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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lumbierres, Maria; Abecasis, David; Alcaraz-Segura, Domingo; Alison, Jamie; +172 Authors

    The information provided here represents the EBV workflow templates collected during the EuropaBON online workshop on Essential Biodiversity Variable (EBV) workflows from 22–24 February 2023. The templates were designed to capture comprehensive descriptions about the three workflow components (data collection and sampling, data integration, and modelling) that are typical for generating EBVs. Recognising the potential value of those EBV templates for European biodiversity monitoring, our objective is to share them for enhancing transparency, knowledge exchange and collaboration, and promoting the operationalisation of EBVs across Europe. EuropaBON (https://europabon.org/) is a Horizon 2020 research and innovation action funded by the European Commission that seeks to co-design a European Biodiversity Observation Network. This network aims to bridge the gap between the biodiversity data needs of policy-makers and authorities on the one hand and the existing reporting streams and available data sources on the other hand, considering both present obligations and forthcoming policy needs. Essential Biodiversity Variables (EBVs) are a central concept of EuropaBON as they provide a standardised framework for biodiversity monitoring and reporting. In 2023, EuropaBON had identified 70 EBVs (Junker et al., 2023) that are policy-relevant for the EU, and measurable with available and existing technologies and with a proven track record of feasibility in ongoing initiatives. EBVs require workflows to process the raw data (primary observations) through data integration and modelling into spatially-explicit EBV data products (Kissling et al., 2018; Schmeller et al., 2017). These workflows can be broken down into three main components (data collection and sampling, data integration, and modelling), with additional aspects of data interoperability and IT infrastructure being recognised as crucial for transnational data streams (Kissling & Lumbierres, 2023). To capture information about the EBV workflows, an online workshop was held on 22–24 February 2023 with 520 registered participants from 49 countries, covering a large range of expertise (Lumbierres & Kissling, 2023). Participants contributed information on EBV workflow components and advanced monitoring techniques, discussed initiatives, and identified tools and requirements for implementing 70 proposed EBVs. The information from the workshop participants was collected through pre-defined EBV workflow templates (provided as Google Docs). Templates were organised into rows representing the workflow components (‘Data collection and sampling’, ‘Data integration’, and ‘Modelling’) and columns reflecting the levels of maturity ('Current initiatives', 'Emerging tools and projects' or 'Future needs'). Prior to the workshop, some information on existing workflows was pre-filled based on previous EuropaBON deliverables, namely an assessment of the current biodiversity monitoring gaps in the EU (Santana et al., 2023) and an assessment of current EU monitoring workflows and bottlenecks (Morán-Ordóñez et al., 2023). After the workshop, the EBV workflow templates were processed to ensure the accuracy and relevance of the information. Each listed initiative was verified to be part of an active biodiversity monitoring scheme and pertinent to the specific EBV under consideration, cross-referencing with the initiative’s websites and other data collected by the EuropaBON deliverables (Morán-Ordóñez et al., 2023; Santana et al., 2023). Moreover, we ensured correct alignment of each initiative and listed requirements and needs with the appropriate workflow components and maturity levels. The EBV workflow templates provide insights into the current biodiversity monitoring landscape in Europe and how EBV production could be operationalized at the EU level. They offer detailed information about ongoing initiatives and projects, methodologies, and technologies that can be used to generate EBVs at a continental scale. Nevertheless, it is important to note that they do not encompass an exhaustive list of all ongoing or proposed initiatives of biodiversity monitoring in all member states of the EU. It is suggested to use them as a starting point and baseline for the further development of EBVs in a European context.

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    ZENODO
    Other literature type . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Other literature type . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Other literature type . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Other literature type . 2024
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lampert, Adam;

    # Software for optimizing treatment to slow the spatial propagation of invasive species: code and results ## ## Description: The complete code and simulation results for finding the optimal treatment of a population front, to slow its propagation to a speed v. The algorithm is described in the paper "Optimizing strategies for slowing the spread of invasive species" by Adam Lampert (PLOS Computational Biology, DOI: 10.1371/journal.pcbi.1011996). Some of the results are demonstrated in Figs. 2-5 in that paper. ## Authorship: The code was written by Adam Lampert, Institute of Environmental Sciences, Robert H. Smith Faculty of Agriculture, Food and Environment, the Hebrew University of Jerusalem, Israel. ## Installation: Running the Matlab code requires the installation of Matlab 2021b for Windows (or a similar version of Matlab). ## Running the code – general model: 1\. Extract all files from "general_model_code.zip" into a single folder. 2\. Open "main.m" and "calc_cost.m" using Matlab. 3\. Change the parameter values, run "main.m," and wait until Matlab completes the execution. ## Running the code – spongy moth model: 1\. Extract all files from "spongy_moth_model_code.zip" into a single folder. 2\. Open "main_F2.m" using Matlab. 3\. Change the parameter values run the code, and wait until Matlab completes the execution. ## Description of the data files: The results for the general model's simulations are given as raw data in the folder "general_model_simulation_results.zip". The data files can be accessed with Matlab. Some of these results are demonstrated in the main article, Fig. 4. The results for the spongy moth model simulations are given as raw data in the folder "spongy_moth_model_simulation_results.zip". The data files can be accessed with Matlab. Some of these results are demonstrated in the main article, Fig. 5. Each data file in "general_model_simulation_results.zip" and in "spongy_moth_model_simulation_results.zip" includes the simulation results for a given set of parameters. The name of the file specifies the parameter values used. Specifically, for the general model, the file name indicates the values of α and v used for the simulation. For the spongy moth model, the file name indicates first the value of (kλ₀) and then the values of v used for the simulation. Each data file includes the following variables: * *n_front:* an array that includes the value of the population front (n-opt) as a function of the location (x). * *treatment:* an array that includes the value of the optimal treatment (A-opt) as a function of the location (x). * *Nx:* size of the n_front and the treatment arrays. The general model data files also cinclude the following parameter value: * *Dt:* time resolution (Δt) Spongy moth model data files also include the following parameter values: * *num_moves:* number of spatial steps the front moves per time unit (equivalent to v). * *delta:* the spatial resolution (σ). * *lambda:* the parameter λ₀. Slowing the spread of invasive species is a major challenge. How can we achieve this goal in the most cost-effective manner? This package includes the complete code and simulation results that help finding the optimal, most cost-effective treatment to slow the spread of a propagating species. This package accompanies the paper "Optimizing strategies for slowing the spread of invasive species" by Adam Lampert (PLOS Computational Biology, DOI: 10.1371/journal.pcbi.1011996). The file general_model_code.zip contains the code for the general model; the file spongy_moth_model_code.zip contains the code for the spongy moth model; and the file general_model_simulation_results.zip contains the results for the general model; and the file spongy_moth_model_simulation_results.zip contains the results for the spongy moth model. The code for the simulations was written in Matlab and the simulation results were obtained by running the code.  Opening the code and results requires an installation of Matlab (2021b for Windows or a similar version).

