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Wyświetlanie 1-10 z 23
Tytuł:
All-sky medium energy gamma-ray observatory : exploring the extreme multimessenger universe
Autorzy:
Kocveski, Daniel
da Silva, Rui Curado
Guiriec, Sylvain
Agudo, Ivan
Hays, Elizabeth
Kanbach, Gottfried
Beacom, John
D'Ammando, Filippo
Doro, Michele
Digel, Seth
Williams, David
Funk, Stefan
Giordano, Francesco
Patricelli, Barbara
Coppi, Paolo
Zoglauer, Andreas
Manousakis, Antonios
Giuliani, Andrea
Dominguez, Alberto
Beckmann, Volker
Auricchio, Natalia
Paliya, Vaidehi S.
Oberlack, Uwe
Klimenko, Alexei V.
Vianello, Giacomo
Kerr, Matthew
Grefenstette, Brian
Neilson, Naoko Kurahashi
Harding, Pat
Boettcher, Markus
Mitchell, John
Zampieri, Luca
Longo, Francesco
Grenier, Isabelle
Gelfand, Joseph
Walter, Roland
Hartmann, Dieter
Holder, Jamie
Rando, Riccardo
Hernanz, Margarita
Thompson, David
Gouiffes, Christian
Kierans, Carolyn
Marcowith, Alexandre
Georganopoulos, Markos
Grove, Eric
Moiseev, Alexander
Prescod-Weinstein, Chanda
Kopp, Joachim
Lommler, Jan
Omodei, Nicola
Krawczynski, Henric
Moss, Michael
Zane, Silvia
Wood, Matthew
Linden, Tim
Petropoulou, Maria
Nakazawa, Kazuhiro
Dietrich, Stefano
Zhang, Haocheng
Knodlseder, Jurgen
De Becker, Michaël
Finke, Justin
Griffin, Sean
Cenko, S. Brad
Morcuende, Daniel
Stawarz, Łukasz
Bambi, Cosimo
Barrio, Juan Abel
Ajello, Marco
Fukazawa, Yasushi
Bozhilov, Vladimir
Harding, Alice
Lenain, Jean-Philippe
Fryer, Chris
Blumer, Harsha
Del Sordo, Stefano
Tajima, Hiro
Jones, Sam
Wulf, Eric
Uhm, Lucas
Meyer, Eileen
Cardillo, Martina
Kubo, Hidetoshi
Wilson-Hodge, Colleen
Hewitt, Jack
Anton, Sonia
Gasparrini, Dario
Bolotnikov, Aleksey
Caputo, Regina
Wadiasingh, Zorawar
Castro, Daniel
Zimmer, Stephan
Bernard, Denis
Moskalenko, Igor
Kislat, Fabian
Racusin, Judith
Bastieri, Denis
Campana, Riccardo
Fields, Brian
Venters, Tonia
Ohno, Masanori
Porter, Troy
Baring, Matthew
Ansoldi, Stefano
Buckley, Jim
Smith, Karl
Ciprini, Stefano
Sanchez-Conde, Miguel A.
Younes, George
De Angelis, Alessandro
Prandini, Elisa
Laurent, Philippe
Li, Hui
Inglis, Andrew
Buson, Sara
Orlando, Elena
Caroli, Ezio
Lovellette, Michael
Ferrara, Elizabeth
Saz Parkinson, Pablo
Tanaka, Yasuyuki
Krizmanic, John
Charles, Eric
Metcalfe, Jessica
Hui, Michelle
Wang, Xilu
Woolf, Richard
Ribó, Marc
Bloser, Peter
Chen, Wenlei
Bozzo, Enrico
Shawhan, Peter
Schirato, Richard
Boggs, Steven
Stephen, John B.
McConnell, Marc
Smith, Jacob
Meyer, Manuel
McEnery, Julie E.
Marcotulli, Lea
Pittori, Carlotta
Foffano, Luca
Cheung, Teddy
Oikonomou, Foteini
Tatischeff, Vincent
Stamerra, Antonio
Stumke, Inga
Lien, Amy
Perkins, Jeremy
Bednarek, Wlodek
van der Horst, Alexander
Di Mauro, Mattia
Tomsick, John
Mizuno, Tsunefumi
Mignani, Roberto
The, Lih-Sin
Bottacini, Eugenio
Martinez, Manel
López, Marcos
Briggs, Michael
Mazziotta, M. Nicola
Kaufmann, S.
