Search

The availability of the sheer volume of Copernicus Sentinel-2 imagery has created new opportunities for exploiting deep learning (DL) methods for land use land cover (LULC) image classification. However, an extensive set of benchmark experiments is currently lacking, i.e. DL models tested on the same dataset, with a common and consistent set of metrics, and in the same hardware. In this work, we use the BigEarthNet Sentinel-2 dataset to benchmark for the first time different state-of-the-art DL models for the multi-label, multi-class LULC image classification problem, contributing with an exhaustive zoo of 60 trained models. Our benchmark includes standard CNNs, as well as non-convolutional methods. We put to the test EfficientNets and Wide Residual Networks (WRN) architectures, and leverage classification accuracy, training time and inference rate. Furthermore, we propose to use the EfficientNet framework for the compound scaling of a lightweight WRN. Enhanced with an Efficient Channel Attention mechanism, our scaled lightweight model emerged as the new state-of-the-art. It achieves 4.5% higher averaged F-Score classification accuracy for all 19 LULC classes compared to a standard ResNet50 baseline model, with an order of magnitude less trainable parameters. We provide access to all trained models, along with our code for distributed training on multiple GPU nodes. This model zoo of pre-trained encoders can be used for transfer learning and rapid prototyping in different remote sensing tasks that use Sentinel-2 data, instead of exploiting backbone models trained with data from a different domain, e.g., from ImageNet. We validate their suitability for transfer learning in different datasets of diverse volumes. Our top-performing WRN achieves state-of-the-art performance (71.1% F-Score) on the SEN12MS dataset while being exposed to only a small fraction of the training dataset., 25 pages

The enzyme NADPH oxidase 1 (NOX1) is a major producer of superoxide which together with other reactive oxygen and nitrogen species (ROS/RNS) are implicated in maintaining a healthy epithelial barrier in the gut. While previous studies have indicated NOX1's involvement in microbial modulation in the small intestine, less is known about the effects of NOX1-dependent ROS/RNS formation in the colon. We investigated the role of NOX1 in the colon of NOX1 knockout (KO) and wild type (WT) mice, under mild and subclinical low-grade colon inflammation induced by 1% dextran sulfate sodium (DSS). Ex vivo imaging of ROS/RNS in the colon revealed that absence of NOX1 strongly decreased ROS/RNS production, particularly during DSS treatment. Furthermore, while absence of NOX1 did not affect disease activity, some markers of inflammation (mRNA: Tnfa, Il6, Ptgs2; protein: lipocalin 2) in the colonic mucosa tended to be higher in NOX1 KO than in WT mice following DSS treatment. Lack of NOX1 also extensively modulated the bacterial community in the colon (16S rRNA gene sequencing), where NOX1 KO mice were characterized mainly by lower α-diversity (richness and evenness), higher abundance of Firmicutes, Akkermansia, and Oscillibacter, and lower abundance of Bacteroidetes and Alistipes. Together, our data suggest that NOX1 is pivotal for colonic ROS/RNS production in mice both during steady-state (i.e., no DSS treatment) and during 1% DSS-induced low-grade inflammation and for modulation of the colonic microbiota, with potential beneficial consequences for intestinal health.

The Copernicus era in Europe has resulted in an increase in the processing of satellite data, making the space sectoran attractive target for cyber-attacks. RF attacks can potentially disrupt the management and distribution of satellite data and pose a significant risk to public safety and national security. The 7SHIELD project aims to develop a comprehensive framework for the protection of ground segments of space systems, which includes innovative services such as e-fences, passive radars, andlaser technologies. Among the key modules developed within the project, the Cyber-Attack Detection Framework (CADF) hasbeen developed to provide real-time correlation and detailed alerts in case anomalies are detected, providing support to the modules in charge of mitigation strategy. The CADF has been successfully tested against RF jamming attacks along with the rest of the 7SHIELD framework within an actual Critical Infrastructure, the Satellite Ground Station in Penteli, Athens, Greece.

Due to climate change, we expect an exacerbation of fire in Europe and around the world, with major wildfire events extending to northern latitudes and boreal regions [1]. In this context, it is important to improve our capabilities to anticipate fire danger and understand its driving mechanisms at a global scale. As the earth is an interconnected system, large-scale processes can have an effect on the global climate and fire seasons. For example, extreme fires in Siberia have been linked to previous-year surface moisture conditions and anomalies in the Arctic Oscillation [2]. As part of the ESA-funded project SeasFire (https://seasfire.hua.gr), we gather and harmonize data related to seasonal fire drivers and develop deep learning models that are able to capture spatiotemporal associations with the goal to forecast burned area sizes on a seasonal scale, globally. We publish a global analysis-ready datacube for seasonal fire forecasting for the years 2001-2021 at a spatiotemporal resolution of 0.25 deg x 0.25 deg x 8 days [3]. The datacube includes a combination of variables describing the seasonal fire drivers , namely climate, vegetation, oceanic indices, human factors, land cover and the burned areas. We leverage the availability of big EO data and the advances in Deep Learning modeling [4, 5] to forecast global burned areas, capture the spatio-temporal interactions of the Earth System variables and identify potential teleconnections that determine wildfire regimes under the light of climate change. We present deep learning models that handle the Earth as a system, such as graph neural networks and transformer-based architectures. Applied on the prediction of wildfires at different temporal horizons we reveal that our deep learning models skillfully predict burned area patterns. Exploring the explanation of the models, we reveal important spatio-temporal links.Our approach, using AI to model the earth as a system and capture long spatio-temporal interactions, showcases the potential of an application-specific digital twin. The SeasFire datacube can be exploited as a baseline digital twin for modeling different natural hazards, including floods, heatwaves, and droughts. Thus, we will discuss insights and future directions for digital twins in anticipating climate extremes, inspired by our global wildfire prediction paradigm. [1] Wu, Chao, et al. "Historical and future global burned area with changing climate and human demography." One Earth 4.4 (2021): 517-530.[2] Kim, Jin-Soo, et al. "Extensive fires in southeastern Siberian permafrost linked to preceding Arctic Oscillation." Science advances 6.2 (2020): eaax3308.[3] Alonso, Lazaro, et al. Seasfire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System. Zenodo, 15 July 2022, p., doi:10.5281/zenodo.6834584.[4] Kondylatos, Spyros et al. “Wildfire Danger Prediction and Understanding with Deep Learning.” Geophysical Research Letters, 2022. doi: 10.1029/2022GL099368[5] Prapas, Ioannis et al. “Deep Learning for Global Wildfire Forecasting.” NeurIPS 2022 workshop on Tackling Climate Change with Machine Learning, doi: 10.48550/arXiv.2211.00534

