Petermann.Comment: 51 pages, to be published in Journal of Mathematical Physic

Learning and climate change

Learning – i.e. the acquisition of new information that leads to changes in our assessment of uncertainty – plays a prominent role in the international climate policy debate. For example, the view that we should postpone actions until we know more continues to be influential. The latest work on learning and climate change includes new theoretical models, better informed simulations of how learning affects the optimal timing of emissions reductions, analyses of how new information could affect the prospects for reaching and maintaining political agreements and for adapting to climate change, and explorations of how learning could lead us astray rather than closer to the truth. Despite the diversity of this new work, a clear consensus on a central point is that the prospect of learning does not support the postponement of emissions reductions today.Learning; Uncertainty; Climate change; Decision analysis

Treatment of Infantile Inflammatory Bowel Disease and Autoimmunity by Allogeneic Stem Cell Transplantation in LPS-Responsive Beige-Like Anchor Deficiency

Inflammatory bowel disease (IBD) in young children can be a clinical manifestation of various primary immunodeficiency syndromes. Poor clinical outcome is associated with poor quality of life and high morbidity from the complications of prolonged immunosuppressive treatment and malabsorption. In 2012, mutations in the lipopolysaccharide-responsive beige-like anchor (LRBA) gene were identified as the cause of an autoimmunity and immunodeficiency syndrome. Since then, several LRBA-deficient patients have been reported with a broad spectrum of clinical manifestations without reliable predictive prognostic markers. Allogeneic hematopoietic stem cell transplantation (alloHSCT) has been performed in a few severely affected patients with complete or partial response. Herein, we present a detailed course of the disease and the transplantation procedure used in a LRBA-deficient patient suffering primarily from infantile IBD with immune enteropathy since the age of 6 weeks, and progressive autoimmunity with major complications following long-term immunosuppressive treatment. At 12 years of age, alloHSCT using bone marrow of a fully matched sibling donor-a healthy heterozygous LRBA mutant carrier-was performed after conditioning with a reduced-intensity regimen. During the 6-year follow-up, we observed a complete remission of enteropathy, autoimmunity, and skin vitiligo, with complete donor chimerism. The genetic diagnosis of LRBA deficiency was made post-alloHSCT by detection of two compound heterozygous mutations, using targeted sequencing of DNA samples extracted from peripheral blood before the transplantation

Deficiency of caspase recruitment domain family, member 11 (CARD11), causes profound combined immunodeficiency in human subjects.

BACKGROUND: Profound combined immunodeficiency can present with normal numbers of T and B cells, and therefore the functional defect of the cellular and humoral immune response is often not recognized until the first severe clinical manifestation. Here we report a patient of consanguineous descent presenting at 13 months of age with hypogammaglobulinemia, Pneumocystis jirovecii pneumonia, and a suggestive family history. OBJECTIVE: We sought to identify the genetic alteration in a patient with combined immunodeficiency and characterize human caspase recruitment domain family, member 11 (CARD11), deficiency. METHODS: Molecular, immunologic, and functional assays were performed. RESULTS: The immunologic characterization revealed only subtle changes in the T-cell and natural killer cell compartment, whereas B-cell differentiation, although normal in number, was distinctively blocked at the transitional stage. Genetic evaluation revealed a homozygous deletion of exon 21 in CARD11 as the underlying defect. This deletion abrogated protein expression and activation of the canonical nuclear factor κB (NF-κB) pathway in lymphocytes after antigen receptor or phorbol 12-myristate 13-acetate stimulation, whereas CD40 signaling in B cells was preserved. The abrogated activation of the canonical NF-κB pathway was associated with severely impaired upregulation of inducible T-cell costimulator, OX40, cytokine production, proliferation of T cells, and B cell-activating factor receptor expression on B cells. CONCLUSION: Thus in patients with CARD11 deficiency, the combination of impaired activation and especially upregulation of inducible T-cell costimulator on T cells, together with severely disturbed peripheral B-cell differentiation, apparently leads to a defective T-cell/B-cell cooperation and probably germinal center formation and clinically results in severe immunodeficiency. This report discloses the crucial and nonredundant role of canonical NF-κB activation and specifically CARD11 in the antigen-specific immune response in human subjects

