Multivariate Statistical Analyses Demonstrate Unique Host Immune Responses to Single and Dual Lentiviral Infection

Feline immunodeficiency virus (FIV) and human immunodeficiency virus (HIV) are recently identified lentiviruses that cause progressive immune decline and ultimately death in infected cats and humans. It is of great interest to understand how to prevent immune system collapse caused by these lentiviruses. We recently described that disease caused by a virulent FIV strain in cats can be attenuated if animals are first infected with a feline immunodeficiency virus derived from a wild cougar. The detailed temporal tracking of cat immunological parameters in response to two viral infections resulted in high-dimensional datasets containing variables that exhibit strong co-variation. Initial analyses of these complex data using univariate statistical techniques did not account for interactions among immunological response variables and therefore potentially obscured significant effects between infection state and immunological parameters.Here, we apply a suite of multivariate statistical tools, including Principal Component Analysis, MANOVA and Linear Discriminant Analysis, to temporal immunological data resulting from FIV superinfection in domestic cats. We investigated the co-variation among immunological responses, the differences in immune parameters among four groups of five cats each (uninfected, single and dual infected animals), and the "immune profiles" that discriminate among them over the first four weeks following superinfection. Dual infected cats mount an immune response by 24 days post superinfection that is characterized by elevated levels of CD8 and CD25 cells and increased expression of IL4 and IFNgamma, and FAS. This profile discriminates dual infected cats from cats infected with FIV alone, which show high IL-10 and lower numbers of CD8 and CD25 cells.Multivariate statistical analyses demonstrate both the dynamic nature of the immune response to FIV single and dual infection and the development of a unique immunological profile in dual infected cats, which are protected from immune decline

Environmental risk factors for dementia: a systematic review

Background - Dementia risk reduction is a major and growing public health priority. While certain modifiable risk factors for dementia have been identified, there remains a substantial proportion of unexplained risk. There is evidence that environmental risk factors may explain some of this risk. Thus, we present the first comprehensive systematic review of environmental risk factors for dementia. Methods - We searched the PubMed and Web of Science databases from their inception to January 2016, bibliographies of review articles, and articles related to publically available environmental data. Articles were included if they examined the association between an environmental risk factor and dementia. Studies with another outcome (for example, cognition), a physiological measure of the exposure, case studies, animal studies, and studies of nutrition were excluded. Data were extracted from individual studies which were, in turn, appraised for methodological quality. The strength and consistency of the overall evidence for each risk factor identified was assessed. Results - We screened 4784 studies and included 60 in the review. Risk factors were considered in six categories: air quality, toxic heavy metals, other metals, other trace elements, occupational-related exposures, and miscellaneous environmental factors. Few studies took a life course approach. There is at least moderate evidence implicating the following risk factors: air pollution; aluminium; silicon; selenium; pesticides; vitamin D deficiency; and electric and magnetic fields. Conclusions - Studies varied widely in size and quality and therefore we must be circumspect in our conclusions. Nevertheless, this extensive review suggests that future research could focus on a short list of environmental risk factors for dementia. Furthermore, further robust, longitudinal studies with repeated measures of environmental exposures are required to confirm these associations

Misty Mountain clustering: application to fast unsupervised flow cytometry gating

<p>Abstract</p> <p>Background</p> <p>There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 10⁶points that are often generated by high throughput experiments.</p> <p>Results</p> <p>To circumvent these limitations, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain, that is based on percolation theory and that efficiently analyzes large data sets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing only once the cross sections of the histogram. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. The clustering of 10⁶data points in 2D data space takes place within about 15 seconds on a standard laptop PC. Comparison of the performance of this algorithm with other state of the art automated flow cytometry gating methods indicate that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment.</p> <p>Conclusions</p> <p>Misty Mountain is fast, unbiased for cluster shape, identifies stable clusters and is robust to noise. It provides a useful, general solution for multidimensional clustering problems. We demonstrate its suitability for automated gating of flow cytometry data.</p

Testosterone levels are negatively associated with childlessness in males, but positively related to offspring count in fathers

Variation in testosterone (T) is thought to affect the allocation of effort between reproductive and parenting strategies. Here, using a large sample of elderly American men (n = 754) and women (n = 669) we examined the relationship between T and self-reported parenthood, as well as the relationship between T and number of reported children. Results supported previous findings from the literature, showing that fathers had lower T levels than men who report no children. Furthermore, we found that among fathers T levels were positively associated with the number of children a man reports close to the end of his lifespan. Results were maintained when controlling for a number of relevant factors such as time of T sampling, participant age, educational attainment, BMI, marital status and reported number of sex partners. In contrast, T was not associated with either motherhood or the number of children women had, suggesting that, at least in this sample, T does not influence the allocation of effort between reproductive and parenting strategies among women. Findings from this study contribute to the growing body of literature suggesting that, among men, pair bonding and paternal care are associated with lower T levels, while searching and acquiring sex partners is associated with higher T levels.27 Jun 2013: Pollet TV, Cobey KD, van der Meij L (2013) Correction: Testosterone Levels Are Negatively Associated with Childlessness in Males, but Positively Related to Offspring Count in Fathers. PLoS ONE 8(6): 10.1371/annotation/bccccb7e-48a7-4594-b3e6-ce8c9d2489a2

