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322 results on '"Kuznetsov,Dmitry"'

1. A large collection of bioinformatics question-query pairs over federated knowledge graphs: methodology and applications

Author

Bolleman, Jerven, Emonet, Vincent, Altenhoff, Adrian, Bairoch, Amos, Blatter, Marie-Claude, Bridge, Alan, Duvaud, Severine, Gasteiger, Elisabeth, Kuznetsov, Dmitry, Moretti, Sebastien, Michel, Pierre-Andre, Morgat, Anne, Pagni, Marco, Redaschi, Nicole, Zahn-Zabal, Monique, de Farias, Tarcisio Mendes, and Sima, Ana Claudia

Subjects
Computer Science - Databases, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

Background. In the last decades, several life science resources have structured data using the same framework and made these accessible using the same query language to facilitate interoperability. Knowledge graphs have seen increased adoption in bioinformatics due to their advantages for representing data in a generic graph format. For example, yummydata.org catalogs more than 60 knowledge graphs accessible through SPARQL, a technical query language. Although SPARQL allows powerful, expressive queries, even across physically distributed knowledge graphs, formulating such queries is a challenge for most users. Therefore, to guide users in retrieving the relevant data, many of these resources provide representative examples. These examples can also be an important source of information for machine learning, if a sufficiently large number of examples are provided and published in a common, machine-readable and standardized format across different resources. Findings. We introduce a large collection of human-written natural language questions and their corresponding SPARQL queries over federated bioinformatics knowledge graphs (KGs) collected for several years across different research groups at the SIB Swiss Institute of Bioinformatics. The collection comprises more than 1000 example questions and queries, including 65 federated queries. We propose a methodology to uniformly represent the examples with minimal metadata, based on existing standards. Furthermore, we introduce an extensive set of open-source applications, including query graph visualizations and smart query editors, easily reusable by KG maintainers who adopt the proposed methodology. Conclusions. We encourage the community to adopt and extend the proposed methodology, towards richer KG metadata and improved Semantic Web services.

Published
2024

4. Identification of biomarkers for glycaemic deterioration in type 2 diabetes

Author

Slieker, Roderick C., Donnelly, Louise A., Akalestou, Elina, Lopez-Noriega, Livia, Melhem, Rana, Güneş, Ayşim, Abou Azar, Frederic, Efanov, Alexander, Georgiadou, Eleni, Muniangi-Muhitu, Hermine, Sheikh, Mahsa, Giordano, Giuseppe N., Åkerlund, Mikael, Ahlqvist, Emma, Ali, Ashfaq, Banasik, Karina, Brunak, Søren, Barovic, Marko, Bouland, Gerard A., Burdet, Frédéric, Canouil, Mickaël, Dragan, Iulian, Elders, Petra J. M., Fernandez, Celine, Festa, Andreas, Fitipaldi, Hugo, Froguel, Phillippe, Gudmundsdottir, Valborg, Gudnason, Vilmundur, Gerl, Mathias J., van der Heijden, Amber A., Jennings, Lori L., Hansen, Michael K., Kim, Min, Leclerc, Isabelle, Klose, Christian, Kuznetsov, Dmitry, Mansour Aly, Dina, Mehl, Florence, Marek, Diana, Melander, Olle, Niknejad, Anne, Ottosson, Filip, Pavo, Imre, Duffin, Kevin, Syed, Samreen K., Shaw, Janice L., Cabrera, Over, Pullen, Timothy J., Simons, Kai, Solimena, Michele, Suvitaival, Tommi, Wretlind, Asger, Rossing, Peter, Lyssenko, Valeriya, Legido Quigley, Cristina, Groop, Leif, Thorens, Bernard, Franks, Paul W., Lim, Gareth E., Estall, Jennifer, Ibberson, Mark, Beulens, Joline W. J., ’t Hart, Leen M, Pearson, Ewan R., and Rutter, Guy A.

Published
2023
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6. MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers

Author

Song, Wei, Li, Xuezixiang, Afroz, Sadia, Garg, Deepali, Kuznetsov, Dmitry, and Yin, Heng

Subjects
Computer Science - Cryptography and Security
Abstract

Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs). Previous works have shown that ML malware classifiers are fragile to the white-box adversarial attacks. However, ML models used in commercial antivirus products are usually not available to attackers and only return hard classification labels. Therefore, it is more practical to evaluate the robustness of ML models and real-world AVs in a pure black-box manner. We propose a black-box Reinforcement Learning (RL) based framework to generate AEs for PE malware classifiers and AV engines. It regards the adversarial attack problem as a multi-armed bandit problem, which finds an optimal balance between exploiting the successful patterns and exploring more varieties. Compared to other frameworks, our improvements lie in three points. 1) Limiting the exploration space by modeling the generation process as a stateless process to avoid combination explosions. 2) Due to the critical role of payload in AE generation, we design to reuse the successful payload in modeling. 3) Minimizing the changes on AE samples to correctly assign the rewards in RL learning. It also helps identify the root cause of evasions. As a result, our framework has much higher black-box evasion rates than other off-the-shelf frameworks. Results show it has over 74\%--97\% evasion rate for two state-of-the-art ML detectors and over 32\%--48\% evasion rate for commercial AVs in a pure black-box setting. We also demonstrate that the transferability of adversarial attacks among ML-based classifiers is higher than the attack transferability between purely ML-based and commercial AVs.

Published
2020

9. Incorporating heterogeneous sampling probabilities in continuous phylogeographic inference - Application to H5N1 spread in the Mekong region.

Author

Dellicour, Simon, Lemey, Philippe, Artois, Jean, Lam, Tommy T, Fusaro, Alice, Monne, Isabella, Cattoli, Giovanni, Kuznetsov, Dmitry, Xenarios, Ioannis, Dauphin, Gwenaelle, Kalpravidh, Wantanee, Von Dobschuetz, Sophie, Claes, Filip, Newman, Scott H, Suchard, Marc A, Baele, Guy, and Gilbert, Marius

Subjects
Emerging Infectious Diseases, Animals, Disease Outbreaks, Influenza A Virus, H5N1 Subtype, Influenza in Birds, Phylogeny, Phylogeography, Probability, Mathematical Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

MotivationThe potentially low precision associated with the geographic origin of sampled sequences represents an important limitation for spatially explicit (i.e. continuous) phylogeographic inference of fast-evolving pathogens such as RNA viruses. A substantial proportion of publicly available sequences is geo-referenced at broad spatial scale such as the administrative unit of origin, rather than more precise locations (e.g. geographic coordinates). Most frequently, such sequences are either discarded prior to continuous phylogeographic inference or arbitrarily assigned to the geographic coordinates of the centroid of their administrative area of origin for lack of a better alternative.ResultsWe here implement and describe a new approach that allows to incorporate heterogeneous prior sampling probabilities over a geographic area. External data, such as outbreak locations, are used to specify these prior sampling probabilities over a collection of sub-polygons. We apply this new method to the analysis of highly pathogenic avian influenza H5N1 clade data in the Mekong region. Our method allows to properly include, in continuous phylogeographic analyses, H5N1 sequences that are only associated with large administrative areas of origin and assign them with more accurate locations. Finally, we use continuous phylogeographic reconstructions to analyse the dispersal dynamics of different H5N1 clades and investigate the impact of environmental factors on lineage dispersal velocities.Availability and implementationOur new method allowing heterogeneous sampling priors for continuous phylogeographic inference is implemented in the open-source multi-platform software package BEAST 1.10.Supplementary informationSupplementary data are available at Bioinformatics online.

Published
2020

13. Extraterrestrial sedimentary rocks on Earth

Author

Anfinogenova, Yana, Anfinogenov, John, Budaeva, Larisa, and Kuznetsov, Dmitry

Subjects
Astrophysics - Earth and Planetary Astrophysics
Abstract

This concept article discusses the possibilities for identifying sedimentary-origin meteorites. The paper concerns (i) the macroscopic candidate for sedimentary meteorite in the epicenter of the 1908 Tunguska catastrophe; (ii) potential parent bodies for sedimentary meteorites; (iii) isotopic heterogeneity of unmixed silicate reservoirs on Mars; (iv) possible terrestrial loss or contamination in the noble gas signatures in new type meteorites that spent time in extreme weather conditions; (v) cosmogenic isotopes and shielding; and (vi) pseudo meteorites. We conclude that the list of candidate parent bodies for sedimentary meteorites includes, but is not limited by the Earth, Mars, Enceladus, Ganymede, Europa, and hypothetical planets that could exist between orbits of Mars and Jupiter in the past. A parent body for extraterrestrial sedimentary rocks on the Earth should be identified based on the entire body of evidence which is not limited solely by tests of oxygen and noble gas isotopes whose signatures may undergo terrestrial contamination and may exhibit significant heterogeneity within the parent bodies. Observed fall of cosmic body, evidence of hypervelocity impact complying with the criteria of impact structures, and the presence of fusion crust on the fragments should be considered as priority signs of meteoritic origin., Comment: 20 pages, 3 figures, 75 references. arXiv admin note: text overlap with arXiv:1605.01892