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    DRYAD; ZENODO
    Dataset . 2024
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2024
      License: CC 0
      Data sources: Datacite; ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Aylin Ulman; Abdel Fattah Nazmi Abd Rabou; Sara Al Mabruk; Michel Bariche; +13 Authors

    The silver-cheeked toadfish Lagocephalus sceleratus (Gmelin 1789), and to a lesser degree the orange spotted toadfish Torquigener hypselogeneion (Bleeker, 1852), pose threats to human health from physical attacks and poisonings in the Eastern Mediterranean Sea. This study reviewed human health-related impacts resulting from these pufferfish, compiling and assessing records from online sources, the peer-reviewed literature, medical records, personal interviews, and observations across the Eastern Mediterranean in the years 2004 to 2023. A total of 198 events impacting human health were documented: 28 records of physical attacks, at least 144 non-lethal poisoning episodes, and 27 human fatalities resulting from consumption. The majority of the reported incidences occurred in Syria, Türkiye, and Lebanon. Most physical attacks occurred in summer, while most poisoning events occurred during winter. The number of recorded incidents greatly increased after 2019, especially with regard to poisonings, yet whether this is related to greater media attention, or to increased fish abundance is unclear. This is the first comprehensive study to collate findings on attacks, poisonings and fatalities caused by these pufferfish in the Mediterranean Sea, and may help in improving national health policies. We urge the continuation of national campaigns to caution residents and tourists of these species’ high toxicities and potential aggressiveness.

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    Biology
    Article . 2024 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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      Biology
      Article . 2024 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ismael Soto; Paride Balzani; Laís Carneiro; Ross N. Cuthbert; +81 Authors

    ABSTRACTStandardised terminology in science is important for clarity of interpretation and communication. In invasion science – a dynamic and rapidly evolving discipline – the proliferation of technical terminology has lacked a standardised framework for its development. The result is a convoluted and inconsistent usage of terminology, with various discrepancies in descriptions of damage and interventions. A standardised framework is therefore needed for a clear, universally applicable, and consistent terminology to promote more effective communication across researchers, stakeholders, and policymakers. Inconsistencies in terminology stem from the exponential increase in scientific publications on the patterns and processes of biological invasions authored by experts from various disciplines and countries since the 1990s, as well as publications by legislators and policymakers focusing on practical applications, regulations, and management of resources. Aligning and standardising terminology across stakeholders remains a challenge in invasion science. Here, we review and evaluate the multiple terms used in invasion science (e.g. ‘non‐native’, ‘alien’, ‘invasive’ or ‘invader’, ‘exotic’, ‘non‐indigenous’, ‘naturalised’, ‘pest’) to propose a more simplified and standardised terminology. The streamlined framework we propose and translate into 28 other languages is based on the terms (i) ‘non‐native’, denoting species transported beyond their natural biogeographic range, (ii) ‘established non‐native’, i.e. those non‐native species that have established self‐sustaining populations in their new location(s) in the wild, and (iii) ‘invasive non‐native’ – populations of established non‐native species that have recently spread or are spreading rapidly in their invaded range actively or passively with or without human mediation. We also highlight the importance of conceptualising ‘spread’ for classifying invasiveness and ‘impact’ for management. Finally, we propose a protocol for classifying populations based on (i) dispersal mechanism, (ii) species origin, (iii) population status, and (iv) impact. Collectively and without introducing new terminology, the framework that we present aims to facilitate effective communication and collaboration in invasion science and management of non‐native species.

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    Biological Reviews
    Article . 2024 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      Biological Reviews
      Article . 2024 . Peer-reviewed
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    Authors: Huang, Zengle; Yi, Hemian; Kaplan, Daniel; Min, Lujin; +6 Authors

    Rare-earth monopnictides are a family of materials simultaneously displaying complex magnetism, strong electronic correlation, and topological band structure. The recently discovered emergent arc-like surface states in these materials have been attributed to the multi-wave-vector antiferromagnetic order, yet the direct experimental evidence has been elusive. Here we report the observation of non-collinear antiferromagnetic order with multiple modulations using spin-polarized scanning tunneling microscopy. Moreover, we discover a hidden spin-rotation transition of single-to-multiple modulations 2 K below the Neel temperature. The hidden transition coincides with the onset of the surface state splitting observed by our angle-resolved photoemission spectroscopy measurements. Single modulation gives rise to a band inversion with induced topological surface states in a local momentum region while the full Brillouin zone carries trivial topological indices, and multiple modulation further splits the surface bands via non-collinear spin tilting, as revealed by our calculations. The direct evidence of the non-collinear spin order in NdSb not only clarifies the mechanism of the emergent topological surface states but also opens up a new paradigm of control and manipulation of band topology with magnetism. # Data for: Hidden non-collinear spin-order induced topological surface states ## Description of the data and file structure There are three files in the dataset: Dataset.zip, filter.ipf, and DriftCorrection.ipf. filter.ipf is the IgorPro procedure for filtering out high-frequency noise of topographic images.\ DriftCorrection.ipf is the IgorPro procedure for drift correction of topographic images by the Lawler-Fujita algorithm. The dataset.zip contains folders arranged by the figures in the article "Hidden non-collinear spin-order induced topological surface states" to be published in Nature Communications. Each folder contains the STM raw data for plotting the STM images in the corresponding figure. Please refer to the article for a more detailed description of the data and methods. The STM data was collected by Omicron LT-STM at 4K. MTRX files were exported to the IgorPro files with the software Vernissage. Data analysis was performed on IgorPro. The figures are plotted with Origin and arranged in Adobe Illustrator. Use Vernissage to open the head file ending with "_0001.mtrx" and inspect the data contained within. The data can be exported as IgorPro files and further analyzed in IgorPro. IPF files are IgorPro procedures for data analysis.

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    DRYAD; ZENODO
    Dataset . 2024
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2024
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    Authors: Chust, Guillem; Villarino, Ernesto; McLean, Matthew; Mieszkowska, Nova; +34 Authors