Rodi, James
Zhang, Bing
Parker, Lucas
Di Venere, Leonardo
Baldini, Luca
Barres, Ulisses
Bissaldi, Elisabetta
Ojha, Roopesh
Tibaldo, Luigi
Johnson, Robert
Takahashi, Hiromitsu
Cutini, Sara
Vestrand, Tom
Rani, Bindu
Kargaltsev, Oleg
Shrader, Chris
Strong, Andy
Pohl, Martin
Álvarez, José-Manuel
De Nolfo, Georgia
Otte, Nepomuk
Dostawca treści:
Repozytorium Uniwersytetu Jagiellońskiego
Artykuł
Tytuł:
A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave
Autorzy:
Ożański, Tomasz
Görgen, Konstantin
Bhatia, Sangeeta
Kirsten, Holger
Soni, Saksham
Bodych, Marcin
Hotz, Thomas
Krueger, Tyll
Castro, Lauren
Ullrich, Alexander
Meinke, Jan H.
Gogolewski, Krzysztof
Zielinski, Jakub
Rakowski, Franciszek
Gu, Quanquan
Scholz, Markus
Gneiting, Tilmann
Krymova, Ekaterina
Deuschel, Jannik
Bracher, Johannes
Bertsimas, Dimitris
Funk, Sebastian
Niedzielewski, Karol
Fuhrmann, Jan
Wolffram, Daniel
Burgard, Jan Pablo
Barbarossa, Maria Vittoria
Heyder, Stefan
Zou, Difan
Kheifetz, Yuri
Li, Michael Lingzhi
Fairchild, Geoffrey
Michaud, Isaac
Srivastava, Ajitesh
Ketterer, Jakob L.
Abbott, Sam
Bosse, Nikos I.
Schienle, Melanie
Wydawca:
Springer Nature
Cytata wydawnicza:
Bracher, J., Wolffram, D., Deuschel, J. et al. A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave. Nat Commun 12, 5173 (2021). https://doi.org/10.1038/s41467-021-25207-0
Opis:
Open Access funding enabled and organized by Projekt DEAL
Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.
The online version contains supplementary materialavailable athttps://doi.org/10.1038/s41467-021-25207-0.
Dostawca treści:
Repozytorium Centrum Otwartej Nauki
Artykuł
Tytuł:
RP11-362K2.2:RP11-767I20.1 genetic variation is associated with post-reperfusion therapy parenchymal hematoma. A GWAS meta-analysis
Autorzy:
Martínez-Domeńo, Alejandro
Gallego-Fabrega, Cristina
Fernández-Cadenas, Israel
Ibańez, Laura
Lledós, Miquel
del Mar Freijo-Guerrero, Maria
Tur, Silvia
Krupinsky, Jerzy
Strbian, Daniel
Arenillas, Juan F.