Published
2017

16. Contemporary Management of Severe Symptomatic Aortic Stenosis

Author

Iung, Bernard, Bax, Jeroen, De Bonis, Michele, Delgado, Victoria, Haude, Michael, Hindricks, Gerhard, Maggioni, Aldo P., Pierard, Luc, Popescu, Bogdan A., Prendergast, Bernard, Price, Susanna, Rosenhek, Raphael, Ruschitzka, Frank, Vahanian, Alec, Wendler, Olaf, Windecker, Stephan, Mekhaldi, Souad, Lemaitre, Katell, Authier, Sébastien, Laroche, Cécile, Abdelhamid, Magdy, Apor, Astrid, Bajraktari, Gani, Beleslin, Branko, Bogachev-Prokophiev, Alexander, Demarco, Daniela Cassar, Pasquet, Agnes, Dogan, Sait Mesut, Erglis, Andrejs, Evangelista, Arturo, Goda, Artan, Ihlemann, Nikolaj, Ince, Huseyin, Katsaros, Andreas, Linhartova, Katerina, Mascherbauer, Julia, Mirrakhimov, Erkin, Mizariene, Vaida, Rahman-Haley, Shelley, Ribeiras, Regina, Samadov, Fuad, Saraste, Antti, Simkova, Iveta, Kostovska, Elizabeta Srbinovska, Tomkiewicz-Pajak, Lidia, Tribouilloy, Christophe, Zera, Eliverta, Metalla, Mimoza, Shirka, Ervina, Dado, Elona, Bica, Loreta, Aleksi, Jorida, Knuti, Gerti, Gjyli, Lidra, Pjeci, Rudina, Shuperka, Eritinka, Lleshi, Erviola, Rustemaj, Joana, Qordja, Marsjon, Gina, Mirald, Husi, Senada, Basic, Daniel, Steringer-Mascherbauer, Regina, Huber, Charlotte, Ebner, Christian, Sigmund, Elisabeth, Ploechl, Andrea, Sturmberger, Thomas, Eder, Veronica, Koppler, Tanja, Heger, Maria, Kammerlander, Andreas, Duca, Franz, Binder, Christina, Koschutnik, Matthias, Perschy, Leonard, Puskas, Lisa, Ho, Chen-Yu, Aliyev, Farid, Guluzada, Vugar, Imanov, Galib, Ibrahimov, Firdovsi, Abbasaliyev, Abbasali, Ahmedov, Tahir, Muslumova, Fargana, Babayev, Jamil, Rustamova, Yasmin, Jahangirov, Tofig, Samadov, Rauf, Museyibov, Muxtar, Isayev, Elnur, Musayev, Oktay, Xalilov, Shahin, Huseynov, Saleh, Yuzbashova, Madina, Zamanov, Vuqar, Mammadov, Vusal, Van Camp, Gery, Penicka, Martin, Batjoens, Hedwig, Debonnaire, Philippe, Dendooven, Daniel, Knecht, Sebastien, Duytschaever, Mattias, Vandekerckhove, Yves, Missault, Luc, Muyldermans, Luc, Tavernier, René, De Grande, Tineke, Coussement, Patrick, DeTroyer, Joyce, Derycker, Katrien, De Jaegher, Kelly, Bondue, Antoine, Beauloye, Christophe, Goffinet, Céline, Mirica, Daniela Corina, Eynden, Frédéric Vanden, Van de Borne, Philippe, Van Frachen, Béatrice, Vancraeynest, David, Vanoverschelde, Jean Louis, Pierard, Sophie, Malanca, Mihaela, Sinnaeve, Florence, Tahon, Séverine, De Clippel, Marie, Gayet, Frederic, Loiseau, Jacques, Van de Veire, Nico, Moerman, Veronique, Willems, Anne-Marie, Cosyns, Bernard, Droogmans, Steven, Motoc, Andreea, Kerkhove, Dirk, Plein, Daniele, Roosens, Bram, Weytjens, Caroline, Lancellotti, Patrizio, Dulgheru, Elena Raluca, Parenicova, Ilona, Bedanova, Helena, Tousek, Frantisek, Sindelarova, Stepanka, Canadyova, Julia, Taborsky, Milos, Ostransky, Jiri, Ivona simkova, Vicha, Marek, Jelinek, Libor, Opavska, Irena, Homza, Miroslav, Kvrayola, Miriam, Brat, Radim, Mrozek, Dan, Lichnerova, Eva, Docekalova, Iveta, Zarybnicka, Marta, Peskova, Marketa, Roucka, Patrik, Stastna, Vlasta, Vondrackova, Dagmar Jungwirtova, Hornig, Alfred, Niznansky, Matus, Branny, Marian, Vodzinska, Alexandra, Dorda, Miloslav, Snkouril, Libor, Kluz, Krystyna, Kypusova, Jana, Nezvalova, Radka, Olsen, Niels Thue, Ali, Hosam Hasan, Taha, Salma, Hassan, Mohamed, Afifi, Ahmed, Kabil, Hamza, Mady, Amr, Ebaid, Hany, Ahmed, Yasser, Nour, Mohammad, Talaat, Islam, Sayed, CairoMaiy El, Mostafa, Ahmad Elsayed, Sadek, CairoYasser, Eltobgi, CairoSherif, Bakhoum, Sameh, Doss, Ramy, Sheashea, Mahmoud, Elasry, Abd Allah, Fouad, Ahmed, Baraka, Mahmoud, Samir, Sameh, Roshdy, Alaa, AbdelRazek, Yasmin, Abd Rabou, Mostafa M., Abobakr, Ahmed, Moaaz, Moemen, Mokhtar, Mohamed, Ashry, Mohamed, Elkhashab, Khaled, Ghareeb, Haytham Soliman, Kamal, Mostafa, AbdelRazek, Gomaa, Farag, GizaNabil, Elbarbary, Giza:Ahmed, Wahib, Evette, Kazamel, Ghada, Kamal, Diaa, Tantawy, Mahmoud, Alansary, Adel, Yahia, Mohammed, Mahmoud, Raouf, El Banna, Tamer, Atef, Mohamed, Nasr, Gamela, Ahmed, Salah, El Hefny, Ehab E., Saifelyazal, Islam, El Ghany, Mostafa Abd, El Rahman El Hadary, Abd, Khairy, Ahmed, Lommi, Jyri, Laine, Mika, Kylmala, Minna, Kankanen, Katja, Turpeinen, Anu, Hartikainen, Juha, Kujanen, Lari, Airaksinen, Juhani, Vasankari, Tuija, Szymanski, Catherine, Bohbot, Yohann, Gun, Mesut, Rousseaux, Justine, Biere, Loic, Mateus, Victor, Audonnet, Martin, Rautureau, Jérémy, Cornet, Charles, Sorbets, Emmanuel, Mear, BourgesKarine, Issa, Adi, Jobic, Yannick, Le Ven, Florent, Pouliquen, Marie-Claire, Gilard, Martine, Ohanessian, Alice, Farhat, Ali, Vlase, Alina, Said, Fkhar, Lasgi, Caroline, Sanchez, Carlos, Breil, Romain, Peignon, Marc, Elkaim, Jean-Philippe, Jan-Blin, Virginie, BertrandM'Ban, Sylvain Ropars, Bardet, Hélène, Sawadogo, Samuel, Muschoot, Aurélie, Tchatchoua, Dieudonné, Elhadad, Simon, Maubert, Aline, Lazizi, Tahar, Ourghi, Kais, Bonnet, Philippe, Menager-Gangloff, Clarisse, Gafsi, Sofiene, Mansouri, Djidjiga, Aboyans, Victor, Magne, Julien, Martins, Elie, Karm, Sarah, Mohty, Dania, Briday, Guillaume, David, Amandine, Marechaux, Sylvestre, Le Goffic, Caroline, Binda, Camille, Menet, Aymeric, Delelis, Francois, Ringlé, Anne, Castel, Anne-Laure, Appert, Ludovic, Tristram, Domitille, Trouillet, Camille, Nacer, Yasmine, Ngoy, Lucas, Habib, MarseilleGilbert, Thuny, Franck, Haentjens, Julie, Cautela, Jennifer, Lavoute, Cécile, Robin, Floriane, Armangau, Pauline, Vergeylen, Ugo, Sanhadji, Khalil, Abdallah, Nessim Hamed, Kerzazi, Hassan, Perianu, Mariana, Plurien, François, Oueslati, Chaker, Debauchez, Mathieu, Monin, Jean-Luc, Konstantinos, Zannis, Berrebi, Alain, Dibie, Alain, Lansac, Emmanuel, Veugeois, Aurélie, Diakov, Christelle, Caussin, Christophe, Czitrom, Daniel, Salvi, Suzanna, Amabile, Nicolas, Dervanian, Patrice, Lejeune, Stéphanie, Bagdadi, Imane, Mokrane, Yemmi, Rouault, Gilles, Abalea, Jerome, Leledy, Marion, Horen, Patrice, Donal, Erwan, Bosseau, Christian, Paven, Elise, Galli, Elena, Collette, Edouard, Urien, Jean-Marie, Bridonneau, Valentin, Gervais, Renaud, Bauer, Fabrice, Chopra, Houzefa, Charbonnier, Arthur, Attias, David, Dahouathi, Nesrine, Khounlaboud, Moukda, Daudin, Magalie, Thebault, Christophe, Hamon, Cécile, Couffon, Philippe, Bellot, Catherine, Vomscheid, Maelle, Bernard, Anne, Dion, Fanny, Naudin, Djedjiga, Mouzouri, Mohammed, Rudelin, Mathilde, Berenfeld, Alain, Vanzwaelmen, Thibault, Alloui, Tarik, Radovikj, Marija Gjerakaroska, Jordanova, Slavica, Scholtz, Werner, Liberda-Knoke, Eva, Wiemer, Melanie, Mugge, Andreas, Nickenig, Georg, Sinning, Jan-Malte, Sedaghat, Alexander, Heintzen, Matthias, Ballof, Jan, Frenk, Daniel, Hambrecht, Rainer, Wienbergen, Harm, Seidel, Annemarie, Osteresch, Rico, Kramer, Kirsten, Ziemann, Janna, Schulze, Ramona, Fehske, Wolfgang, Eifler, Clarissa, Wafaisade, Bahram, Kuhn, Andreas, Fischer, Sören, Lichtenberg, Lutz, Brunold, Mareike, Simons, Judith, Balling, Doris, Buck, Thomas, Plicht, Bjoern, Schols, Wolfgang, Ebelt, Henning, Chamieh, Marwan, Anacker, Jelena, Rassaf, Tienush, Janosi, Alexander, Lind, Alexander, Lortz, Julia, Lüdike, Peter, Kahlert, Philipp, Rittger, Harald, Eichinger, Gabriele, Kuhls, Britta, Felix, Stephan B., Lehnert, Kristin, Pedersen, Ann-Louise, Dorr, Marcus, Empen, Klaus, Kaczmarek, Sabine, Busch, Mathias, Baly, Mohammed, Er, Fikret, Duman, Erkan, Gabriel, Linda, Weinbrenner, Christof, Bauersachs, Johann, Wider, Julian, Kempf, Tibor, Bohm, Michael, Schulze, Paul-Christian, Poerner, C. Tudor, Möbius-Winkler, Sven, Lenk, Karsten, Heitkamp, Kerstin, Franz, Marcus, Krauspe, Sabine, Schumacher, Burghard, Windmuller, Volker, Kurwitz, Sarah, Thiele, Holger, Kurz, Thomas, Meyer-Saraei, Roza, Akin, Ibrahim, Fastner, Christian, Lossnitzer, Dirk, Hoffmann, Ursula, Borggrefe, Martin, Baumann, Stefan, Kircher, Brigitte, Foellinger, Claudia, Dietz, Heike, Schieffer, Bernhard, Niroomand, Feraydoon, Mudra, Harald, Maier, Lars, Camboni, Daniele, Birner, Christoph, Debl, Kurt, Paulus, Michael, Seither, Benedikt, El Mokhtari, Nour Eddine, Oner, Alper, Caglayan, Evren, Sherif, Mohammed, Yucel, Seyrani, Custodis, Florian, Schwinger, Robert, Vorpahl, Marc, Seyfarth, Melchior, Nover, Ina, Koehler, Till, Christiani, Sarah, Sanchez, David Calvo, Schanze, Barbel, Sigusch, Holger, Salman, Athir, Hancock, Jane, Chambers, John, Demetrescue, Camelia, Prendergast, Claire, Dalby, Miles, Smith, Robert, Rogers, Paula, Riley, Cheryl, Tousoulis, Dimitris, Kanakakis, Ioannis, Spargias, Konstantinos, Lampropoulos, Konstantinos, Panagiotis, Tolis, Koutsoukis, Athanasios, Michalis, Lampros, Goudevenos, Ioannis, Bellos, Vasileios, Papafaklis, Michail, Lakkas, Lampros, Hahalis, George, Makris, Athanasios, Karvounis, Haralampos, Kamperidis, Vasileios, Ninios, Vlasis, Sachpekidis, Vasileios, Rouskas, Pavlos, Poulimenos, Leonidas, Charalampidis, Georgios, Hamodraka, Eftihia, Manolis, Athanasios, Kiss, Robert Gabor, Borsanyi, Tunde, Jarai, Zoltan, Zsary, Andras, Bartha, Elektra, Kosztin, Annamaria, Doronina, Alexandra, Kovacs, Attila, Imre, Barabas Janos, Chao, Chun, Benke, Kalman, Karoczkai, Istvan, Keltai, Kati, Förchécz, Zsolt, Pozsonyi, Zoltán, Jenei, Zsigmond, Patthy, Adam, Sallai, Laszlo, Majoros, Zsuzsanna, Pál, Tamás, Bencze, Jusztina, Sagi, Ildiko, Molnar, Andrea, Kurczina, Anita, Kolodzey, Gabor, Edes, Istvan, Szatmari, Valeria, Zajacz, Zsuzsanna, Cziraki, Attila, Nemeth, Adam, Faludi, Reka, Vegh, Laszlone, Jebelovszki, Eva, Lupkovics, Geza