Local proliferation dominates lesional macrophage accumulation in atherosclerosis

During the inflammatory response that drives atherogenesis, macrophages accumulate progressively in the expanding arterial wall1,2. The observation that circulating monocytes give rise to lesional macrophages3–9 has reinforced the concept that monocyte infiltration dictates macrophage build-up. Recent work indicates, however, that macrophages do not depend on monocytes in some inflammatory contexts10. We therefore revisited the mechanism of macrophage accumulation in atherosclerosis. We show that murine atherosclerotic lesions experience a surprisingly rapid, 4-week, cell turnover. Replenishment of macrophages in these experimental atheromata depends predominantly on local macrophage proliferation rather than monocyte influx. The microenvironment orchestrates macrophage proliferation via the involvement of scavenger receptor (SR)-A. Our study reveals macrophage proliferation as a key event in atherosclerosis and identifies macrophage self-renewal as a therapeutic target for cardiovascular disease

Unit bar architecture in a highly‐variable fluvial discharge regime: Examples from the Burdekin River, Australia

Unit bars are relatively large bedforms that develop in rivers over a wide range of climatic regimes. Unit bars formed within the highly-variable discharge Burdekin River in Queensland, Australia, were examined over three field campaigns between 2015 and 2017. These bars had complex internal structures, dominated by co-sets of cross-stratified and planar-stratified sets. The cross-stratified sets tended to down-climb. The development of complex internal structures was primarily a result of three processes: (i) superimposed bedforms reworking the unit bar avalanche face; (ii) variable discharge triggering reactivation surfaces; and (iii) changes in bar growth direction induced by stage change. Internal structures varied along the length and across the width of unit bars. For the former, down-climbing cross-stratified sets tended to pass into single planar cross-stratified deposits at the downstream end of emergent bars; such variation related to changes in fluvial conditions whilst bars were active. A hierarchy of six categories of fluvial unsteadiness is proposed, with these discussed in relation to their effects on unit bar (and dune) internal structure. Across-deposit variation was caused by changes in superimposed bedform and bar character along bar crests; such changes related to the three-dimensionality of the channel and bar geometry when bars were active. Variation in internal structure is likely to be more pronounced in unit bar deposits than in smaller bedform (for example, dune) deposits formed in the same river. This is because smaller bedforms are more easily washed out or modified by changing discharge conditions and their smaller dimensions restrict the variation in flow conditions that occur over their width. In regimes where unit bar deposits are well-preserved, their architectural variability is a potential aid to their identification. This complex architecture also allows greater resolution in interpreting the conditions before and during bar initiation and development

High resolution 148Nd(3He,nγ) two proton stripping reaction and the structure of the 02 + state in 150Sm

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Study of 0+ States at iThemba LABS

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Harmonizing methods for wildlife abundance estimation and pathogen detection in Europe-a questionnaire survey on three selected host-pathogen combinations

__Background:__ The need for wildlife health surveillance as part of disease control in wildlife, domestic animals and humans on the global level is widely recognized. However, the objectives, methods and intensity of existing wildlife health surveillance programs vary greatly among European countries, resulting in a patchwork of data that are difficult to merge and compare. This survey aimed at evaluating the need and potential for data harmonization in wildlife health in Europe. The specific objective was to collect information on methods currently used to estimate host abundance and pathogen prevalence. Questionnaires were designed t

SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues.

There is pressing urgency to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2), which causes the disease COVID-19. SARS-CoV-2 spike (S) protein binds angiotensin-converting enzyme 2 (ACE2), and in concert with host proteases, principally transmembrane serine protease 2 (TMPRSS2), promotes cellular entry. The cell subsets targeted by SARS-CoV-2 in host tissues and the factors that regulate ACE2 expression remain unknown. Here, we leverage human, non-human primate, and mouse single-cell RNA-sequencing (scRNA-seq) datasets across health and disease to uncover putative targets of SARS-CoV-2 among tissue-resident cell subsets. We identify ACE2 and TMPRSS2 co-expressing cells within lung type II pneumocytes, ileal absorptive enterocytes, and nasal goblet secretory cells. Strikingly, we discovered that ACE2 is a human interferon-stimulated gene (ISG) in vitro using airway epithelial cells and extend our findings to in vivo viral infections. Our data suggest that SARS-CoV-2 could exploit species-specific interferon-driven upregulation of ACE2, a tissue-protective mediator during lung injury, to enhance infection

The Public Repository of Xenografts enables discovery and randomized phase II-like trials in mice

More than 90% of drugs with preclinical activity fail in human trials, largely due to insufficient efficacy. We hypothesized that adequately powered trials of patient-derived xenografts (PDX) in mice could efficiently define therapeutic activity across heterogeneous tumors. To address this hypothesis, we established a large, publicly available repository of well-characterized leukemia and lymphoma PDXs that undergo orthotopic engraftment, called the Public Repository of Xenografts (PRoXe). PRoXe includes all de-identified information relevant to the primary specimens and the PDXs derived from them. Using this repository, we demonstrate that large studies of acute leukemia PDXs that mimic human randomized clinical trials can characterize drug efficacy and generate transcriptional, functional, and proteomic biomarkers in both treatment-naive and relapsed/refractory disease

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

Measurement of the View the tt production cross-section using eμ events with b-tagged jets in pp collisions at √s = 13 TeV with the ATLAS detector

This paper describes a measurement of the inclusive top quark pair production cross-section (σtt¯) with a data sample of 3.2 fb−1 of proton–proton collisions at a centre-of-mass energy of √s = 13 TeV, collected in 2015 by the ATLAS detector at the LHC. This measurement uses events with an opposite-charge electron–muon pair in the final state. Jets containing b-quarks are tagged using an algorithm based on track impact parameters and reconstructed secondary vertices. The numbers of events with exactly one and exactly two b-tagged jets are counted and used to determine simultaneously σtt¯ and the efficiency to reconstruct and b-tag a jet from a top quark decay, thereby minimising the associated systematic uncertainties. The cross-section is measured to be: σtt¯ = 818 ± 8 (stat) ± 27 (syst) ± 19 (lumi) ± 12 (beam) pb, where the four uncertainties arise from data statistics, experimental and theoretical systematic effects, the integrated luminosity and the LHC beam energy, giving a total relative uncertainty of 4.4%. The result is consistent with theoretical QCD calculations at next-to-next-to-leading order. A fiducial measurement corresponding to the experimental acceptance of the leptons is also presented

Search for TeV-scale gravity signatures in high-mass final states with leptons and jets with the ATLAS detector at sqrt [ s ] = 13TeV

A search for physics beyond the Standard Model, in final states with at least one high transverse momentum charged lepton (electron or muon) and two additional high transverse momentum leptons or jets, is performed using 3.2 fb−1 of proton–proton collision data recorded by the ATLAS detector at the Large Hadron Collider in 2015 at √s = 13 TeV. The upper end of the distribution of the scalar sum of the transverse momenta of leptons and jets is sensitive to the production of high-mass objects. No excess of events beyond Standard Model predictions is observed. Exclusion limits are set for models of microscopic black holes with two to six extra dimensions