    cti_cross_region_taxon This repository provides the data and codes used in the manuscript: Guillem Chust, Ernesto Villarino, Matthew McLean, Nova Mieszkowska, Lisandro Benedetti-Cecchi, Fabio Bulleri, Chiara Ravaglioli, Angel Borja, Iñigo Muxika, José A. Fernandes-Salvador, Leire Ibaibarriaga, Ainhize Uriarte, Marta Revilla, Fernando Villate, Arantza Iriarte, Ibon Uriarte, Soultana Zervoudaki, Jacob Carstensen, Paul J. Somerfield, Ana M. Queirós , Andrea J. McEvoy, Arnaud Auber, Manuel Hidalgo, Marta Coll, Joaquim Garrabou, Daniel Gómez-Gras, Cristina Linares, Francisco Ramírez, Núria Margarit, Mario Lepage, Chloé Dambrine, Jérémy Lobry, Myron A. Peck, Paula de la Barra, Anieke van Leeuwen, Gil Rilov, Erez Yeruham, Anik Brind'Amour, and Martin Lindegren. Cross-basin and cross-taxa patterns of marine community tropicalization and deborealization in warming European seas. Nature Communications Please check the github repo cti_cross_region_taxon for updates. Abstract Ocean warming and acidification, decreases in dissolved oxygen concentrations, and changes in primary production are causing an unprecedented global redistribution of marine life. The identification of underlying ecological processes underpinning marine species turnover, particularly the prevalence of tropicalization over deborealization, has been recently debated in the context of ocean warming. Here, we track changes in the mean thermal affinity of marine communities across European seas by calculating the Community Temperature Index for 65 biodiversity time series collected over four decades and containing 1,817 species from different communities (zooplankton, coastal benthos, pelagic and demersal invertebrates and fish). We show most communities and sites have clearly responded to ongoing ocean warming via abundance increases of warm-water species (tropicalization; 54%) and decreases of cold-water species (deborealization; 18%). Tropicalization dominated Atlantic sites compared to semi-enclosed basins such as the Mediterranean and Baltic Seas, probably due to physical barrier constraints to connectivity and species colonization. Semi-enclosed basins appeared to be particularly vulnerable to ocean warming, experiencing the fastest rates of warming and biodiversity loss through deborealization. Keywords: climate change, community temperature index, CTI, ocean connectivity. Acknowledgements This study has been supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No 869300 (FutureMARES project) (G.C., E.V., J.F-S., M.P., M.Lin., C.D., D.G-G., G.R., L.B., C.R., M.C., F.R., G.R., P.B., M.P., M.Lep., J.L., A.B., N.M., J.G., F.B., L.B.C, A.Q., F.V., A.I., and I.U.), and by the Urban Klima 2050 -- LIFE 18 IPC 000001 project, which has been received funding from European Union's LIFE programme (G.C., E.V., L.I., A.B., A.U., and M.R.). Additional financial support was obtained from the Basque Government (PIBA2020-1-0028 & IT1723-22). We thank the NOAA Climate Prediction Center for providing Sea Temperature data through the NCEP Global Ocean Data Assimilation System (GODAS) www.cpc.ncep.noaa.gov/products/GODAS. We also thank Ocean Biodiversity Information System (OBIS) for providing global occurrences of the biological group studied here. Data from the Basque Country were obtained from the Basque Water Agency (URA) monitoring network, through a Convention with AZTI. M.C., J.G., D.G.-G. and F.R. acknowledge the 'Severo Ochoa Centre of Excellence' accreditation (CEX2019-000928-S). Authors M.H. and M.Lin. are grateful for the support from ICES Working Group on Comparative Ecosystem-based Analyses of Atlantic and Mediterranean marine systems (WGCOMEDA) for this research. This paper is contribution nº xxxx from AZTI, Marine Research, Basque Research and Technology Alliance (BRTA)

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    Authors: Gupta, Kapish; Xu, Jimmy P.; Diamond, Tamir; de Jong, Iris E. M.; +8 Authors

