ATM activation accompanies histone H2AX phosphorylation in A549 cells upon exposure to tobacco smoke

<p>Abstract</p> <p>Background</p> <p>In response to DNA damage or structural alterations of chromatin, histone H2AX may be phosphorylated on <it>Ser</it>139 by phosphoinositide 3-kinase related protein kinases (PIKKs) such as <it>ataxia telangiectasia </it>mutated (ATM), ATM-and Rad-3 related (ATR) kinase, or by DNA dependent protein kinase (DNA-PKcs). When DNA damage primarily involves formation of DNA double-strand breaks (DSBs), H2AX is preferentially phosphorylated by ATM rather than by the other PIKKs. We have recently reported that brief exposure of human pulmonary adenocarcinoma A549 cells or normal human bronchial epithelial cells (NHBE) to cigarette smoke (CS) induced phosphorylation of H2AX.</p> <p>Results</p> <p>We report here that H2AX phosphorylation in A549 cells induced by CS was accompanied by activation of ATM, as revealed by ATM phosphorylation on <it>Ser</it>1981 (ATM-S1981^P) detected immunocytochemically and by Western blotting. No cell cycle-phase specific differences in kinetics of ATM activation and H2AX phosphorylation were observed. When cells were exposed to CS from cigarettes with different tobacco and filter combinations, the expression levels of ATM-S1981^Pcorrelated well with the increase in expression of phosphorylated H2AX (γH2AX) (R = 0.89). In addition, we note that while CS-induced γH2AX expression was localized within discrete foci, the activated ATM was distributed throughout the nucleoplasm.</p> <p>Conclusion</p> <p>These data implicate ATM as the PIKK that phosphorylates H2AX in response to DNA damage caused by CS. Based on current understanding of ATM activation, expression and localization, these data would suggest that, in addition to inducing potentially carcinogenic DSB lesions, CS may also trigger other types of DNA lesions and cause chromatin alterations. As checkpoint kinase (Chk) 1, Chk2 and the p53 tumor suppressor gene are known to be phosphorylated by ATM, the present data indicate that exposure to CS may lead to their phosphorylation, with the downstream consequences related to the halt in cell cycle progression and increased propensity to undergo apoptosis. Defining the nature and temporal sequence of molecular events that are disrupted by CS through activation and eventual dysregulation of normal defense mechanisms such as ATM and its downstream effectors may allow a more precise understanding of how CS promotes cancer development.</p

Advancing Smart Manufacturing in Europe: Experiences from Two Decades of Research and Innovation Projects

In the past two decades, a large amount of attention has been devoted to the introduction of smart manufacturing concepts and technologies into industrial practice. In Europe, these efforts have been supported by European research and innovation programs, bringing together research and application parties. In this paper, we provide an overview of a series of four content-wise connected projects on the European scale that are aimed at advancing smart manufacturing, with a focus on connecting processes on smart factory shop floors to manufacturing equipment on the one hand and enterprise-level business processes on the other hand. These projects cover several tens of application cases across Europe. We present our experiences in the form of a single, informal longitudinal case study, highlighting both the major advances and the current limitations of developments. To organize these experiences, we place them in the context of the well-known RAMI4.0 reference framework for Industry 4.0 (covering the ISA-95 standard). Then, we analyze the experiences, both the positive ones and those including problems, and draw our learnings from these. In doing so, we do not present novel technological developments in this paper—these are presented in the papers we refer to—but concentrate on the main issues we have observed to guide future developments in research efforts and industrial innovation in the smart industry domain

Color Me Optically Shallow: A Simple And Adaptive Method For Standardized Analysis Ready Data For Coastal Ecosystem Assessments

Coastal aquatic remote sensing (RS) can help monitor the immensely valuable ecosystems of the global seascape, such as seagrasses and corals, by providing information on their extent, condition (e.g., water quality, bathymetry), ecosystem services (e.g., carbon sequestration, biodiversity maintenance), and trajectories. Unlike terrestrial RS, coastal aquatic RS applications require an additional consideration of the water column and its interactions with the light signal. This introduces new challenges as the water column attenuates light differently across the wavelengths, which has implications for signals from the benthic seabed where these subtidal ecosystems thrive. When the object(s) of interest is located on the benthic floor and not floating near the water surface, the additional depth increases the influence of the water column on light and affects the signals sensed by satellites at the top of the atmosphere. Besides these, other effects such as turbidity, waves, and sunglint introduce wide-ranging reflectance values as well. While these challenges have been traditionally handled through often complex methods in local computing environments, contemporary advances in cloud computing and big satellite data analytics offer highly scalable and effective solutions within the same context. The parallel processing of cloud platforms like the Google Earth Engine allows multitemporal composition of thousands of satellite images in a defined area over a defined time range through highly efficient statistical aggregations. As such, this approach yields Analysis Ready Data which are less redundant and more time efficient than the conventional laborious manual search for suitable single satellite image(s) which is often a yearlong assessment over cloud-dense coastal regions like the tropics. Regardless of the method, the pre-processing of the image and/or image composite remains a critical component of a successful coastal ecosystem assessment using RS. The impact of light attenuation changes the returning spectral signal, resulting in different signal profiles for the same seabed cover at different depths. In particular, at deeper depths, darker covers such as vegetated coastal beds (e.g., dense seagrass, microalgal mats) and optically deep water pixels are more likely to be confused and misclassified. A possible solution is to identify and remove these deep water pixels, where the water is too deep and thus no bottom signals are able to return to the sensor. By using a HSV-transformed B1-B2-B3 false-colour composite, namely the hue and saturation bands, of the Sentinel-2 image archive within the cloud computing platform of the Google Earth Engine, we are able to disentangle optically deep from optically shallow waters across four sites (Tanzania, the Bahamas, Caspian Sea (Kazakhstan) and Wadden Sea (Denmark and Germany)) with wide-ranging water qualities to improve the optically shallow benthic habitat classification. Furthermore, we compare our method with the three band ratios from a combination of the same three bands. While the band ratios may perform better in some sites, the specific band combination is site specific and thus might perform worse in others. In comparison, the hue and saturation bands show more consistent performance across all four sites. By using simple statistical reduction, the multitemporal composite is able to automatically mitigate common coastal aquatic RS showstoppers like clouds, cloud shadows or other temporal phenomena. However, there is also a need to remove images with explicitly no useful information, so that it does not affect the statistical approach. The use of metadata properties in the image archive is therefore additionally needed to filter out “bad” images, reducing the unnecessary computational costs of processing these low quality images. Case in point, this is a recommended procedure to filter for lower cloud covers prior to multitemporal composition in Google Earth Engine. We extend this approach further by integrating the various solar and viewing angles to estimate the presence of sunglint, on the basis that the spectral reflectance angle of the scene is a major factor to sunglint presence in satellite images. Finally, we draw comparisons with less pre-processed composites, showcasing methodological benefits for national coastal ecosystem assessments in the Bahamas, Seychelles, and East Africa

A Simple Cloud-Native Spectral Transformation Method to Disentangle Optically Shallow and Deep Waters in Sentinel-2 Images.

This study presents a novel method to identify optically deep water using purely spectral approaches. Optically deep waters, where the seabed is too deep for a bottom reflectance signal to be returned, is uninformative for seabed mapping. Furthermore, owing to the attenuation of light in the water column, submerged vegetation at deeper depths is easily confused with optically deep waters, thereby inducing misclassifications that reduce the accuracy of these seabed maps. While bathymetry data could mask out deeper areas, they are not always available or of sufficient spatial resolution for use. Without bathymetry data and based on the coastal aerosol blue green (1,2,3) bands of the Sentinel 2 imagery, this study investigates the use of band ratios and a false colour HSV transformation of both L1C and L2A images to separate optically deep and shallow waters across varying water quality over four tropical and temperate submerged sites: Tanzania, the Bahamas, the Caspian Sea (Kazakhstan) and the Wadden Sea (Denmark and Germany). Two supervised thresholds based on annotated reference data and an unsupervised Otsu threshold were applied. The band ratio group usually featured the best overall accuracies (OA), F1 scores and Matthews correlation coefficients, although the individual band combination might not perform consistently across different sites. Meanwhile, the saturation and hue band yielded close to best performance for the L1C and L2A images, featuring OA of up to 0.93 and 0.98, respectively, and a more consistent behaviour than the individual band ratios. Nonetheless, all these spectral methods are still susceptible to sunglint, the Sentinel 2 parallax effect, turbidity and water colour. Both supervised approaches performed similarly and were superior to the unsupervised Otsus method: the supervised methods featuring OA were usually over 0.70, while the unsupervised OA were usually under 0.80. In the absence of bathymetry data, this method could effectively remove optically deep water pixels in Sentinel 2 imagery and reduce the issue of dark pixel misclassification, thereby improving the benthic mapping of optically shallow waters and their seascapes

A Full Cloud-Native Dive into Bioregional-Scale Seagrass Mapping in the Mediterranean using Sentinel-2 Multitemporal Data

The seagrass Posidonia oceanica is the main habitat-forming species of the coastal Mediterranean, providing millennia-scale ecosystem services including habitat provisioning, biodiversity maintenance, food security, coastal protection, and carbon sequestration. Yet, projected temperature extremes and sea level rise due to climate change, the current knowledge gaps in its basin-wide extent, and its slow growth increase the risk of reduction and loss of these wide-scope services. As a result, accurate and efficient mapping of its distribution and trajectories of change is needed. Here, we leverage recent advances in Earth Observation—cloud computing, open satellite data, and machine learning—and field observations through a cloud-native geoprocessing framework to estimate the pan-Mediterranean extent of P. oceanica species. Employing 279,186 Sentinel-2 images taken between 2015 and 2019, and a human-labeled training dataset of 62,928 pixels, we map 19,020 km2 of P. oceanica meadows up to 25 m of depth in 22 Mediterranean countries, across a total seabed area of 56,783 km2. Using 2,480 independent, field-based points, we observe an overall accuracy of 72%. Given suitable reference data, our highly-scalable cloud-native framework can provide effective and data-driven seagrass mapping products to timely support pertinent Multilateral Environmental Agreements—from national to continental and global scale