Showing papers by "Hellenic Military Academy published in 2021"

Nanotechnology and artificial intelligence to enable sustainable and precision agriculture.

Abstract: Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, in which farmers respond in real time to changes in crop growth with nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery, nutrient capture and long-term impacts on soil microbial communities will enable design of nanoscale agrochemicals that combine optimal safety and functionality profiles.

Advances in de Novo Drug Design: From Conventional to Machine Learning Methods.

Abstract: De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development

Chemotherapy-Induced Brain Effects in Small-Cell Lung Cancer Patients: A Multimodal MRI Study.

Abstract: The golden standard of treating Small Cell Lung Cancer (SCLC) entails application of platinum-based chemotherapy, is often accompanied by Prophylactic Cranial Irradiation (PCI), which have been linked to neurotoxic side-effects in cognitive functions. The related existing neuroimaging research mainly focuses on the effect of PCI treatment in life quality and expectancy, while little is known regarding the distinct adverse effects of chemotherapy. In this context, a multimodal MRI analysis based on structural and functional brain data is proposed in order to evaluate chemotherapy-specific effects on SCLC patients. Data from 20 patients (after chemotherapy and before PCI) and 14 healthy controls who underwent structural MRI, DTI and resting state fMRI were selected in this study. From a structural aspect, the proposed analysis included volumetry and thickness measurements on structural MRI data for assessing gray matter dissimilarities, as well as deterministic tractography and Tract-Based Spatial Statistics (TBSS) on DTI data, aiming to investigate potential white matter abnormalities. Functional data were also processed on the basis of connectivity analysis, evaluating brain network parameters to identify potential manifestation of functional inconsistencies. By comparing patients to healthy controls, the obtained results revealed statistically significant differences, with the patients' brains presenting reduced volumetry/thickness and fractional anisotropy values, accompanied by prominent differences in functional connectivity measurements. All above mentioned findings were observed in patients that underwent chemotherapy.

In Silico Identification and Evaluation of Natural Products as Potential Tumor Necrosis Factor Function Inhibitors Using Advanced Enalos Asclepios KNIME Nodes

Abstract: Tumor necrosis factor (TNF) is a regulator of several chronic inflammatory diseases, such as rheumatoid arthritis. Although anti-TNF biologics have been used in clinic, they render several drawbacks, such as patients’ progressive immunodeficiency and loss of response, high cost, and intravenous administration. In order to find new potential anti-TNF small molecule inhibitors, we employed an in silico approach, aiming to find natural products, analogs of Ampelopsin H, a compound that blocks the formation of TNF active trimer. Two out of nine commercially available compounds tested, Nepalensinol B and Miyabenol A, efficiently reduced TNF-induced cytotoxicity in L929 cells and production of chemokines in mice joints’ synovial fibroblasts, while Nepalensinol B also abolished TNF-TNFR1 binding in non-toxic concentrations. The binding mode of the compounds was further investigated by molecular dynamics and free energy calculation studies, using and advancing the Enalos Asclepios pipeline. Conclusively, we propose that Nepalensinol B, characterized by the lowest free energy of binding and by a higher number of hydrogen bonds with TNF, qualifies as a potential lead compound for TNF inhibitors’ drug development. Finally, the upgraded Enalos Asclepios pipeline can be used for improved identification of new therapeutics against TNF-mediated chronic inflammatory diseases, providing state-of-the-art insight on their binding mode.

Discursive constructions of populism in opinion-based journalism: A comparative European study

Abstract: This study offers a contribution to current research on populism and the media by exploring the attribution of populism as a discursive label in editorials and opinion-based articles from France, Greece, Sweden and the UK. Taking a corpus-assisted, discourse analytic approach, we undertake a comparative, empirical analysis of the use of the terms ‘populism/populist’, focusing on the attribution of populist characteristics to political actors, parties and practices, and how these function in producing journalistic stance in relation to political populism as a phenomenon in 2017. We examine the patterns and variations in the range of salient semantic fields and metaphorically evaluative language identified across the corpus, and argue that there is strong evidence for the existence of a shared meta-language in relation to the conceptualization of populism and constructions of stance towards it, which nevertheless serves as a flexible resource for journalistic positioning in different socio-political contexts.

Two-compartment stochastic single vehicle routing problems with simultaneous pickups and deliveries from N ordered customers

Abstract: We consider a vehicle with two compartments that starts its route from a depot and visits N ordered customers in order to deliver new (or fresh or useful) products and to collect old (or expired or...

Bibliometrics EEG Metrics Associations and Connections Between Military Medicine and the Differentiate Post Traumatic Stress Disorder (PTSD)

Abstract: In this paper we investigate the bibliometric association and connection between Electroencephalography (EEG) metrics of human brain and connections between military medicine and the post traumatic stress disorder (PTSD). In EEG metrics included various metrics used from scientists in order to map the brain activity. Inrecent years there has been an increasingly amount of data connect EEG metrics with PTSD and military medicine. Due to breakthroughs in biology and bioinformatics, more and more data are stored in various large databases as biomedical databases. In recent years, biomedical information has become the center of research, and its data volume has continued to grow. Therefore, obtaining effective information from scientists has become increasingly challenging. As a new scientific field of bioinformatics, new tools and applications are needed to extract important scientific data based on experimental results and information provided by papers and journals. In this paper we are going to investigate methods based in acustom made IT system, more specifically a programmable in Python toolin order to find connections between the differentiate post traumatic stress disorder and the brain operation and signaling. This IT system could become a useful tool against the struggle of scientists and medicalprofessionalsin the near future.

Deterministic Prediction Theory

Abstract: We give a general method for predicting spatio-temporal regions with “strange” systemic occurrences. To do so, we consider systemic indices and their measurements into the under consideration fixed spatio-temporal region. Given a set of preselected future points, the magnitude of the (Euclidean or not) distance between the surface of these systemic indices and a parametrized surface that interpolates or passes very close to the points of systemic measurements and given preselected vector values may be viewed as a measure for assessing the appearance of peculiar systemic incidents over the region under consideration; so, depending on these preselected points, we provide a general algorithmic framework for predicting spatio-temporal regions into which crucial systemic events are expected.

The human GPCR signal transduction network

Abstract: The eukaryotic cell surface G protein-coupled receptors (GPCRs) interact with a wide spectrum of ligands. The intracellular transmission of the extracellular signal is mediated by the selective coupling of GPCRs to G proteins, which, in turn, activate downstream effectors. GPCRs are of paramount pharmacological importance, with approximately 40% of all commercial drugs targeting these proteins. Herein, we have made an effort to unravel the molecular mechanisms underlying the GPCR-mediated signaling pathway and the way this pathway is associated with diseases. Network-based approaches were utilized to delineate the GPCR pathway, incorporating data from gene expression profiles across eleven healthy tissues and disease–gene associations from three diverse resources. The associations between the tissue-specific expression profiles of the disease-related genes along with the relative risk of disease development were further investigated. In the GPCR-activated pathway, the signal was found to be amplified at the successive steps of the pathway so that the effector molecules are highly expressed compared to ligands. This amplification effect was more pronounced when the respective genes encoding the particular proteins were associated with diseases. It was also found that co-expressed genes, corresponding to interacting molecules in affected tissues, may constitute powerful predictive markers for disease development. A disease risk prediction model based on tissue-specific expression profiles of the disease-associated genes was also generated. These findings could be applied to clinical settings.