Artificial intelligence (AI) methods have emerged as a powerful tool to study and in some cases forecast natural disasters [1,2]. Recent works have successfully combined deep learning modeling with scientific knowledge stemming from the SAR Interferometry domain propelling research on tasks like volcanic activity monitoring [3], associated with ground deformation. A milestone in this interdisciplinary field has been the release of the Hephaestus [4] InSAR dataset, facilitating automatic InSAR interpretation, volcanic activity localization as well as the detection and categorization of atmospheric contributions in wrapped interferograms. Hephaestus contains annotations for approximately 20,000 InSAR frames, covering the 44 most active volcanoes in the world. The annotation was performed by a team of InSAR experts that manually examined each InSAR frame individually. However, even with such a large dataset, class imbalance remains a challenge, i.e. the InSAR samples containing volcano deformation fringes are orders of magnitude less than those that do not. This is anticipated since natural hazards are in principle rare in nature. To counter that, the authors of Hephaestus provide more than 100,000 unlabeled InSAR frames to be used for global large-scale self-supervised learning, which is more robust to class imbalance when compared to supervised learning [5]. Motivated by the Hephaestus dataset and the insights provided by [2], we train global, task-agnostic models in a self-supervised learning fashion that can handle distribution shifts caused by spatio-temporal variability as well as major class imbalances. By finetuning such a model to the labeled part of Hephaestus we obtain the backbone for a global volcanic activity alerting system, namely Pluto. Pluto is a novel end-to-end AI based system that provides early warnings of volcanic unrest on a global scale.Pluto automatically synchronizes its database with the Comet-LiCS [6] portal to receive newly generated Sentinel-1 InSAR data acquired over volcanic areas. The new samples are fed to our volcanic activity detection model. If volcanic activity is detected, an automatic email is sent to the service users, which contains information about the intensity, the exact location and the type (Mogi, Sill, Dyk) of the event. To ensure a robust and ever-improving service we augment Pluto with an iterative pipeline that collects samples that were misclassified in production, and uses them to further improve the existing model. [1] Kondylatos et al. "Wildfire danger prediction and understanding with Deep Learning." Geophysical Research Letters 49.17 (2022): e2022GL099368.[2] Bountos et al. "Self-supervised contrastive learning for volcanic unrest detection." IEEE Geoscience and Remote Sensing Letters 19 (2021): 1-5.[3] Bountos et al. "Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection." IEEE Transactions on Geoscience and Remote Sensing (2022).[4] Bountos et al. "Hephaestus: A large scale multitask dataset towards InSAR understanding." Proceedings of the IEEE/CVF CVPR. 2022.[5] Liu et al. "Self-supervised learning is more robust to dataset imbalance." arXiv preprint arXiv:2110.05025 (2021).[6] Lazecký et al. "LiCSAR: An automatic InSAR tool for measuring and monitoring tectonic and volcanic activity." Remote Sensing 12.15 (2020): 2430.

Background: There is a growing body of evidence suggesting that endocervical crypt involvement by high-grade cervical intraepithelial neoplasia (CIN) may represent a risk factor for disease recurrence after cervical treatment. Objectives: To provide a systematic review and meta-analysis on whether endocervical crypt involvement by high-grade CIN on the excised cervical specimen is associated with high-grade histopathological recurrence during the follow-up of women after cervical excisional treatment. Search Strategy: We searched the Medline, Scopus, Central and Clinical Trials.gov databases from inception till June 2021. Selection Criteria: Studies that reported on women with a single cervical treatment with any method of excision for CIN2 or CIN3 lesion, negative excision margins and whose recurrence was defined histopathologically were included. Data Collection and Analysis: Two reviewers independently evaluated study eligibility. We used the fixed effects model for meta-analysis. Main Results: There were 4 eligible studies included in the present systematic review that evaluated 1088 women treated with either large loop excision of the transformation zone (LLETZ) or with cold knife conization (CKC). The between study heterogeneity was not significant. We found no significant association of endocervical crypt involvement by CIN2-3 with high-grade histopathological recurrence at follow-up after cervical excision (OR:1.93; 95%CI:0.51-3.35). The subgroup analysis of women with LLETZ cervical excision showed again no significant association with high-grade histopathological recurrence at follow-up (OR:2.00; 95%CI:0.26-3.74). Conclusions: Endocervical crypt involvement by high-grade CIN does not seem to be a risk factor for high-grade histopathological recurrence after cervical excision with negative excision margins.

The artery of Adamkiewicz (AKA) provides blood supply to the thoracolumbar spinal cord. Any disruption of the AKA can lead to the anterior spinal artery (ASA) syndrome, with devastating systematic and neurologic complications for the patient. This is a narrative review of the anatomy of AKA, the characteristics of ASA syndrome and the role of radiologic techniques in diagnosis and treatment. A detailed search of the PubMed database was conducted from January 2000 until April 2020, to locate articles relevant to our study. The references of the included studies were also retrieved in order not to miss any information. The ASA syndrome can present as a possible post-operative complication after minimally invasive or open surgeries of multiple specialties that involve the field of spine. Risk factors associated with ASA syndrome include; kyphosis of the patient, corresponding spinal surgical approach, intraoperative hypotension, multiple ligations of the AKA, a left side approach and a 360-combined or revision surgery. The incidence varies among different operations. Many different imaging modalities have been used in preoperative plan, including but not limited to computed tomography angiography, magnetic resonance angiography, and subtraction angiography. The use of computed tomography angiography or magnetic resonance angiography preoperatively can play a major role in the prevention of the ASA syndrome. However, more research needs to be done before making any final assumptions.

Wildfires are increasingly exacerbated as a result of climate change, necessitating advanced proactive measures for effective mitigation. It is important to forecast wildfires weeks and months in advance to plan forest fuel management, resource procurement and allocation. To achieve such accurate long-term forecasts at a global scale, it is crucial to employ models that account for the Earth system's inherent spatio-temporal interactions, such as memory effects and teleconnections. We propose a teleconnection-driven vision transformer (TeleViT), capable of treating the Earth as one interconnected system, integrating fine-grained local-scale inputs with global-scale inputs, such as climate indices and coarse-grained global variables. Through comprehensive experimentation, we demonstrate the superiority of TeleViT in accurately predicting global burned area patterns for various forecasting windows, up to four months in advance. The gain is especially pronounced in larger forecasting windows, demonstrating the improved ability of deep learning models that exploit teleconnections to capture Earth system dynamics. Code available at https://github.com/Orion-Ai-Lab/TeleViT.

We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables representing wildfire drivers (meteorology, vegetation, human activity) and historical records of wildfire ignitions and burned areas for 17 years (2006-2022). It is designed as a cloud-friendly spatio-temporal dataset, namely a datacube, harmonizing all variables in a grid of 1km x 1km x 1-day resolution. The datacube structure offers opportunities to assess machine learning (ML) usage in various wildfire modeling tasks. We extract two ML-ready datasets that establish distinct tracks to demonstrate this potential: (1) short-term wildfire danger forecasting and (2) final burned area estimation given the point of ignition. We define appropriate metrics and baselines to evaluate the performance of models in each track. By publishing the datacube, along with the code to create the ML datasets and models, we encourage the community to foster the implementation of additional tracks for mitigating the increasing threat of wildfires in the Mediterranean.

Submission note: A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Psychology in Clinical Neuropsychology to the School of Psychological Science, Faculty of Science, Technology and Engineering, La Trobe University, Bundoora.

Aims: Numerous beneficial effects of vitamin C (ascorbic acid) supplementation have been reported in the literature. However, data on its effects toward the gut microbiome are limited. We assessed the effect of vitamin C supplementation on the abundance of beneficial bacterial species in the gut microbiome. Materials and methods: Stool samples were analyzed for relative abundance of gut microbiome bacteria using next-generation sequencing-based profiling and metagenomic shotgun analysis. Results: Supplementation with vitamin C increased the abundance of bacteria of the genus Bifidobacterium (p = 0.0001) and affected various species. Conclusion: The beneficial effects of vitamin C supplementation may be attributed to modulation of the gut microbiome and the consequent health benefits thereof.

