Lipopolysaccharide (LPS)-binding protein is carried on lipoproteins and acts as a cofactor in the neutralization of LPS.

Lipoproteins isolated from normal human plasma can bind and neutralize bacterial lipopolysaccharide (LPS) and may represent an important mechanism in host defense against gram-negative septic shock. Recent studies have shown that experimentally elevating the levels of circulating high-density lipoproteins (HDL) provides protection against death in animal models of endotoxic shock. We sought to define the components of HDL that are required for neutralization of LPS. To accomplish this we have studied the functional neutralization of LPS by native and reconstituted HDL using a rapid assay that measures the CD14-dependent activation of leukocyte integrins on human neutrophils. We report here that reconstituted HDL particles (R-HDL), prepared from purified apolipoprotein A-I (apoA-I) combined with phospholipid and free cholesterol, are not sufficient to neutralize the biologic activity of LPS. However, addition of recombinant LPS binding protein (LBP), a protein known to transfer LPS to CD14 and enhance responses of cells to LPS, enabled prompt binding and neutralization of LPS by R-HDL. Thus, LBP appears capable of transferring LPS not only to CD14 but also to lipoprotein particles. In contrast with R-HDL, apoA-I containing lipoproteins (LpA-I) isolated from plasma by selected affinity immunosorption (SAIS) on an anti-apoA-I column, neutralized LPS without addition of exogenous LBP. Several lines of evidence demonstrated that LBP is a constituent of LpA-I in plasma. Passage of plasma over an anti-apoA-I column removed more than 99% of the LBP detectable by ELISA, whereas 31% of the LBP was recovered by elution of the column. Similarly, the ability of plasma to enable activation of neutrophils by LPS (LBP/Septin activity) was depleted and recovered by the same process. Furthermore, an immobilized anti-LBP monoclonal antibody coprecipitated apoA-I. The results described here suggest that in addition to its ability to transfer LPS to CD14, LBP may also transfer LPS to lipoproteins. Since LBP appears to be physically associated with lipoproteins in plasma, it is positioned to play an important role in the neutralization of LPS

Triglyceride-rich lipoproteins prevent septic death in rats.

Triglyceride-rich lipoproteins bind and inactive bacterial endotoxin in vitro and prevent death when given before a lethal dose of endotoxin in animals. However, lipoproteins have not yet been demonstrated to improve survival in polymicrobial gram-negative sepsis. We therefore tested the ability of triglyceride-rich lipoproteins to prevent death after cecal ligation and puncture (CLP) in rats. Animals were given bolus infusions of either chylomicrons (1 g triglyceride/kg per 4 h) or an equal volume of saline for 28 h after CLP. Chylomicron infusions significantly improved survival (measured at 96 h) compared with saline controls (80 vs 27%, P < or = 0.03). Chylomicron infusions also reduced serum levels of endotoxin, measured 90 min (26 +/- 3 vs 136 +/- 51 pg/ml, mean +/- SEM, P < or = 0.03) and 6 h (121 +/- 54 vs 1,026 +/- 459 pg/ml, P < or = 0.05) after CLP. The reduction in serum endotoxin correlated with a reduction in serum tumor necrosis factor, measured 6 h after CLP (0 +/- 0 vs 58 +/- 24 pg/ml, P < or = 0.03), suggesting that chylomicrons improve survival in this model by limiting macrophage exposure to endotoxin and thereby reducing secretion of inflammatory cytokines. Infusions of a synthetic triglyceride-rich lipid emulsion (Intralipid; KabiVitrum, Inc., Alameda, CA) (1 g triglyceride/kg) also significantly improved survival compared with saline controls (71 vs 27%, P < or = 0.03). These data demonstrate that triglyceride-rich lipoproteins can protect animals from lethal polymicrobial gram-negative sepsis

Humans as the third evolutionary stage of biosphere engineering of rivers

We examine three fundamental changes in river systems induced by innovations of the biosphere, these being: (1) the evolution of oxygenic photosynthesis; (2) the development of vascular plants with root systems; and (3) the evolution of humans. The first two innovations provide context for the degree of human-induced river change. Early river systems of the Precambrian Archean Eon developed in an atmosphere with no free oxygen, and fluvial sediments accumulated ‘reduced detrital’ minerals. By 2.4 Ga the evolution of oxygenic photosynthesis produced an oxygenated atmosphere and ‘reduced detrital’ minerals mostly disappeared from rivers, affording a distinct mineralogical difference from subsequent fluvial deposits. Rivers of the Precambrian and early Phanerozoic were dominantly braided, but from 0.416 Ga, the evolution of vascular plants with roots bound floodplain sediments and fostered fine-grained meandering rivers. Early meandering river deposits show extensive animal activity including fish and arthropod tracks and burrows. Homo sapiens, appearing about 150 ka BP, has, in recent millennia, profoundly modified river systems, altering their mineralogical, morphological and sedimentary state. Changes in sediment fluxes caused by human ‘reverse engineering’ of the terrestrial biosphere include deforestation, irrigation and agriculture. Sediment retention has been encouraged by the construction of dams. Modern river systems are associated with extensive human trace fossils that show a developing complexity from ancient civilizations through to megacities. Changes induced by humans rank in scale with those caused by earlier biosphere innovations at 2.4 and 0.416 Ga, but would geologically soon revert to a “pre-human” state were humans to become extinct.This is the author accepted manuscript. The final version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S2213305415000089

