The World Spider Trait database: a centralized global open repository for curated data on spider traits

Spiders are a highly diversified group of arthropods and play an important role in terrestrial ecosystems as ubiquitous predators, which makes them a suitable group to test a variety of eco-evolutionary hypotheses. For this purpose, knowledge of a diverse range of species traits is required. Until now, data on spider traits have been scattered across thousands of publications produced for over two centuries and written in diverse languages. To facilitate access to such data, we developed an online database for archiving and accessing spider traits at a global scale. The database has been designed to accommodate a great variety of traits (e.g. ecological, behavioural and morphological) measured at individual, species or higher taxonomic levels. Records are accompanied by extensive metadata (e.g. location and method). The database is curated by an expert team, regularly updated and open to any user. A future goal of the growing database is to include all published and unpublished data on spider traits provided by experts worldwide and to facilitate broad cross-taxon assays in functional ecology and comparative biology.Fil: Pekár, Stano. Masaryk University; República ChecaFil: Wolff, Jonas O. University of Greifswald; AlemaniaFil: Cernecká, L'udmila. Slovak Academy of Sciences; ArgentinaFil: Birkhofer, Klaus. Brandenburgische Technische Universität Cottbus; AlemaniaFil: Mammola, Stefano. University of Helsinki; FinlandiaFil: Lowe, Elizabeth C.. Macquarie University; AustraliaFil: Fukushima, Caroline S.. University of Helsinki; FinlandiaFil: Herberstein, Marie E.. Macquarie University; AustraliaFil: Kucera, Adam. Masaryk University; República ChecaFil: Buzatto, Bruno A.. University of Western Australia; AustraliaFil: Djoudi, El Aziz. Brandenburgische Technische Universität Cottbus; AlemaniaFil: Domenech, Marc. Universidad de Barcelona; EspañaFil: Enciso, Alison Vanesa. Fundación Protectora Ambiental Planadas Tolima; ColombiaFil: Piñanez Espejo, Yolanda María Guadalupe. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas; ArgentinaFil: Febles, Sara. No especifíca;Fil: García, Luis F. Universidad de la República; UruguayFil: Gonçalves Souza, Thiago. Universidad Federal Rural Pernambuco; BrasilFil: Isaia, Marco. Università di Torino; ItaliaFil: Lafage, Denis. Universite de Rennes I; FranciaFil: Líznarová, Eva. Masaryk University; República ChecaFil: Macías Hernández, Nuria. Universidad de La Laguna; EspañaFil: Fiorini de Magalhaes, Ivan Luiz. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaFil: Malumbres Olarte, Jagoba. Universidade Dos Açores; PortugalFil: Michálek, Ondrej. Masaryk University; República ChecaFil: Michalik, Peter. ERNST MORITZ ARNDT UNIVERSITÄT GREIFSWALD (UG);Fil: Michalko, Radek. No especifíca;Fil: Milano, Filippo. Università di Torino; ItaliaFil: Munévar, Ana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Nentwig, Wolfgang. University of Bern; SuizaFil: Nicolosi, Giuseppe. Università di Torino; ItaliaFil: Painting, Christina J. No especifíca;Fil: Pétillon, Julien. Universite de Rennes I; FranciaFil: Piano, Elena. Università di Torino; ItaliaFil: Privet, Kaïna. Universite de Rennes I; FranciaFil: Ramirez, Martin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaFil: Ramos, Cândida. No especifíca;Fil: Rezác, Milan. No especifíca;Fil: Ridel, Aurélien. Universite de Rennes I; FranciaFil: Ruzicka, Vlastimil. No especifíca;Fil: Santos, Irene. No especifíca;Fil: Sentenská, Lenka. Masaryk University; República ChecaFil: Walker, Leilani. No especifíca;Fil: Wierucka, Kaja. Universitat Zurich; SuizaFil: Zurita, Gustavo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas; ArgentinaFil: Cardoso, Pedro. No especifíca

The World Spider Trait database : a centralised global open repository for curated data on spider traits

Publisher Copyright: © The Author(s) 2021. Published by Oxford University Press.Spiders are a highly diversified group of arthropods and play an important role in terrestrial ecosystems as ubiquitous predators, which makes them a suitable group to test a variety of eco-evolutionary hypotheses. For this purpose, knowledge of a diverse range of species traits is required. Until now, data on spider traits have been scattered across thousands of publications produced for over two centuries and written in diverse languages. To facilitate access to such data, we developed an online database for archiving and accessing spider traits at a global scale. The database has been designed to accommodate a great variety of traits (e.g. ecological, behavioural and morphological) measured at individual, species or higher taxonomic levels. Records are accompanied by extensive metadata (e.g. location and method). The database is curated by an expert team, regularly updated and open to any user. A future goal of the growing database is to include all published and unpublished data on spider traits provided by experts worldwide and to facilitate broad cross-taxon assays in functional ecology and comparative biology. Database URL:https://spidertraits.sci.muni.cz/.Peer reviewe

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Study of aeroelastic interference effect among four cylinders arranged in rectangular configuration

project No. 19-21817S of the Czech Science Foundation (GAČR

Investigation of flow around and in wake of a heated circular cylinder at moderate Reynolds numbers

The flow around a heated circular cylinder and the wake behind it were studied in wind tunnel flow using two methods of anemometry, i.e., particle image velocimetry (PIV) and constant temperature anemometry (CTA) with a special technique using a rotating slanted hot-wire probe. The Reynolds number ranged from 2000 to 20,000 and the cylinder wall temperatures varied between 27 degrees C and 177 degrees C. The wake is characterized by mean wind and fluctuation contour maps. Significant changes in wake patterns were observed while the cylinder was being heated, thus increasing its wall temperatures at low Reynolds numbers. At higher Reynolds numbers, the effects of cylinder heating on wake properties were negligible. The research fills a gap observed in the literature for a certain combination of velocity, cylinder aspect ratio, and cylinder surface temperature.Web of Science14211art. no. 11180