Vitamin C, also known as ascorbic acid, is used as a supplement for fighting infectious disorders. Many disorders, including COVID-19 and cancer, harmfully disrupt the levels of bacteria that naturally reside in the gut, which may contribute to symptoms. The aim of the study was to understand whether high-dose vitamin C could improve the types of bacteria in the human gut. To do this we characterized the gut bacteria before and after 23 individuals took vitamin C, as prescribed by their respective physicians. We observed that vitamin C increased levels of a gut bacterium called

We present a multifaceted approach to effectively probe complex local protein environments utilizing the vibrational reporter unnatural amino acid (UAA) 4-cyano-l-phenylalanine (pCNPhe) in the model system superfolder green fluorescent protein (sfGFP). This approach combines temperature-dependent infrared (IR) spectroscopy, X-ray crystallography, and molecular dynamics (MD) simulations to provide a molecular interpretation of the local environment of the nitrile group in the protein. Specifically, a two-step enantioselective synthesis was developed that provided an 87% overall yield of pCNPhe in high purity without the need for chromatography. It was then genetically incorporated individually at three unique sites (74, 133, and 149) in sfGFP to probe these local protein environments. The incorporation of the UAA site-specifically in sfGFP utilized an engineered, orthogonal tRNA synthetase in

The spectrophotometric properties of the green fluorescent protein (GFP) result from the post-translationally cyclized chromophore composed of three amino acids including a tyrosine at the center of the β-barrel protein. Altering the amino acids in the chromophore or the nearby region has resulted in numerous GFP variants with differing photophysical properties. To further examine the effect of small atomic changes in the chromophore on the structure and photophysical properties of GFP, the hydroxyl group of the chromophore tyrosine was replaced with a nitro or a cyano group. The structures and spectrophotometric properties of these superfolder GFP (sfGFP) variants with the unnatural amino acids (UAAs) 4-nitro-L-phenylalanine or 4-cyano-L-phenylalanine were explored. Notably, the characteristic 487 nm absorbance band of wild-type (wt) sfGFP is absent in both unnatural amino-acid-containing protein constructs (Tyr66pNO2Phe-sfGFP and Tyr66pCNPhe-sfGFP). Consequently, neither Tyr66pNO2Phe-sfGFP nor Tyr66pCNPhe-sfGFP exhibited the characteristic emission of wt sfGFP centered at 511 nm when excited at 487 nm. Tyr66pNO2Phe-sfGFP appeared orange due to an absorbance band centered at 406 nm that was not present in wt sfGFP, while Tyr66pCNPhe-sfGFP appeared colorless with an absorbance band centered at 365 nm. Mass spectrometry and X-ray crystallography confirmed the presence of a fully formed chromophore and no significant structural changes in either of these UAA-containing protein constructs, signaling that the change in the observed photophysical properties of the proteins is the result of the presence of the UAA in the chromophore.

Cyber Threat Intelligence helps organisations in their fight against cyber threats to strategically design their defences and support decision making by continuously providing information regarding the cyber threat landscape. In this context, honeypots are a widespread solution for gathering intelligence about threat actors. However, honeypots do not inherently provide information about the origin of threat groups, their resources, capabilities, and potential impact. Thus, we propose an approach that classifies threats, as highly or less abusive, based on their behaviour characteristics using four ensemble machine learning algorithms applied on security incidents identified in a rule-based manner on a deployed honeypot. After prepossessing and hyper-tuning of the parameters, the four examined models, Random Forest Classifier (RFC), Adaptive Boosting Classifier (AdaBoost), Light Gradient Boosting Machine (LGBM) and Extreme Gradient Boosting (XGBoost), achieve good results, with RFC and LGBM achieving the best recall (84%, 83%) and LGBM and XGB the best AUC (91%, 90%).

Attack pattern identification is a significant step for protecting organisations from cyber-threats, as it can be used to reveal valuable patterns, enabling the better detection and analysis of the respective attacks that can be leveraged for the development of effective and efficient Intrusion Detection Systems. In this work, Association Rule Learning (ARL), a data mining technique, is used for the identification of attack patterns from data collected from a public honeypot. Using the FP-Growth ARL algorithm, we identified different patterns of attacks and correlated the respective commands executed by various attackers. To our knowledge, this is the first time ARL has been used to extract attack patterns from commands run by the attackers using real-world log data collected at the host level., This is the accepted version of the paper. The final version of the paper can be found at https://ieeexplore.ieee.org/xpl/conhome/9850275/proceeding

Objective: The aim of this study was to provide early-term evaluation, safety, and efficacy of the novel CGuard (Inspire MD, Tel Aviv, Israel) micromesh self-expanding stent with embolic protection system (EPS) during carotid artery stenting (CAS) procedure. Materials and Methods: All patients who underwent CAS with CGuard carotid stent system from January 2018 to September 2020 in a tertiary center were prospectively evaluated. Primary endpoints included technical success and perioperative neurological events (0–48 hours). Secondary endpoint was the rate of neurologic, cardiac events, and death (major adverse event or MAE) at 30 days. Patency of CGuard, peak systolic velocity (PSV), and end diastolic velocity (EDV) were evaluated at 30 days with duplex ultrasound. Results: A total of 103 patients underwent CAS procedure. Forty patients were symptomatic, and 63 patients were asymptomatic with stenosis greater than 70%. Technical success was 100%. Local anesthesia was applied in 94 patients (93%). Embolic protection devices were used in 6 patients (5.8%). Access site complications were 1.9%. Mean internal carotid artery stenosis diameter reduced from 79.47% to 14.68%. Two patients had transient ischemic attack (1.9%) and 1 patient experienced a cerebral hyperperfusion syndrome (CHS) perioperatively. External carotid artery (ECA) occlusion occurred in 1.9%. Four patients experienced jaw claudication (3.8%) postoperatively. Mean time of operation was 41 minutes. Mean duration of hospitalization was 3.1 days. The 30-day rate of MAE was 0%. CGuard patency was 100%, mean internal carotid PSV was reduced from 251.57 to 77.29 cm/s, and mean internal carotid EDV was reduced from 154.62 to 24.63 cm/s at 30 days. Conclusion: Our study shows that CGuard stent with EPS is an effective and safe device for treatment of carotid artery stenosis with acceptable low perioperative neurologic events, even with low embolic protection device usage. Larger multicenter and randomized studies are necessary to confirm its long-term efficacy.

Background and Objectives: Fetal growth abnormalities increase the risk of negative perinatal and long-term outcomes. Bisphenol A (BPA) is a ubiquitous endocrine-disrupting chemical to which humans may be exposed in a number of ways, such as from the environment, via various consumer products, and through the individual’s diet. Since the compound possesses estrogen-mimicking properties and exerts epigenetic and genotoxic effects, it has been associated with harmful effects impacting the entire spectrum of human life, including, vitally, the intrauterine period. We investigated the role of maternal exposure to BPA in abnormal fetal growth velocity, both impaired and excessive. Materials and Methods: Amniotic fluid samples were collected from 35 women who underwent amniocentesis early in the second trimester due to medical reasons. Pregnancies were followed until delivery, and birth weights were recorded. The amniotic fluid samples were subsequently divided into three groups based on fetal birth weight, as follows: AGA (appropriate for gestational age), SGA (small for gestational age), and LGA (large for gestational age). Amniotic fluid BPA levels were determined by gas chromatography coupled with mass spectrometry. Results: BPA was detected in 80% (28/35) of our amniotic fluid samples. Median concentration was 281.495 pg/mL and ranged from 108.82 pg/mL to 1605.36 pg/mL. No significant association was observed between the study groups regarding BPA concentration. A significant positive correlation between amniotic fluid BPA concentration and birth weight centile (r = 0.351, p-value = 0.039) was identified. BPA levels were also inversely associated with gestational age in pregnancies at term (between 37 and 41 weeks) (r = −0.365, p-value = 0.031). Conclusions: Our findings suggest that maternal exposure to BPA during the early second trimester of pregnancy can potentially contribute to increased birthweight percentiles and to decreased gestational age in pregnancies at term.

Purpose The purpose of this review is to summarize the existing knowledge on flualprazolam, a novel ‘designer’ benzodiazepine that derives from the fluorination of the phenyl moiety in the ortho-position of alprazolam. Methods An extensive literature search was carried out in PubMed, Google Scholar and World Wide Web using relevant keywords. All articles found were gathered, and the available information is presented. Results This article reviews the existing knowledge on chemistry, pharmacology, toxicology, prevalence and current legal status of flualprazolam. Moreover, forensic and clinical cases where flualprazolam was involved worldwide, as well as flualprazolam seizures, along with the methods for its determination in biological samples are presented. Conclusions The recent flualprazolam-related cases have raised concerns to regulatory authorities and international stakeholders suggesting that flualprazolam should be under international control. The urgent international control of flualprazolam, despite the limited information on clinical effects and pharmacologic characteristics available, is an important measure for the prevention of its increasing abuse worldwide.