    Materials The bile acids ursodeoxycholic acid (UDCA), hyodeoxycholic acid (HDCA), taurodeoxycholic acid (TDCA), taurocholic acid (TCA), cholic acid (CA), taurochenodeoxycholic acid (TCDCA), glycocholic acid (GCA), β-muricholic acid (BMA), tauro-β-muricholic acid (TBMCA), α-muricholic acid (AMA), deoxycholic acid (DCA), glycoursodeoxycholic acid (GUDCA), glycohyodeoxycholic acid (GHDCA), glycochenodeoxycholic acid (GCDCA) and glycine-β-muricholic acid (GBMCA) as well as an internal standard stock solution containing a mixture of CA-d4, GCA-d4 and DCA-d4 were obtained from Cayman Chemical (Ann Arbor, Michigan, USA). Stock solutions of 1 mg/mL in 100% methanol were diluted to obtain calibration curves for concentrations ranging from 15.6 to 1000 nM. Percoll was obtained from GE Healthcare Life Sciences (Chicago, Illinois, USA). Antibodies against CD4, Ly6G and CD90.2 were obtained from BD Pharmingen (San Diego, CA, USA) and antibodies against CD8α, NK1.1, B220 and Ly6C from BioLegend (San Diego, CA, USA). Goat anti-collagen I antibody for staining was obtained from Southern Biotech (Birmingham, AL, USA). Rabbit anti-Cytokeratin 19 (KRT 19) antibody was obtained from Abcam (Boston, MA, USA). Biotinylated Goat Anti-Rabbit IgG Antibody was obtained from Vector Laboratories, Inc. (Burlington, CA, USA). Biotinylated HA-binding protein for staining HA was obtained from EMD Millipore (Burlington, MA, USA). LIVE/DEAD fixable viability dye was from Life Technologies (Carlsbad, CA, USA). Biliatresone was synthesized as previously described [31]. Stock solutions were made in DMSO and diluted in 1X PBS for gavage. Animal experiments We used BALB/c mice obtained from Jackson Laboratories as animal model. All animal experiments were conducted following the National Institutes of Health policy, and the study was approved by the Institutional Animal Care and Use Committee at the University of Pennsylvania under protocol #804862, ensuring that all procedures were performed ethically and with minimal harm to the animals. To investigate the effects of biliatresone, pregnant female mice were administered either 15 mg/kg of biliatresone or vehicle (containing an equivalent concentration of DMSO, at 0.3 ml/kg) via gavage on days 14 and 15 post mating. All gavaging procedures were carried out with utmost consideration to ensure humane and ethical treatment throughout the experiment. Animals were closely monitored post-gavaging for any signs of distress, and no instances of stressed animals were recorded during the observation period. We used a dosage of 15 mg/kg of biliatresone in order to avoid pregnancy loss, which was reported by Yang et al. [1] with higher doses. The average litter size in the biliatresone-treated group was 6, comparable to the control group. We chose E14 and 15 for biliatresone treatment in alignment with the time of hepatoblast differentiation and early biliary network formation [2]. Half of the pups from each litter (chosen randomly) were euthanized at P5, and the other half were euthanized at P21. All animals were handled with humane care. Carbon dioxide was utilized as the primary method for euthanasia. Additionally, P5 pups underwent a secondary physical method of euthanasia through decapitation, while other animals underwent cervical dislocation as the secondary method..We did not determine sex given that anogenital distance measurements at P5 lack accuracy, but our random selection of pups for euthanasia at a specific day typically yields a balanced male-female distribution. Mother mice and P21 pups were euthanized on the same day, and blood, EHBDs, and liver samples were collected from all the animals. Placental tissue was not collected. Although in utero exposure is the most likely route of biliatresone toxicity in pups, we could not rule out transfer in milk [3] and therefore kept nursing mothers with pups until P21, then euthanized both. Serum samples were analyzed for albumin, alkaline phosphatase (ALP) and aspartate transaminase (AST) levels, as well as bile acid concentrations. Liver samples were analyzed for bile acids and immune cells. EHBD and liver samples were fixed and stained for further analysis. There were 13-15 total pups in each group; however, due to limited serum sample volumes, especially in P5 pups, not all analyses could be performed on all samples. Histochemistry and immunostaining After collection, EHBDs and liver samples were fixed in 10% formalin and embedded in paraffin. The embedded samples were then sectioned at 5 µm thickness, and slides were stained for Hematoxylin and Eosin (H&E). Standard protocols were followed for processing the slides. For antibody staining, EHBD sections were deparaffinized with xylene and rehydrated through a graded series of alcohols and distilled water. Antigen retrieval was performed in 10 mM citric acid buffer (pH 6.0). Sections were blocked with 5% bovine serum albumin and permeabilized with 0.4% Triton X-100 prior to antibody incubation. Sections were stained for collagen I, HA and DAPI as described in [4]. For collagen cy3 anti-goat antibodies and for HA-binding protein, Cy2-streptavidin secondary antibodies were used (1:500, Vector Laboratories). Liver sections were stained for KRT 19 and labelled with diaminobenzidine. Sections were incubated with 3% H2O2 to quench endogenous peroxidases and blocked with StartingBlock™ T20/PBS Blocking Buffer (Thermo Fisher Scientific, Waltham, MA, USA) and Avidin D and Biotin Blocking Reagents, prior to incubation with primary KRT 19 antibodies (1:500) overnight at 4oC. The next day, sections were incubated with secondary antibodies (1:500) for 30 minutes at 37oC and visualized using an Avidin-Biotin Complex detection system (Vector Elite Kit, Vector Laboratories, Burlingame, CA, USA). Signals were developed by a diaminobenzidine substrate kit for peroxidases (Vector Laboratories) and counterstained with hematoxylin. Histology assessment For bile duct H&E-stained slides, a qualitative assessment of damage was performed by grading the slides as normal or abnormal based on several features. These features included the presence of luminal debris, marked inflammation, detachment of surface epithelium, and signs of regeneration (including multi-layered surface epithelium and peribiliary gland expansion). Similarly, liver H&E-stained slides were graded as normal or abnormal based on the presence of bile duct damage, ductular reaction and the presence of bile plugs. The grading was performed independently by two researchers, IDJ and NDT, with more than eight animals per group being analyzed. Image analysis Image analysis of stained sections was performed with Fiji ImageJ and QuPath v0.2.0 software. The QuPath selection tool was used to calculate the area of the biliary submucosa that was occupied by HA (based on HA-binding protein staining) relative to the entire submucosal area. As a second measure for the thickness of the HA layer, the width between the lumen and the HA-collagen interface was measured in at least 5 different places, and was adjusted relative to the entire thickness of the bile duct wall. For KRT 19 stained samples, the QuPath pixel classification tool was utilized to measure the KRT 19 positive area relative to the field area. The number of KRT 19-positive foci per portal triad was counted manually. Liver immunology Intrahepatic leukocytes were isolated by Percoll density gradient centrifugation and stained with LIVE/DEAD fixable viability dye, or with antibodies against CD4, CD8α, NK1.1, B220, Ly6C, Ly6G, and CD90.2. All samples were separated on a MACSQuant flow cytometer (Miltenyi Biotec, Gaithersburg, MD, USA) and analyzed using FlowJo software version 10.6 (Tree Star) (Fig 4A). Sample processing for HPLC Bile acids were extracted from homogenized liver and serum samples as described [5,6]. Separations were performed on a Waters BEH C18 Column (2.1 mm x 50 mm 1.7 μm). Mobile phase A was water with 0.1% formic acid, and mobile phase B was methanol with 0.1% formic acid at 0.4 mL/min flow. The gradient started at 5% B and was changed to 40% B over 2 min, then to 99% B over 2 min, held constant for 3 minutes then back to the initial composition for equilibration of the column, for a total chromatographic separation time of 12 min. Analysis was conducted on a Thermo Q Exactive HF coupled to an Ultimate 3000 UHPLC interfaced with a heated electrospray ionization (HESI-II) source. The instrument was operated in negative ion mode alternating between full scan from 250-800 m/z at a resolution of 120,000 and parallel reaction monitoring at 60,000 resolution with a precursor isolation window of 0.7 m/z. Since sample amounts were limited, not all analyses were performed on all samples. Bile acid values were normalized to average values obtained for control pups in each set of experiments. Some bile acids could not be detected in all samples; for purposes of the analysis, only those bile acids detected in at least 5 samples were considered. Statistical analysis Statistical significance was calculated by one and two-tailed Student’s t-tests. Differences in variance were tested using the F test [7]. The number of samples tested for each experiment is given in parentheses in the graphs. All data are shown as boxplots. References 1. Yang Y, Wang J, Zhan Y, Chen G, Shen Z, Zheng S, et al. The synthetic toxin biliatresone causes biliary atresia in mice. Lab Investig. 2020;100: 1425–1435. doi:10.1038/s41374-020-0467-7 2. Su X, Shi Y, Zou X, Lu ZN, Xie G, Yang JYH, et al. Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development. BMC Genomics. 2017;18: 946. doi:10.1186/s12864-017-4342-x 3. Kotb MA, Kotb A, Talaat S, Shehata SM, El Dessouki N, Elhaddad AA, et al. Congenital aflatoxicosis, mal-detoxification genomics & ontogeny trigger immune-mediated Kotb disease biliary atresia variant: SANRA compliant review. Med (United States). 2022;101: E30368. doi:10.1097/MD.0000000000030368 4.de Jong IEM, Hunt ML, Chen D, Du Y, Llewellyn J, Gupta K, et al. A fetal wound healing program after intrauterine bile duct injury may contribute to biliary atresia. J Hepatol. 