Thijs, Vincent
Pera, Joanna
Obach, Victor
Cruchaga, Carlos
Martí-Fŕbregas, Joan
Delgado-Mederos, Raquel
Guasch, Marina
Muińo, Elena
Lee, Jin-Moo
Camps-Renom, Pol
Montaner, Joan
Millán, Mňnica
Campos, Francisco
Bustamante, Alejandro
Prats-Sánchez, Luis
Cabezas, Juan Antonio
Sobrino, Tomás
Muńoz-Narbona, Lucía
Lemmens, Robin
Cullell, Natalia
Rodríguez-Castro, Emilio
Castellanos, Mar
Słowik, Agnieszka
Roquer, Jaume
Dhar, Rajat
Castillo, José
Jiménez-Conde, Jordi
Lluciŕ-Carol, Laia
Soriano-Tárraga, Carolina
Tatlisumak, Turgut
Segura, Tomás
Guisado, Daniel
Cárcel-Márquez, Jara
López-Cancio, Elena
Ribó, Marc
Álvarez-Sabín, José
Giralt-Steinhauer, Eva
Vives-Bauza, Cristófol
Díaz Navarro, Rosa
Marin, Rebeca
Serrano-Heras, Gemma
Heitsch, Laura
Moniche, Francisco
Carrera, Caty
Opis:
Stroke is one of the most common causes of death and disability. Reperfusion therapies are the only treatment available during the acute phase of stroke. Due to recent clinical trials, these therapies may increase their frequency of use by extending the time-window administration, which may lead to an increase in complications such as hemorrhagic transformation, with parenchymal hematoma (PH) being the more severe subtype, associated with higher mortality and disability rates. Our aim was to find genetic risk factors associated with PH, as that could provide molecular targets/pathways for their prevention/treatment and study its genetic correlations to find traits sharing genetic background. We performed a GWAS and meta-analysis, following standard quality controls and association analysis (fastGWAS), adjusting age, NIHSS, and principal components. FUMA was used to annotate, prioritize, visualize, and interpret the meta-analysis results. The total number of patients in the meta-analysis was 2034 (216 cases and 1818 controls). We found rs79770152 having a genome-wide significant association (beta 0.09, p-value 3.90 × 10−8) located in the RP11-362K2.2:RP11-767I20.1 gene and a suggestive variant (rs13297983: beta 0.07, p-value 6.10 × 10−8) located in PCSK5 associated with PH occurrence. The genetic correlation showed a shared genetic background of PH with Alzheimer’s disease and white matter hyperintensities. In addition, genes containing the ten most significant associations have been related to aggregated amyloid-β, tau protein, white matter microstructure, inflammation, and matrix metalloproteinases.
Dostawca treści:
Repozytorium Uniwersytetu Jagiellońskiego
Artykuł
Tytuł:
Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies
Autorzy:
Schoonman, Marten
Dalmon, Anne
Filipiak, Michał
Schäfer, Marc O
Castro, Sílvia
Flener, Claude
Lopes, Sara
Verbeke, Wim
van Gennip, Pim
Peters, Jeroen
Ulgezen, Zeynep
Godeau, Ugoline
Matthijs, Severine
Alaux, Cedric
Mcveigh, Adam
Topping, Christopher J
Streicher, Tabea
Sousa, José Paulo
Giurgiu, Alexandru I
Bencsik, Martin
Duan, Xiaodong
Capela, Nuno
Loureiro, João
Leufgen, Kirsten
Valkenburg, Dirk-Jan
van Dooremalen, Coby
Dall’olio, Raffaele
Alves, Joana
Simões, Sandra
Bica, João
Mikołajczyk, Łukasz
Alves da Silva, António
de Smet, Lina
Kumar, Tarun
Horčičková, Eva
de Graaf, Dirk C
Boúúaert, David Claeys
Ziółkowska, Elżbieta
van Delden, April
Beaurepaire, Alexis
Schaafsma, Famke
Le Conte, Yves
Alves, Fátima
Freshley, Dana
Williams, James Henty
van den Bosch, Trudy
Xu, Mang
Moro, Arrigo
Tehel, Anja
Dezmirean, Daniel S
Paxton, Robert J
Wydawca:
MDPI
Cytata wydawnicza:
van Dooremalen, C.; Ulgezen, Z.N.; Dall’Olio, R.; Godeau, U.; Duan, X.; Sousa, J.P.; Schäfer, M.O.; Beaurepaire, A.; van Gennip, P.; Schoonman, M.; et al. Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies. Insects 2024, 15, 76. https://doi.org/10.3390/insects15010076
Opis:
Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies’ exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony’s health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project’s data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.
Honey bees are very important for nature and food production. However, beekeepers’ work is continuously challenged by pests, pathogens, pesticides, and other impacts of the environment on their honey bee colonies, and, therefore, they would greatly benefit from up-to-date insights on the health condition of their bees. To disturb those bee colonies as little as possible, it is preferable that this information be collected in an automated way. In this article, we present the B-GOOD project as a case study to monitor the health of honey bee colonies in an automated, standardized way. The use of a similar approach by researchers in their future studies would allow the combination of different datasets on bee health. More data combinations would facilitate the use of machine learning to better and more accurately determine the thresholds for beekeeper interventions, the underlying mechanisms of honey bee colony health, and the prediction of health and colony losses, among other indicators.
Dostawca treści:
Repozytorium Centrum Otwartej Nauki
Artykuł
Wyświetlanie 1-10 z 23

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