T2K neutrino flux prediction

cited By 15 art_number: 012001 affiliation: Centre for Particle Physics, Department of Physics, University of Alberta, Edmonton, AB, Canada; Albert Einstein Center for Fundamental Physics, Laboratory for High Energy Physics (LHEP), University of Bern, Bern, Switzerland; Department of Physics, Boston University, Boston, MA, United States; Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada; Department of Physics and Astronomy, University of California Irvine, Irvine, CA, United States; IRFU, CEA Saclay, Gif-sur-Yvette, France; Institute for Universe and Elementary Particles, Chonnam National University, Gwangju, South Korea; Department of Physics, University of Colorado at Boulder, Boulder, CO, United States; Department of Physics, Colorado State University, Fort Collins, CO, United States; Department of Physics, Dongshin University, Naju, South Korea; Department of Physics, Duke University, Durham, NC, United States; IN2P3-CNRS, Laboratoire Leprince-Ringuet, Ecole Polytechnique, Palaiseau, France; Institute for Particle Physics, ETH Zurich, Zurich, Switzerland; Section de Physique, DPNC, University of Geneva, Geneva, Switzerland; H. Niewodniczanski Institute of Nuclear Physics PAN, Cracow, Poland; High Energy Accelerator Research Organization (KEK), Tsukuba, Ibaraki, Japan; Institut de Fisica d’Altes Energies (IFAE), Bellaterra (Barcelona), Spain; IFIC (CSIC and University of Valencia), Valencia, Spain; Department of Physics, Imperial College London, London, United Kingdom; INFN Sezione di Bari, Dipartimento Interuniversitario di Fisica, Università e Politecnico di Bari, Bari, Italy; INFN Sezione di Napoli and Dipartimento di Fisica, Università di Napoli, Napoli, Italy; INFN Sezione di Padova, Dipartimento di Fisica, Università di Padova, Padova, Italy; INFN Sezione di Roma, Università di Roma la Sapienza, Roma, Italy; Institute for Nuclear Research, Russian Academy of Sciences, Moscow, Russian Federation; Kobe University, Kobe, Japan; Department of Physics, Kyoto University, Kyoto, Japan; Physics Department, Lancaster University, Lancaster, United Kingdom; Department of Physics, University of Liverpool, Liverpool, United Kingdom; Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, United States; Université de Lyon, Université Claude Bernard Lyon 1, IPN Lyon (IN2P3), Villeurbanne, France; Department of Physics, Miyagi University of Education, Sendai, Japan; National Centre for Nuclear Research, Warsaw, Poland; State University of New York at Stony Brook, Stony Brook, NY, United States; Department of Physics and Astronomy, Osaka City University, Department of Physics, Osaka, Japan; Department of Physics, Oxford University, Oxford, United Kingdom; UPMC, Université Paris Diderot, Laboratoire de Physique Nucléaire et de Hautes Energies (LPNHE), Paris, France; Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, United States; School of Physics, Queen Mary University of London, London, United Kingdom; Department of Physics, University of Regina, Regina, SK, Canada; Department of Physics and Astronomy, University of Rochester, Rochester, NY, United States; III. Physikalisches Institut, RWTH Aachen University, Aachen, Germany; Department of Physics and Astronomy, Seoul National University, Seoul, South Korea; Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom; University of Silesia, Institute of Physics, Katowice, Poland; STFC, Rutherford Appleton Laboratory, Harwell Oxford, Warrington, United Kingdom; Department of Physics, University of Tokyo, Tokyo, Japan; Institute for Cosmic Ray Research, Kamioka Observatory, University of Tokyo, Kamioka, Japan; Institute for Cosmic Ray Research, Research Center for Cosmic Neutrinos, University of Tokyo, Kashiwa, Japan; Department of Physics, University of Toronto, Toronto, ON, Canada; TRIUMF, Vancouver, BC, Canada; Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada; Faculty of Physics, University of Warsaw, Warsaw, Poland; Institute of Radioelectronics, Warsaw University of Technology, Warsaw, Poland; Department of Physics, University of Warwick, Coventry, United Kingdom; Department of Physics, University of Washington, Seattle, WA, United States; Department of Physics, University of Winnipeg, Winnipeg, MB, Canada; Faculty of Physics and Astronomy, Wroclaw University, Wroclaw, Poland; Department of Physics and Astronomy, York University, Toronto, ON, Canada references: Astier, P., (2003) Nucl. 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Measurement and interpretation of same-sign W boson pair production in association with two jets in pp collisions at s = 13 TeV with the ATLAS detector

This paper presents the measurement of fducial and diferential cross sections for both the inclusive and electroweak production of a same-sign W-boson pair in association with two jets (W±W±jj) using 139 fb−1 of proton-proton collision data recorded at a centre-of-mass energy of √s = 13 TeV by the ATLAS detector at the Large Hadron Collider. The analysis is performed by selecting two same-charge leptons, electron or muon, and at least two jets with large invariant mass and a large rapidity diference. The measured fducial cross sections for electroweak and inclusive W±W±jj production are 2.92 ± 0.22 (stat.) ± 0.19 (syst.)fb and 3.38±0.22 (stat.)±0.19 (syst.)fb, respectively, in agreement with Standard Model predictions. The measurements are used to constrain anomalous quartic gauge couplings by extracting 95% confdence level intervals on dimension-8 operators. A search for doubly charged Higgs bosons H±± that are produced in vector-boson fusion processes and decay into a same-sign W boson pair is performed. The largest deviation from the Standard Model occurs for an H±± mass near 450 GeV, with a global signifcance of 2.5 standard deviations

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion