Hetero-association of aromatic molecules in aqueous solution

Knowledge of the physical chemistry of small molecules complexation (the hetero-association) in aqueous solution is increasingly important in view of the rapidly emerging branch of supramolecular chemistry dealing with the formation of heterogeneous polymeric structures having specific functional roles. In this paper, the 50-year history of scientific studies of hetero-association of heterocyclic aromatic molecules in aqueous solution has been reviewed. Some important correlations of structural and thermodynamic parameters of complexation have been reported based on large data-set of hetero-association parameters accumulated to date. The fundamental problem of ‘energetic composition’ of π-stacking is extensively discussed. The review has shown that there are some gaps in our understanding of heteroassociation, which provides a challenge for further studies in this are

Mapping the national seagrass extent in Seychelles using PlanetScope NICFI data

Seagrasses provide ecosystem services worth USD 2.28 trillion annually. However, their direct threats and our incomplete knowledge hamper our capabilities to protect and manage them. This study aims to evaluate if the NICFI Satellite Data Program basemaps could map Seychelles’ extensive seagrass meadows, directly supporting the country’s ambitions to protect this ecosystem. The Seychelles archipelago was divided into three geographical regions. Half-yearly basemaps from 2015 to 2020 were combined using an interval mean of the 10th percentile and median before land and deep water masking. Additional features were produced using the Depth Invariant Index, Normalised Differences, and segmentation. With 80% of the reference data, an initial Random Forest followed by a variable importance analysis was performed. Only the top ten contributing features were retained for a second classification, which was validated with the remaining 20%. The best overall accuracies across the three regions ranged between 69.7% and 75.7%. The biggest challenges for the NICFI basemaps are its four-band spectral resolution and uncertainties owing to sampling bias. As part of a nationwide seagrass extent and blue carbon mapping project, the estimates herein will be combined with ancillary satellite data and contribute to a full national estimate in a near-future report. However, the numbers reported showcase the broader potential for using NICFI basemaps for seagrass mapping at scale

Business model prototyping for intelligent transport systems: a service-dominant approach

Case Study for Praktijkproef Amsterdam Fase 2 deelproject Zuidoos

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

A Cloud-Based Framework for the Quantification of the Uncertainty of a Machine Learning Produced Satellite-Derived Bathymetry

The estimation of accurate and precise Satellite-Derived Bathymetries (SDBs) is important in marine and coastal applications for a better understanding of the ecosystems and science-based decision-making. Despite the advancements in related Machine Learning (ML) studies, quantifying the anticipated bias per pixel in the SDBs remains a significant challenge. This study aims to address this knowledge gap by developing a spatially explicit uncertainty index of a ML-derived SDB, capable of providing a quantifiable anticipation for biases of 0.5, 1, and 2 m. In addition, we explore the usage of this index for model optimization via the exclusion of training points of high or moderate uncertainty via a six-fold iteration loop. The developed methodology is applied across the national coastal extent of Belize in Central America (~7017 km2) and utilizes remote sensing data from the European Space Agency’s twin satellite system Sentinel-2 and Planet’s NICFI PlanetScope. In total, 876 Sentinel-2 images, nine NICFI six-month basemaps and 28 monthly PlanetScope mosaics are processed in this study. The training dataset is based on NASA’s system Ice, Cloud and Elevation Satellite (ICESat-2), while the validation data are in situ measurements collected with scientific equipment (e.g., multibeam sonar) and were provided by the National Oceanography Centre, UK. According to our results, the presented approach is able to provide a pixel-based (i.e., spatially explicit) uncertainty index for a specific prediction bias and integrate it to refine the SDB. It should be noted that the efficiency of the optimization of the SDBs as well as the correlations of the proposed uncertainty index with the absolute prediction error and the true depth are low. Nevertheless, spatially explicit uncertainty information produced by a ML-related SDB provides substantial insight to advance coastal ecosystem monitoring thanks to its capability to showcase the difficulty of the model to provide a prediction. Such spatially explicit uncertainty products can also aid the communication of coastal aquatic products with decision makers and provide potential improvements in SDB modeling

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