Increased plasma CD14 levels 1 year postpartum in women with pre-eclampsia during pregnancy: a case–control plasma proteomics study

Epidemiological data suggest that pre-eclampsia (PE) is associated with an increased risk of post-delivery metabolic dysregulation. The aim of the present case–control observational study was to examine the global plasma proteomic profile 1 year postpartum in women who developed PE during pregnancy (n = 5) compared to controls (n = 5), in order to identify a novel predictive marker linking PE with long-term metabolic imbalance. Key findings were verified with enzyme-linked immunosorbent assay (ELISA) in a separate cohort (n = 17 women with PE and n = 43 controls). One hundred and seventy-two proteins were differentially expressed in the PE vs. control groups. Gene ontology analysis showed that Inflammatory|Immune responses, Blood coagulation and Metabolism were significantly enriched terms. CD14, mapping to the inflammatory response protein network, was selected for verification based on bibliographic evidence. ELISA measurements showed CD14 to be significantly increased 1 year postpartum in women with PE during pregnancy compared to controls [PE group (median ± SD): 296.5 ± 113.6; control group (median ± SD): 128.9 ± 98.5; Mann–Whitney U test p = 0.0078]. Overall, the identified proteins could provide insight into the long-term disease risk among women with PE during pregnancy and highlight the need for their postpartum monitoring. CD14 could be examined in larger cohorts as a predictive marker of insulin resistance and type II diabetes mellitus among women with PE

Polycystic Ovary Syndrome and Insulin Physiology: An Observational Quantitative Serum Proteomics Study in adolescent, Normal-Weight Females

Background: Polycystic ovary syndrome (PCOS) is a common endocrine disorder associated with insulin resistance, even in the absence of overweight/obesity. The aim of the present study is to examine the global serum proteomic profile of adolescent, normal‐weight females with PCOS in order to gain novel insight in the association of this endocrine disorder with insulin physiology and to identify novel circulating markers that can guide intervention protocols. Methods: Non‐depleted serum from normal‐weight (BMI: 18–23 kg m^(−2)), adolescent females (13–21 years old) with PCOS (n = 20) is compared to BMI‐ and age‐matched healthy controls (n = 20) using our 3D quantitative proteomics methodology. Serum samples from study participants are randomly pooled to form four biological replicates of females with PCOS and four of healthy controls (n = 5 per sample pool). Results: One‐hundred and twenty‐six proteins are differentially expressed in females with PCOS compared to controls. Gene ontology analysis shows significant enrichment for terms related to inflammatory immune response, metabolism and insulin‐like growth factor receptor signaling pathway. Circulating levels of IGF‐1 and ‐2 and IGFBP‐2, ‐3, and ‐4 are found to be lower in females with PCOS compared to healthy controls. Conclusions: The present serum proteomics study provides insight into the pro‐inflammatory status and insulin dysregulation in young females with PCOS and identifies potential serological markers that can guide early intervention protocols

Improving knowledge discovery from synthetic aperture radar images using the linked open data cloud and Sextant

In the last few years, thanks to projects like TELEIOS, the linked open data cloud has been rapidly populated with geospatial data some of it describing Earth Observation products (e.g., CORINE Land Cover, Urban Atlas). The abundance of this data can prove very useful to the new missions (e.g., Sentinels) as a means to increase the usability of the millions of images and EO products that are expected to be produced by these missions. In this paper, we explain the relevant opportunities by demonstrating how the process of knowledge discovery from TerraSAR-X images can be improved using linked open data and Sextant, a tool for browsing and exploration of linked geospatial data, as well as the creation of thematic maps

Wildfire monitoring via the integration of remote sensing with innovative information technologies

In the Institute for Space Applications and Remote Sensing of the National Observatory of Athens (ISARS/NOA) volumes of Earth Observation images of different spectral and spatial resolutions are being processed on a systematic basis to derive thematic products that cover a wide spectrum of applications during and after wildfire crisis, from fire detection and fire-front propagation monitoring, to damage assessment in the inflicted areas. The processed satellite imagery is combined with auxiliary geo-information layers, including land use/land cover, administrative boundaries, road and rail network, points of interest, and meteorological data to generate and validate added-value fire-related products. The service portfolio has become available to institutional End Users with a mandate to act on natural disasters and that have activated Emergency Support Services at a European level in the framework of the operational GMES projects SAFER and LinkER. Towards the goal of delivering integrated services for fire monitoring and management, ISARS/NOA employs observational capacities which include the operation of MSG/SEVIRI and NOAA/AVHRR receiving stations, NOA's in-situ monitoring networks for capturing meteorological parameters to generate weather forecasts, and datasets originating from the European Space Agency and third party satellite operators. The qualified operational activity of ISARS/NOA in the domain of wildfires management is highly enhanced by the integration of state-of-the-art Information Technologies that have become available in the framework of the TELEIOS (EC/ICT) project. TELEIOS aims at the development of fully automatic processing chains reliant on a) the effective storing and management of the large amount of EO and GIS data, b) the post-processing refinement of the fire products using semantics, and c) the creation of thematic maps and added-value services. The first objective is achieved with the use of advanced Array Database technologies, such as MonetDB, to enable efficiency in accessing large archives of image data and metadata in a fully transparent way, without worrying for their format, size, and location, as well as efficiency in processing such data using state-of-the-art implementations of image processing algorithms expressed in a high-level Scientific Query Language (SciQL). The product refinement is realized through the application of update operations that incorporate human evidence and human logic, with semantic content extracted from thematic information coming from auxiliary geo-information layers and sources, for reducing considerably the number of false alarms in fire detection, and improving the credibility of the burnt area assessment. The third objective is approached via the combination of the derived fire-products with Linked Geospatial Data, structured accordingly and freely available in the web, using Semantic Web technologies. These technologies are built on top of a robust and modular computational environment, to facilitate several wildfire applications to run efficiently, such as real-time fire detection, fire-front propagation monitoring, rapid burnt area mapping, after crisis detailed burnt scar mapping, and time series analysis of burnt areas. The approach adopted allows ISARS/NOA to routinely serve requests from the end-user community, irrespective of the area of interest and its extent, the observation time period, or the data volume involved, granting the opportunity to combine innovative IT solutions with remote sensing techniques and