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available.This work is supported by the EPSRC First Grant scheme (grant ref no. EP/N023668/1) and partially funded under the 7th Framework Programme by the European Commission (TBIcare: http: //www.tbicare.eu/ ; CENTER-TBI: https://www.center-tbi.eu/). This work was further supported by a Medical Research Council (UK) Program Grant (Acute brain injury: heterogeneity of mechanisms, therapeutic targets and outcome effects [G9439390 ID 65883]), the UK National Institute of Health Research Biomedical Research Centre at Cambridge and Technology Platform funding provided by the UK Department of Health. KK is supported by the Imperial College London PhD Scholarship Programme. VFJN is supported by a Health Foundation/Academy of Medical Sciences Clinician Scientist Fellowship. DKM is supported by an NIHR Senior Investigator Award. We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs for our research

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the networks soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumors, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network’s soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumors, and ischemic stroke. We improve on the state-of-theart for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly availabl

Bino Dark Matter and Big Bang Nucleosynthesis in the Constrained E6SSM with Massless Inert Singlinos

We discuss a new variant of the E6 inspired supersymmetric standard model (E6SSM) in which the two inert singlinos are exactly massless and the dark matter candidate has a dominant bino component. A successful relic density is achieved via a novel mechanism in which the bino scatters inelastically into heavier inert Higgsinos during the time of thermal freeze-out. The two massless inert singlinos contribute to the effective number of neutrino species at the time of Big Bang Nucleosynthesis, where the precise contribution depends on the mass of the Z' which keeps them in equilibrium. For example for mZ' > 1300 GeV we find Neff \approx 3.2, where the smallness of the additional contribution is due to entropy dilution. We study a few benchmark points in the constrained E6SSM with massless inert singlinos to illustrate this new scenario.Comment: 24 pages, revised for publication in JHE

Graphene for spintronics: giant Rashba splitting due to hybridization with Au

Graphene in spintronics has so far primarily meant spin current leads of high performance because the intrinsic spin-orbit coupling of its pi-electrons is very weak. If a large spin-orbit coupling could be created by a proximity effect, the material could also form active elements of a spintronic device such as the Das-Datta spin field-effect transistor, however, metal interfaces often compromise the band dispersion of massless Dirac fermions. Our measurements show that Au intercalation at the graphene-Ni interface creates a giant spin-orbit splitting (~100 meV) in the graphene Dirac cone up to the Fermi energy. Photoelectron spectroscopy reveals hybridization with Au-5d states as the source for the giant spin-orbit splitting. An ab initio model of the system shows a Rashba-split dispersion with the analytically predicted gapless band topology around the Dirac point of graphene and indicates that a sharp graphene-Au interface at equilibrium distance will account for only ~10 meV spin-orbit splitting. The ab initio calculations suggest an enhancement due to Au atoms that get closer to the graphene and do not violate the sublattice symmetry.Comment: 16 pages (3 figures) + supplementary information 16 pages (14 figures

Topological Crystalline Insulators in the SnTe Material Class

Topological crystalline insulators are new states of matter in which the topological nature of electronic structures arises from crystal symmetries. Here we predict the first material realization of topological crystalline insulator in the semiconductor SnTe, by identifying its nonzero topological index. We predict that as a manifestation of this nontrivial topology, SnTe has metallic surface states with an even number of Dirac cones on high-symmetry crystal surfaces such as {001}, {110} and {111}. These surface states form a new type of high-mobility chiral electron gas, which is robust against disorder and topologically protected by reflection symmetry of the crystal with respect to {110} mirror plane. Breaking this mirror symmetry via elastic strain engineering or applying an in-plane magnetic field can open up a continuously tunable band gap on the surface, which may lead to wide-ranging applications in thermoelectrics, infrared detection, and tunable electronics. Closely related semiconductors PbTe and PbSe also become topological crystalline insulators after band inversion by pressure, strain and alloying.Comment: submitted on Feb. 10, 2012; to appear in Nature Communications; 5 pages, 4 figure

An alternative approach to measuring treatment persistence with antipsychotic agents among patients with schizophrenia in the Veterans Health Administration

Prior studies have demonstrated the importance of treatment persistence with anti-psychotic agents in sustaining control of schizophrenic symptoms. However, the conventional approach in measuring treatment persistence tended to use only the first prescription episode even though some patients received multiple prescriptions (or multiple treatment episodes) of the same medication within one year following the initiation of the index drug. In this study, we used data from the Veterans Health Administration in the United States to assess the extent to which patients received multiple prescriptions. The study found that about a quarter of the patients had two or more treatment episodes and that levels of treatment persistence tended to vary across treatment episodes. Based on these results, we offered an alternative approach in which we calculated treatment persistence with typical and atypical antipsychotic agents separately for patients with one, two, or three treatment episodes. Considering that patients with different number of treatment episodes might differ in disease profiles, this treatment episode-specific approach offered a fair comparison of the levels of treatment persistence across patients with different number of treatment episodes. Future research needs to extend the analyses beyond two antipsychotic classes to individual antipsychotic agents. A more comprehensive assessment using appropriate analytic methods should help physicians make prescription choices that will ultimately improve the care of patients with schizophrenia