Karoly, Kovacs, Zsofia, Horvath, Andras, Berisha, Gezim, Ibrahimi, Pranvera, Percuku, Luan, Arapova, Rano, Laahunova, Elmira, Neronova, Kseniia, Zhakypova, Zarema, Naizabekova, Gulira, Muratova, Gulnazik, Sime, Iveta, Sorokins, Nikolajs, Kamzola, Ginta, Cgojeva-Sproge, Irina, Rancane, Gita, Valentinaviciene, Ramune, Rudiene, Laima, Raugaliene, Rasa, Bardzilauske, Aiste, Jonkaitiene, Regina, Petrauskaite, Jurate, Bieseviciene, Monika, Verseckaite, Raimonda, Zvirblyte, Ruta, Kalibatiene, Danute, Radauskaite, Greta, Janaviciute-Matuzeviciene, Gabija, Jancauskaite, Dovile, Balkute, Deimile, Maneikyte, Juste, Mileryte, Ingrida, Vaisvilaite, Monika, Gedvilaite, Lina, Biliukas, Mykolas, Karpaviciene, Vaiva, Xuereb, Robert George, Pllaha, Elton, Djaberi, Roxana, Komor, Klaudiusz, Gorgon-Komor, Agnieszka, Loranc, Beata, Myszor, Jaroslaw, Mizia-Stec, Katarzyna, Berger-Kucza, Adrianna, Mizia, Magdalena, Polak, Mateusz, Bogacki, Piotr, Podolec, Piotr, Komar, Monika, Sedziwy, Ewa, Sliwiak, Dorota, Sobien, Bartosz, Rog, Beata, Hlawaty, Marta, Gancarczyk, Urszula, Libiszewska, Natasza, Sorysz, Danuta, Gackowski, Andrzej, Cieply, Malgorzata, Misiuda, Agnieszka, Racibor, Franciszek, Nytko, Anna, Widenka, Kazimierz, Kolowca, Maciej, Bak, Janusz, Curzytek, Andrzej, Regulski, Mateusz, Kamela, Malgorzata, Wisniowski, Mateusz, Hryniewiecki, Tomasz, Szymanski, Piotr, Rozewicz, Monika, Grabowski, Maciej, Duchnowski, Piotr, Budaj, Andrzej, Zaborska, Beata, Pilichowska-Paskiet, Ewa, Sikora-Frac, Malgorzata, Slomski, Tomasz, Joao, Isabel, Cruz, Ines, Pereira, Hélder, Cale, Rita, Marques, Ana, Pereira, Ana Rita, Morais, Carlos, Freitas, Antonio, Roque, David, Antunes, Nuno, Pereira, Antonio Costeira, Vieira, Catarina, Salome, Nuno, Martins, Juliana, Campos, Isabel, Cardoso, Goncalo, Silva, Claudia, Oliveira, Afonso, Goncalves, Mariana, Martins, Rui, Quintal, Nuno, Mendes, Bruno, Silva, Joseline, Ferreira, Joao, Milner, James, Alves, Patricia, Marinho, Vera, Gago, Paula, Amado, Jose, Bispo, Joao, Bento, Dina, Machado, Inocencia, Oliveira, Margarida, Calvo, Lucy, von Hate, Pedro, Faria, Bebiana, Galrinho, Ana, Branco, Luisa, Goncalves, Antonio, Mendonca, Tiago, Selas, Mafalda, Macedo, Filipe, Sousa, Carla, Cabral, Sofia, Oliveira, Filomena, Trepa, Maria, Fontes-Oliveira, Marta, Nunes, Alzira, Araújo, Paulo, Ribeiro, Vasco Gama, Almeida, Joao, Rodrigues, Alberto, Braga, Pedro, Dias, Sonia, Carvalho, Sofia, Ferreira, Catarina, Ferreira, Alberto, Mateus, Pedro, Moz, Miguel, Leao, Silvia, Margato, Renato, Moreira, Ilidio, Guimanaes, Jose, Ribeiro, Joana, Goncalves, Fernando, Cabral, Jose, Almeida, Ines, Goncalves, Luisa, Tarusi, Mariana, Pop, Calin, Matei, Claudia, Tint, Diana, Barbulescu, Sanziana, Micu, Sorin, Pop, Ioana, Baba, Costica, Dimulescu, Doina, Dorobantu, Maria, Ginghina, Carmen, Onut, Roxana, Popescu, Andreea, Zamfirescu, Brandusa, Aflorii, Raluca, Popescu, Mihaela, Ghilencea, Liviu, Rachieru, Andreeea, Stoian, Monica, Oprescu, Nicoleta, Iancovici, Silvia, Petre, Iona, Mateescu, Anca Doina, Calin, Andreea, Botezatu, Simona, Enache, Roxana, Rosca, Monica, Ciuperca, Daniela, Babalac, Evelyn, Beyer, Ruxandra, Cadis, Laura, Rancea, Raluca, Tomoaia, Raluca, Rosianu, Adela, Kovacs, Emese, Militaru, Constantin, Craciun, Alina, Mirea, Oana, Florescu, Mihaela, Grigorica, Lucica, Dragusin, Daniela, Nechita, Luiza, Marinescu, Mihai, Chiscaneanu, Teodor, Botezatu, Lucia, Corciova, Costela, Petris, Antoniu Octavian, Arsenescu-Georgescu, Catalina, Salaru, Delia, Alexandrescu, Dan Mihai, Plesoianu, Carmjen, Tanasa, Ana, Mitu, Ovidiu, Costache, Irina Iuliana, Tudorancea, Ionut, Usurelu, Catalin, Eminovici, Gabriela, Manitiu, Ioan, Stoia, Oana, Mitre, Adriana, Nistor, Dan-Octavian, Maier, Anca, Lupu, Silvia, Opris, Mihaela, Ionac, Adina, Popescu, Irina, Crisan, Simina, Mornos, Cristian, Goanta, Flavia, Gruescu, Liana, Voinescu, Oana, Petcu, Madalina, Cozlac, Ramona, Damrina, Elena, Khilova, Liliya, Ryazantseva, Irina, Kozmin, Dmitry, Kiseleva, Maria, Goncharova, Marina, Kitalaeva, Kamila, Demetskay, Victoria, Verevetinov, Artem, Fomenko, Mikhail, Skripkina, Elena, Tsoi, Viktor, Antipov, Georgii, Schneider, Yuri, Yazikov, Denis, Makarova, Marina, Cherkes, Aleksei, Ermakova, Natalya, Medvedev, Aleksandr, Sarosek, Anastasia, Isayan, Mikhail, Voronova, Tatyana, Kulumbegov, Oleg, Tuchina, Alina, Stefanov, Sergei, Klimova, Margarita, Smolyaninov, Konstantin, Dandarova, Zhargalma, Magamet, Victoriya, Spiropulos, Natalia, Boldyrev, Sergey, Barbukhatty, Kirill, Buyankov, Dmitrii, Yurin, Vladimir, Gross, Yuriy, Boronin, Maksim, Mikhaleva, Mariya, Shablovskaya, Mariya, Zotov, Alex, Borisov, Daniil, Tereshchenko, Vasily, Zubova, Ekaterina, Kuzmin, A., Tarasenko, Ivan, Gamzaev, Alishir, Borovkova, Natalya, Koroleva, Tatyana, Botova, Svetlana, Pochinka, Ilya, Dunaeva, Vera, Teplitskaya, Victoria, Semenova, Elena I., Korabel'Nikova, Olga V., Simonov, Denis S., Denisenko, Elena, Harina, Natalia, Yarohno, Natalia, Alekseeva, Svetlana, Abydenkova, Julia, Shabalkina, Lyubov, Mayorova, Olga, Tsechanovich, Valeriy, Medvedev, Igor, Lepilin, Michail, Nemchenko, PenzaEvgenii, Karnahin, Vadim, Safina, Vasilya, Slastin, Yaroslav, Gilfanova, Venera, Gorbunov, Roman, Jakubov, Ramis, Fazylova, Aigul, Poteev, Mansur, Vazetdinova, Laysan, Tarasova, Indira, Irgaliyev, Rishat, Moiseeva, Olga, Gordeev, Mikhail, Irtyuga, Olga, Moiseeva, Raisa, Ostanina, Nina, Zverev, Dmitry, Murtazalieva, Patimat, Kuznetsov, Dmitry, Skurativa, Mariya, Polyaeva, Larisa, Mihaiilov, Kirill, Obrenovic-Kircanski, Biljana, Putnik, Svetozar, Simic, Dragan, Petrovic, Milan, Nikolic, Natasa Markovic, Jovovic, Ljiljana, Ostric, Dimitra Kalimanovska, Brajovic, Milan, Manojlovic, Milica Dekleva, Novakovic, Vladimir, Zamaklar-Trifunovic, Danijela, Orbovic, Bojana, Petrovic, Olga, Boricic-Kostic, Marija, Andjelkovic, Kristina, Milanov, Marko, Despotovic-Nikolic, Maja, Budisavljevic, Sreten, Veljkovic, Sanja, Cvetinovic, Nataša, Lepojevic, Daniijela, Todorovic, Aleksandra, Nikolic, Aleksandra, Borzanovic, Branislava, Trkulja, Ljiljana, Tomic, Slobodan, Vukovic, Milan, Milosavljevic, Jelica, Milanovic, Mirjana, Stakic, Vladan, Cvetkovic, Aleksandra, Milutinovic, Suzana, Bozic, Olivera, Miladinovic, Miodrag, Nikolic, Zoran, Despotovic, Dinka, Jovanovic, Dimitrije, Stojsic-Milosavljevic, Anastazija, Ilic, Aleksandra, Sladojevic, Mirjana, Susak, Stamenko, Maletin, Srdjan, Pavlovic, Salvo, Kuzmanovic, Vladimir, Ivanovic, Nikola, Dejanovic, Jovana, Ruzicic, Dusan, Drajic, Dragana, Cvetanovic, Danijel, Mirkovic, Marija, Omoran, Jon, Margoczy, Roman, Sedminova, Katarina, Reptova, Adriana, Baranova, Eva, Valkovicova, Tatiana, Valocik, Gabriel, Kurecko, Marian, Vachalcova, Marianna, Kollarova, Alzbeta, Studencan, Martin, Alusik, Daniel, Kozlej, Marek, Macakova, Jana, Moral, Sergio, Cladellas, Merce, Luiso, Daniele, Calvo, Alicia, Palet, Jordi, Carballo, Juli, Tura, Gisela Teixido, Maldonado, Giuliana, Gutierrez, Laura, Gonzalez-Alujas, Teresa, Jose Fernando, Rodriguez Palomares, Villalva, Nicolas, Molina-Mora, Ma Jose, Paton, Ramon Rubio, Martinez Diaz, Juan Jose, Ruiz, Pablo Ramos, Valle, Alfonso, Rodriguez, Ana, Alania, Edgardo, Galcera, Emilio, Seller, Julia, Valenzuela, Gonzalo de la Morena, Espin, Daniel Saura, Garcia, Dolores Espinosa, Oliva Sandoval, Maria Jose, Gonzalez, Josefa, Navarro, Miguel Garcia, Perez-Martinez, Maria Teresa, Ortega Trujillo, Jose Ramon, Gallego, Irene Menduina, San Roman, Daniel, Perez Nogales, Eliu David, Medina, Olga, Montiel Quintero, Rodolfo Antonio, Bujanda Morun, Pablo Felipe, Perez, Marta Lopez, Huaripata, Jimmy Plasencia, Morales Gonzalez, Juan Jose, Nelson, Veronica Quevedo, Zamorano, Jose Luis, Gomez, Ariana Gonzalez, Fraile, Alfonso, Alberca, Maria Teresa, Martin, Joaquin Alonso, Fernandez-Golfin, Covadonga, Ramos, Javier, Jimenez, Sergio Hernandez, Mitroi, Cristina, Sanchez Fernandez, Pedro L., Diaz-Pelaez, Elena, Garde, Beatriz, Caballero, Luis, Garcia, Fermin Martinez, Cambronero, Francisco, Castro, Noelia, Castro, Antonio, De La Rosa, Alejandro, Gallego, Pastora, Mendez, Irene, Villegas, David Villagomez, Correa, Manuel Gonzalez, Calvo, Roman, Florian, Francisco, Paya, Rafael, Esteban, Esther, Buendia, Francisco, Cubillos, Andrés, Fernandez, Carmen, Cárdenas, Juan Pablo, Pérez-Boscá, José Leandro, Vano, Joan, Belchi, Joaquina, Iglesia-Carreno, Cristina, Iglesias, Francisco Calvo, Escudero-Gonzalez, Aida, Zapateria-Lucea, Sergio, Duarte, Juan Sterling, Perez-Davila, Lara, Cobas-Paz, Rafael, Besada-Montenegro, Rosario, Fontao-Romeo, Maribel, Lopez-Rodriguez, Elena, Paredes-Galan, Emilio, Caneiro-Queija, Berenice, Gonzalez, Alba Guitian, Bozkurt, Abdi, Demir, Serafettin, Unlu, Durmus, Cagliyan, Caglar Emre, Ikikardes, Muslum Firat, Tangalay, Mustafa, Kuloglu, Osman, Ozer, Necla, Canpolat, Ugur, Kemaloglu, Melek Didem, Demirtas, Abdullah Orhan, Akgün, Didar Elif, Avci, Eyup, Taylan, Gokay, Yilmaztepe, Mustafa Adem, Ucar, Fatih Mehmet, Altay, Servet, Gurdogan, Muhammet, Gudul, Naile Eris, Aktas, Mujdat, Buyuklu, Mutlu, Degirmenci, Husnu, Turan, Mehmet Salih, Mert, Kadir Ugur, Mert, Gurbet Ozge, Dural, Muhammet, Arslan, Sukru, Sayar, Nurten, Kanar, Batur, Sadic, Beste Ozben, Sahin, Ahmet Anil, Buyuk, Ahmet, Kilicarslan, Onur, Bostan, Cem, Yildirim, Tarik, Yildirim, Seda Elcim, Cosansu, Kahraman, Varim, Perihan, Ilguz, Ersin, Demirbag, Recep, Yesilay, Asuman, Cirit, Abdullah, Tusun, Eyyup, Erkus, Emre, Sayin, Muhammet Rasit, Kazaz, Zeynep, Kul, Selim, Karabag, Turgut, Kalayci, Belma, Eugène, Marc, and Bax, Jeroen J.

