Foundation for Research and Technology - Hellas
Recent publications
To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology.
Stress conditions are manifested in different human body’s physiological processes and the human face. Facial expressions are modelled consistently through the Facial Action Coding System (FACS) using the facial Action Units (AU) parameters. This paper focuses on the automated recognition and analysis of AUs in videos as quantitative indices to discriminate between neutral and stress states. A novel deep learning pipeline for automatic recognition of facial action units is proposed, relying on two publicly available annotated facial datasets for training, the UNBC and the BOSPHORUS datasets. Two types of descriptive facial features are extracted from the input images, geometric features (non-rigid 3D facial deformations due to facial expressions) and appearance features (deep facial appearance features). The extracted facial features are then fed to deep fully connected layers that regress AU intensities and robustly perform AU classification. The proposed algorithm is applied to the SRD’15 stress dataset, which contains neutral and stress states related to four types of stressors. We present thorough experimental results and comparisons, which indicate that the proposed methodology yields particularly promising performance in terms of both AU detection and stress recognition accuracy. Furthermore, the AUs relevant to stress were experimentally identified, providing evidence that their intensity is significantly increased during stress, which leads to a more expressive human face as compared to neutral states.
Purpose Τhe study examined changes in hemodynamics and functional connectivity in patients with systemic lupus erythematosus (SLE) with or without neuropsychiatric manifestations. Methods Participants were 44 patients with neuropsychiatric SLE (NPSLE), 20 SLE patients without such manifestations (non-NPSLE), and 35 healthy controls. Resting-state functional MRI (rs-fMRI) was used to obtain whole-brain maps of (a) perfusion dynamics derived through time shift analysis (TSA), (b) regional functional connectivity (intrinsic connectivity contrast (ICC) coefficients), and (c) hemodynamic-connectivity coupling. Group differences were assessed through independent samples t-tests, and correlations of rs-fMRI indices with clinical variables and neuropsychological test scores were, also, computed. Results Compared to HC, NPSLE patients demonstrated intrinsic hypoconnectivity of anterior Default Mode Network (DMN) and hyperconnectivity of posterior DMN components. These changes were paralleled by elevated hemodynamic lag. In NPSLE, cognitive performance was positively related to higher intrinsic connectivity in these regions, and to higher connectivity-hemodynamic coupling in posterior DMN components. Uncoupling between hemodynamics and connectivity in the posterior DMN was associated with worse task performance. Non-NPSLE patients displayed hyperconnectivity in posterior DMN and sensorimotor regions paralleled by relatively increased hemodynamic lag. Conclusion Adaptation of regional brain function to hemodynamic changes in NPSLE may involve locally decreased or locally increased intrinsic connectivity (which can be beneficial for cognitive function). This process may also involve elevated coupling of hemodynamics with functional connectivity (beneficial for cognitive performance) or uncoupling, which may be detrimental for the cognitive skills of NPSLE patients.
Neurons are highly polarized cells with significantly long axonal and dendritic extensions that can reach distances up to hundreds of centimeters away from the cell bodies in higher vertebrates. Their successful formation, maintenance, and proper function highly depend on the coordination of intricate molecular networks that allow axons and dendrites to quickly process information, and respond to a continuous and diverse cascade of environmental stimuli, often without enough time for communication with the soma. Two seemingly unrelated processes, essential for these rapid responses, and thus neuronal homeostasis and plasticity, are local mRNA translation and cytoskeletal reorganization. The axonal cytoskeleton is characterized by high stability and great plasticity; two contradictory attributes that emerge from the powerful cytoskeletal rearrangement dynamics. Cytoskeletal reorganization is crucial during nervous system development and in adulthood, ensuring the establishment of proper neuronal shape and polarity, as well as regulating intracellular transport and synaptic functions. Local mRNA translation is another mechanism with a well-established role in the developing and adult nervous system. It is pivotal for axonal guidance and arborization, synaptic formation, and function and seems to be a key player in processes activated after neuronal damage. Perturbations in the regulatory pathways of local translation and cytoskeletal reorganization contribute to various pathologies with diverse clinical manifestations, ranging from intellectual disabilities (ID) to autism spectrum disorders (ASD) and schizophrenia (SCZ). Despite the fact that both processes are essential for the orchestration of pathways critical for proper axonal and dendritic function, the interplay between them remains elusive. Here we review our current knowledge on the molecular mechanisms and specific interaction networks that regulate and potentially coordinate these interconnected processes.
