1,944 research outputs found
Effect of tacrolimus (FK506) in dystrophic epidermolysis bullosa: rationale and preliminary results.
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
From medical charts to national census, healthcare has traditionally operated
under a paper-based paradigm. However, the past decade has marked a long and
arduous transformation bringing healthcare into the digital age. Ranging from
electronic health records, to digitized imaging and laboratory reports, to
public health datasets, today, healthcare now generates an incredible amount of
digital information. Such a wealth of data presents an exciting opportunity for
integrated machine learning solutions to address problems across multiple
facets of healthcare practice and administration. Unfortunately, the ability to
derive accurate and informative insights requires more than the ability to
execute machine learning models. Rather, a deeper understanding of the data on
which the models are run is imperative for their success. While a significant
effort has been undertaken to develop models able to process the volume of data
obtained during the analysis of millions of digitalized patient records, it is
important to remember that volume represents only one aspect of the data. In
fact, drawing on data from an increasingly diverse set of sources, healthcare
data presents an incredibly complex set of attributes that must be accounted
for throughout the machine learning pipeline. This chapter focuses on
highlighting such challenges, and is broken down into three distinct
components, each representing a phase of the pipeline. We begin with attributes
of the data accounted for during preprocessing, then move to considerations
during model building, and end with challenges to the interpretation of model
output. For each component, we present a discussion around data as it relates
to the healthcare domain and offer insight into the challenges each may impose
on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20
Pages, 1 Figur
Public understandings of addiction: where do neurobiological explanations fit?
Developments in the field of neuroscience, according to its proponents, offer the prospect of an enhanced understanding and treatment of addicted persons. Consequently, its advocates consider that improving public understanding of addiction neuroscience is a desirable aim. Those critical of neuroscientific approaches, however, charge that it is a totalising, reductive perspective–one that ignores other known causes in favour of neurobiological explanations. Sociologist Nikolas Rose has argued that neuroscience, and its associated technologies, are coming to dominate cultural models to the extent that 'we' increasingly understand ourselves as 'neurochemical selves'. Drawing on 55 qualitative interviews conducted with members of the Australian public residing in the Greater Brisbane area, we challenge both the 'expectational discourses' of neuroscientists and the criticisms of its detractors. Members of the public accepted multiple perspectives on the causes of addiction, including some elements of neurobiological explanations. Their discussions of addiction drew upon a broad range of philosophical, sociological, anthropological, psychological and neurobiological vocabularies, suggesting that they synthesised newer technical understandings, such as that offered by neuroscience, with older ones. Holding conceptual models that acknowledge the complexity of addiction aetiology into which new information is incorporated suggests that the impact of neuroscientific discourse in directing the public's beliefs about addiction is likely to be more limited than proponents or opponents of neuroscience expect
Placental syncytiotrophoblast constitutes a major barrier to vertical transmission of Listeria monocytogenes.
Listeria monocytogenes is an important cause of maternal-fetal infections and serves as a model organism to study these important but poorly understood events. L. monocytogenes can infect non-phagocytic cells by two means: direct invasion and cell-to-cell spread. The relative contribution of each method to placental infection is controversial, as is the anatomical site of invasion. Here, we report for the first time the use of first trimester placental organ cultures to quantitatively analyze L. monocytogenes infection of the human placenta. Contrary to previous reports, we found that the syncytiotrophoblast, which constitutes most of the placental surface and is bathed in maternal blood, was highly resistant to L. monocytogenes infection by either internalin-mediated invasion or cell-to-cell spread. Instead, extravillous cytotrophoblasts-which anchor the placenta in the decidua (uterine lining) and abundantly express E-cadherin-served as the primary portal of entry for L. monocytogenes from both extracellular and intracellular compartments. Subsequent bacterial dissemination to the villous stroma, where fetal capillaries are found, was hampered by further cellular and histological barriers. Our study suggests the placenta has evolved multiple mechanisms to resist pathogen infection, especially from maternal blood. These findings provide a novel explanation why almost all placental pathogens have intracellular life cycles: they may need maternal cells to reach the decidua and infect the placenta
To respond or not to respond - a personal perspective of intestinal tolerance
For many years, the intestine was one of the poor relations of the immunology world, being a realm inhabited mostly by specialists and those interested in unusual phenomena. However, this has changed dramatically in recent years with the realization of how important the microbiota is in shaping immune function throughout the body, and almost every major immunology institution now includes the intestine as an area of interest. One of the most important aspects of the intestinal immune system is how it discriminates carefully between harmless and harmful antigens, in particular, its ability to generate active tolerance to materials such as commensal bacteria and food proteins. This phenomenon has been recognized for more than 100 years, and it is essential for preventing inflammatory disease in the intestine, but its basis remains enigmatic. Here, I discuss the progress that has been made in understanding oral tolerance during my 40 years in the field and highlight the topics that will be the focus of future research
Physics of Neutron Star Crusts
The physics of neutron star crusts is vast, involving many different research
fields, from nuclear and condensed matter physics to general relativity. This
review summarizes the progress, which has been achieved over the last few
years, in modeling neutron star crusts, both at the microscopic and macroscopic
levels. The confrontation of these theoretical models with observations is also
briefly discussed.Comment: 182 pages, published version available at
<http://www.livingreviews.org/lrr-2008-10
Deriving a mutation index of carcinogenicity using protein structure and protein interfaces
With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/
Measurement of the production of a W boson in association with a charm quark in pp collisions at √s = 7 TeV with the ATLAS detector
The production of a W boson in association with a single charm quark is studied using 4.6 fb−1 of pp collision data at s√ = 7 TeV collected with the ATLAS detector at the Large Hadron Collider. In events in which a W boson decays to an electron or muon, the charm quark is tagged either by its semileptonic decay to a muon or by the presence of a charmed meson. The integrated and differential cross sections as a function of the pseudorapidity of the lepton from the W-boson decay are measured. Results are compared to the predictions of next-to-leading-order QCD calculations obtained from various parton distribution function parameterisations. The ratio of the strange-to-down sea-quark distributions is determined to be 0.96+0.26−0.30 at Q 2 = 1.9 GeV2, which supports the hypothesis of an SU(3)-symmetric composition of the light-quark sea. Additionally, the cross-section ratio σ(W + +c¯¯)/σ(W − + c) is compared to the predictions obtained using parton distribution function parameterisations with different assumptions about the s−s¯¯¯ quark asymmetry
Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector
The inclusive and dijet production cross-sections have been measured for jets
containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass
energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The
measurements use data corresponding to an integrated luminosity of 34 pb^-1.
The b-jets are identified using either a lifetime-based method, where secondary
decay vertices of b-hadrons in jets are reconstructed using information from
the tracking detectors, or a muon-based method where the presence of a muon is
used to identify semileptonic decays of b-hadrons inside jets. The inclusive
b-jet cross-section is measured as a function of transverse momentum in the
range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet
cross-section is measured as a function of the dijet invariant mass in the
range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets
and the angular variable chi in two dijet mass regions. The results are
compared with next-to-leading-order QCD predictions. Good agreement is observed
between the measured cross-sections and the predictions obtained using POWHEG +
Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet
cross-section. However, it does not reproduce the measured inclusive
cross-section well, particularly for central b-jets with large transverse
momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final
version published in European Physical Journal
Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector
Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente
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