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For this, we additionally acquired measures of resting- and flight metabolic rates of the study individuals using flow-through respirometry.\n3.\tAre there physiological mechanisms that may more directly link toxicity and flight, e.g. recycling cyanogenic glucosides to release nitrogenous compounds that could provide energy for flight?\n\nSAMPLE COLLECTION AND PREPARATION\n\nSample collection and RNA extraction: All individuals were sampled within three days following emergence, were unmated and unfed. One-half of the thorax was flash-frozen in liquid nitrogen and stored in -80°C. RNA was extracted from thorax tissue samples (one quarter of thorax) with Bioline TRIsure protocol, and re-dissolved in RNA-free water. DNA was removed with Ambion DNA-free kit. RNA concentration was analysed with Qubit RNA HS kit, RNA purity was assessed with Nanodrop and integrity with Agilent 2100 (using aliquots of a subset of 12 samples). RIN value is non-applicable for Lepidoptera RNA, but visual inspection proves the sample integrity if high (there is negligible degradation of the RNA product. Extracted RNA samples were further processed and sequenced by Novogene.\n\nRNA quantification and qualification: RNA degradation and contamination was monitored on 1% agarose gels. RNA purity was checked using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). RNA concentration was measured using Qubit® RNA Assay Kit in Qubit® 2.0 Flurometer (Life Technologies, CA, USA). RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA).\n\nLibrary preparation for Transcriptome sequencing: A total amount of 3 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer（5X). First strand cDNA was synthesized using random hexamer primer and M MuLV Reverse Transcriptase（RNase H-）. Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3’ ends of DNA fragments, NEBNext Adaptor with hairpin loop structure were ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 150~200 bp in length, the library fragments were purified with AMPure XP system (Beckman Coulter, Beverly, USA). Then 3 μl USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95 °C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. At last, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system.\nClustering and sequencing (Novogene Experimental Department): The clustering of the index-coded samples was performed on a cBot Cluster Generation System using HiSeq PE Cluster Kit cBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and 125 bp/150 bp paired-end reads were generated.\n\nQuantification of cyanogenic compounds linamarin and lotaustralin using nuclear magnetic resonance (NMR): The biosynthesized cyanogenic glucoside compounds linamarin and lotaustralin were identified and quantified from the H. erato samples using nuclear magnetic resonance (NMR). For details of the NMR method, see Mattila et al. 2021 (https://doi.org/10.7717/peerj.11523)\n\nMetabolic rates: Measures of resting- and flight metabolic rates of the study individuals were acquired using flow-through respirometry (see Niitepõld et al. 2009, https://doi.org/10.1890/08-1498.1) For the measurement, the individual was placed in a 1-L transparent respirometry chamber, through which CO2-free dry air was pumped at the rate of 1.04 L/min. After acclimatization in the darkened measurement chamber for ~30 min, resting metabolic rate (RMR) was measured during one minute of complete immobility. The individual was then stimulated to fly for 10 min in the exposed chamber, and the highest peak value in the CO2 emission rate during the flight was used as a measure of flight metabolic rate (FMR). This measure is expected to represent the maximal flight performance of an individual butterfly. Metabolic rate generally scales positively with body mass, and in intraspecific comparisons, this effect should be accounted for (e.g., Kleiber 1947). To remove the effect of body mass on FMR and enable the examination of mass-independent FMR differences, the residual from a linear model of FMR against body mass was used, calculated separately for females and males. In addition to body mass, measurement temperature and time of day of measurement were controlled for.\n\n \nDESCRIPTIONS OF THE RNASeq DATASETS:\n\nRNASeq dataset 1: \n•\tMain question: Genes associated with cyanogen biosynthesis and cyanogen-flight metabolic rate trade-off\n•\tSequenced samples: 54 H. erato (27 males, 27 females) with phenotype data of 1) biosynthesized cyanogenic toxicity (based on NMR analysis), 2) resting- and flight metabolic rates.\n•\tTwo sequencing runs: run 1 and run 2. Additional run 2 was done because of low coverage in some samples in run 1. Preliminary choice of higher quality sequencing run has been made per sample, provided in the associated phenotype data file.\n•\tRaw data of RNASeq dataset 1, sequencing run 1, found in folder named: “Heliconius erato cyanogenic toxicity RNAseq Dataset1_54 samples sequencing run 1”\n•\tRaw data of RNASeq dataset 1, sequencing run 2, found in folder named: “Heliconius erato cyanogenic toxicity RNAseq Dataset1_54 samples sequencing run 2 additional sequencing”\n\nRNASeq dataset 2:\n•\tMain question: Verification of cyanogen biosynthesis-associated differentially expressed genes and testing family effect\n•\tSequenced samples: 37 H. erato females across 8 full-sibling families with phenotype data of biosynthesized cyanogenic toxicity (based on NMR analysis).\n•\tRaw sequence data of RNASeq dataset 2 found in folder named: “Heliconius erato cyanogenic toxicity RNAseq Dataset2_37 females across 8 full-sibling families”\n\n\nDESCRIPTIONS OF ASSOCIATED DATASETS:\n\nIndividual and phenotype data: Basic information of study individuals, including origin, sex, mass, eclosion date and values of measured phenotypes.\n•\tDatafile names: \no\t“Heliconius erato cyanogenic glucoside toxicity RNASeq_dataset1_phenotypes.csv”\no\t“Heliconius erato cyanogenic glucoside toxicity RNASeq_dataset2_phenotypes.csv”\n\nNMR data: \n•\tProcessed NMR data found in datafile named: “Heliconius erato cyanogenic glucoside concentration data from NMR.csv”\n•\tRaw nuclear magnetic resonance data of 904 H. erato individuals found in data folder named: “Heliconius erato NMR data”\n•\tThese data also include NMR data of individuals sequenced for RNASeq datasets 1 and 2."