Two-dimensional iron chalcogenide intercalates display a remarkable correlation of the interlayer spacing with enhancement of the superconducting critical temperature (Tc). In this work, synchrotron X-ray absorption (XAS; at the Fe and Se K-edges) and emission (XES; at the Fe Κβ) spectroscopies allow one to discuss how the important rise of Tc (∼44 K) in the molecule-intercalated Lix(C5H5N)yFe2-zSe2 relates to the electronic and local structural changes felt by the inorganic host upon doping (x). XES shows that widely separated layers of edge-sharing FeSe4 tetrahedra carry low-spin moieties, with a local Fe magnetic moment slightly reduced compared to the parent β-Fe2-zSe2. Pre-edge XAS expresses the progressively reduced mixing of metal 3d-4p states upon lithiation. Doping-mediated local lattice modifications, probed by conventional Tc optimization measures (cf. the anion height and FeSe4 tetrahedra regularity), become less relevant when layers are spaced far away. On the basis of extended X-ray absorption fine structure, such distortions are compensated by a softer Fe network that relates to Fe-site vacancies, alleviating electron-lattice correlations and superconductivity. Density functional theory (DFT) guided modification of the isolated Fe2-zSe2 (z, vacant sites) planes, resembling the host layers, identify that Fe-site deficiency occurs at low energy cost, giving rise to stretched Fe sheets, in accordance with experiments. The robust high-Tc in Lix(C5H5N)yFe2-zSe2, arises from the interplay of electron-donating spacers and the iron selenide layer's tolerance to defect chemistry, a tool to favorably tune its Fermi surface properties.
Pediatric adrenocortical hyperplasias are rare; they usually present with Cushing syndrome (CS); of them, isolated micronodular adrenal disease and its variant, primary pigmented adrenocortical disease are the most commonly encountered. Most cases are due to defects in the cyclic AMP/protein kinase A (cAMP/PKA) pathway, although a few cases remain without an identified genetic defect. Another cause of adrenal hyperplasia in childhood is congenital adrenal hyperplasia, a group of autosomal recessive disorders that affect steroidogenic enzymes in the adrenal cortex. Clinical presentation varies and depends on the extent of the underlying enzymatic defect. The most common form is due to 21-hydroxylase deficiency; it accounts for more than 90% of the cases. In this article, we discuss the genetic etiology of adrenal hyperplasias in childhood.
Conference Title: 2 nd QuIESCENT (Quantifying the Indirect Effect: from Sources to Climate Effects of Natural and Transported aerosol in the Arctic) Workshop What: Atmospheric scientists, shared and discussed recent work to understand the complex interactions between aerosols, clouds, precipitation, radiation and dynamics at northern high latitudes, as well as recent and upcoming field campaigns to improve that understanding. When: 30 March – 1 April, 2022 Where: Tromsø, Norway
Development of insecticide resistance in insect populations is a major challenge to sustainable agriculture and food security worldwide. Buprofezin, one of the commonly used chitin synthesis inhibitors, has severely declined its control efficacy against the brown planthopper (BPH, Nilaparvata lugens), a devastating rice insect species. To date, however, mechanism of buprofezin resistance in target pests remains elusive. We conducted a long-term (25 years from 1996 to 2020) and large geographical scale (11 provinces and cities in China) resistance monitoring program for buprofezin in BPH, a notorious pest of rice crop in East and Southeast Asia. BPH rapidly developed resistance with > 1,000-fold resistance being detected in nearly all the field populations after 2015. Using the bulk segregant mapping method, we uncovered a novel mutation (G932C) in chs1 gene encoding chitin synthase 1 from a near isogeneic buprofezin-resistant (> 10,000-fold) strain harboring recessive, monogenic resistance. Using CRISPR/Cas9-based genome-modified Drosophila melanogaster possessing the same mutation as a model, we found that the G932C mutation was not only responsible for buprofezin resistance but also conferred a cross-resistance to cyromazine, an insect molting disruptor, on which the mode of action is largely unknown. Taken together, our study for the first time revealed the molecular mechanism conferring buprofezin resistance in BPH and implicated that cyromazine also targets chitin biosynthesis to confer its toxicity.