2023;79: 1396–1407. doi:10.1016/j.jhep.2023.08.010 5. Huang J, Bathena SPR, Csanaky IL, Alnouti Y. Simultaneous characterization of bile acids and their sulfate metabolites in mouse liver, plasma, bile, and urine using LC-MS/MS. J Pharm Biomed Anal. 2011;55: 1111–1119. doi:10.1016/j.jpba.2011.03.035 6. Ghaffarzadegan T, Essén S, Verbrugghe P, Marungruang N, Hållenius FF, Nyman M, et al. Determination of free and conjugated bile acids in serum of Apoe(−/−) mice fed different lingonberry fractions by UHPLC-MS. Sci Rep. 2019;9: 3800. doi:10.1038/s41598-019-40272-8 7. Mohr DL, Wilson WJ, Freund RJ. Statistical Methods. Statistical Methods. New Delhi: Wiley Blackwell; 2021. doi:10.1016/B978-0-12-823043-5.00015-1 # Low-dose biliatresone treatment of pregnant mice causes subclinical biliary disease in their offspring: evidence for a spectrum of neonatal injury This dataset comprises data collected from pups born to mothers administered either biliatresone or a vehicle control via gavage. Analysis was conducted on pups at postnatal days 5 and 21, with mothers analyzed alongside pups at postnatal day 5. Physical measurements, liver, bile duct, and serum samples were collected. Serum samples were utilized for assessing serum biochemistry relevant to liver diseases and bile acid content. Liver samples underwent analysis for cytokeratin-19 (KRT19) staining, immune profiling, and bile acid measurements. Bile duct samples were subjected to hyaluronic acid staining. ## Description of the data and file structure The data are structured in accordance with the organization of figures in our manuscript, with two comparison groups designated: "D" representing the vehicle control group and "B" representing the biliatresone-treated group. Each cell value within the groups represents data obtained from individual pups. High-performance liquid chromatography (HPLC) measurements are presented as normalized area ratios for specific bile acids. The first cell in each sheet provides a brief background on the experiment and the specific measurement presented in the sheet. ## Sheet 1 Sheet title: Fig 1B.P5 serum biochemistry Description: Pregnant mothers (E14 and E15) were treated with 15 mg/kg of biliatresone or vehicle control (DMSO), and P5 pups were analyzed. Serum biochemistry and physical parameters for P5 pups. The number of pups is shown in parentheses. D denotes DMSO treated, B denotes Biliatresone treated. Each cell denote value from individual pup. ALP (U/L), AST (U/L), Albumin (g/dL), and Weight (gm) were analyzed and are reported. ## Sheet 2 Sheet title: Fig 1D.P5 KRT19 quantification Description: Pregnant mothers (E14 and E15) were treated with 15 mg/kg of biliatresone or vehicle control (DMSO), and P5 pups were analyzed. Quantification of the number of cytokeratin 19 (KRT19) positive foci per portal triad and KRT19 positive area per field in P5 pups, with the number of pups indicated in parentheses. D denotes DMSO treated, B denotes Biliatresone treated. Each cell denote value from individual pup. KRT 19 +ve Area per field (%) and KRT 19 +ve duct/PT were analyzed and are reported. PT: Portal traid. ## Sheet 3 Sheet title: Fig 1F.P5 hyaluronic acid Description: Pregnant mothers (E14 and E15) were treated with 15 mg/kg of biliatresone or vehicle control (DMSO), and P5 pups were analyzed. Quantification of submucosal area stained by hyaluronic acid (HA)-binding protein. The number of P5 pups is shown in parentheses. D denotes DMSO treated, B denotes Biliatresone treated. Each cell denote value from individual pup.HA content (%) is reported in this sheet. ## Sheet 4 Sheet title: Fig 2B.P21 serum biochemistry Description: Pregnant mothers (E14 and E15) were treated with 15 mg/kg of biliatresone or vehicle control (DMSO), and P21 pups were analyzed. Serum biochemistry and physical parameters for P21 pups. The number of pups is shown in parentheses. D denotes DMSO treated, B denotes Biliatresone treated. Each cell denote value from individual pup. ALP (U/L), AST (U/L), Albumin (g/dL), and Weight (gm) were analyzed and are reported. ## Sheet 5 Sheet title: Fig 2D.P21 KRT19 quantification Description: Pregnant mothers (E14 and E15) were treated with 15 mg/kg of biliatresone or vehicle control (DMSO), and P21 pups were analyzed. Quantification of the number of cytokeratin 19 (KRT19) positive foci per portal triad and KRT19 positive area per field in P21 pups, with the number of pups indicated in parentheses. D denotes DMSO treated, B denotes Biliatresone treated. Each cell denote value from individual pup. KRT 19 +ve Area per field (%) and KRT 19 +ve duct/PT were analyzed and are reported. PT: Portal traid. ## Sheet 6 Sheet title: Fig 3A.P5 liver bile acid Pregnant mothers (E14 and E15) were treated with 15 mg/kg of biliatresone or vehicle control (DMSO). Liver from P5 pups were analyzed. Relative amounts of various bile acids from P5 liver are reported here. Individual bile acids were normalized to the corresponding mean from control pups from each experiment. Each column denote value from individual pup. D denotes DMSO treated. B denotes Biliatresone treated. Empty cells represent the values that could not be determined. Each row denotes individual bile acid that were determined. The abbreviations for bile acid could be found in method section. ## Sheet 7 Sheet title: Fig 3B.P5 serum bile acid Pregnant mothers (E14 and E15) were treated with 15 mg/kg of biliatresone or vehicle control (DMSO). Serum from P5 pups were analyzed. Relative amounts of various bile acids from P5 Serum are reported here. Individual bile acids were normalized to the corresponding mean from control pups from each experiment. Each Column denote value from individual pup. D denotes DMSO treated. B denotes Biliatresone treated. Empty cells represent the values that could not be determined. Each row denotes individual bile acid that were determined. The abbreviations for bile acid could be found in method section. ## Sheet 8 Sheet title: Fig 3C.P21 liver bile acid Pregnant mothers (E14 and E15) were treated with 15 mg/kg of biliatresone or vehicle control (DMSO). Liver from P21 pups were analyzed. Relative amounts of various bile acids from P21 liver are reported here. Individual bile acids were normalized to the corresponding mean from control pups from each experiment. Each Column denote value from individual pup. D denotes DMSO treated. B denotes Biliatresone treated. Empty cells represent the values that could not be determined. Each row denotes individual bile acid that were determined. The abbreviations for bile acid could be found in method section. ## Sheet 9 Sheet title: Fig 3D.P21 serum bile acid Pregnant mothers (E14 and E15) were treated with 15 mg/kg of biliatresone or vehicle control (DMSO). Serum from P21 pups were analyzed. Relative amounts of various bile acids from P21 Serum are reported here. Individual bile acids were normalized to the corresponding mean from control pups from each experiment. Each Column denote value from individual pup. D denotes DMSO treated. B denotes Biliatresone treated. Empty cells represent the values that could not be determined. Each row denotes individual bile acid that were determined. The abbreviations for bile acid could be found in method section. ## Sheet 10 Sheet title: Fig 4B.P21 Immune cells Pregnant mothers (E14 and E15) were treated with 15 mg/kg of biliatresone or vehicle control (DMSO). Liver from P21 pups were analyzed. Quantification showing numbers of T-cells, CD4 cells, CD8 cells, B-cells, monocytes and neutrophils in livers isolated from P21 pups. The number of pups is shown in parentheses. Each cell denote value from individual pup. D denotes DMSO treated. B denotes Biliatresone treated. ## Sheet 11 Sheet title: Fig 5BMother serum biochemistry Pregnant mothers (E14 and E15) were treated with 15 mg/kg of biliatresone or vehicle control (DMSO). Mothers were euthanized along with P21 pups, and serum biochemistry and physical parameters were measured for both control and biliatresone-treated mothers.Each cell denote value from individual pup. D denotes DMSO treated. B denotes Biliatresone treated. ## Sheet 12 Sheet title: Fig Fig 5C.Mother immune cells Pregnant mothers (E14 and E15) were treated with 15 mg/kg of biliatresone or vehicle control (DMSO). Mothers were euthanized along with P21 pups, and the numbers of immune cells in livers isolated from both control and biliatresone-treated mothers were quantified. Quantification showing numbers of T-cells, CD4 cells, CD8 cells, B-cells, monocytes and neutrophils in livers isolated from mothers. Biliary atresia is a neonatal disease characterized by damage, inflammation, and fibrosis of the liver and bile ducts and by abnormal bile metabolism. It likely results from a prenatal environmental exposure that spares the mother and affects the fetus. Our aim was to develop a model of fetal injury by exposing pregnant mice to low-dose biliatresone, a plant toxin implicated in biliary atresia in livestock, and then to determine whether there was a hepatobiliary phenotype in their pups. Pregnant mice were treated orally with 15 mg/kg/d biliatresone for 2 days. Histology of the liver and bile ducts, serum bile acids, and liver immune cells of pups from treated mothers were analyzed at P5 and P21. Pups had no evidence of histological liver or bile duct injury or fibrosis at either timepoint. In addition, growth was normal. However, serum levels of glycocholic acid were elevated at P5, suggesting altered bile metabolism, and the serum bile acid profile became increasingly abnormal through P21, with enhanced glycine conjugation of bile acids. There was also immune cell activation observed in the liver at P21. These results suggest that prenatal exposure to low doses of an environmental toxin can cause subclinical disease including liver inflammation and aberrant bile metabolism even in the absence of histological changes. This finding suggests a wide potential spectrum of disease after fetal biliary injury.