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"en": "These files (version 1) comprise the research data and code underlying the manuscript \"Integrating Cultural Heritage with the Biodiversity-Climate Nexus in Cities\", as well as the pre-print -versions of the manuscript text, manuscript figures, and the supporting information files.\r\n\r\nNote that the materials have not yet been subjected to peer-review in the context of this record, and may change before publication in a scientific journal.\r\n\r\nSources and citations critical to the creation and use of the data in this research include the following:\r\n\r\nCity of Helsinki (2024) City of Helsinki Map Service, layers describing cultural enviroments and heritage. Accessed: https://kartta.hel.fi/?setlanguage=en\r\n\r\nCity of Helsinki (2025) City of Helsinki Map Service, layers describing currently ongoing city zonation initiatives concerning local detailed plans. Accessed: https://kartta.hel.fi/?setlanguage=en\r\n\r\nHavu M, Lee HS, Soininen J, Järvi L (2022) Spatial variability of biogenic CO2 flux in Helsinki in 2020 (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7198140\r\n\r\nHavu M, Kulmala L, Lee HS, Saranko O, Soininen J, Ahongshangbam J, Järvi L (2024) CO2 uptake of urban vegetation in a warming Nordic city. Urban Forestry & Urban Greening 94: 128261. https://doi.org/10.1016/j.ufug.2024.128261\r\n\r\nHSY Helsinki Region Environmental Services Authority (2025a) Housing production in the Helsinki region 2012-2015, 2016-2019, 2020-2023, and 2024. License CC BY 4.0. Accessed,e.g.:https://www.hsy.fi/en/environmental-information/open-data/avoin-data---sivut/housing-production-in-the-helsinki-region-2024/\r\n\r\nHSY Helsinki Region Environmental Services Authority (2025b) Lot reserve in the Helsinki Metropolitan Area per block (SeutuRAMAVA). License CC BY 4.0. Accessed: https://www.hsy.fi/en/environmental-information/open-data/avoin-data---sivut/lot-reserve-in-the-helsinki-metropolitan-area-per-block-seuturamava/\r\n\r\nJalkanen J, Vierikko K, Moilanen A (2020) Spatial prioritization for urban biodiversity quality using biotope maps and expert opinion. Urban Forestry & Urban Greening 49: 126586. https://doi.org/10.1016/j.ufug.2020.126586\r\n\r\nFor more detailed references to input data sources, see manuscript text file."
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"en": "Abstract Background Social interaction is essential for health and well-being, given the growing public health concern of social isolation and loneliness. The role of the built environment in supporting social interaction has been widely studied. However, previous research has often treated social interaction as a single, undifferentiated category, although different types of interaction may serve distinct social functions and be influenced by different environmental factors. Moreover, most studies have focused primarily on residential neighborhood contexts. This study addresses these key gaps by distinguishing between two types of social interaction—tie formation and tie maintenance—and by examining built environment characteristics across broader, individualized multidimensional activity space models. Method Using data from a Public Participatory GIS (PPGIS) survey (n = 386) in Turku, Finland, this study analyzed how residential urban form and built environment features relate to tie formation and tie maintenance. Built environment features were assessed using three activity space models: 500-meter home buffer, combined buffer around home and daily destinations, and individualized activity range spanning between home and destinations. Structural Equation Modeling was used to examine how these factors influence each type of social interaction and associated psychosocial outcomes. Results Residing in urban areas was significantly associated with tie maintenance but not with tie formation. Walkability around the home supported both types of interaction, whereas parks and green spaces near daily destinations were positively associated with tie formation. A similar pattern was observed within individualized activity ranges, where park ratio predicted tie formation. These two types of social interaction influenced psychosocial outcomes through distinct pathways: tie formation had direct positive effects on health and well-being, while tie maintenance contributed indirectly through increased relationship satisfaction. Conclusion The findings emphasize the importance of distinguishing between different types of social interaction and accounting for their unique spatial and functional drivers. Urban planning and public health efforts should consider how different aspects of the built environment foster both the formation and maintenance of social ties. Promoting environments that support diverse forms of social interaction is essential not only for enhancing health and well-being but also for reducing the risk of loneliness."
