Involution of the mouse mammary gland is associated with an immune cascade and an acute-phase response, involving LBP, CD14 and STAT3

INTRODUCTION: Involution of the mammary gland is a complex process of controlled apoptosis and tissue remodelling. The aim of the project was to identify genes that are specifically involved in this process. METHODS: We used Affymetrix oligonucleotide microarrays to perform a detailed transcript analysis on the mechanism of controlled involution after withdrawal of the pups at day seven of lactation. Some of the results were confirmed by semi-quantitative reverse transcriptase polymerase chain reaction, Western blotting or immunohistochemistry. RESULTS: We identified 145 genes that were specifically upregulated during the first 4 days of involution; of these, 49 encoded immunoglobulin genes. A further 12 genes, including those encoding the signal transducer and activator of transcription 3 (STAT3), the lipopolysaccharide receptor (CD14) and lipopolysaccharide-binding protein (LBP), were involved in the acute-phase response, demonstrating that the expression of acute-phase response genes can occur in the mammary gland itself and not only in the liver. Expression of LBP and CD14 was upregulated, at both the RNA and protein level, immediately after pup withdrawal; CD14 was strongly expressed in the luminal epithelial cells. Other genes identified suggested neutrophil activation early in involution, followed by macrophage activation late in the process. Immunohistochemistry and histological staining confirmed the infiltration of the involuting mammary tissue with neutrophils, plasma cells, macrophages and eosinophils. CONCLUSION: Oligonucleotide microarrays are a useful tool for identifying genes that are involved in the complex developmental process of mammary gland involution. The genes identified are consistent with an immune cascade, with an early acute-phase response that occurs in the mammary gland itself and resembles a wound healing process

Supermarket Transaction Records In Dietary Evaluation – The STRIDE study: validation against self-reported dietary intake

Objective: Scalable methods are required for population dietary monitoring. The Supermarket Transaction Records In Dietary Evaluation (STRIDE) study compares dietary estimates from supermarket transactions with an online FFQ. Design: Participants were recruited in four waves, accounting for seasonal dietary variation. Purchases were collected for 1 year during and 1 year prior to the study. Bland–Altman agreement and limits of agreement (LoA) were calculated for energy, sugar, fat, saturated fat, protein and sodium (absolute and relative). Setting: This study was partnered with a large UK retailer. Participants: Totally, 1788 participants from four UK regions were recruited from the retailer’s loyalty card customer database, according to breadth and frequency of purchases. Six hundred and eighty-six participants were included for analysis. Results: The analysis sample were mostly female (72 %), with a mean age of 56 years (SD 13). The ratio of purchases to intakes varied depending on amounts purchased and consumed; purchases under-estimated intakes for smaller amounts on average, but over-estimated for larger amounts. For absolute measures, the LoA across households were wide, for example, for energy intake of 2000 kcal, purchases could under- or over-estimate intake by a factor of 5; values could be between 400 kcal and 10000 kcal. LoA for relative (energy-adjusted) estimates were smaller, for example, for 14 % of total energy from saturated fat, purchase estimates may be between 7 % and 27 %. Conclusions: Agreement between purchases and intake was highly variable, strongest for smaller loyal households and for relative values. For some customers, relative nutrient purchases are a reasonable proxy for dietary composition indicating utility in population-level dietary research

Instantons and the endpoint of the lepton energy spectrum in charmless semileptonic BB decays

A recent calculation by Chay and Rey has shown that instantons may make a significant contribution to the lepton energy spectrum near its endpoint. Using an ansatz borrowed from the study of high energy baryon number violating processes, we investigate whether these corrections could spoil the relation between the nonperturbative contributions to this spectrum and to the photon energy spectrum in radiative $B$ decays. We find, in general, that this universality may well fail unless the spectrum is smeared over a region which is considerably larger than had previously been thought necessary. This result affects the possibility of performing a reliable measurement of $V_{ub}$ using inclusive decays.Comment: Slightly revised version, to appear in Phys. Rev. D. A few additional comments have been added on the approximations which are used. 13 pages, 2 embedded uuencoded figures, uses REVTe

Efficient Algorithm on a Non-staggered Mesh for Simulating Rayleigh-Benard Convection in a Box

An efficient semi-implicit second-order-accurate finite-difference method is described for studying incompressible Rayleigh-Benard convection in a box, with sidewalls that are periodic, thermally insulated, or thermally conducting. Operator-splitting and a projection method reduce the algorithm at each time step to the solution of four Helmholtz equations and one Poisson equation, and these are are solved by fast direct methods. The method is numerically stable even though all field values are placed on a single non-staggered mesh commensurate with the boundaries. The efficiency and accuracy of the method are characterized for several representative convection problems.Comment: REVTeX, 30 pages, 5 figure

Tree method for quantum vortex dynamics

We present a numerical method to compute the evolution of vortex filaments in superfluid helium. The method is based on a tree algorithm which considerably speeds up the calculation of Biot-Savart integrals. We show that the computational cost scales as Nlog{(N) rather than N squared, where $N$ is the number of discretization points. We test the method and its properties for a variety of vortex configurations, ranging from simple vortex rings to a counterflow vortex tangle, and compare results against the Local Induction Approximation and the exact Biot-Savart law.Comment: 12 pages, 10 figure

New Lump-like Structures in Scalar-field Models

In this work we investigate lump-like solutions in models described by a single real scalar field. We start considering non-topological solutions with the usual lump-like form, and then we study other models, where the bell-shape profile may have varying amplitude and width, or develop a flat plateau at its top, or even induce a lump on top of another lump. We suggest possible applications where these exotic solutions might be used in several distinct branches of physics.Comment: REvTex4, twocolumn, 10 pages, 9 figures; new reference added, to appear in EPJ

The sensitivity of the vortex filament method to different reconnection models

We present a detailed analysis on the effect of using different algorithms to model the reconnection of vortices in quantum turbulence, using the thin-filament approach. We examine differences between four main algorithms for the case of turbulence driven by a counterflow. In calculating the velocity field we use both the local induction approximation (LIA) and the full Biot-Savart integral. We show that results of Biot-Savart simulations are not sensitive to the particular reconnection method used, but LIA results are.Comment: 9 pages, 9 figure

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Background: Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. Results: We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. Conclusion: Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.</p

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Background: Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. Results: We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. Conclusion: Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.</p

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Background: Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. Results: We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. Conclusion: Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.</p