Aiming to develop more robust, mechanically advanced, Fused Filament Fabrication (FFF) ma-terials, High-Density Polyethylene (HDPE) nanocomposites were developed in the current re-search work. Titanium Dioxide (TiO2) was selected as filler to be incorporated into the HDPE matrix in concentration steps of 0.5, 2.5, 5, and 10 wt.%. 3D printing nanocomposite filaments were extruded in ~1.75 mm diameter and used to 3D print and test tensile and flexion specimens ac-cording to international standards. Reported results indicate that the filler contributes to in-creasing the mechanical strength of the virgin HDPE at certain filler and filler type concentrations; with the highest values reported to be 37.8% higher in tensile strength with HDPE/TiO2 10 wt.%. Morphological and thermal characterization was performed utilizing Scanning Electron Mi-croscopy (SEM), Raman, Thermogravimetric Analysis (TGA), and Differential Scanning Calo-rimetry (DSC), while the results were correlated with the available literature.
One of the main challenges in brain research is to link all aspects of brain function: on a cellular, systemic, and functional level. Multimodal neuroimaging methodology provides a continuously evolving platform. Being able to combine calcium imaging, optogenetics, electrophysiology, chemogenetics, and functional magnetic resonance imaging (fMRI) as part of the numerous efforts on brain functional mapping, we have a unique opportunity to better understand brain function. This review will focus on the developments in application of these tools within fMRI studies and highlight the challenges and choices neurosciences face when designing multimodal experiments.
In the current study, we propose a simple hydrothermal pathway to synthesize nano-structured Mg(OH)2 after application of thermal decomposition followed by hydration of commercial minerals based on hydromagnesite and huntite. The synthesis of nano-materials is performed without the use of any catalyst. The effect of decomposition temperature on the hydrothermal synthesis of Mg(OH)2 is extensively studied. It is shown that the morphology of resulting structures consists typically of particles ~200 nm in diameter and ~10 nm in thickness. Study of the structure at the molecular level designates the composition and supports the nano-sized characteristics of the produced materials. The associated thermal properties combined with the corresponding optical properties suggest that the material may be used as a flame retardant filler with enhanced transparency. In this concept, the flame retardancy of composite coatings containing the produced nano-sized Mg(OH)2 was examined in terms of limiting oxygen index (LOI), i.e., the minimum concentration of oxygen that just supports flaming combustion.
The Arctic is warming at more than twice the rate of the global average. This warming is influenced by clouds which modulate the solar and terrestrial radiative fluxes, and thus, determine the surface energy budget. However, the interactions among clouds, aerosols, and radiative fluxes in the Arctic are still poorly understood. To address these uncertainties, the Ny-Ålesund AeroSol Cloud ExperimeNT (NASCENT) study was conducted from September 2019 to August 2020 in Ny-Ålesund Svalbard. The campaign’s primary goal was to elucidate the life cycle of aerosols in the Arctic and to determine how they modulate cloud properties throughout the year. In-situ and remote sensing observations were taken on the ground at sea-level and at a mountaintop station, and with a tethered balloon system. An overview of the meteorological and the main aerosol seasonality encountered during the NASCENT year is introduced, followed by a presentation of first scientific highlights. In particular, we present new findings on aerosol physicochemical properties which also include molecular properties. Further, the role of cloud droplet activation and ice crystal nucleation in the formation and persistence of mixed-phase clouds, and the occurrence of secondary ice processes, are discussed and compared to the representation of cloud processes within the regional Weather Research and Forecasting model. The paper concludes with research questions that are to be addressed in upcoming NASCENT publications.