During the last decades, massive amounts of satellite images are becoming available that can be enriched with semantic annotations for the creation of value-added Earth Observation products. One challenge is to extract knowledge from the raw satellite data in an automated way and to effectively manage the extracted information in a semantic way, to allow fast and accurate decisions of spatio-temporal nature in a real operational scenario. In this work we present a framework that combines supervised learning for crop type classification on satellite imagery time-series with Semantic Web and Linked Data technologies to assist in the implementation of rule sets by the European Common Agricultural Policy (CAP). The framework collects georeferenced data that are available online and satellite images from the Sentinel-2 mission. We analyze image time-series that cover the entire cultivation period and link each parcel with a specific crop. On top of that, we introduce a semantic layer to facilitate a knowledge-driven management of the available information, capitalizing on ontologies for knowledge representation and semantic rules, to identify possible farmers noncompliance according to the Greening 1 (Crop Diversification) and SMR 1 rule (protection of waters against pollution caused by nitrates) rules of the CAP. Experiments show the effectiveness of the proposed integrated approach in three different scenarios for crop type monitoring and consistency-checking for noncompliance to the CAP rules; the smart sampling of on-thespot-checks; the automatic detection of CAP’s Greening 1 rule; and the automatic detection of susceptible parcels according to the CAP’s SMR 1 rule.

Krüppel-like factor 4 (KLF4) is known to preserve vascular homeostasis. In the present study, we sought to correlate serum KLF4 levels with arterial aneurysm size and their clinical presentation. We also explored the association between serum KLF4 levels and the severity of extracranial carotid and peripheral arterial disease.Patients undergoing surgery for various forms of atheromatosis (ATH group) or for arterial aneurysm repair (AA group) were eligible for inclusion. KLF4 levels were measured via enzyme-linked immunosorbent assay.Patients in the atheromatic and aneurysmal groups had significantly higher serum KLF4 levels compared with controls. Patients with permanent end-organ damage (ATH3) had higher serum KLF4 (6.96 ± 0.75 pg/mL) compared with patients with asymptomatic internal carotid stenosis70% or claudication (ATH1) (2.76 ± 0.68 pg/mL; mean difference [MD], -4.20; 95% confidence interval [95% CI], -5.35 to -3.04; P 0.01) and those with transient ischemic attack or rest pain (ATH2) (4.47 ± 1.08 pg/mL; MD, -2.48; 95% CI, -3.76 to -1.21). Furthermore, patients with an asymptomatic aneurysm of a diameter 250-300% of that of the normal artery (AA1, 5.01 ± 1.08 pg/mL) had considerably lower serum KLF4 compared with those suffering from either a symptomatic aneurysm or an asymptomatic aneurysm of a diameter350% of that of normal artery (AA3, 6.63 ± 1.92 pg/mL; MD, -2.61; 95% CI, -5.04 to -0.18; P 0.01).Serum KLF4 levels are significantly increased in patients with end-organ damage related to atheromatosis as well as those with extensive aneurysmal disease.

Introduction To determine whether expansile endocervical crypt involvement (ECI) on pretreatment cervical punch biopsies is a risk factor for high grade cytology recurrence in women following cold coagulation for cervical intraepithelial neoplasia (CIN). Materials and Methods This was a secondary analysis on the results of an observational study of women who had a single cold coagulation cervical treatment between 2001 – 2011 and who were followed up for cytology recurrence. Women with a previous cervical treatment were excluded. Results 559 women were identified with a mean age of 28.7 ± 6.2 years. Expansile and non-expansile ECI were identified in 5.4 and 4.3% of women, respectively. The proportion of women with high grade cytology recurrence was 10% for those with expansile ECI and 2.3% for those without. Multivariate analysis showed that women with expansile ECI when compared to those without, had a four-fold greater risk for high grade cytology recurrence (HR = 4.22; 95% CI: 1.10 – 16.29, p = 0.036). There was no significant association found between non-expansile ECI and overall or high grade cytology recurrence. The increased biopsy depth and the CIN3 grade of pretreatment cervical punch biopsies were significantly associated with greater odds for the detection of expansile ECI. We calculated that the optimal-cut off of pretreatment cervical punch biopsy depth for the detection of expansile ECI was 4 mm (sensitivity: 73.3%; specificity: 55.1%). Conclusions Expansile ECI is a risk factor that increases the likelihood of high grade cytology recurrence following cold coagulation. Deeper pretreatment cervical punch biopsies need to be taken so as not to miss expansile ECI prior to ablative treatment.

For aerodynamic and conjugate heat transfer problems, the work reported in this article investigated the effect of the grid displacement model on the derivatives of an objective function with respe...

Background\ud A large amount of mushroom waste is generated during mushroom production (accounting for up to 20% of total production) and is mainly composed of mushrooms that do not meet the specifications set by retailers because of misshapen caps and/or stalks. Mushrooms are notable for their ergosterol (a precursor of vitamin D2) content which is converted to vitamin D2 after exposure to natural or artificial ultraviolet (UV) irradiation. Therefore, mushroom waste could be used as a source for the recovery of both ergosterol and vitamin D2 which could be valorized by both pharmaceutical and food industries.\ud \ud Scope and approach\ud The current review presents a comprehensive summary of research performed regarding the extraction, purification and determination of ergosterol and vitamin D2 (ergocalciferol) from mushroom matrices. Additionally, studies related to the impact of sample preparation and especially of drying methods on the retention of ergosterol and vitamin D2 are presented. Finally, the potential valorization of mushroom waste sterols by food and pharmaceutical industries is discussed.\ud \ud Key findings and conclusions\ud Ergosterol and vitamin D2 contents vary among different mushroom species. Sample drying is a crucial step that precedes sterol extraction and has a significant impact on the retention of ergosterol and vitamin D2. The extraction of sterols from mushrooms can be conducted by either conventional (e.g., Soxhlet extraction) or non-conventional methods (e.g., ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), deep eutectic solvents (DES) extraction, supercritical fluid extraction (SFE), and pressurized liquid extraction (PLE)) or their combination. The application of non-conventional methods such as UAE and MAE facilitate in shorter extraction times than the conventional methods. The valorization of mushroom extracts enriched in ergosterol and vitamin D2 by both pharmaceutical and food industries requires further work.

Solid–liquid extraction (SLE) (extraction time (5.5–69.5 min) and temperature (25.5–89.5 °C)) and ultrasound-assisted extraction (UAE) (extraction time (5–25 min) and temperature (25–85 °C), and amplitude (50–100%)) were investigated and optimised as a means to recover steroidal alkaloids from potato peel. Optimisation for the recovery of α-solanine, α-chaconine, and total glycoalkaloid content (TGA) from potato peels was performed using a central composite design (CCD) and Box–Behnken design for SLE and UAE, respectively. For SLE, the recovery of glycoalkaloids increased with increasing temperature, while time had no significant effect (at p

Rodent studies have shown that legumes can reduce chemical induced colonic inflammation, but the role of faba bean fractions for colon health has not been described. We have investigated the role of protein and fiber fractions of faba beans for colonic health and microbiota composition in a low-grade inflammation mice-model when incorporated in a Western diet (WD). The diet of sixty C57BL/6JRj male mice was standardized to a WD (41% fat, 43% carbohydrates) before were randomly assigned to four groups (n = 12) receiving either 1) WD with 30% of the protein replaced with faba-bean proteins, 2) WD with 7% of the fiber replaced with faba-bean fibers, 3) WD with protein and fiber fractions or 4) plain WD (n = 24). Low-grade inflammation was induced by 1% dextran sodium sulfate (DSS) given to mice for the last six days of the trial. Half (n = 12) in group 4) were given only water (controls). Prior to DSS, body weight, energy intake, glucose and insulin tolerance assays were performed. Inflammatory status in the colon was assessed by biomarkers of inflammation and qRT-PCR analyses of inflammatory related genes. Fecal microbiota composition was assessed by 16S rRNA gene sequencing. 1% DSS treatment increased levels in fecal lipocalin-2 and induced disease activity index score, but the presence of faba bean fractions in WD did not influence these indicators nor the expression level of inflammatory associated genes. However, the mice that had faba-bean proteins had a lower amount of Proteobacteria compared the group on plain WD. The Actinobacteria abundance was also lower in the group that had fiber fraction from faba-beans. Overall, outcomes indicated that in a low-grade inflammation model, replacement of protein and or fiber in a WD with faba bean fractions had marginal effects on inflammatory parameters and colonic microbiota.