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    Authors: Toledano, Hila; Perry, Noam; Braun, Racheli; Ben-Hamo-Arad, Aya; +3 Authors

    # Integrin restriction by miR-34 protects germline progenitors from cell death during aging The dataset contains 16 samples. Each Sample contains 2 fastq.gz files from 2 lanes. There are 4 experimental groups: * w1118 1 day * w1118 30 day * mir34 knockout 1 day * mir34 knockout 30 day Each group contains 4 biological replicates. ## Description of the data and file structure w1118 1 day Rep1: w1118_1day_Rep1_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 1 day, Replicate #1, Lane 6 w1118_1day_Rep1_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 1 day, Replicate #1, Lane 7 w1118 1 day Rep2: w1118_1day_Rep2_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 1 day, Replicate #2, Lane 6 w1118_1day_Rep2_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 1 day, Replicate #2, Lane 7 w1118 1 day Rep3: w1118_1day_Rep3_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 1 day, Replicate #3, Lane 6 w1118_1day_Rep3_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 1 day, Replicate #3, Lane 7 w1118 1 day Rep4: w1118_1day_Rep4_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 1 day, Replicate #4, Lane 6 w1118_1day_Rep4_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 1 day, Replicate #4, Lane 7 w1118 30 days Rep1: w1118_30day_Rep1_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 30 days, Replicate #1, Lane 6 w1118_30day_Rep1_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 30 days, Replicate #1, Lane 7 w1118 30 days Rep2: w1118_30day_Rep2_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 30 days, Replicate #2, Lane 6 w1118_30day_Rep2_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 30 days, Replicate #2, Lane 7 w1118 30 days Rep3: w1118_30day_Rep3_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 30 days, Replicate #3, Lane 6 w1118_30day_Rep3_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 30 days, Replicate #3, Lane 7 w1118 30 days Rep4: w1118_30day_Rep4_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 30 days, Replicate #4, Lane 6 w1118_30day_Rep4_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype w1118, Age 30 days, Replicate #4, Lane 7 mir34 KO 1 day Rep1: mir34_KO_1day_Rep1_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 1 day, Replicate #1, Lane 6 mir34_KO_1day_Rep1_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 1 day, Replicate #1, Lane 7 mir34 KO 1 day Rep2: mir34_KO_1day_Rep2_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 1 day, Replicate #2, Lane 6 mir34_KO_1day_Rep2_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 1 day, Replicate #2, Lane 7 mir34 KO 1 day Rep3: mir34_KO_1day_Rep3_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 1 day, Replicate #3, Lane 6 mir34_KO_1day_Rep3_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 1 day, Replicate #3, Lane 7 mir34 KO 1 day Rep4: mir34_KO_1day_Rep4_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 1 day, Replicate #4, Lane 6 mir34_KO_1day_Rep4_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 1 day, Replicate #4, Lane 7 mir34 KO 30 days Rep1: mir34_KO_30day_Rep1_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 30 days, Replicate #1, Lane 6 mir34_KO_30day_Rep1_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 30 days, Replicate #1, Lane 7 mir34 KO 30 days Rep2: mir34_KO_30day_Rep2_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 30 days, Replicate #2, Lane 6 mir34_KO_30day_Rep2_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 30 days, Replicate #2, Lane 7 mir34 KO 30 days Rep3: mir34_KO_30day_Rep3_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 30 days, Replicate #3, Lane 6 mir34_KO_30day_Rep3_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 30 days, Replicate #3, Lane 7 mir34 KO 30 days Rep4: mir34_KO_30day_Rep4_L6.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 30 days, Replicate #4, Lane 6 mir34_KO_30day_Rep4_L7.fastq.gz * RNA-Sequencing data from Drosophila testes, Genotype mir34 knockout, Age 30 days, Replicate #4, Lane 7 During aging, regenerative tissues must dynamically balance the two opposing processes of proliferation and cell death. While many microRNAs are differentially expressed during aging, their roles as dynamic regulators of tissue regeneration have yet to be described. We show that in the highly regenerative Drosophila testis, miR-34 levels are significantly elevated during aging. miR-34 modulates germ cell death and protects the progenitor germ cells from accelerated aging. However, miR-34 is not expressed in the progenitors themselves but rather in neighboring cyst cells that kill the progenitors. Transcriptomics followed by functional analysis revealed that during aging, miR-34 modifies integrin signaling by limiting the levels of the heterodimeric integrin receptor αPS2 and βPS subunits. In addition, we found that in cyst cells, this heterodimer is essential for inducing phagoptosis and degradation of the progenitor germ cells. Together, these data suggest that the miR-34 – integrin signaling axis acts as a sensor of progenitor germ cell death to extend progenitor functionality during aging. Illumina cDNA libraries were prepared from 1 µg total RNA extracted from testes of young and aged control w1118 and miR-34 null mutants. Sequencing libraries were prepared using INCPM mRNA Sequence Single-Read. Sixty reads were sequenced on two lanes of an Illumina HiSeq apparatus. The output was ~22 million reads per sample. Poly-A/T stretches and Illumina adapters were trimmed from the reads using cutadapt. Resulting reads shorter than 30 bp were discarded. Reads were mapped to the Drosophila melanogaster dmel reference genome using STAR, supplied with gene annotations downloaded from FlyBase (r6.18) (and with the EndToEnd option and outFilterMismatchNoverLmax was set to 0.04).

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    ZENODO; DRYAD
    Dataset . 2024
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      ZENODO; DRYAD
      Dataset . 2024
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    Authors: Mizrahi, Itzhak; Zorea, Alvah; Bayer, Ed; Levin, Liron; +9 Authors

