Searching for light dark matter with a spherical proportional counter

The vast majority of mass in the universe is comprised of an unknown form of matter – Dark Matter. The NEWS-G collaboration are using a novel gaseous detector, the spherical propor- tional counter, in a search for Dark Matter particles with masses down to sub-GeV. Having set the first exclusion limit on the spin-independent DM-nucleon cross sections for a 0.5 GeV DM particle in 2017, NEWS-G is now turning its focus to it’s new detector, SNOGLOBE. The 140 cm-diameter spherical proportional counter was constructed and commissioned in France and has now been shipped to SNOLAB, Canada, for a direct DM search. For SNOGLOBE to achieve it’s physics potential, several developments are required, in- cluding the understanding of the detector, the properties of gases, background suppression techniques and the physics potential of future experiments. Developments in the spherical proportional counter read-out technology are presented, which uses high-resistivity electrodes to improve stability and energy resolution. The multi-anode sensor, ACHINOS, enables the operation of larger detectors at higher pressures. A simulation framework for the spherical proportional counter has also been developed, which is an important tool for understanding how the detector operates. Another critical component to understanding the operation of the detector when looking for low-energy nuclear recoils induced by DM interactions is the ionisation quenching factor. Measurements of this in gases are scarce, and so a method to calculate this from measurements of the W-value has been developed and applied to several gases. The suppression of radioactive backgrounds is of paramount importance for future NEWS-G spherical proportional counters, and all rare-event search experiments. A method for producing highly radiopure copper is electroforming, which has been used to apply a layer to SNOGLOBE’s inner surface and suppress experimental backgrounds. The application of this technique to produce future NEWS-G detectors is discussed, along with their physics potential

Association between rheumatoid arthritis disease activity, progression of functional limitation and long-term risk of orthopaedic surgery : Combined analysis of two prospective cohorts supports EULAR treat to target DAS thresholds

Objectives: To examine the association between disease activity in early rheumatoid arthritis (RA), functional limitation and long-term orthopaedic episodes. Methods: Health Assessment Questionnaire (HAQ) disability scores were collected from two longitudinal early RA inception cohorts in routine care; Early Rheumatoid Arthritis Study and Early Rheumatoid Arthritis Network from 1986 to 2012. The incidence of major and intermediate orthopaedic surgical episodes over 25 years was collected from national data sets. Disease activity was categorised by mean disease activity score (DAS28) annually between years 1 and 5; remission (RDAS≤2.6), low (LDAS>2.6-3.2), low-moderate (LMDAS≥3.2-4.19), high-moderate (HMDAS 4.2-5.1) and high (HDAS>5.1). Results: Data from 2045 patients were analysed. Patients in RDAS showed no HAQ progression over 5 years, whereas there was a significant relationship between rising DAS28 category and HAQ at 1 year, and the rate of HAQ progression between years 1 and 5. During 27 986 person-years follow-up, 392 intermediate and 591 major surgeries were observed. Compared with the RDAS category, there was a significantly increased cumulative incidence of intermediate surgery in HDAS (OR 2.59 CI 1.49 to 4.52) and HMDAS (OR 1.8 CI 1.05 to 3.11) categories, and for major surgery in HDAS (OR 2.48 CI 1.5 to 4.11), HMDAS (OR 2.16 CI 1.32 to 3.52) and LMDAS (OR 2.07 CI 1.28 to 3.33) categories. There was no significant difference in HAQ progression or orthopaedic episodes between RDAS and LDAS categories. Conclusions: There is an association between disease activity and both poor function and long-term orthopaedic episodes. This illustrates the far from benign consequences of persistent moderate disease activity, and supports European League Against Rheumatism treat to target recommendations to secure low disease activity or remission in all patients.Peer reviewedFinal Published versio

A spatially resolved atlas of the human lung characterizes a gland-associated immune niche

Single-cell transcriptomics has allowed unprecedented resolution of cell types/states in the human lung, but their spatial context is less well defined. To (re)define tissue architecture of lung and airways, we profiled five proximal-to-distal locations of healthy human lungs in depth using multi-omic single cell/nuclei and spatial transcriptomics (queryable at lungcellatlas.org ). Using computational data integration and analysis, we extend beyond the suspension cell paradigm and discover macro and micro-anatomical tissue compartments including previously unannotated cell types in the epithelial, vascular, stromal and nerve bundle micro-environments. We identify and implicate peribronchial fibroblasts in lung disease. Importantly, we discover and validate a survival niche for IgA plasma cells in the airway submucosal glands (SMG). We show that gland epithelial cells recruit B cells and IgA plasma cells, and promote longevity and antibody secretion locally through expression of CCL28, APRIL and IL-6. This new 'gland-associated immune niche' has implications for respiratory health

Ocean currents shape the microbiome of Arctic marine sediments

Prokaryote communities were investigated on the seasonally stratified Alaska Beaufort Shelf (ABS). Water and sediment directly underlying water with origin in the Arctic, Pacific or Atlantic oceans were analyzed by pyrosequencing and length heterogeneity-PCR in conjunction with physicochemical and geographic distance data to determine what features structure ABS microbiomes. Distinct bacterial communities were evident in all water masses. Alphaproteobacteria explained similarity in Arctic surface water and Pacific derived water. Deltaproteobacteria were abundant in Atlantic origin water and drove similarity among samples. Most archaeal sequences in water were related to unclassified marine Euryarchaeota. Sediment communities influenced by Pacific and Atlantic water were distinct from each other and pelagic communities. Firmicutes and Chloroflexi were abundant in sediment, although their distribution varied in Atlantic and Pacific influenced sites. Thermoprotei dominated archaea in Pacific influenced sediments and Methanomicrobia dominated in methane-containing Atlantic influenced sediments. Length heterogeneity-PCR data from this study were analyzed with data from methane-containing sediments in other regions. Pacific influenced ABS sediments clustered with Pacific sites from New Zealand and Chilean coastal margins. Atlantic influenced ABS sediments formed another distinct cluster. Density and salinity were significant structuring features on pelagic communities. Porosity co-varied with benthic community structure across sites and methane did not. This study indicates that the origin of water overlying sediments shapes benthic communities locally and globally and that hydrography exerts greater influence on microbial community structure than the availability of methane

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University