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

Dose-dependent effects of exogenous gonadotrophins on the endometrium of the rat

We compared the serwn levels of oestrogen and progesterone and the endometrial morphology of normal pregnant rats at 5,5 days' gestation with those of pregnant rats given either low (10 IU) or high (20 IU) doses of two gonadotrophins: follicle-stimulating hormone (FSH) and hwnan chorionic gonadotrophin (HCG). Evidence of ovarian hyperstimulation was observed in the high- but not the low-dose group; both treatment regimens caused significant changes in the endometrial surface, epithelial height, the microvillous border, the glycocalyx, the subepithelial stromal cells and the mitotic activity of the surface epithelial and stromal connective tissue cells. The effects of the highdose treatment were Inore severe than those of the low-dose treatment. The serum oestradiol and progesterone levels of the treated groups were not significantly different from those of the control group. The changes in the endometrium after both treatment regimens may interfere with normal trophoblastic-endometrial interactions and could influence the maintenance of pregnancy. This investigation demonstrated that even low doses of gonadotrophins, which do not cause obvious ovarian stimulation, affect uterine morphology. The findings haveimportant implications for in vitro fertilisation and embryo transfer programmes

Fourier Analysis of Gapped Time Series: Improved Estimates of Solar and Stellar Oscillation Parameters

Quantitative helio- and asteroseismology require very precise measurements of the frequencies, amplitudes, and lifetimes of the global modes of stellar oscillation. It is common knowledge that the precision of these measurements depends on the total length (T), quality, and completeness of the observations. Except in a few simple cases, the effect of gaps in the data on measurement precision is poorly understood, in particular in Fourier space where the convolution of the observable with the observation window introduces correlations between different frequencies. Here we describe and implement a rather general method to retrieve maximum likelihood estimates of the oscillation parameters, taking into account the proper statistics of the observations. Our fitting method applies in complex Fourier space and exploits the phase information. We consider both solar-like stochastic oscillations and long-lived harmonic oscillations, plus random noise. Using numerical simulations, we demonstrate the existence of cases for which our improved fitting method is less biased and has a greater precision than when the frequency correlations are ignored. This is especially true of low signal-to-noise solar-like oscillations. For example, we discuss a case where the precision on the mode frequency estimate is increased by a factor of five, for a duty cycle of 15%. In the case of long-lived sinusoidal oscillations, a proper treatment of the frequency correlations does not provide any significant improvement; nevertheless we confirm that the mode frequency can be measured from gapped data at a much better precision than the 1/T Rayleigh resolution.Comment: Accepted for publication in Solar Physics Topical Issue "Helioseismology, Asteroseismology, and MHD Connections

Determination of the high-twist contribution to the structure function xF3νNxF^{\nu N}_3

We extract the high-twist contribution to the neutrino-nucleon structure function $xF_3^{(\nu+\bar{\nu})N}$ from the analysis of the data collected by the IHEP-JINR Neutrino Detector in the runs with the focused neutrino beams at the IHEP 70 GeV proton synchrotron. The analysis is performed within the infrared renormalon (IRR) model of high twists in order to extract the normalization parameter of the model. From the NLO QCD fit to our data we obtained the value of the IRR model normalization parameter $\Lambda^2_{3}=0.69\pm0.37~({\rm exp})\pm0.16~({\rm theor})~{\rm GeV}^2$. We also obtained $\Lambda^2_{3}=0.36\pm0.22~({\rm exp})\pm0.12~({\rm theor})~{\rm GeV}^2$ from a similar fit to the CCFR data. The average of both results is $\Lambda^2_{3}=0.44\pm0.19~({\rm exp})~{\rm GeV}^2$.Comment: preprint IHEP-01-18, 7 pages, LATEX, 1 figure (EPS

Ambulatory cancer care electronic symptom self-reporting (ACCESS) for surgical patients: A randomised controlled trial protocol

Introduction An increasing proportion of cancer surgeries are ambulatory procedures requiring a stay of 1 day or less in the hospital. Providing patients and their caregivers with ongoing, real-time support after discharge aids delivery of high-quality postoperative care in this new healthcare environment. Despite abundant evidence that patient self-reporting of symptoms improves quality of care, the most effective way to monitor and manage this self-reported information is not known. Methods and analysis This is a two-armed randomised, controlled trial evaluating two approaches to the management of patient-reported data: (1) team monitoring, symptom monitoring by the clinical team, with nursing outreach if symptoms exceed normal limits, and (2) enhanced feedback, real-time feedback to patients about expected symptom severity, with patient-activated care as needed. Patients with breast, gynaecologic, urologic, and head and neck cancer undergoing ambulatory cancer surgery (n=2750) complete an electronic survey for up to 30 days after surgery that includes items from a validated instrument developed by the National Cancer Institute, the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). Information provided to patients in the Enhanced Feedback group is procedure-specific and based on updated PRO-CTCAE data from previous patients. Qualitative interviews are also performed. The primary study outcomes assess unplanned emergency department visits and symptom-triggered interventions (eg, nursing calls and pain management referrals) within 30 days, and secondary outcomes assess the patient and caregiver experience (ie, patient engagement, patient anxiety and caregiver burden). Ethics and dissemination This study is approved by the Institutional Review Board at Memorial Sloan Kettering Cancer Center. The relationships between the study team and stakeholders will be leveraged to disseminate study findings. Findings will be relevant in designing future coordinated care models targeting improved healthcare quality and patient experience. Trial registration number NCT03178045

Precision Measurement of the Proton and Deuteron Spin Structure Functions g2 and Asymmetries A2

We have measured the spin structure functions g2p and g2d and the virtual photon asymmetries A2p and A2d over the kinematic range 0.02 < x < 0.8 and 0.7 < Q^2 < 20 GeV^2 by scattering 29.1 and 32.3 GeV longitudinally polarized electrons from transversely polarized NH3 and 6LiD targets. Our measured g2 approximately follows the twist-2 Wandzura-Wilczek calculation. The twist-3 reduced matrix elements d2p and d2n are less than two standard deviations from zero. The data are inconsistent with the Burkhardt-Cottingham sum rule if there is no pathological behavior as x->0. The Efremov-Leader-Teryaev integral is consistent with zero within our measured kinematic range. The absolute value of A2 is significantly smaller than the sqrt[R(1+A1)/2] limit.Comment: 12 pages, 4 figures, 2 table

Measurements of the Q2Q^2-Dependence of the Proton and Neutron Spin Structure Functions g1p and g1n

The structure functions g1p and g1n have been measured over the range 0.014 < x < 0.9 and 1 < Q2 < 40 GeV2 using deep-inelastic scattering of 48 GeV longitudinally polarized electrons from polarized protons and deuterons. We find that the Q2 dependence of g1p (g1n) at fixed x is very similar to that of the spin-averaged structure function F1p (F1n). From a NLO QCD fit to all available data we find $\Gamma_1^p - \Gamma_1^n =0.176 \pm 0.003 \pm 0.007$ at Q2=5 GeV2, in agreement with the Bjorken sum rule prediction of 0.182 \pm 0.005.Comment: 17 pages, 3 figures. Submitted to Physics Letters

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem