Article
Biochemistry & Molecular Biology
Petroula Laiou, Andrea Biondi, Elisa Bruno, Pedro F. Viana, Joel S. Winston, Zulqarnain Rashid, Yatharth Ranjan, Pauline Conde, Callum Stewart, Shaoxiong Sun, Yuezhou Zhang, Amos Folarin, Richard J. B. Dobson, Andreas Schulze-Bonhage, Matthias Duempelmann, Mark P. Richardson
Summary: This study used brain network metrics to characterize the temporal evolution of epileptic functional networks prior to seizures. The findings show that these metrics vary across days and exhibit a circadian periodicity. Additionally, the distribution of strength variance in the days before seizure occurrence is significantly different compared to previous days. These results suggest that brain network metrics could potentially be used to characterize brain network changes before seizures and contribute to the development of seizure warning systems.
Article
Environmental Sciences
Patrick R. Pata, Aletta T. Yniguez
Summary: The marine habitats in the Philippines are highly biodiverse but only a small percentage of its seas are designated as marine protected areas (MPAs). Larval dispersal connectivity of reefs play a significant role in regional resilience against disturbances. Existing MPAs do not fully capture regional connectivity patterns, highlighting the need to expand protected areas to better protect national-scale connectivity and meet global conservation objectives.
FRONTIERS IN MARINE SCIENCE
(2021)
Article
Neurosciences
Farzad Farahani, Waldemar Karwowski, Mark D'Esposito, Richard F. Betzel, Pamela K. Douglas, Anna Maria Sobczak, Bartosz Bohaterewicz, Tadeusz Marek, Magdalena Fafrowicz
Summary: Circadian rhythms have an impact on brain function, particularly on functional connectivity patterns and local/regional changes. Time of day affects areas associated with somatomotor, attention, frontoparietal, and default networks. The somatomotor, ventral attention, and visual networks are highly connected areas that show changes between morning and evening sessions.
Article
Mathematics
Jiakang Xu, Wolfgang Mayer, Hongyu Zhang, Keqing He, Zaiwen Feng
Summary: This paper proposes a novel method for semantically annotating structured data sources using machine learning, graph matching, and modified frequent subgraph mining to improve the automatic inference of relationships between attributes. Knowledge graph is used as prior knowledge. The evaluation shows that this method outperforms two state-of-the-art solutions in complex cases where only a few semantic models are known.
Article
Mathematics
Seonghun Kim, Seockhun Bae, Yinhua Piao, Kyuri Jo
Summary: Integrating gene expression data and biological networks into the analysis framework for drug response prediction can improve prediction accuracy. DrugGCN successfully achieves this goal through graph convolutional network technology and demonstrates its success in biological data.
Article
Anatomy & Morphology
W. Tyler Ketchabaw, Andrew T. DeMarco, Sachi Paul, Elizabeth Dvorak, Candace van der Stelt, Peter E. Turkeltaub
Summary: Localization of language function in the brain has shifted towards a distributed network, and the semantic system plays a crucial role in integrating information from multiple cortical regions.
BRAIN STRUCTURE & FUNCTION
(2022)
Article
Engineering, Environmental
Yike Shen, Marianthi-Anna Kioumourtzoglou, Haotian Wu, Pantel Vokonas, Avron Spiro III, Ana Navas-Acien, Andrea A. Baccarelli, Feng Gao
Summary: Contemporary environmental health sciences rely on large-scale longitudinal studies to uncover the impact of environmental exposures and behavior factors on disease risk and underlying mechanisms. However, the publications from these studies are often not well-organized or summarized, hampering knowledge dissemination. To address this issue, a Cohort Network is proposed, utilizing a multilayer knowledge graph approach to extract and visualize the connections between exposures, outcomes, and publications. The Cohort Network was applied to 121 peer-reviewed papers from the Veterans Affairs Normative Aging Study, revealing important associations and potential mediators in environmental health research. This approach facilitates knowledge-driven discovery and dissemination in cohort studies with rich information on environmental exposures and health outcomes.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Zikang Wang, Linjing Li, Daniel Zeng, Xiaofei Wu
Summary: This paper focuses on mining prior knowledge from knowledge graphs based on counterfactuals and using it to enhance reasoning models. Experiments show that the extracted prior knowledge is effective in improving the performance of multi-hop reasoning models, and it also has the advantage of being path-length independent.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Plant Sciences
Lin Jin, Youngkeun Song
Summary: This study aims to analyze the changes in urban forest landscape connectivity and identify the most important forest patches for connectivity. A case study in Seoul, South Korea showed that forest landscape connectivity significantly increased from 2001 to 2018. The top ten forest patches with the highest importance for connectivity remained stable in all four years, and these patches were generally large in size. Based on these findings, the study also examined the impact of afforestation of small patches on overall connectivity and identified the 50 most important patches for afforestation as a guide for effective ecological afforestation planning.
URBAN FORESTRY & URBAN GREENING
(2023)
Article
Computer Science, Artificial Intelligence
Mei Yu, Qianyu Zhang, Jian Yu, Mankun Zhao, Xuewei Li, Di Jin, Ming Yang, Ruiguo Yu
Summary: Knowledge graphs are semantic networks designed to describe real-world facts. Existing graphs are incomplete, hence the need for knowledge graph completion. However, current graph completion models have limitations in distinguishing relations and aggregating multi-perspective features.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Neurosciences
Wenjing Luo, Abigail S. Greene, R. Todd Constable
Summary: Studies on the organization of the brain using graph theory methods have shown that functional nodes in the brain reconfigure with different brain states. The influence of voxel-level changes leading to node reconfigurations should be considered when investigating connectivity contrasts between brain states and/or groups.
Article
Materials Science, Multidisciplinary
H. T. Vo, P. Pinney, M. M. Schneider, M. Arul Kumar, R. J. Mccabe, C. N. Tome, L. Capolungo
Summary: The advent of three-dimensional analysis techniques has been crucial in various fields, but the understanding of microstructure-property linkages in materials science is still fragmented. This study introduces a graph-theory-based framework and network science approach to automatically extract and analyze three-dimensional microstructures, providing new insights into the ability of metals to withstand severe microstructure changes without failing.
MATERIALS TODAY ADVANCES
(2023)
Article
Geography, Physical
Matthew Hiatt, Elisabeth A. Addink, Maarten G. Kleinhans
Summary: This article uses graph theory to quantify the connectivity of multidirectional estuarine channel networks, finding higher levels of structural connectivity in larger networks with looping structures. Real-world networks contain signatures of both mutually evasive flood and ebb channels, as well as branching structures, with flow direction influencing dynamical connectivity.
EARTH SURFACE PROCESSES AND LANDFORMS
(2022)
Article
Anesthesiology
Camille Fauchon, David Meunier, Anton Rogachov, Kasey S. Hemington, Joshua C. Cheng, Rachael L. Bosma, Natalie R. Osborne, Junseok A. Kim, Peter Shih-Ping Hung, Robert D. Inman, Karen D. Davis
Summary: The study identified sex-specific brain network characteristics in both healthy individuals and those with chronic pain. People with chronic pain displayed higher cross-network connectivity, with females showing higher functional segregation in certain brain regions and lower connectivity in certain modules compared to males. Classification models based on nodal graph metrics could accurately classify an individual's sex and chronic pain status.
Article
Computer Science, Interdisciplinary Applications
Tianfang Zhu, Gang Yao, Dongli Hu, Chuangchuang Xie, Pengcheng Li, Xiaoquan Yang, Hui Gong, Qingming Luo, Anan Li
Summary: This study proposes MorphoGNN, a single neuron morphological embedding based on a graph neural network. By considering the point-level structure information of reconstructed nerve fibers, MorphoGNN captures the lower-dimensional representation of a single neuron and demonstrates cutting-edge performance in tasks such as neuron classification, retrieval, reconstruction quality classification, and neuron clustering.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Economics
Niki-Artemis Spyridaki, Vassilis Stavrakas, Yiannis Dendramis, Alexandros Flamos
Letter
Hematology
Karolina Akinosoglou, Foteini Paliogianni, Alexandros Spyridonidis, Argiris Symeonidis, Leonidas G. Alexopoulos, Dimitrios Ziazias, Alexandra Kouraklis-Symeonidis, Markos Marangos, Charalambos Gogos
BRITISH JOURNAL OF HAEMATOLOGY
(2021)
Review
Biochemistry & Molecular Biology
Laura Baumgartner, Karin Wuertz-Kozak, Christine L. Le Maitre, Francis Wignall, Stephen M. Richardson, Judith Hoyland, Carlos Ruiz Wills, Miguel A. Gonzalez Ballester, Michael Neidlin, Leonidas G. Alexopoulos, Jerome Noailly
Summary: Degenerative changes in the intervertebral disc are a major risk factor for low back pain, with accelerated progression in some individuals as they age. Understanding the disease requires identifying key regulatory processes at various levels, from cells to organs. Experimental research and computational modeling have contributed significantly to our understanding of cell signaling pathways and complex interactions within the intervertebral disc.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Multidisciplinary Sciences
Christos Fotis, Nikolaos Meimetis, Nikos Tsolakos, Marianna Politou, Karolina Akinosoglou, Vaia Pliaka, Angeliki Minia, Evangelos Terpos, Ioannis P. Trougakos, Andreas Mentis, Markos Marangos, George Panayiotakopoulos, Meletios A. Dimopoulos, Charalampos Gogos, Alexandros Spyridonidis, Leonidas G. Alexopoulos
Summary: A Luminex-based multiplex immunoassay was developed to detect antibodies against SARS-CoV-2 antigens in Greek blood donors, revealing the influence of antigen type and cut-off values on seroprevalence estimates. A multi-antigen approach showed reduced impact of cut-off values, leading to more accurate seroprevalence estimation.