Published
2021
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17. Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study

Author

Li, Shiying, primary, Dragan, Iulian, additional, Tran, Van Du T., additional, Fung, Chun Ho, additional, Kuznetsov, Dmitry, additional, Hansen, Michael K., additional, Beulens, Joline W. J., additional, Hart, Leen M. ‘t, additional, Slieker, Roderick C., additional, Donnelly, Louise A., additional, Gerl, Mathias J., additional, Klose, Christian, additional, Mehl, Florence, additional, Simons, Kai, additional, Elders, Petra J. M., additional, Pearson, Ewan R., additional, Rutter, Guy A., additional, and Ibberson, Mark, additional

Published
2024
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18. A Federated Database for Obesity Research: An IMI-SOPHIA Study

Author

Delfin, Carl, primary, Dragan, Iulian, additional, Kuznetsov, Dmitry, additional, Tajes, Juan Fernandez, additional, Smit, Femke, additional, Coral, Daniel E., additional, Farzaneh, Ali, additional, Haugg, André, additional, Hungele, Andreas, additional, Niknejad, Anne, additional, Hall, Christopher, additional, Jacobs, Daan, additional, Marek, Diana, additional, Fraser, Diane P., additional, Thuillier, Dorothee, additional, Ahmadizar, Fariba, additional, Mehl, Florence, additional, Pattou, Francois, additional, Burdet, Frederic, additional, Hawkes, Gareth, additional, Arts, Ilja C. W., additional, Blanch, Jordi, additional, Van Soest, Johan, additional, Fernández-Real, José-Manuel, additional, Boehl, Juergen, additional, Fink, Katharina, additional, van Greevenbroek, Marleen M. J., additional, Kavousi, Maryam, additional, Minten, Michiel, additional, Prinz, Nicole, additional, Ipsen, Niels, additional, Franks, Paul W., additional, Ramos, Rafael, additional, Holl, Reinhard W., additional, Horban, Scott, additional, Duarte-Salles, Talita, additional, Tran, Van Du T., additional, Raverdy, Violeta, additional, Leal, Yenny, additional, Lenart, Adam, additional, Pearson, Ewan, additional, Sparsø, Thomas, additional, Giordano, Giuseppe N., additional, Ioannidis, Vassilios, additional, Soh, Keng, additional, Frayling, Timothy M., additional, Le Roux, Carel W., additional, and Ibberson, Mark, additional

Published
2024
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22. Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study

Author

Slieker, Roderick C., Donnelly, Louise A., Fitipaldi, Hugo, Bouland, Gerard A., Giordano, Giuseppe N., Åkerlund, Mikael, Gerl, Mathias J., Ahlqvist, Emma, Ali, Ashfaq, Dragan, Iulian, Festa, Andreas, Hansen, Michael K., Mansour Aly, Dina, Kim, Min, Kuznetsov, Dmitry, Mehl, Florence, Klose, Christian, Simons, Kai, Pavo, Imre, Pullen, Timothy J., Suvitaival, Tommi, Wretlind, Asger, Rossing, Peter, Lyssenko, Valeriya, Legido-Quigley, Cristina, Groop, Leif, Thorens, Bernard, Franks, Paul W., Ibberson, Mark, Rutter, Guy A., Beulens, Joline W. J., ‘t Hart, Leen M., and Pearson, Ewan R.

