1,282 research outputs found

    Deformation modes and geometries in the EPICA-DML ice core, Antarctica

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    Combination of physical-properties methods (crystal-orientation-fabrics, grain-elongation-data, line-scan stratigraphy-documentation) reveal evidences for five deformation geometry regimes:1. Random c-axes distributions and crystal elongation directions (~2020 m depth). Here bed-parallel simple shear deforms the ice causing folding and inclination of stratigraphic layers.5. A last change of geometries is observed at ~2370 m depth, with a locally very restricted (~10 m) backslide to girdle fabric, isoclinal z-folding and borehole closure. Below that an inclined single maximum fabric reoccurs.Simple shear can easily produce the observed small-scale folding of layers which however may belong to disturbances on a larger scale with possible overturning and thus age reversal of layers. Below ~2020 m the EDML climate record has to be interpreted with great care

    Peculiarities of synthesis of matrix metalloproteinase-9 in the lungs during experimental development of distress syndrome

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    Acute respiratory distress syndrome (ARDS) was reproduced in nonlinear male rats by intratracheal instillation of 45—55 thousand, lysate rat neutrophils (method patented in RF); the control group was injected with saline solution. The animals were taken from the experiment at 1, 3 and 6 days, the expression of matrix metallopro-teinase-9 (MMP-9) by cells of the lungs was determined, by immunohistochemistry at each days. The expression of MMP-9 was found in neutrophils, macrophages, fibroblasts, endothelium and. type 2 alveolocytes in the both groups. In the control group the expression, of MMP-9 was found equally low in the all cells, except type 2 alveolocytes. It was decreased in type 2 alveolocytes on day 6. A significant increase in the expression of MMP-9 was found in neutrophils, macrophages, fibroblasts endothelium and type 2 alveolocytes in the exudative stage of ARDS. In the proliferative stage the expression of MMP-9 was high in all the cells, decreasing only in type 2 alveolocytes. In the fibrotic stage of expression of MMP-9 returned to the level of the control group in macrophages, type 2 alveolocytes, the endothelium; in neutrophils and fibroblasts it remained high

    The role of HSP-70 in the pathogenesis of respiratory distress syndrome induced by influenza pneumonia

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    Lung paraffin sections of 35 died during the epidemic of influenza A/H1N1 of 2009-2010 in Zabaikalskiy krai were investigated. Exudative stage was diagnosed, in 10 cases, proliferative stage - in 16 cases and fibrotic stage of acute respiratory distress syndrome (ARDS) was diagnosed in 9 cases at the autopsy. Expression of heat shock protein 70 (HSP-70) by cells of the lungs was determined in sections by immunohistochemistry. We revealed that HSP-70 is expressed by neutrophils, macrophages, fibroblasts and alveolocytes type 1 and. 2. It was found that regardless of the stage of ARDS the highest expression of HSP-70 was realized by neutrophils, the lowest - by the endothelium alveolocytes type 2 and. fibroblasts. HSP-70 expression in neutrophils, macrophages, fibroblasts and. alveolocytes type 1 was the same in all stages of ARDS. In alveolocytes type 2 in exudative and. proliferative phases it was lower than at the development of fibrosis. We concluded that one of the mechanisms of developments of acute lungs injury/acute respiratory distress syndrome at influenza pneumonia is comparatively low synthesis of HSP-70 by endotheliocytes of lung capillary and. alveolocytes type 2

    Utilizing stimulated Raman scattering microscopy to study intracellular distribution of label-free ponatinib in live cells

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    Stimulated Raman scattering (SRS) microscopy represents a powerful method for imaging label-free drug dis-tribution with high resolution. SRS was applied to image label-free ponatinib with high sensitivity and speci-ficity in live human chronic myeloid leukemia (CML) cell lines. This was achieved at biologically relevant, na-nomolar concentrations; allowing determination of ponatinib uptake and sequestration into lysosomes during the development of acquired drug resistance and an improved understanding of target engagement

    Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

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    The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations
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