The study objective was to compare gut microbiome diversity and composition in SARS-CoV-2 PCR-positive patients whose symptoms ranged from asymptomatic to severe versus PCR-negative exposed controls.Using a cross-sectional design, we performed shotgun next-generation sequencing on stool samples to evaluate gut microbiome composition and diversity in both patients with SARS-CoV-2 PCR-confirmed infections, which had presented to Ventura Clinical Trials for care from March 2020 through October 2021 and SARS-CoV-2 PCR-negative exposed controls. Patients were classified as being asymptomatic or having mild, moderate or severe symptoms based on National Institute of Health criteria. Exposed controls were individuals with prolonged or repeated close contact with patients with SARS-CoV-2 infection or their samples, for example, household members of patients or frontline healthcare workers. Microbiome diversity and composition were compared between patients and exposed controls at all taxonomic levels.Compared with controls (n=20), severely symptomatic SARS-CoV-2-infected patients (n=28) had significantly less bacterial diversity (Shannon Index, p=0.0499; Simpson Index, p=0.0581), and positive patients overall had lower relative abundances ofWe hypothesise that low bacterial diversity and depletion ofNCT04031469 (PCR-) and 04359836 (PCR+).

Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) products in particular, are one of the largest sources of Earth Observation data. InSAR provides unique information on diverse geophysical processes and geology, and on the geotechnical properties of man-made structures. However, there are only a limited number of applications that exploit the abundance of InSAR data and deep learning methods to extract such knowledge. The main barrier has been the lack of a large curated and annotated InSAR dataset, which would be costly to create and would require an interdisciplinary team of experts experienced on InSAR data interpretation. In this work, we put the effort to create and make available the first of its kind, manually annotated dataset that consists of 19,919 individual Sentinel-1 interferograms acquired over 44 different volcanoes globally, which are split into 216,106 InSAR patches. The annotated dataset is designed to address different computer vision problems, including volcano state classification, semantic segmentation of ground deformation, detection and classification of atmospheric signals in InSAR imagery, interferogram captioning, text to InSAR generation, and InSAR image quality assessment., Comment: This work has been accepted for publication in EARTHVISION 2022, in conjuction with the Computer Vision and Pattern Recognition (CVPR) 2022 Conference

We develop a "dual cone" model for millimeter wavelength emission near a spinning black hole. The model consists of optically thin, luminous cones of emission, centered on the spin axis, which are meant to represent jet walls. The resulting image is dominated by a thin ring. We first consider the effect of black hole's spin on the image, and show that the dominant effect is to displace the ring perpendicular to the projection of the spin axis on the sky by $2 a_* \sin i + \mathcal{O}(a_*^3)$. This effect is lower order in $a_*$ than changes in the shape and size of the photon ring itself, but is undetectable without a positional reference. We then show that the centerline of the jet can provide a suitable reference: its location is exactly independent of spin if the observer is outside the cone, and nearly independent of spin if the observer is inside the cone. If astrophysical uncertainties can be controlled for, then spin displacement is large enough to be detectable by future space VLBI missions. Finally, we consider ring substructure in the dual cone model and show that features in total intensity are not universal and depend on the cone opening angle., Comment: 8 pages, 5 figures. Accepted for publication in ApJ

Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In this work, we create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale with the use of segmentation deep learning models. Particularly, we present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers (climate, vegetation, oceanic indices, human-related variables), as well as the historical burned areas and wildfire emissions for 2001-2021. We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time. Our work motivates the use of deep learning for global burned area forecasting and paves the way towards improved anticipation of global wildfire patterns., Comment: Accepted at the NeurIPS 2022 workshop on Tackling Climate Change with Machine Learning

Crop classification is an important remote sensing task with many applications, e.g., food security monitoring, ecosystem service mapping, climate change impact assessment, etc. This work focuses on mapping 10 crop types at the field level in an agricultural region located in the Spanish province of Navarre. For this, multi-temporal Synthetic Aperture Radar Polarimetric (PolSAR) Sentinel-1 imagery and multi-spectral Sentinel-2 data were jointly used. We applied the Cloude–Pottier polarimetric decomposition on PolSAR data to compute 23 polarimetric indicators and extracted vegetation indices from Sentinel-2 time-series to generate a big feature space of 818 features. In order to assess the relevance of the different features for the crop mapping task, we run a number of scenarios using a Support Vector Machines (SVM) classifier. The model that was trained using only the polarimetric data demonstrates a very promising performance, achieving an overall accuracy over 82%. A genetic algorithm was also implemented as a feature selection method for deriving an optimal feature subset. To showcase the positive effect of using polarimetric data over areas suffering from cloud coverage, we contaminated the original Sentinel-2 time-series with simulated cloud masks. By incorporating the genetic algorithm, we derived a high informative feature subset of 120 optical and polarimetric features, as the corresponding classification model increased the overall accuracy by 5% compared to the model trained only with Sentinel-2 features. The feature importance analysis indicated that apart from the Sentinel-2 spectral bands and vegetation indices, several polarimetric parameters, such as Shannon entropy, second eigenvalue and normalised Shannon entropy are of high value in identifying crops. In summary, the findings of our study highlight the significant contribution of Sentinel-1 PolSAR data in crop classification in areas with frequent cloud coverage and the effectiveness of the genetic algorithm in discovering the most informative features.