Published
2021
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23. Response to the Comment by Haack et al. (2015) on the paper by Anfinogenov et al. (2014): John's stone: A possible fragment of the 1908 Tunguska meteorite

Author

Anfinogenova, Yana, Anfinogenov, John, Budaeva, Larisa, and Kuznetsov, Dmitry

Subjects
Astrophysics - Earth and Planetary Astrophysics
Abstract

The article provides an open discussion and a critical feedback to the comments of Haack et al. (2015) and emphasizes a significance of the first macroscopic evidence for a candidate meteorite of a new type: planetary-origin meteorite composed of silica-rich sedimentary rock. Discussion concerns the arguments for (i) candidate parental bodies including the Earth, Mars and icy moons of Saturn and Jupiter; (ii) PGE anomaly versus glassy silicate microspherules and quartz grains anomaly in the area of the 1908 Tunguska catastrophe; (iii) isotopic heterogeneity of unmixed silicate reservoirs on Mars; (iv) possible terrestrial loss or contamination in the noble gas signatures in meteorites that spent time in the extreme weather conditions; (v) cosmogenic isotopes and shielding; and (vi) pseudo meteorites. We conclude that the list of candidate parental bodies for hypothetical sedimentary-origin meteorites includes, but is not limited by the Earth, Mars, Enceladus, Ganymede, and Europa. A parental body should be identified based on the entire body of evidence which is not limited solely by tests of oxygen and noble gas isotopes whose signatures may undergo terrestrial contamination and may exhibit significant heterogeneity within the parental bodies., Comment: 11 pages, 49 references

Published
2016

24. Longitudinal support for the correlative triad among aging, dopamine D2-like receptor loss, and memory decline

Author

Karalija, Nina, Papenberg, Goran, Johansson, Jarkko, Wåhlin, Anders, Salami, Alireza, Andersson, Micael, Axelsson, Jan, Kuznetsov, Dmitry, Riklund, Katrine, Lövdén, Martin, Lindenberger, Ulman, Bäckman, Lars, Nyberg, Lars, Karalija, Nina, Papenberg, Goran, Johansson, Jarkko, Wåhlin, Anders, Salami, Alireza, Andersson, Micael, Axelsson, Jan, Kuznetsov, Dmitry, Riklund, Katrine, Lövdén, Martin, Lindenberger, Ulman, Bäckman, Lars, and Nyberg, Lars

Abstract

Dopamine decline is suggested to underlie aging-related cognitive decline, but longitudinal examinations of this link are currently missing. We analyzed 5-year longitudinal data for a sample of healthy, older adults (baseline: n = 181, age: 64–68 years; 5-year follow-up: n = 129) who underwent positron emission tomography with 11C-raclopride to assess dopamine D2-like receptor (DRD2) availability, magnetic resonance imaging to evaluate structural brain measures, and cognitive tests. Health, lifestyle, and genetic data were also collected. A data-driven approach (k-means cluster analysis) identified groups that differed maximally in DRD2 decline rates in age-sensitive brain regions. One group (n = 47) had DRD2 decline exclusively in the caudate and no cognitive decline. A second group (n = 72) had more wide-ranged DRD2 decline in putamen and nucleus accumbens and also in extrastriatal regions. The latter group showed significant 5-year working memory decline that correlated with putamen DRD2 decline, along with higher dementia and cardiovascular risk and a faster biological pace of aging. Taken together, for individuals with more extensive DRD2 decline, dopamine decline is associated with memory decline in aging.

Published
2024
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25. An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study

Author

Slieker, Roderick C. (author), Münch, Magnus (author), Donnelly, Louise A. (author), Bouland, G.A. (author), Dragan, Iulian (author), Kuznetsov, Dmitry (author), Elders, Petra J.M. (author), Rutter, Guy A. (author), Ibberson, Mark (author), Slieker, Roderick C. (author), Münch, Magnus (author), Donnelly, Louise A. (author), Bouland, G.A. (author), Dragan, Iulian (author), Kuznetsov, Dmitry (author), Elders, Petra J.M. (author), Rutter, Guy A. (author), and Ibberson, Mark (author)

Abstract

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel’s C statistic. Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0–11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3–11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HD, Pattern Recognition and Bioinformatics

Published
2024
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26. A Federated Database for Obesity Research: An IMI-SOPHIA Study

Author

RWE/Causal inference, Child Health, Delfin, Carl, Dragan, Iulian, Kuznetsov, Dmitry, Tajes, Juan Fernandez, Smit, Femke, Coral, Daniel E, Farzaneh, Ali, Haugg, André, Hungele, Andreas, Niknejad, Anne, Hall, Christopher, Jacobs, Daan, Marek, Diana, Fraser, Diane P, Thuillier, Dorothee, Ahmadizar, Fariba, Mehl, Florence, Pattou, Francois, Burdet, Frederic, Hawkes, Gareth, Arts, Ilja C W, Blanch, Jordi, Van Soest, Johan, Fernández-Real, José-Manuel, Boehl, Juergen, Fink, Katharina, van Greevenbroek, Marleen M J, Kavousi, Maryam, Minten, Michiel, Prinz, Nicole, Ipsen, Niels, Franks, Paul W, Ramos, Rafael, Holl, Reinhard W, Horban, Scott, Duarte-Salles, Talita, Tran, Van Du T, Raverdy, Violeta, Leal, Yenny, Lenart, Adam, Pearson, Ewan, Sparsø, Thomas, Giordano, Giuseppe N, Ioannidis, Vassilios, Soh, Keng, Frayling, Timothy M, Le Roux, Carel W, Ibberson, Mark, RWE/Causal inference, Child Health, Delfin, Carl, Dragan, Iulian, Kuznetsov, Dmitry, Tajes, Juan Fernandez, Smit, Femke, Coral, Daniel E, Farzaneh, Ali, Haugg, André, Hungele, Andreas, Niknejad, Anne, Hall, Christopher, Jacobs, Daan, Marek, Diana, Fraser, Diane P, Thuillier, Dorothee, Ahmadizar, Fariba, Mehl, Florence, Pattou, Francois, Burdet, Frederic, Hawkes, Gareth, Arts, Ilja C W, Blanch, Jordi, Van Soest, Johan, Fernández-Real, José-Manuel, Boehl, Juergen, Fink, Katharina, van Greevenbroek, Marleen M J, Kavousi, Maryam, Minten, Michiel, Prinz, Nicole, Ipsen, Niels, Franks, Paul W, Ramos, Rafael, Holl, Reinhard W, Horban, Scott, Duarte-Salles, Talita, Tran, Van Du T, Raverdy, Violeta, Leal, Yenny, Lenart, Adam, Pearson, Ewan, Sparsø, Thomas, Giordano, Giuseppe N, Ioannidis, Vassilios, Soh, Keng, Frayling, Timothy M, Le Roux, Carel W, and Ibberson, Mark