Physical processes working in parallel with digital ones have transformed the way we view systems and have led to the creation of applications that boost the quality of people’s lives, increase security as well as decrease production costs of goods. Critical to this evolution is the cost decrease in the components of such systems, among which are gas sensors. In this work, a custom-made Co3O4 gas sensing element is presented, which can potentially be used as part of a cyber-physical system (CPS) for O3 monitoring. To investigate its performance, a CPS is developed using low-cost, low-power micro-controller units (MCUs) and comparisons both with the laboratory equipment and commercial off-the-shelf (COTS) ozone sensors are provided. The experiments show that the Co3O4 sensor works at room temperature with low input voltage and low power consumption when used with the proposed MCUs. Moreover, an enhanced gas sensing performance against ozone is observed under low-pressure conditions due to the detection of low ozone concentrations (85.90 ppb) and good sensor response (113.1%) towards 1100 ppb O3. However, the drawbacks that need improvement relate to the kinetics of the charge carriers, which affect the response time and recovery behavior. The effect of humidity needs to be clarified in further works.
Particulate sulfate is one of the most important components in the atmosphere. The observation of rapid sulfate aerosol production during haze events provoked scientific interest in the multiphase oxidation of SO2 in aqueous aerosol particles. Diverse oxidation pathways can be enhanced or suppressed under different aerosol acidity levels and high ionic strength conditions of atmospheric aerosol. The importance of ionic strength to sulfate multiphase chemistry has been verified under laboratory conditions, though studies in the actual atmosphere are still limited. By utilizing online observations and developing an improved solute strength-dependent chemical thermodynamics and kinetics model (EF-T&K model, EF is the enhancement factor that represents the effect of ionic strength on an aerosol aqueous-phase reaction), we provided quantitative evidence that the H2O2 pathway was enhanced nearly 100 times and dominated sulfate formation for entire years (66%) in Tianjin (a northern city in China). TMI (oxygen catalyzed by transition-metal ions) (14%) and NO2 (14%) pathways got the second-highest contributions. Machine learning supported the result that aerosol sulfate production was more affected by the H2O2 pathway. The collaborative effects of atmospheric oxidants and SO2 on sulfate aerosol production were further investigated using the improved EF-T&K model. Our findings highlight the effectiveness of adopting target oxidant control as a new direction for sustainable mitigation of sulfate, given the already low SO2 concentrations in China.
The energy performance of the building stock and the resulting emissions of greenhouse gases must be taken seriously into account for securing a sustainable future. Smart windows can transform a building to reach the standards of the Net Zero Emissions by 2050 Scenario. Among the currently available technologies, the Photoelectrochromic Devices (PECDs) promise extended functionalities beyond that of thermal and glare control, making them ideal for future buildings. PECDs use solar energy to generate and store electrical energy, simultaneously modulating their optical and thermal properties. A state-of-the art PECD can achieve a high optical modulation (<70%) and a photoconversion efficiency up to 7%, with switching times ranging from a few seconds to several minutes. In general, the performance of the devices is affected not only by the adopted materials properties, but also by the architecture of the devices. Therefore, they have a potential for remarkable energy savings in heating, cooling and electric lighting and are expected to play a significant role in the realization of energy efficient buildings. This review article summarizes the developments in the field of PECDs, since their first appearance ca. 25 years ago, giving emphasis on specific material properties and mechanisms that affect optical performance and solar energy harvesting.
The production of either CO or CH4 via the hydrogenation of CO2 is amongst the most promising routes for CO2 utilization. However, kinetic barriers necessitate the use of a catalyst, with Ni/CeO2 being one of the most investigated systems. Nevertheless, surface chemistry fine-tuning via appropriate promotional routes can induce significant modifications on the solid-state properties of catalysts and in turn on their activity/selectivity. In the present work, we originally report on the outstanding selectivity alteration of Ni/CeO2 by ZnO doping. Specifically, Ni-based catalysts supported on ZnO, CeO2 nanorods or a mixed ZnO-CeO2 oxide were synthesized by a modified hydrothermal method and characterized by various physicochemical methods. Notable changes in the reaction pathway were demonstrated, as the presence of ZnO largely favored CO production at T < 450 oC for both Ni/ZnO and Ni/ZnO-CeO2, whereas Ni/CeO2 was completely selective to CH4. These findings were interpreted on the basis of ZnO-induced inhibitory effects on key activity/selectivity descriptors like the redox and basic properties, as well as on the adsorption affinity of CO species, which are considered as intermediate species for CO2 methanation.