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

A challenge in solving the genotype-to-phenotype relationship is to predict a cell\u27s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype. Predicting metabolomes from gene expression data is a key challenge in understanding the genotype-phenotype relationship. Studying the enzyme expression proteome in kinase knockouts, we reveal the importance of a so far overlooked metabolism-regulatory mechanism. Enzyme expression changes are impacting on metabolite levels through many changes acting in concert. We show that one can map regulatory enzyme expression patterns using machine learning and use them to predict the metabolome of kinase-deficient cells on the basis of their enzyme expression proteome. Our study quantifies the role of enzyme abundance in the regulation of metabolism and by doing so reveals the potential of machine learning in gaining understanding about complex metabolism regulation

Reliability of multi-site UK Biobank MRI brain phenotypes for the assessment of neuropsychiatric complications of SARS-CoV-2 infection: The COVID-CNS travelling heads study.

Funder: National Institute for Health Research (NIHR)INTRODUCTION: Magnetic resonance imaging (MRI) of the brain could be a key diagnostic and research tool for understanding the neuropsychiatric complications of COVID-19. For maximum impact, multi-modal MRI protocols will be needed to measure the effects of SARS-CoV-2 infection on the brain by diverse potentially pathogenic mechanisms, and with high reliability across multiple sites and scanner manufacturers. Here we describe the development of such a protocol, based upon the UK Biobank, and its validation with a travelling heads study. A multi-modal brain MRI protocol comprising sequences for T1-weighted MRI, T2-FLAIR, diffusion MRI (dMRI), resting-state functional MRI (fMRI), susceptibility-weighted imaging (swMRI), and arterial spin labelling (ASL), was defined in close approximation to prior UK Biobank (UKB) and C-MORE protocols for Siemens 3T systems. We iteratively defined a comparable set of sequences for General Electric (GE) 3T systems. To assess multi-site feasibility and between-site variability of this protocol, N = 8 healthy participants were each scanned at 4 UK sites: 3 using Siemens PRISMA scanners (Cambridge, Liverpool, Oxford) and 1 using a GE scanner (King's College London). Over 2,000 Imaging Derived Phenotypes (IDPs), measuring both data quality and regional image properties of interest, were automatically estimated by customised UKB image processing pipelines (S2 File). Components of variance and intra-class correlations (ICCs) were estimated for each IDP by linear mixed effects models and benchmarked by comparison to repeated measurements of the same IDPs from UKB participants. Intra-class correlations for many IDPs indicated good-to-excellent between-site reliability. Considering only data from the Siemens sites, between-site reliability generally matched the high levels of test-retest reliability of the same IDPs estimated in repeated, within-site, within-subject scans from UK Biobank. Inclusion of the GE site resulted in good-to-excellent reliability for many IDPs, although there were significant between-site differences in mean and scaling, and reduced ICCs, for some classes of IDP, especially T1 contrast and some dMRI-derived measures. We also identified high reliability of quantitative susceptibility mapping (QSM) IDPs derived from swMRI images, multi-network ICA-based IDPs from resting-state fMRI, and olfactory bulb structure IDPs from T1, T2-FLAIR and dMRI data. CONCLUSION: These results give confidence that large, multi-site MRI datasets can be collected reliably at different sites across the diverse range of MRI modalities and IDPs that could be mechanistically informative in COVID brain research. We discuss limitations of the study and strategies for further harmonisation of data collected from sites using scanners supplied by different manufacturers. These acquisition and analysis protocols are now in use for MRI assessments of post-COVID patients (N = 700) as part of the ongoing COVID-CNS study