SCIENTIFIC REPORTS
(2021)
Article
Oncology
Laura Gadeyne, Yannick Van Herck, Giorgia Milli, Zeynep Kalender Atak, Maddalena Maria Bolognesi, Jasper Wouters, Lukas Marcelis, Angeliki Minia, Vaia Pliaka, Jan Roznac, Leonidas G. Alexopoulos, Giorgio Cattoretti, Oliver Bechter, Joost Van Den Oord, Frederik De Smet, Asier Antoranz, Francesca Maria Bosisio
Summary: The study revealed that HLA-DR positive areas in melanoma attract anti-tumor immune cell infiltration by creating a dystrophic germinal center-like microenvironment where enhanced antigen presentation lead to exhausted immune environment. This could actually represent a fertile ground for better efficacy of anti-PD-1 inhibitors due to simultaneous higher levels of PD-1 in immune cells and PD-L1 in HLA-DR positive melanoma cells.
FRONTIERS IN ONCOLOGY
(2021)
Article
Biochemistry & Molecular Biology
Nicole Rufo, Dimitris Korovesis, Sofie Van Eygen, Rita Derua, Abhishek D. Garg, Francesca Finotello, Monica Vara-Perez, Jan Rozanc, Michael Dewaele, Peter A. de Witte, Leonidas G. Alexopoulos, Sophie Janssens, Lasse Sinkkonen, Thomas Sauter, Steven H. L. Verhelst, Patrizia Agostinis
Summary: Immunogenic therapies engaging the unfolded protein response (UPR) following endoplasmic reticulum (ER) stress stimulate immunomodulatory/proinflammatory factors by stressed cancer cells, with NF-kappa B/AP-1 inflammatory stress response being a key mechanism. However, the anti-inflammatory effect of IRE1 alpha kinase inhibitor KIRA6 can impact inflammation responses, urging caution in interpreting its action.
CELL DEATH AND DIFFERENTIATION
(2022)
Article
Thermodynamics
Diana Sasser, Hannes Gaschnig, Andrzej Ceglarz, Vassilis Stavrakas, Alexandros Flamos, Johan Lilliestam
Summary: The study shows that user needs and ongoing improvements of energy system models are largely aligned, suggesting that future models are likely to be better than current models. However, mismatches exist between model improvements perceived by modellers and the actual needs of users, especially in the modelling of social, behavioural and political aspects, model complexity and understandability, as well as communication of model results.
Article
Urology & Nephrology
Insa M. Schmidt, Mia R. Colona, Bryan R. Kestenbaum, Leonidas G. Alexopoulos, Ragnar Palsson, Anand Srivastava, Jing Liu, Isaac E. Stillman, Helmut G. Rennke, Vishal S. Vaidya, Haojia Wu, Benjamin D. Humphreys, Sushrut S. Waikar
Summary: Analysis of gene expression profiles and the development of Luminex-based assays were used to identify promising non-invasive biomarkers of kidney fibrosis. CDH11, SMOC2, and PEDF were found to be associated with the severity of interstitial fibrosis and tubular atrophy, as well as progression to end-stage kidney disease in independent cohorts of chronic kidney disease patients.