Published
2024

27. A Federated Database for Obesity Research:An IMI-SOPHIA Study

Author

Delfin, Carl, Dragan, Iulian, Kuznetsov, Dmitry, Tajes, Juan Fernandez, Smit, Femke, Coral, Daniel E., Farzaneh, Ali, Haugg, Andre, Hungele, Andreas, Niknejad, Anne, Hall, Christopher, Jacobs, Daan, Marek, Diana, Fraser, Diane P., Thuillier, Dorothee, Ahmadizar, Fariba, Mehl, Florence, Pattou, Francois, Burdet, Frederic, Hawkes, Gareth, Arts, Ilja C. W., Blanch, Jordi, Van Soest, Johan, Fernandez-Real, Jose-Manuel, Boehl, Juergen, Fink, Katharina, van Greevenbroek, Marleen M. J., Kavousi, Maryam, Minten, Michiel, Prinz, Nicole, Ipsen, Niels, Franks, Paul W., Ramos, Rafael, Holl, Reinhard W., Horban, Scott, Duarte-Salles, Talita, Tran, Van Du T., Raverdy, Violeta, Leal, Yenny, Lenart, Adam, Pearson, Ewan, Sparso, Thomas, Giordano, Giuseppe N., Ioannidis, Vassilios, Soh, Keng, Frayling, Timothy M., Le Roux, Carel W., Ibberson, Mark, Delfin, Carl, Dragan, Iulian, Kuznetsov, Dmitry, Tajes, Juan Fernandez, Smit, Femke, Coral, Daniel E., Farzaneh, Ali, Haugg, Andre, Hungele, Andreas, Niknejad, Anne, Hall, Christopher, Jacobs, Daan, Marek, Diana, Fraser, Diane P., Thuillier, Dorothee, Ahmadizar, Fariba, Mehl, Florence, Pattou, Francois, Burdet, Frederic, Hawkes, Gareth, Arts, Ilja C. W., Blanch, Jordi, Van Soest, Johan, Fernandez-Real, Jose-Manuel, Boehl, Juergen, Fink, Katharina, van Greevenbroek, Marleen M. J., Kavousi, Maryam, Minten, Michiel, Prinz, Nicole, Ipsen, Niels, Franks, Paul W., Ramos, Rafael, Holl, Reinhard W., Horban, Scott, Duarte-Salles, Talita, Tran, Van Du T., Raverdy, Violeta, Leal, Yenny, Lenart, Adam, Pearson, Ewan, Sparso, Thomas, Giordano, Giuseppe N., Ioannidis, Vassilios, Soh, Keng, Frayling, Timothy M., Le Roux, Carel W., and Ibberson, Mark

Abstract

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.

Published
2024

28. The Significance of Stochastic CTMC Over Deterministic Model in Understanding the Dynamics of Lymphatic Filariasis With Asymptomatic Carriers.

Author

Stephano, Mussa A., Irunde, Jacob I., Mayengo, Maranya M., and Kuznetsov, Dmitry

Subjects
STOCHASTIC models, MARKOV processes, BRANCHING processes, FILARIASIS
Abstract

Lymphatic filariasis is a leading cause of chronic and irreversible damage to human immunity. This paper presents deterministic and continuous-time Markov chain (CTMC) stochastic models regarding lymphatic filariasis dynamics. To account for randomness and uncertainties in dynamics, the CTMC model was formulated based on deterministic model possible events. A deterministic model's outputs suggest that disease extinction is feasible when the secondary threshold infection number is below one, while persistence becomes likely when the opposite holds true. Furthermore, the significant contribution of asymptomatic carriers was identified. Results indicate that persistence is more likely to occur when the infection results from asymptomatic, acutely infected, or infectious mosquitoes. Consequently, the CTMC stochastic model is essential in capturing variabilities, randomness, associated probabilities, and validity across different scales, whereas oversimplification and unpredictability of inherent may not be featured in a deterministic model. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Tunguska cosmic body of 1908: is it from planet Mars?

Author

Anfinogenov, John, Budaeva, Larisa, Kuznetsov, Dmitry, and Anfinogenova, Yana

Subjects
Astrophysics - Earth and Planetary Astrophysics
Abstract

The aim of the study was to discover remnants of the 1908 Tunguska meteorite. Main objective of the field studies was identification of exotic rocks, furrows, and penetration funnels reported by the first eyewitnesses, residents of the area with severe forest destruction. Main methods included decoding of aerial survey photographs, systematic survey of the epicenter area of the Tunguska explosion, exploratory excavations of the objects of interest, reconstruction studies of exotic boulder by using its splinters, mineralogical and spectral analysis of specimens, experimental attempt of plasma-induced reproduction of the fusion crust on specimen. The authors present results on discovery of penetration funnel-like structures; exotic boulder (known as John's Stone)) with its shear-fractured splinters and fresh furrow in the permafrost; several splinters with glassy coatings; evidence of high-speed John's Stone deceleration in the permafrost; and clear consistency in geometry of spacial arrangements of all splinters, furrows, cleaved pebbles according to data of reconstruction studies. John's Stone is composed of highly silicified gravelite sandstone (98.5% SiO2) with grain size of 0.5 to 1.5 cm. Outer surface of several splinters showed continuous glassy coating similar to shiny fusion crust reminiscent of freshly applied enamel. Plasma-induced heating of John's Stone specimen led to its explosive disintegration; residue presented with whitish semi-transparent pumice-like grains and irregularly shaped fused particles. Overall, our data suggest that John's Stone is Tunguska meteorite candidate. Recent discoveries of sedimentary rocks, lithified gravelite sandstone, clay, and quartz on Mars provide rationale for search and identification of silica-rich Martian meteorites of sedimentary origin., Comment: Number of Pages: 19 Number of References: 27 Number of Tables: 1 Number of Figures: 5

Published
2014

33. Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study.

Author

Shiying Li, Dragan, Iulian, Tran, Van Du T., Chun Ho Fung, Kuznetsov, Dmitry, Hansen, Michael K., Beulens, Joline W. J., t Hart, Leen M., Slieker, Roderick C., Donnelly, Louise A., Gerl, Mathias J., Klose, Christian, Mehl, Florence, Simons, Kai, Elders, Petra J. M., Pearson, Ewan R., Rutter, Guy A., and Ibberson, Mark

Subjects
TYPE 2 diabetes, MULTIOMICS, INTERLEUKIN receptors, BLOOD lipids, INSULIN, INSULIN sensitivity, INSULIN aspart
Abstract

Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Molecular phenotyping of multiple mouse strains under metabolic challenge uncovers a role for Elovl2 in glucose-induced insulin secretion

Author

Cruciani-Guglielmacci, Céline, Bellini, Lara, Denom, Jessica, Oshima, Masaya, Fernandez, Neïké, Normandie-Levi, Priscilla, Berney, Xavier P., Kassis, Nadim, Rouch, Claude, Dairou, Julien, Gorman, Tracy, Smith, David M., Marley, Anna, Liechti, Robin, Kuznetsov, Dmitry, Wigger, Leonore, Burdet, Frédéric, Lefèvre, Anne-Laure, Wehrle, Isabelle, Uphues, Ingo, Hildebrandt, Tobias, Rust, Werner, Bernard, Catherine, Ktorza, Alain, Rutter, Guy A., Scharfmann, Raphael, Xenarios, Ioannis, Le Stunff, Hervé, Thorens, Bernard, Magnan, Christophe, and Ibberson, Mark

Published
2017
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38. Maximizing the value of twin studies in health and behaviour

Author

Hagenbeek, Fiona A., Hirzinger, Jana S., Breunig, Sophie, Bruins, Susanne, Kuznetsov, Dmitry V., Schut, Kirsten, Odintsova, Veronika V., Boomsma, Dorret I., Hagenbeek, Fiona A., Hirzinger, Jana S., Breunig, Sophie, Bruins, Susanne, Kuznetsov, Dmitry V., Schut, Kirsten, Odintsova, Veronika V., and Boomsma, Dorret I.

Abstract

In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, and gene–environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and we conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.

Published
2023
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39. Trends in green transformation economic policy: experience of leading countries and russia

Author

Alieva Inobbat, Omurova Saltanat, Kuznetsov Dmitry, Pankina Inga, and Shchepkova Irina

Subjects
Environmental sciences, GE1-350
Abstract

Given the demand for green economic growth and sustainable development, many economic practices demonstrate different rates and characteristics of the transition to a green economy, which in one way or another are determined by the initial socio-economic factors and the economic policy implemented by the state. Stimulating green growth and increasing the well-being of society through the rational use of natural resources, natural capital and ecosystems require the elaboration and development of alternative measures for ensuring the rational use of natural resources, the development of environmental innovations and human capital. An equally important factor contributing to green growth is the formation of a state policy of green economic growth, as well as tools and mechanisms for its implementation. The conceptual foundations of a supranational green growth policy have already been developed and the methodological basis has been elaborated. The study of the experience and best practices of green growth in different countries allows us to identify certain shortcomings that require a deeper study and revision of state regulation documents. In addition, the post-Covid economy requires a separate consideration from the point of view of both the relevance of the policy of green growth and the measures and tools for its implementation. Thus, the study of modern trends and principles of the implementation of the state policy of green growth is a topical area of research that requires critical rethinking for the introduction of more constructive measures for reaching the set goals and objectives.