Η παρούσα διδακτορική διατριβή ασχολείται με τη μαθηματική ανάπτυξη, τονπρογραμματισμό και την πιστοποίηση συζυγών (adjoint) μεθόδων για τον υπολογι-σμό (πρώτης και υψηλότερων τάξεων) παραγώγων ευαισθησίας συναρτήσεων-στόχωνστους τομείς της αερο/υδροδυναμικής καθώς και την ένταξη των τελευταίων σε αλ-γόριθμους ϐελτιστοποίησης. Παρουσιάζονται μέθοδοι που ϐασίζονται στη συνεχή καιτη διακριτή συζυγή τεχνική, με έμφαση στην πρώτη. Εξετάζονται ακαδημαϊκά καιϐιομηχανικά προβλήματα ϐελτιστοποίησης μορφής (shape optimization) με εφαρ-μογή, μεταξύ άλλων, στη ϐελτιστοποίηση επιβατικών αυτοκινήτων και στροβιλομηχα-νών, σε προβλήματα τοπολογίας (topology optimization) καθώς και σε προβλήματαϐελτιστοποίησης υπό αβεβαιότητες (στιβαρός σχεδιασμός, robust design).Ως προς τη ϐελτιστοποίηση μορφής, διερευνάται η σημασία της παραγώγισηςμοντέλων τύρβης, τόσο χαμηλών όσο και υψηλών αριθμών Reynolds, για χρονικάμόνιμες, ασυμπίεστες ϱοές. Συγκεκριμένα, επεκτείνεται η συνεχής συζυγής μέθο-δος ώστε να καλύπτει ϱοές που διέπονται από το μοντέλο τύρβης k − ǫ Reynolds τωνLaunder–Sharma. Δίνεται έμφαση στην επίδραση που ϑα είχε ενδεχόμενη παράλει-ψη της παραγώγισης των εξισώσεων του μοντέλου (η ευρύτατα χρησιμοποιούμενηπαραδοχή της ‘παγωμένης τυρβώδους συνεκτικότητας’) στη διαδικασία της ϐελτιστο-ποίησης μορφής. Επιπρόσθετα, αναπτύσσεται και υλοποιείται η συνεχής συζυγήςμέθοδος για το μοντέλο υψηλών αριθμών Reynolds των Spalart–Allmaras, για πρώτηϕορά στη ϐιβλιογραφία. Επιπλέον, χρησιμοποιείται η eikonal εξίσωση για τον υπο-λογισμό αποστάσεων από τα στερεά τοιχώματα και αναπτύσσεται η συζυγής της, ώστεη τελική μορφή των παραγώγων ευαισθησίας να καταστεί ανεξάρτητη των παραγώγων των αποστάσεων ως προς τις μεταβλητές σχεδιασμού.Σημαντικό κομμάτι της διατριβής αποτελεί η μετέπειτα εφαρμογή των μεθόδωναυτών και του αναπτυχθέντος λογισμικού σε ϐιομηχανικά προβλήματα ϐελτιστο-ποίησης μορφής. Συγκεκριμένα, εξετάζεται η ελαχιστοποίηση της οπισθέλκουσαςσε πρωτότυπο αυτοκίνητο ευρωπαϊκής αυτοκινητοβιομηχανίας χρησιμοποιώντας τιςRANS (Reynolds-Averaged Navier Stokes) εξισώσεις με το μοντέλο τύρβης χαμηλώναριθμών Reynolds των Spalart–Allmaras, το αναπτυχθέν λογισμικό για την εύρε-ση παραγώγων ευαισθησίας και τεχνικές παραμόρφωσης πλέγματος. Επιπρόσθε-τα, το αναπτυχθέν λογισμικό χρησιμοποιείται για τον εντοπισμό των αεροδυναμικάευαίσθητων περιοχών (χάραξη χαρτών ευαισθησίας) ως προς τον ϑόρυβο που γίνε-ται αντιληπτός από τον οδηγό. Επιπλέον, ακολουθώντας τη συνήθη προσέγγιση τηςϐιομηχανίας για την ανάλυση της ϱοής χρησιμοποιώντας το DES (Detached EddySimulation) μοντέλο τύρβης των Spalart–Allmaras σε συνδυασμό με συναρτήσειςτοιχώματος, προτείνεται διαδικασία για τη χάραξη χαρτών ευαισθησίας ως προς τηνασκούμενη άνωση στον πίσω άξονα του οχήματος, χρησιμοποιώντας τη συνεχή συ-Ϲυγή τεχνική. ΄Εμφαση δίνεται στη μείωση του υπολογιστικού κόστους και της α-παιτούμενης μνήμης ώστε η διαδικασία να ολοκληρώνεται σε χρόνο αποδεκτό για τηϐιομηχανία. Παράλληλα, αναπτύσσονται δυο νέοι συζευγμένοι αλγόριθμοι επίλυσηςτων συζυγών εξισώσεων με στόχο την επιτάχυνση της σύγκλισης και την αύξηση της ευστάθειας του συζυγούς επιλύτη σε ϐιομηχανικές εφαρμογές.Το αναπτυχθέν λογισμικό εφαρμόζεται επίσης για τον σχεδιασμό δρομέα υδρο-στροβίλου τύπου Francis. Συγκεκριμένα, χρησιμοποιείται η συνεχής συζυγής μέθο-δος για τον υπολογισμό παραγώγων ευαισθησίας ως προς την κάθετη μετατόπισητων οριακών κόμβων του πλέγματος για διάφορες συναρτήσεις κόστους που περι-λαμβάνουν : επιθυμητές κατανομές ταχύτητας στην εξόδο του δρομέα, αλλαγή τουυδραυλικού ύψους ώστε να ταιριάζει με το επιθυμητό σημείο λειτουργίας και τηνεξάλειψη ενδεχόμενης σπηλαίωσης στην επιφάνεια του δρομέα.Ως προς τη ϐελτιστοποίηση τοπολογίας, η οποία στοχεύει στον αρχικό σχεδιασμόαεροδυναμικών μορφών υπολογίζοντας τη ϐέλτιστη κατανομή ενός τεχνητού πεδίουπορώδους, η παρούσα διατριβή επεκτείνει το μαθηματικό υπόβαθρο της συνεχούςσυζυγούς μεθόδου ώστε να καλύπτει 3Δ ασυμπίεστες ϱοές με το μοντέλο υψηλώναριθμών Reynolds των Spalart–Allmaras, σε συνδυασμό με περιορισμούς που χρη-σιμοποιούνται συχνά από τη ϐιομηχανία. Το αναπτυχθέν λογισμικό εφαρμόζεταιστη ϐελτιστοποίηση τοπολογίας ενός αγωγού κλιματισμού επιβατικού αυτοκινήτουκαι στον σχεδιασμού ενός ϑαλάμου plenum οχήματος πανεπιστημιακού διαγωνι-σμού ταχύτητας.Παρά τη χρήση των χαμηλού κόστους συζυγών μεθόδων, η διαδικασία της ϐελ-τιστοποίησης μπορεί να αποδειχθεί χρονοβόρα λόγω του αριθμού των απαραίτητωνκύκλων ϐελτιστοποίησης για τη σύγκλιση του αλγορίθμου. Στοχεύοντας στη μείωσητου συνολικού χρόνου ϐελτιστοποίησης/σχεδιασμού, αναπτύσσεται και εφαρμόζε-ται για πρώτη ϕορά στη ϐιβλιογραφία της ϐελτιστοποίησης τοπολογίας η μέθοδος Newton με αποκοπή (truncated Newton). Αυτή χρησιμοποιεί προβολές του Εσσια-νού μητρώου σε διανύσματα για να προσεγγίσει τη δεύτερης τάξης διόρθωση τωνμεταβλητών σχεδιασμού. Τα ανωτέρω γινόμενα υπολογίζονται μέσω συνδυασμού τηςσυνεχούς συζυγούς μεθόδου και της μεθόδου ευθείας διαφόρισης.Ως στιβαρός σχεδιασμός ορίζεται η διαδικασία σχεδιασμού/βελτιστοποίησης α-εροδυναμικών σχημάτων σε ένα εύρος συνθηκών λειτουργίας. Για την ποσοτικο-ποίηση της συμπεριφοράς ενός αεροδυναμικού σχήματος σε εύρος συνθηκών λει-τουργίας απαιτείται ο υπολογισμός της μέσης τιμής και της τυπικής απόκλισηςσυναρτήσεων-στόχων. Στη παρούσα διατριβή χρησιμοποιείται η δεύτερης τάξηςμέθοδος των στατιστικών ϱοπών (Second-Order Second-Moment method) που α-παιτεί τον υπολογισμό παραγώγων ευαισθησίας της συνάρτησης-στόχου, πρώτης καιδεύτερης τάξης ως προς τις αβέβαιες μεταβλητές. Η ελαχιστοποίηση της μέσης τι-μής και της τυπικής απόκλισης της συνάρτησης-στόχου με αιτιοκρατικές μεθόδουςαπαιτεί τον υπολογισμό μέχρι και τρίτης τάξης παραγώγων ευαισθησίας. Στην πα-ϱούσα διατριβή παρουσιάζεται ένας αποδοτικός συνδυασμός της μεθόδου ευθείαςδιαφόρισης και της συζυγούς τεχνικής για τον υπολογισμό των ανωτέρω παραγώγων.