    # Cryptic diversity of cellulose-degrading gut bacteria in industrialized humans Data sources files for all figures and supplementary material Description of the data and file structure Data S1 Figure 1B tree.nwk is an unrooted phylogenetic tree, computed with the maximum likelihood method, of 62 selected genomes and MAGs, using the sequence of the ScaC scaffoldin as a phylotyping marker. The file contains the newick format used to create phylogenetic tree. Data S2 Figure 1C data.xlsx Genomic dissimilarity computed by Mash distance within the novel identified ruminococcal cellulosomal species and pairwise comparisons to each other as well as to the ruminal R. flavefaciens species and the human species, R. champanellensis. Each column represents the mash distances. Data S3 Figure 2A.xlsx, Observed collective prevalence of the MAGs for fiber-degrading strains in various human, apes and NHP cohorts. Values in columns represents the number of positive individuals, and percentages are also given for each host category. Data S4 Figure 2Ci.xlsx Distribution of each human cellulosomal strain (R. champanellensis, R. hominiciens, R. ruminiciens and R. primaciens) across the sample cohorts. For each genome (column A), samples (column B) and host (column C), average fold and number of strains are given. Data S5 Figure 2Cii.xlsx, Distribution of the human cellulosomal strains among the human- and NHP-positive samples. 1st sheet for each genome and host, the number of samples is given (column C) as well as the percentage of positive samples (column D), the 2nd sheet includes the siginificant indval statistics for each genome group. Data S6 Figure 3A host tree.nwk is the newick format used to create phylogenetic host tree in Figure 3A, a phylogenetic tree of the mammalian host species. Data S7 Figure 3A tree.nwk is the newick format used to create phylogenetic tree in Figure 3A, a core protein phylogenetic tree illustrating the co-speciation hypothesis. Data S8 Figure 3B tree.nwk is the newick format used to create phylogenetic tree in Figure 3B, a phylogenetic tree of 197 concatenated core proteins. Data S9 Figure 4C.xlsx concentration of reducing sugars is given for the two enzymes at the different time points. Comparative cellulolytic activity of ruminococcal GH5 orthologs of either human (R. primaciens) or rumen origin (R. flavefaciens FD-1). Enzyme samples were examined using microcrystalline cellulose (Avicel) as the substrate at 37°C. Data S10 Figure 5A presence/absence (0 or 1) is giving for each MAG and gene clusters (column 1). Analysis (PCA) of the overall predicted ORFs of the MAGs. Data S11 Figure 5B.xlsx column A verticality values for common genes, column B verticality values for specific genes. Rank distribution of verticality values for core proteins across the three host types versus host-specific proteins indicates that specific genes are likely to be transferred via horizontal gene transfer within a given type of host. Data S12 Figure 5C in each column number of GH genes for each specific MAG. Analysis of the fibrolytic system [indicating glycoside hydrolase (GH) families] of the MAGs, according to their hosts. Data S13 Figure 5D transcripts expression of fibrolytic genes for the 3 hosts, 3 individual each. Analysis of the expression of the fibrolytic system, as examined by transcriptomic analysis of three fecal samples of the three hosts (macaque, human and sheep rumen). Data S14 Figure 5E middle panel number of specific genes copies for each specific MAG. The statistically significant GH families that statistically distinguish the strains associated with the three gut ecosystems as determined by the Kruskal-Wallis test p* *<0.05 after FDR correction. Data S15 Figure 5E right panel transcripts expression of specific genes for the 3 hosts, 3 individual each. Statistically significant GH expression (metatranscripts in FPKM) between the three types of hosts. Data S16 Figure 5E verticality values.xlsx verticality values are given for the indicated genes. Verticality values for each of statistically significant GH families. Data S17 Supplementary Figure S1.xlsx Prevalence of the fibrolytic strains in 1989 gut metagenomic samples. Sheet 1 presence is given as 1 in column C for a specific genome in column B, host group (column D) and host animal (column E) and run (column A). Average fold are given in column E, lifestyle in G, country in H, number of strains in the samples in I, and host category in J. Sheet 2 is the raw data, sheet 3 is same as sheet 2 but a filtered coverage at 20% for the covered percent column (column T) Data S18 Supplementary Figure S2A Prevalence and abundance of the fibrolytic strains in various human and NHPs gut samples. Prevalence of the cellulosomal strains (R. hominiciens, R. ruminiciens, R. primaciens and R. flavefaciens) is given for various subsampling read depths (5, 10, 20, 40 and 60 M). The maximal numbers of individuals in the cohorts are given for each host category at 5 M read depths. Sheet 1 prevalences are given in column D for a specific genome (column A) readsubsampling (column B), host categories are given in column C. Sheet 2 statistics Chi-square test , including pvalue, significance and adj. pvalue are given between the specific host categories and genomes. In sheet 4 additional metadata for the samples with a coverage above 20 % are given including the original study in column A. Data S19 Supplementary Figure S2B.xlsx Prevalence of the cellulosomal strains for each host category at 10 M read depths. Sheet 1 prevalences are given in column D for a specific genome (column A) readsubsampling (column B), host categories are given in column C. Additional informations include country of origin (column E), host (column F), lifestyle (column G), host catergory (column H), depth (column I), host species (column J), host group (column K), additional host category (column L). Sheet 2 statistics Chi-square test , including pvalue, significance and adj. pvalue are given between the specific host categories and genomes. Data S20 Supplementary Figure S2C.xlsx Abundance of the cellulosomal strains (R. hominiciens, R. ruminiciens, R. primaciens and R. flavefaciens) for each category at 20 M read depths. Sheet 1 abundances are given in column D for a specific genome (column A) readsubsampling (column B), samples are given in column C. Sheet 2 statistics Chi-square test , including pvalue, significance and adj. pvalue are given between the specific host categories and genomes. Data S21 Supplementary Figure S3.xlsx Prevalence of the fibrolytic strains in NHP gut samples. Prevalence of the cellulosomal strains (R. hominiciens, R. ruminiciens, R. primaciens and R. flavefaciens) is given for various subsampling read depths (5, 10, 20, 40 and 60 M). Sheet 1 prevalences are given in column D for a specific genome (column A) readsubsampling (column B), lsamples hosts are given in column C. Sheet 2 statistics Chi-square test , including pvalue, significance and adj. pvalue are given between the specific hosts and genomes. Data S22 Supplementary Figure S4.xlsx : Prevalence of the fibrolytic strains in industrialized countries. Prevalence of the cellulosomal strains (R. hominiciens, R. ruminiciens, R. primaciens and R. flavefaciens) is given for 10 M read depths. Sheet 1 prevalences are given in column D for a specific genome (column A) readsubsampling (column B), locations of the samples are given in column C. Sheet 2 statistics Chi-square test , including pvalue, significance and adj. pvalue are given between the specific locations and genomes. Data S23 Supplementary Figure S5.xlsx Prevalence of the fibrolytic strains in rural societies countries. Prevalence of the cellulosomal strains (R. hominiciens, R. ruminiciens, R. primaciens and R. flavefaciens) is given for 10 M read depths. Sheet 1 prevalences are given in column D for a specific genome (column A) readsubsampling (column B), locations of the samples are given in column C. Sheet 2 statistics Chi-square test , including pvalue, significance and adj. pvalue are given between the specific locations and genomes. Data S24 Supplementary Figure S6.xlsx Abundance of the MAGs in their ecosystem. Abundance of each MAG in each sample is given Data S25 Supplementary Figure S7.xlsx Prevalence of the fibrolytic strains in captive and wild NHPs. Prevalence of the cellulosomal strains (R. hominiciens, R. ruminiciens, R. primaciens and R. flavefaciens) is given for 10 M read depths in various animals (apes: chimpanzees, gorillas, and orangutang, other NHPs: macaques, tamarins, baboons, mandrills, capuchins, colobus monkeys, guerezas and geladas, and ruminants: cows, sheep, camels, yaks and deer). Sheet 1 number of positive (column E) and negative samples (column F) out of total number of samples examined (column D) is given for each host group (column A) and genomes (column B), lifestyle appears in column C. Sheet 2 statistics Chi-square test , including pvalue, significance and adj. pvalue are given for the specific host groups and genomes. Data S26 Supplementary Figure S8.xlsx Prevalence of the fibrolytic strains in omnivore and folivore NHPs. Prevalence of the cellulosomal strains (R. hominiciens, R. ruminiciens, R. primaciens and R. flavefaciens) is given for 10 M read depths in various NHPs (not including apes that are all omnivores). Sheet 1 prevalence (column D) of a specific genome (column A) is given, column B is the reading depth and column C the diet of the animal. Sheet 2 statistics Pearson's Chi-squared test with Yates' continuity correction and p value are given for the 3 genomes examined Data S27 Supplementary Figure S9.xlsx Identification of core proteins. Presence/absence (0 or 1) is giving for each MAG and gene clusters (column 1) Data S28 Supplementary Figure S10 tree.nwk Multilocus sequence analysis of the 30 MAGs. The file contains the newick format used to create phylogenetic tree in Figure S11 Data S29 Supplementary Figure S11.xlsx Comparative cellulolytic activity of ruminococcal GH5 orthologs of either human (R. primaciens) or rumen origin (R. flavefaciens FD-1). Enzyme samples were examined at various concentrations using amorphous cellulose as the substrate at 37°C for 1h incubation. Concentration of reducing sugars is given for the two enzymes at the different enzyme concentrations Data S30 Supplementary Figure S12.xlsx Transcriptomic analysis of R. flavefaciens, R. hominiciens and R. primaciens strains. The total gene expression in percentage of the specific MAGs in the three fecal samples of the three hosts (macaque, human and sheep rumen) is in sheet 1 overall transcripts for the 3 hosts, 3 individuals each. In sheet 2, 3 and 4 the number of transcripts (in FPKM) of each of the indicated cellulosomal genes in three fecal samples from either sheep, human or macaque host. Sheet 2, transcripts for the 3 sheep individuals for each gene, sheet 3, transcripts for the 3 human individuals for each gene, sheet 4, transcripts for the 3 macaque individuals for each gene/ Data S31 Supplementary Figure S13.xlsx Fibrolytic cellulosomal core enzymes of the 30 MAGs with respect to their sample of origin (human-, rumen- and NHPs-assembled MAGs). In each column number of GH genes for each specific MAG Data S32 197 phylogenetic trees.pdf are the compilations of the 197 phylogenetic tree used for evolutionary analysis Humans, like all mammals, depend on the gut microbiome for digestion of cellulose, the main component of plant fiber, but evidence for cellulose fermentation in the human gut is scarce. We have identified ruminococcal species in the gut microbiota of human populations that assemble functional multi-enzymatic cellulosome systems capable of degrading plant cell wall polysaccharides. One of these species, which is strongly associated with humans, likely originated in the ruminant gut and was subsequently transferred to the human gut potentially during domestication, where it underwent diversification and diet-related adaptation through the acquisition of genes from other gut microbes. Collectively, these species are abundant and widespread among ancient humans, hunter-gatherers, and rural populations, but are extremely rare in populations from industrialized societies, suggesting potential disappearance in response to the westernized lifestyle.