KIDNEY INTERNATIONAL
(2021)
Article
Genetics & Heredity
Marti Bernardo-Faura, Melanie Rinas, Jakob Wirbel, Inna Pertsovskaya, Vicky Pliaka, Dimitris E. Messinis, Gemma Vila, Theodore Sakellaropoulos, Wolfgang Faigle, Pernilla Stridh, Janina R. Behrens, Tomas Olsson, Roland Martin, Friedemann Paul, Leonidas G. Alexopoulos, Pablo Villoslada, Julio Saez-Rodriguez
Summary: This study used a network-based modeling approach to uncover differences in signaling activation between healthy individuals and multiple sclerosis patients, aiming to identify targets for combination therapy. Through experimental validation, several combinations were predicted to revert signaling to a healthy state, with one combination validated in an animal model of MS.
Article
Pharmacology & Pharmacy
Nikoleta Karampetsou, Aspasia Tzani, Ilias P. Doulamis, Evanthia Bletsa, Aggeliki Minia, Vaia Pliaka, Nikos Tsolakos, Evangelos Oikonomou, Dimitris Tousoulis, Konstantinos Kontzoglou, Leonidas G. Alexopoulos, Despoina N. Perrea, Paulos Patapis, Ioannis A. Chloroyiannis
Summary: This study explored the regional differences in adipose stores surrounding diseased and non-diseased segments of coronary arteries in patients with advanced CAD. The researchers found that certain cytokines were significantly increased in the diseased segment of epicardial adipose tissue (EAT), and adipocyte-derived TNF-alpha played a prominent role in local inflammation. However, no significant alterations were observed in the circulating levels of these cytokines with respect to CAD-related comorbidities.
CURRENT VASCULAR PHARMACOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Christiana Magkrioti, Georgia Antonopoulou, Dionysios Fanidis, Vaia Pliaka, Theodore Sakellaropoulos, Leonidas G. Alexopoulos, Christoph Ullmer, Vassilis Aidinis
Summary: This study investigated the response of human proximal tubular epithelial cells to LPA and other pathological stimuli, identifying signaling pathways and inflammatory factors that may play a role in the pathogenesis of CKD.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Biochemistry & Molecular Biology
Aphrodite Daskalopoulou, Sotiria G. Giotaki, Konstantina Toli, Angeliki Minia, Vaia Pliaka, Leonidas G. Alexopoulos, Gerasimos Deftereos, Konstantinos Iliodromitis, Dimitrios Dimitroulis, Gerasimos Siasos, Christos Verikokos, Dimitrios Iliopoulos
Summary: This study aimed to identify potential biomarkers for ascending thoracic aneurysm (ATAA) using targeted proteomic analysis. CCL5, HBD1, and ICAM1 were found to be promising biomarkers with satisfying sensitivity and specificity, which could be helpful in the diagnosis and follow-up of ATAA patients. Further studies are warranted to investigate the role of these biomarkers in the pathogenesis of ATAA.
Article
Economics
Dimitra Tzani, Danai Sofia Exintaveloni, Vassilis Stavrakas, Alexandros Flamos
Summary: The European Union (EU) is making slow progress in energy efficiency and needs to adopt various policy measures, such as establishing demanding energy performance standards, valuing energy efficiency as a resource, and promoting metered savings methodologies, to support the deployment of Pay-for-Performance (P4P) programmes.
Article
Environmental Studies
Diana Suesser, Andrzej Ceglarz, Hannes Gaschnig, Vassilis Stavrakas, Alexandros Flamos, George Giannakidis, Johan Lilliestam
Summary: Energy models are increasingly used to support energy policymaking, with models impacting policymaking through assessing impacts and supporting target setting. Policymakers also influence models and modellers by affecting data and assumptions, study scope, and decisions on how modelling results are used.
ENERGY RESEARCH & SOCIAL SCIENCE
(2021)
Article
Biochemistry & Molecular Biology
C. Fotis, N. Meimetis, A. Sardis, L. G. Alexopoulos
Summary: In this study, a deep learning model was developed to represent compound structures as graphs and link them to their biological effects with high precision. By utilizing deep ensembles to estimate uncertainty, reliable and accurate predictions were provided for chemical structures that are very different from the training set. The model was also used to infer important substructures and affected signaling pathways of FDA-approved anticancer drugs.