ObjectiveThe study objective was to compare gut microbiome diversity and composition in SARS-CoV-2 polymerase chain reaction (PCR)-positive patients whose symptoms ranged from asymptomatic to severe, versus PCR-negative exposed controls.DesignUsing a cross-sectional design, we performed shotgun next-generation sequencing (NGS) on stool samples to evaluate gut microbiome composition and diversity in both patients with SARS-CoV-2 PCR- confirmed infections, that had presented to Ventura Clinical Trials for care from March 2020 through October 2021, and SARS-CoV-2 PCR-negative exposed controls. Patients were classified as being asymptomatic or having mild, moderate, or severe symptoms based on NIH criteria. Exposed controls were individuals with prolonged or repeated close contact with patients with SARS-CoV-2 infection or their samples, e.g. household members of patients or frontline healthcare workers. Microbiome diversity and composition were compared between patients and exposed controls at all taxonomic levels.ResultsCompared with controls (n=20), severely symptomatic SARS-CoV-2 infected patients (n=28) had significantly less bacterial diversity (Shannon Index, P=0.0499; Simpson Index, P=0.0581), and positive patients overall had lower relative abundances of Bifidobacterium (PConclusionWe hypothesize that low bacterial diversity and depletion of Bifidobacterium genera either before or after infection led to reduced pro-immune function, thereby allowing SARS-CoV-2 infection to become symptomatic. This particular dysbiosis pattern may be a susceptibility marker for symptomatic severity from SARS-CoV-2 infection and may be amenable to pre-, intra-, or post infection intervention.Keywords: SARS-CoV-2, COVID, Microbiome, Bifidobacterium, Faecalibacterium, Bacteriodes, Shannon Index, Simpson Index, Severity, MicrobiotaRegistrationclinicaltrials.govNCT04031469 (PCR -) and 04359836 (PCR+)