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"en": "These are atomic-scale models of disordered carbon-based materials generated using molecular augmented dynamics (MAD) using experimental constraints (X-ray diffraction, neutron diffraction, X-ray photoelectron spectroscopy) and a control simulation protocol based on regular molecular dynamics (MD) without the experimental constraints.\r\n\r\nThe materials include pure carbon forms:\r\n\r\n\r\n\r\ntetrahedral amorphous carbon (ta-C);\r\n\r\nglassy carbon;\r\n\r\nnanoporous carbon;\r\n\r\n\r\nas well as binary mixtures of carbon compounds:\r\n\r\n\r\n\r\ndeuterated/hydrogenated amorphous carbon (a-C:D, a-C:H);\r\n\r\noxygen-rich amorphous carbon (a-COx).\r\n\r\n\r\nFor a-C:H, experimental a-C:D data was used in the structure generation because of the difficulty in matching the available a-C:H data due to the non-trivial interaction of hydrogen's most common isotope (protium, 1H) with neutrons. Thus, available experimental data with deuterium (2H) was used instead. The XYZ structures labeled as \"aCD\" and \"aCH\" are the same, generated with target a-C:D data, with deuterium (\"D\") or hydrogen (\"H\") labels, respectively. This is to ensure the XYZ files can be imported with common atomistic manipulation software that might give errors with \"D\" labels.\r\n\r\nThe structures where generated with the TurboGAP code (turbogap.fi) using GAP potentials trained for pure-C [1], C-H mixtures [2], C-O mixtures [3] and a core-electron binding energy model trained for C-H-O [3, 4]. The methodology used is a modified Hamiltonian dynamics approach [3] whose extension to analytical forces is known as MAD, with all the details provided in the following pair of papers:\r\n\r\n\r\n\r\nT. Zarrouk and M.A. Caro. Molecular augmented dynamics: Generating experimentally consistent atomistic structures by design. arXiv:2508.17132\r\n\r\nT. Zarrouk and M.A. Caro. Linear-scaling calculation of experimental observables for molecular augmented dynamics simulations. arXiv:2509.22388\r\n\r\n\r\nA gallery of the structures, plotted using ASE [5], Ovito [6] and ase_tools [7], is provided with file name \"gallery.png\". All other files use an obvious naming ocnvention.\r\n\r\nThe experimental data were obtained from the literature [8-12]. All the details are given in the reference papers [13, 14].\r\n\r\nContact\r\n\r\nYou can get in contact with either or both the authors at firstname.lastname(at)aalto.fi\r\n\r\nFunding\r\n\r\nThe authors acknowledge financial support from the Research Council of Finland under projects 330488, 347252, 352484, 355301 and 364778, from the European Union’s M-ERA.NET 3 program (NACAB project under grant agreement No 958174). We acknowledge the EuroHPC Joint Undertaking for awarding this project (XCALE innovation study within the Inno4scale project under grant agreement No 101118139) access to the EuroHPC supercomputer LUMI, hosted by CSC (Finland) and the LUMI consortium through a EuroHPC Regular Access call. The authors also acknowledge other computational resources from CSC – the Finnish IT Center for Science and Aalto University’s Science-IT project.\r\n\r\nReferences\r\n\r\n\r\n\r\nH. Muhli, X. Chen, A. P. Bartók, P. Hernández-León, G. Csányi, T. Ala-Nissila, and M. A. Caro. Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60. Phys. Rev. B 104, 054106 (2021).\r\n\r\nR. Ibragimova, M. S. Kuklin, T. Zarrouk, and M. A. Caro. Unifying the Description of Hydrocarbons and Hydrogenated Carbon Materials with a Chemically Reactive Machine Learning Interatomic Potential. Chem. Mater. 37, 1094 (2025).\r\n\r\nT. Zarrouk, R. Ibragimova, A. P. Bartók, and M. A. Caro. Experiment-driven atomistic materials modeling: A case study combining x-ray photoelectron spectroscopy and machine learning potentials to infer the structure of oxygen-rich amorphous carbon. J. Am. Chem. Soc. 146, 14645 (2024).\r\n\r\nD. Golze, M. Hirvensalo, Hernández-León P., A. Aarva, J. Etula, T. Susi, P. Rinke, T. Laurila, and M. A. Caro. Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining DFT and GW. Chem. Mater. 34, 6240 (2022).\r\n\r\nA. H. Larsen, J. J. Mortensen, J. Blomqvist, I. E. Castelli, R. Christensen, M. Dulak, J. Friis, M. N. Groves, B. Hammer, C. Hargus, E. D. Hermes, P. C. Jennings, P. B. Jensen, J. Kermode, J. R. Kitchin, E. L. Kolsbjerg, J. Kubal, K. Kaasbjerg, S. Lysgaard, J. B. Maronsson, T. Maxson, T. Olsen, L. Pastewka, A. Peterson, C. Rostgaard, J. Schiøtz, O. Schütt, M. Strange, K. S. Thygesen, T. Vegge, L. Vilhelmsen, M. Walter, Z. Zeng, and K. W. Jacobsen. The Atomic Simulation Environment – A Python library for working with atoms. J. Phys.: Condens. Matter 29, 273002 (2017).\r\n\r\nA. Stukowski. Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool. Modelling Simul. Mater. Sci. Eng. 18, 015012 (2009).\r\n\r\ngithub.com/mcaroba/ase_tools\r\n\r\nZ. Zeng, L. Yang, Q. Zeng, H. Lou, H. Sheng, J. Wen, D. J. Miller, Y. Meng, W. Yang, W. L. Mao, and H.-K. Mao. Synthesis of quenchable amorphous diamond. Nat. Commun. 8, 322 (2017).\r\n\r\nK. W. R. Gilkes, P. H. Gaskell, and J. Robertson. Comparison of neutron-scattering data for tetrahedral amorphous carbon with structural models. Phys. Rev. B 51, 12303 (1995).\r\n\r\nT. M. Burke, R. J. Newport, W. S. Howells, K. W. R. Gilkes, and P. H. Gaskell. The structure of a-C:H(D) by neutron diffraction and isotropic enrichment. J. Non-Cryst. Solids 164, 1139 (1993).\r\n\r\nC. A. Santini, A. Sebastian, C. Marchiori, V. P. Jonnalagadda, L. Dellmann, W. W. Koelmans, M. D. Rossell, C. P. Rossel, and E. Eleftheriou. Oxygenated amorphous carbon for resistive memory applications. Nat. Commun. 6, 1 (2015).\r\n\r\nA. C. Forse, C. Merlet, P. K. Allan, E. K. Humphreys, J. M. Griffin, M. Aslan, M. Zeiger, V. Presser, Y. Gogotsi, and C. P. Grey. New insights into the structure of nanoporous carbons from NMR, Raman, and pair distribution function analysis. Chem. Mater. 27, 6848 (2015).\r\n\r\nT. Zarrouk and M. A. Caro. Molecular augmented dynamics: Generating experimentally consistent atomistic structures by design. arXiv:2508.17132.\r\n\r\nT. Zarrouk and M. A. Caro. Linear-scaling calculation of experimental observables for molecular augmented dynamics simulations. arXiv:2509.22388."
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"fi": "Aineisto koostuu kahden työpajan yksilövalmennuskeskusteluiden litteraateista, jotka käsittelevät työpajojen arkista toimintaa sekä työllistymisen edistämistä. Valmennuskeskustelut ovat eri vaiheista valmennusprosessia, ja sama henkilö on tyypillisesti osallistunut useampaan taltioituun valmennuskeskusteluun. Aineistonkeruu toteutettiin osana työpajojen arkista ja tutkimuksesta riippumatonta toimintaa. Aineisto on osa Työmarkkinoiden marginaaleista kohti voimaantumista? (MARTE) -tutkimusprojektia. Vuorovaikutustilanteissa ilmeni sosiaaliseen työllistämiseen, työpajatoimintaan sekä valmennuskäytäntöihin liittyviä teemoja. Valmentajien ja valmennettavien yksilövalmennuskeskusteluissa muun muassa kartoitettiin valmentautujan työkykyä, työllistymisen esteitä, tavoitteita ja toiveita. Näiden pohjalta muodostettiin työllistymistä edistäviä suunnitelmia, jotka koostuvat sosiaalisesta ja ammatillisesta kuntouttamisesta. Keskeisiä valmennuskeskusteluissa esiintyviä aiheita ovat esimerkiksi valmentautujien toimintakyky ja elämänhallinta, työnhaku, kouluttautuminen, opiskelu sekä henkilökohtaiset tavoitteet. Taustatietoina ovat paikka, päivämäärä ja valmennuskeskustelun osallistujat. Aineistosta on tehty html-versio, jonka hakemiston avulla haastatteluja on helppo selata."
},
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"pref_label": {
"en": "Social sciences",
"fi": "YHTEISKUNTATIETEET",
"sv": "Samhällsvetenskaper"
}
}
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"issued": "2023-03-29",
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],
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