Understanding the diverse energy exchange within the city's boundaries would enable better design of future urban planning and environmental policies and update current ones. More importantly, it would facilitate the establishment of smart cities, where human health and wellbeing is a top priority. In this regard, several energy balance models have been proposed for urban areas. Only recently, remote sensing images have been extensively used in these areas, but are still difficult to implement due to the large inputs' sources and types required as well as their complexity. Their accuracy can also be improved. Thus, in this paper, the Surface Energy Balance Model for Urban areas (SEBU) is proposed based on the 100-m Landsat images. It uses other sources as well such as Sentinel-1, MODIS and ERA5. SEBU is based on the innovative hot/cold pixels approach widely known in the agricultural-based models, but also includes several dynamic internal calibrations. It generates monthly turbulent sensible (Qh) and latent (Qe) heat values over a 100-m spatial resolution. When applied over seven different regions (i.e., Denver, New Hampshire, Basel, Heraklion, Singapore, Phoenix and Vancouver) in four contrasting climates (i.e., cold, arid, warm and equatorial) and when compared to local flux tower measurements, absolute mean error varied between 6.13 W m⁻² month⁻¹ for Qe and 14.46 W m⁻² month⁻¹ for Qh. More importantly, the novelty of SEBU does not lie only in providing reliable accuracy and full reliance on the remote sensing database, but also on its open-source nature and easy-accessibility over the Google Earth Engine (GEE) platform. Thus, SEBU has the potential to be scalable using the massive power and huge database found in GEE, where users need to specify the required date only. This would certainly assist researchers to access urban cilmate information in a timely manner. Policy makers and even local dwellers would then benefit from their findings. Furthermore, SEBU can be improved to accommodate current and future needs of its users, while ultimately, enhancing the urban surface energy models and related science.
Background and objective: The cognitive workload is an important component in performance psychology, ergonomics, and human factors. Publicly available datasets are scarce, making it difficult to establish new approaches and comparative studies. In this work, COLET-COgnitive workLoad estimation based on Eye-Tracking dataset is presented. Methods: Forty-seven (47) individuals' eye movements were monitored as they solved puzzles involving visual search activities of varying complexity and duration. The participants' cognitive workload level was evaluated with the subjective test of NASA-TLX and this score is used as an annotation of the activity. Extensive data analysis was performed in order to derive eye and gaze features from low-level eye recorded metrics, and a range of machine learning models were evaluated and tested regarding the estimation of the cognitive workload level. Results: The activities induced four different levels of cognitive workload. Multi tasking and time pressure have induced a higher level of cognitive workload than the one induced by single tasking and absence of time pressure. Multi tasking had a significant effect on 17 eye features while time pressure had a significant effect on 7 eye features. Both binary and multi-class identification attempts were performed by testing a variety of well-known classifiers, resulting in encouraging results towards cognitive workload levels estimation, with up to 88% correct predictions between low and high cognitive workload. Conclusions: Machine learning analysis demonstrated potential in discriminating cognitive workload levels using only eye-tracking characteristics. The proposed dataset includes a much higher sample size and a wider spectrum of eye and gaze metrics than other similar datasets, allowing for the examination of their relations with various cognitive states.
The subclonal evolution of breast cancer is closely related to epigenetic regulation. Secreted microRNAs (miRNAs) spotted within the complex extracellular matrix (ECM) network are responsible for post-transcriptional and functional alterations to matrix constituents affecting vital cell processes for the initiation of metastasis, such as cell proliferation, migration, and invasion. The focus of this chapter is to highlight how the two-way relationship between miRNAs and ECM affects breast cancer pathogenesis and progression. Future investigation on the epigenetic regulation of matrix biomolecules focusing on miRNAs will improve current approaches to target tumor microenvironment and may expand our perspective in the mechanistic aspects of this pathology, contributing to a more effective breast cancer patient management.
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830 members
Panagiotis Siozos
  • Institute of Electronic Structure and Laser (IESL)
Nikos Papadopoulos
  • Geophysical - Satellite Remote Sensing and Archaeo-environment Laboratory (GIS)
Ioanna Ntaikou
  • Institute of Chemical Engineering Sciences (ICE-HT)
George Kenanakis
  • Institute of Electronic Structure and Laser (IESL)
George Potamias
  • Institute of Computer Science, Bio-Informatics Laboratory (BIL)
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