6 research outputs found
Working memory features are embedded in hippocampal place fields.
No full text<p>These are the Matlab scripts / functions used for analyzing electrophysiological data used in the above titled manuscript. Please, contact the lead or the first author of the connected manuscript for help with using these scripts.</p>
Regression plane concept for analysing continuous cellular processes with machine learning
No full textFunding Information: The authors thank Antti Lehmussola and Pekka Ruusuvuori (Tampere University of Technology, Finland) for the information provided about the SIMCEP software; Samuli Ripatti and Ida Surakka (FIMM, University of Helsinki, Finland) for their valuable comments on our experiments related to the genes associated with dyslipidemia; Anna Uro (Faculty of Medicine, University of Helsinki) for providing expertise in the biochemical quantification of lipid levels; Mariliina Arjama (FIMM, University of Helsinki, Finland) for technical expertise in cell culture; the FIMM High Throughput Biomedicine Unit for providing access to high throughput robotics and siRNA library and the FIMM High Content Imaging and Analysis unit for HC-imaging (HiLIFE, University of Helsinki and Biocenter Finland); Olli Kallioniemi (FIMM, University of Helsinki, Finland) for support in HC-imaging capabilities and funding; Gabriella Tick and Máté Görbe (BRC, Szeged, Hungary) for their help with the software documentation; the Finnish Grid and Cloud Infrastructure (urn:nbn:fi:research-infras-2016072533) for computational resources; Dóra Bokor (BRC, Szeged, Hungary) for proofreading the manuscript. A.Sz., B.T., A.B., E.M., Cs.M. and P.H. acknowledge support from the Hungarian National Brain Research Program (MTA-SE-NAP B-BIOMAG), from the LENDULET-BIOMAG Grant (2018-342), from the European Regional Development Funds (GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00026, GINOP-2.3.2-15-2016-00037), from the H2020 (ERAPERMED-COMPASS, DiscovAIR) and from the Chan Zuckerberg Initiative (Deep Visual Proteomics). A.Sz. and E.I. acknowledges support from University of Helsinki (Centre of Excellence matching funds) and Academy of Finland (project 324929). V.P., L.P. and P.H. acknowledge support from the Finnish TEKES FiDiPro Fellow Grant 40294/13 and FIMM High Content Imaging and Analysis Unit (FIMM-HCA; HiLIFE-HELMI) and Biocenter Finland, Finnish Cancer Society, Juselius Foundation, Academy of Finland Centre of Excellence in Translational Cancer Biology, Kymenlaakso and Finnish Cultural Foundation. V.P. acknowledges University of Helsinki post-doctoral research project grant. F.P. acknowledges support from the Union for International Cancer Control (UICC) for a UICC Yamagiwa-Yoshida (YY) Memorial International Cancer Study Grant (ref: UICC-YY/678329). V.H. and I.A. acknowledge the Hungarian National Research Fund (OTKA NKFI‐2 NN118207). V.H. acknowledges support from the National Research, Development and Innovation Office (OTKA K-131484). L.P. and J.P. acknowledge support from the Academy of Finland, decision numbers 295694, 313748, 327352 and 310552. Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool enabling class-free phenotypic supervised machine learning, to describe and explore biological data in a continuous manner. First, we compare traditional classification with regression in a simulated experimental setup. Second, we use our framework to identify genes involved in regulating triglyceride levels in human cells. Subsequently, we analyse a time-lapse dataset on mitosis to demonstrate that the proposed methodology is capable of modelling complex processes at infinite resolution. Finally, we show that hemocyte differentiation in Drosophila melanogaster has continuous characteristics.Peer reviewe
Phylogenomics, divergence times and notes of orders in Basidiomycota
No full textBasidiomycota is one of the major phyla in the fungal tree of life. The outline of Basidiomycota provides essential taxonomic information for researchers and workers in mycology. In this study, we present a time-framed phylogenomic tree with 487 species of Basidiomycota from 127 families, 47 orders, 14 classes and four subphyla; we update the outline of Basidiomycota based on the phylogenomic relationships and the taxonomic studies since 2019; and we provide notes for each order and discuss the history, defining characteristics, evolution, justification of orders, problems, significance, and plates. Our phylogenomic analysis suggests that the subphyla diverged in a time range of 443–490 Myr (million years), classes in a time range of 312–412 Myr, and orders in a time range of 102–361 Myr. Families diverged in a time range of 50–289 Myr, 76–224 Myr, and 62–156 Myr in Agaricomycotina, Pucciniomycotina, and Ustilaginomycotina, respectively. Based on the phylogenomic relationships and divergence times, we propose a new suborder Mycenineae in Agaricales to accommodate Mycenaceae. In the current outline of Basidiomycota, there are four subphyla, 20 classes, 77 orders, 297 families, and 2134 genera accepted. When building a robust taxonomy of Basidiomycota in the genomic era, the generation of molecular phylogenetic data has become relatively easier. Finding phenotypical characters, especially those that can be applied for identification and classification, however, has become increasingly challenging. © The Author(s) under exclusive licence to Mushroom Research Foundation 2024
Measurement of the ratio of inclusive jet cross sections using the anti- kT algorithm with radius parameters R=0.5 and 0.7 in pp collisions at s =7 TeV
No full textPublished by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published articles title, journal citation, and DOI.Measurements of the inclusive jet cross section with the anti-kT clustering algorithm are presented for two radius parameters, R=0.5 and 0.7. They are based on data from LHC proton-proton collisions at s=7TeV corresponding to an integrated luminosity of 5.0fb-1 collected with the CMS detector in 2011. The ratio of these two measurements is obtained as a function of the rapidity and transverse momentum of the jets. Significant discrepancies are found comparing the data to leading-order simulations and to fixed-order calculations at next-to-leading order, corrected for nonperturbative effects, whereas simulations with next-to-leading-order matrix elements matched to parton showers describe the data best