Published
2021
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40. Global subsiding as a way to sustainable fishery

Author

Dorofeeva Viktoria, Klippenstein Elena, and Kuznetsov Dmitry

Subjects
Environmental sciences, GE1-350
Abstract

Government subsidies affect many kinds of world’s fishery and are intended to restore troubled fisheries. The article shows that current information about global subsidies for fishery are fragmentary due to lack of its classification and accounting. But at the same time, many fishery countries allow overfishing, illegal, unreported and unregulated fishing. As a result, bioresources are overexploited, and economical indicators of fishing deteriorate. Depleted reserves of fish in seas, and also economical and social problems within fishery society is a result of exploiting of marine bioresources in terms of lack of proper fishing management system. We have made an attempt to summarize approaches of different organizations to classify global subsidies. The presented analysis shows that the largest share of subsidies falls on fuel, which is undoubtable a negative tendency. The assessment of subsidies level for the largest fishery countries is implemented. Scientific and practical recommendations are formulated, its compliance may provide the growth of economical state without accelerating the depletion of fish stocks.

Published
2021
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42. Using Steel-Smelting Slag as a Raw Material Component in the Production of Asphalt Concrete

Author

Kuznetsov Dmitry, Vysotskaya Marina, and Burgonutdinov Albert

Subjects
Environmental sciences, GE1-350
Abstract

The possibility of using polydisperse steel-smelting slag for the production of type B asphalt concrete is investigated, the possibility of its use as a mineral powder, crushing screening and crushed stone is considered. As a result of the study, it was found that a promising area for the use of slag raw materials in the technology for the preparation of asphalt mixes is: as a mineral powder - without additional processing steps; crushing screening - with the development of additional technological operations. The use of raw materials as crushed stone is impractical because of the high average density of the final asphalt mixture. Based on the test results, the characteristics of the fatigue properties of asphalt concrete from fine-grained dense mixtures of type B made on mineral powders from slag and limestone were obtained. It was established that at the initial moment, a large number of cycles to failure are characterized by compositions on slag powder, however, samples from this series tested after 45 days showed a significant rigidity increase and were destroyed earlier than similar samples on limestone. Obviously, this phenomenon is associated with an increase in the brittle properties of the material due to the formation of crystallization-condensation bonds in the structure of asphalt concrete containing slag.

Published
2020
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45. Information Technologies in the Context of Forming the Synergy of Post-industrial Consciousness and Digital Economy

Author

Vlasova, Natalya V., Kuznetsov, Dmitry V., Mehdiev, Shamsaddin Z., Timofeeva, Ekaterina S., and Chistyakov, Maxim S.

Subjects
Artificial intelligence, O33, NBIC convergence, Postindustrial society, Information technology, Article, Digital economy
Abstract

In the collective author’s work on the basis of general scientific methodological approaches, the revolutionary transformation of modern civilization into the format of a post-industrial state and network thinking under the influence of information technologies is considered, taking into account the relevance of awareness of threats to humanity during the convergence of digitalization into everyday reality. The main approach is a theoretical analysis of the impact of modern information technologies and their extrapolation to the near future of civilization from the point of view of the feasibility of creating favorable conditions for life, human improvement from the standpoint of improving moral, ethical, psychological and intellectual qualities. The results, which were obtained, can be used from the position of an interdisciplinary approach as a subjective scientific position of the authors of the article on the evolution of the post-industrial society and the assessment of possible variations in the development of the post-industrial economy on the platform of information technologies, which are part of the convergent ones that form the basis of the VI technological order of the IV industrial revolution. The authors explain that the reasoning given in the article isn’t a dogma and can be transformed directly by the spokesmen of these opinions and by other authors of other works on the topic of post-industrial development in the process of scientific discussions.

Published
2021

46. The Role of Coal-Mining Museum in Kuzbass Coal Industry Development

Author

Kuznetsov Dmitry, Novoklinova Anna, Rabkina Nadezda, and Valko Olga

Subjects
Environmental sciences, GE1-350
Abstract

A museum cluster is a phenomenon that did not acquire conceptual understanding until the late 1990s. Since then, numerous approaches to defining a museum cluster have been put to the test. It has been unanimously stated that a museum cluster is an environment-creating and culture-forming entity, which catches the spirit of the time/place and embodies the locus genii. The museum, thus, renews and strengthens itself in the urban, scientific and cultural thought. These apply well to Krasnaya Gorka, a museum cluster in Kemerovskaya oblast. Combining all ‘cluster’ characteristics, Krasnaya Gorka features very specific, historically and culturally valuable trends eventually transforming the urban environment and forming local traditions. The study is aimed at comprehensive research of a museum cluster contribution to the local culture, environment, science, economy and, most importantly, people. The study showed that: (1) the Krasnaya Gorka museum is justifiably the heart of a cluster; (2) the archival materials in city museums are valuable as integral historic documents in their own right, but more significant when regarded along with other papers and artefacts in the domain of the museums, forming the cluster; c) the Krasnaya Gorka museum is a culture-forming entity due to its aggregating people and organizing activities.

Published
2019
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47. Poetic image of Kuzbass in the verses of regional authors

Author

Kuznetsov Dmitry, Rabkina Nadezda, and Valko Olga

Subjects
Social Sciences
Abstract

This research features the phenomenon of the so-called literature of space, namely amateur and professional poetic texts that together constitute a cultural supertext devoted to the Siberian coal mining region of Kuzbass. The article employed a multifaceted approach (statistical, pragmatic, textual and temporal/spatial vision analysis) to reconstruct the image of the region in the naïve worldview. Statistical analysis identified the key words and semantic models. The cognitive analysis reveals the basic cognitive structures, while the textual analysis makes it possible to reconstruct narrative and perception. The pragmatic analysis determines the communication role model and the predominant speech acts. The study exposes extended thematic nets of geographical, natural and meteorological terms, time description, and coal mining realia. The predominating cognitive models represent Kuzbass as a territory / a living being / treasure trove / home / family, while the most significant motif for professional oeuvre is legitimizing the peripheral area as an inseparable part of the Russian domain and history. Introspection, simultaneity, and strong modality appears to be the most significant text characteristics. Thus, the image of Kuzbass proves to be a complex comprehensive multitiered phenomenon based on different linguistic units and reflecting the results of the evaluative perception and processing.

Published
2019
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50. Advanced Large-Scale Nanofabrication Route for Ultrasensitive SERS Platforms Based on Precisely Shaped Gold Nanostructures

Author

Akil, Suzanna, Omar, Rana, Kuznetsov, Dmitry, Shur, Vladimir, Naciri, Aotmane En, and Jradi, Safi

Subjects
NANOFABRICATION, Chemistry, SENSORS, nanofabrication, ADVANCED NANOCOMPOSITES, self-assembly, large area, sensors, QD1-999, SELF-ASSEMBLY, Article, advanced nanocomposites, LARGE AREA
Abstract

One of the key issues for SERS-based trace applications is engineering structurally uniform substrates with ultrasensitivity, stability, and good reproducibility. A label-free, cost-effective, and reproducible fabrication strategy of ultrasensitive SERS sensors was reported in this work. Herein, we present recent progress in self-assembly-based synthesis to elaborate precisely shaped and abundant gold nanoparticles in a large area. We demonstrated that shape control is driven by the selective adsorption of a cation (Na+, K+, and H+) on a single facet of gold nanocrystal seeds during the growth process. We studied SERS features as a function of morphology. Importantly, we found a correlation between the shape and experimental SERS enhancement factors. We observed a detection threshold of 10−20 M of bipyridine ethylene (BPE), which matches the lowest value determined in literature for BPE until now. Such novel sensing finding could be very promising for diseases and pathogen detection and opens up an avenue toward predicting which other morphologies could offer improved sensitivity. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Funding: This research was funded by C2MP doctoral school and FEDER “Fond Européen de Développement Regional” and “Région Grand-Est” are acknowledged for their financial support (Pronano project).

Published
2021

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