Η έναρξη της νέα χιλιετίας βρήκε τους επιστήμονες δορυφορικής τηλεπισκόπησης, γεωδαίτες, γεωφυσικούς και μηχανικούς, εξοπλισμένους με νέα και ισχυρά εργαλεία για την μέτρηση της παραμόρφωσης του γήινου φλοιού, με χρήση τεχνικών παρατήρησης της γης. Η αυξανόμενη διαθεσιμότητα δορυφορικών δεδομένων, σε συνδυασμό με την ανάπτυξη καινοτόμων αλγορίθμων για την επεξεργασία τους, επέτρεψε τη συστηματική χαρτογράφηση της επιφανειακής παραμόρφωσης έπειτα από σεισμικά γεγονότα, ηφαιστειακές εκρήξεις, κατολισθήσεις, και καθιζήσεις του εδάφους που προκύπτουν από ανθρωπογενείς δραστηριότητες, με αποτέλεσμα την καλύτερη κατανόηση αρκετών γεωφυσικών φαινομένων και των μηχανισμών που τα διέπουν.Αυτή η διατριβή εστιάζει στη χρήση γεωδαιτικών τεχνικών συμβολομετρίας ραντάρ για τη χαρτογράφηση της μετατόπισης του εδάφους στη βάση πρωτοποριακών ερευνητικών κατευθύνσεων:•Η πρώτη κατεύθυνση αφορά στη χρήση και ολοκλήρωση δεδομένων από πολλαπλούς δορυφορικούς αισθητήρες SAR διαφορετικής χρονικής, χωρικής και φασματικής ανάλυσης και διαφορετικών γεωμετριών απεικόνισης. Συγκεκριμένα, έγινε επεξεργασία δεδομένων από μια σειρά δορυφορικών συστημάτων, συμπεριλαμβανομένου των πλατφορμών ERS-1,2, Envisat, ALOS και COSMO-SkyMed που εκπέμπουν σε συχνότητες C-, L- και englishX-band, με χωρική ανάλυση που ποικίλει από 20×4 μ. εώς και 1×1 μ., με επισκεψιμότητα από 16 εώς 35 μέρες και με ευρεία γκάμα γωνιών πρόσπτωσης (incidence angle). Στην διατριβή συνδυάστηκαν τα οφέλη που παρέχονται από τα διαφορετικά χαρακτηριστικά λειτουργίας των εν λόγω συστημάτων, λαμβάνοντας επίσης υπόψην παραμέτρους που επηρεάζουν τη συνάφεια φάσης ανάλογα με την υποκείμενη χρήση γης (αστικά-περιαστικά, αγροκαλλιέργειες, δασικά), με αποτέλεσμα την αξιοσημείωτη επέκταση της χωρικής κάλυψης, τη βελτίωση της ακρίβειας των γεωδαιτικών μετρήσεων, και την απομόνωση της συμβολής των διακριτών οριζόντιων και κατακόρυφων ταχυτήτων μετατόπισης.•Επεξεργασία δεδομένων SAR χρησιμοποιώντας διάφορες μεθόδους αιχμής, προσαρμόζοντας μια σειρά από παραμέτρους και τροποποιώντας επιμέρους βήματα στις αλυσίδες επεξεργασίας, καταφέρνοντας τη μέγιστη δυνατή αξιοποίηση των δεδομένων για την μέτρηση της εδαφικής παραμόρφωσης με υψηλή ακρίβεια. Αξιοποιήθηκαν τεχνικές σώρευσης χρονοσειρών διαφορικών συμβολογραμμάτων SAR ενσωματώνοντας και βρίσκοντας συνέργειες ανάμεσα σε συμβατικές μεθόδους διαφορικής συμβολομετρίας, αλγόριθμους στοίβαξης συμβολογραμμάτων και μεθοδολογίες σταθερών σκεδαστών (Permanent Scatterers InSAR και Small BAseline Subset). Αποτέλεσμα της ερευνητικής αυτής προσπάθειας ήταν η βελτίωση της χωρικής ανάλυσης του σήματος παραμόρφωσης, αναδεικνύοντας με υψηλή ευκρίνεια τοπικά μοτίβα παραμόρφωσης, αλλά και επιτρέποντας την αποτύπωση μεγάλης κλίμακας μετατοπίσεων με χρήση περιορισμένου σετ δεδομένων (< 15 σκηνές SAR).•Σε ορισμένες περιοχές μελέτης, οι γεωδαιτικές μετρήσεις που προήλθαν από δορυφορικά δεδομένα SAR επικυρώθηκαν με επίγειες παρατηρήσεις από σταθερούς σταθμούς GPS και χωροσταθμήσεις. Η εναρμόνιση των διαφορετικών αυτών τύπων γεωδαιτικών μετρήσεων έγινε με την εφαρμογή μιας σειράς αλγοριθμικών βημάτων, αξιοποιώντας το πυκνό δίκτυο GPS στο Ελλαδικό χώρο που επεξεργάζεται το Κέντρο Δορυφόρων Διονύσου.•Επεξεργασία πρωτοφανούς όγκου δεδομένων SAR, που ανέρχεται σε αρκετά Terrabytes, δημιουργώντας μια μοναδική γεωβάση με διαχρονικές γεωδαιτικές παρατηρήσεις σε κρίσιμες γεωφυσικά περιοχές. Πλέον, για μια σειρά ευαίσθητων περιοχών στον Ελλαδική επικράτεια είναι διαθέσιμοι αξιόπιστοι χάρτες εδαφικής παραμόρφωσης υψηλής προστιθέμενης αξίας και χωρικής ανάλυσης. Οι χάρτες αυτοί αποτελούν μοναδικό σημείο αναφοράς σε ότι αφορά τη δραστηριότητα και τις παραμορφώσεις του φλοιού της Γης στην Ελλάδα.Οι παραπάνω καινοτόμες μεθόδοι εφαρμόστηκαν σε διάφορες περιοχές γεωφυσικού ενδιαφέροντος στην Ελλάδα. Η Ελλάδα έχει μοναδική γεωμορφολογία, μαστίζεται από συχνούς σεισμούς και περιέχει πολλαπλά ηφαιστειακά κέντρα κατά μήκος του Ελληνικού Τόξου. Επιπλέον η Αθήνα, πρωτεύουσα της Ελλάδας, είναι μια έντονα αστικοποιημένη μητρόπολη, όπου κατά τις τελευταίες δύο δεκαετίες έχει λάβει χώρα έντονη κατασκευαστική δραστηριότητα, λαμβάνοντας υπόψη και τις εργασίες για την υποδοχή των Ολυμπιακών Αγώνων του 2004.Αρχικά μελετάται ο καταστροφικός σεισμός (Mw 5.9) που έπληξε την Αθήνα στις 7 Σεπτεμβρίου 1999. Εφαρμόζονται τεχνικές συμβολομετρικής σώρευσης για τον εντοπισμό του πεδίου μετατοπίσεων στο Θριάσιο Πεδίο, οι οποίες δείχνουν σημαντική παραμόρφωση, με τη μέγιστη καθίζηση στην κατεύθυνση στόχου-δορυφόρου (LOS) να ανέρχεται σε 6 περίπου εκατοστά. Στη συνέχεια, το μοντέλο παραμόρφωσης επιβεβαιώνεται χρησιμοποιώντας μια εξωτερική πηγή δεδομένων από επίγειες μετρήσεις κατά μήκος του υδραγωγείου του Μόρνου. Διαπιστώνεται ότι η απόκλιση των προφίλ των καθιζήσεων ανάμεσα στη διαφορική συμβολομετρία ραντάρ και τις χωροσταθμήσεις βρίσκεται εντός του διαστήματος εμπιστοσύνης που ορίζεται για τα χωροσταθμικά δεδομένα.Στη συνέχεια διερευνάται ο σεισμός της Μόβρης (Mw 6.4) που έπληξε την βορειοδυτική Πελοπόννησο το 2008, με δορυφορική συμβολομετρία. Η συμβολομετρική σώρευση ανίχνευσε συν-σεισμική κίνηση μόνο στην περιοχή της Κάτω Αχαϊάς, όπου εντοπίζονται κροσσοί συμβολής που καταδεικνύουν κατακόρυφη μετατόπιση με διακύμανση από 3 έως 6 εκατοστά. Συνδυάζοντας αυτές τις πληροφορίες με επίγειες παρατηρήσεις, ένα μοντέλο μετατόπισης (dislocation model) και ιστορικά αρχεία σεισμικότητας, υποστηρίζεται από γεωφυσικούς ότι η αιτιώδης σύνδεση μεταξύ της μετατόπισης του εδάφους και των αυξημένων καταστροφών στην μια πλευρά του ρήγματος (hanging wall), καθίσταται δυνατή όχι μόνο σε σεισμούς που οφείλονται σε ρηξιγενείς δομές εφελκυσμού, αλλά και στις διατμητικές ρηξιγενείς δομές με μικρή συνιστώσα εφελκυσμού.Η διατριβή καταπιάνεται επίσης με ένα πρόβλημα καθαρά γεωδαιτικής φύσης, την παραγωγή ψηφιακών μοντέλων εδάφους (ΨΜΕ) υψηλής υψομετρικής ακρίβειας, χρησιμοποιώντας μεθοδολογίες αιχμής για την παραγωγή συμβολογραμμάτων με συνδυασμό δορυφορικών δεδομένων ERS και Envisat σε ειδική διάταξη λήψης. Οι διαθέσιμες γραμμές βάσης που χρησιμοποιούνται δεν είναι οι ιδανικές, ωστόσο ΨΜΕ ανακατασκευάζονται για δύο θέσεις στο Νομό Αττικής, εμφανίζοντας σημαντικά χαμηλότερες τοπικές υψομετρικές μετοβολές σε σύγκριση με υφιστάμενο ΨΜΕ, αλλά με μειωμένη χωρική κάλυψη. Επιπλέον, επιβεβαιώνεται ότι ο δείκτης συνάφειας φάσης της συγκεκριμένης τεχνικής αυξάνεται στις αγροτικές περιοχές με μικρές εδαφικές κλίσεις, ενώ μειώνεται σε ορεινές και αστικές περιοχές όπου επικρατεί αποσυσχέτιση σήματος επιφάνειας και όγκου αντίστοιχα.Το βασικό μέρος του ερευνητικού αυτού έργου, είναι η εφαρμογή τεχνικών επεξεργασίας χρονοσειρών συμβολογραμμάτων (PSI & SBAS) για την ακριβή χαρτογράφηση της επιφανειακής παραμόρφωσης στην ευρύτερη περιοχή του νομού Αττικής από το 1992 μέχρι το 2010. Έγινε επεξεργασία δεδομένων από πολλαπλές τροχιές, αποτυπώνοντας για πρώτη φορά τις LOS εδαφικές ταχύτητες που απεικονίζουν τα τοπικά μοτίβα παραμόρφωσης, με πρωτοφανή ακρίβεια και χωρική κάλυψη. Αυτές οι συμβολομετρικές παρατηρήσεις μπορούν να αποτελέσουν σημείο αναφοράς για μελλοντικές γεωδαιτικές έρευνες. Επιπλέον, η ύπαρξη προϊόντων από πολλαπλές τροχιές επιτρέπει την απομόνωση των κάθετων και οριζόντιων συνιστωσών των διανυσμάτων ταχύτητας. Τα αποτελέσματα της ανάλυσης χρονοσειρών δείχνουν ότι μια μεγάλη περιοχή που περιλαμβάνει το δήμο Κηφισιάς είχε υποστεί καθίζηση κατά την περίοδο 1992-1999 (~ 8 χιλ/έτος) ενώ ανυψώνεται από το 2002 (~ 4 χιλ/έτος) και έπειτα. Αυτό αποδίδεται σε δραστηριότητες εξόρυξης νερού που έλαβαν χώρα κατά την πρώτη περίοδο και σταμάτησε περί τα τέλη του 1995. Από τότε, η Κηφισιά βρίσκεται σε μια κατάσταση φυσικής επαναφοράς. Ακόμα πιο σημαντικά ευρήματα εντοπίζονται στο Θριάσιο Πεδίο όπου δεν διαφαίνονται κατακόρυφες παραμορφώσεις με αξιοσημείωτο μέγεθος πριν από το σεισμό του 1999 στην Αθήνα. Ωστόσο, αποκαλυπτικές είναι οι σαφείς ζώνες οριζόντιας μετατόπισης με αντίθετα πρόσημα που εντοπίζονται πλησίον του επίκεντρου του σεισμού.Τέλος, παρουσιάζεται η μελέτη της επιφανειακής παραμόρφωσης που συνδέεται με την πρόσφατη αναταραχή στο ηφαιστειακό συγκρότημα της Σαντορίνης, στο διάστημα Ιανουάριος 2011 με Φεβρουάριος 2012. Οι τεχνικές PSI και SBAS χρησιμοποιούνται εκ νέου για την παραγωγή πυκνών χαρτών παραμόρφωσης του εδάφους, και το πεδίο αυτό μετατόπισης αντιπαραβάλλεται με τις παρατηρήσεις GPS από δέκα σταθμούς απρόσκοπτης λήψης που έχουν εγκατασταθεί στη Σαντορίνη. Τα αποτελέσματα δείχνουν μια σαφή τάση ανύψωσης, έως 150 χιλ/έτος στην κατεύθυνση LOS, μειούμενου μέτρου ακτινωτά από το κέντρο της καλντέρας. Η παραμόρφωση που προκύπτει από τις παρατηρήσεις GPS και συμβολομετρίας μοντελοποιείται χρησιμοποιώντας μια πηγή τύπου Mogi που βρίσκεται βόρεια της νήσου Νέας Καμένης, σε βάθος μεταξύ 3,3 και 6,3 χιλιομέτρων και με ένα ρυθμό μεταβολής του όγκου της περί των 12 με 24 κυβικών μέτρων ανά έτος. Τα χαρακτηριστικά αυτά του μοντέλου, σε συνδυασμό με τη λήξη του επεισοδίου που διαπιστώνεται με χρήση GPS και συμβολομετρικών δεδομένων και εκτιμάται ότι συνέβη στα τέλη Φεβρουαρίου 2012, οδηγεί στο συμπέρασμα ότι η Σαντορίνη έχει εισέλθει σε μια νέα εποχή σχετικής γεωφυσικής ηρεμίας, μειώνοντας σημαντικά την πιθανότητα μιας επικείμενης ηφαιστειακής έκρηξης.