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    DRYAD; ZENODO
    Dataset . 2024
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      DRYAD; ZENODO
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    Authors: Pekarsky, Sasha; Shohami, David; Horvitz, Nir; Bowie, Rauri; +4 Authors

    # Cranes soar on thermal updrafts behind cold fronts as they migrate across the sea This dataset and accompanying R scripts pertain to the research study on crane migration above the sea. The focus of this study is to analyze the migratory patterns of cranes, particularly their utilization of thermal updrafts and soaring-gliding dynamics during sea crossing. The dataset includes high-resolution GPS and accelerometer data, annotated with environmental variables, and the R scripts are tailored for statistical analysis and visualization of these data. ## Description of the data and file structure The data folder contains the following files: **1HzGPS_and_ACC_annotated (multiple files):** * 1Hz data for individual cranes, annotated with environmental variables. * File names include individual crane IDs. **ThermalStats.csv:** * Summarizes 10-minute segments with and without thermal activity. * Used in `ThermalSections_analysis&plot.R` and `time_scales_analysis.R`. **SoarGlide.csv:** * Contains data on coupled soaring-gliding events. * Used in `soaring&gliding_plot&analyse.R`. **AnnotatedTimePointsMedSea.csv:** * Data for annotated time scale data for Mediterranean sea crossing. * Used in `time_scales_analysis.R`. **TimesOfMigrationFall.csv:** * Number of days crane stayed at the last stopover site before crossing the Mediterranean sea * Used in `time_scales_analysis.R`. ## Code/Software The code folder includes R scripts designed for the statistical analysis high-resolution datasets: **ThermalSections_analysis&plot.R:** * Performs statistical analysis of 10-minute thermal sections. * Generates Figure 2B. **soaring&gliding_plot&analyse.R:** * Analyzes coupled soaring-gliding events. * Generates Figure 3A. **time_scales_analysis.R:** * Conducts time-scale analysis. * Generates Figure 4A. ### Metadata Description ### (1) Data 1HzGPS\_and\_ACC\_annotated Tables: NAN indicates not available. 1. **Tag:** Identifier for the tracked animal 2. **TimeCont:** timestamps of observations (UTC). 3. **Latitude:** Geographic latitude where the observation was recorded. 4. **Longitude:** Geographic longitude where the observation was recorded. 5. **Altitude_m:** Altitude of the crane (m). 6. **Speed_km_h:** Speed of the crane (km/h). 7. **direction_deg:** Direction of crane movement (degrees clockwise from north). 8. **OverSeaOrLand:** Indicator of whether the crane is over the sea or land during observation (0=land, 1=Black sea, 2=Mediterranean sea). 9. **mag_x, mag_y, mag_z:** Components of the magnetic field measurement in the x, y, and z directions. 10. **acc_x, acc_y, acc_z:** Accelerometer readings in the x, y, and z directions (m^2/sec). 11. **Interpolated_Lat:** Interpolated latitude values. 12. **Interpolated_Lon:** Interpolated longitude 13. **InterpolatedElevation:** Interpolated elevation values (m). 14. **running_Flap_rate:** estimated flap rate form 1Hz data based on a calibration dataset of 10Hz (flaps per second) 15. **AleAboveTerrain:** Altitude of the crane above the terrain (m). 16. **TerrainHeight:** Height of the terrain above sea level (m). 17. **quartile:** Statistical quartile information. 18. **Individual:** tag number used for environmental annotation. 19. **t2m:** Temperature at 2 meters above surface level (°C). 20. **sst:** Sea surface in C temperature (°C). 21. **blh:** Boundary layer height (m). 22. **cbh:** Cloud base height (m). 23. **cape:** Convective available potential energy (J/kg). 24. **cin:** Convective inhibition (J/kg). 25. **msshf:** Mean sea sensible heat flux (W/m^2). 26. **tcc:** Total cloud cover (%). 27. **msl:** Mean sea level pressure (Pa). 28. **u850, u925:** Zonal wind component at 850 hPa and 925 hPa pressure levels. 29. **v850, v925:** Meridional wind component at 850 hPa and 925 hPa pressure levels. 30. **t850, t925:** Temperature at 850 hPa and 925 hPa pressure levels. 31. **sstDiff:** Difference between sea and air temeperature (°C) 32. **windDirection850, windDirection925:** Wind direction at 850 hPa and 925 hPa pressure levels. 33. **windSpeed850, windSpeed925:** Wind speed at 850 hPa and 925 hPa pressure levels. ### **(2) ThermalStats:** NAN indicates not available. 1. **Individual**: Identifier for the tracked animal. 2. **UniqueSectionCounter**: Sequential counter for a distinct 1 Hz GPS burst (min10 min., but can be longer). 3. **Date**: Date and time of observation (DD-MMM-YYYY HH:MM:SS). 4. **lat_start**: Geographic latitude at the start of a 10 minutes segment (Degrees). 5. **lon_start**: Geographic longitude at the start of a 10 minutes segment (Degrees). 6. **OverSea**: Flag indicating whether the segment is over the sea (0=land, 1=Black sea, 2=Mediterranean sea). 7. **day_time**: Flag indicating it during daytime or during nighttime (0=night, 1=day). 8. **time_since_sunrise_h**: Time since sunrise at the start of the segment (Hours). 9. **time_since_sunset_h**: Time since sunset at the start of the segment (Hours). 10. **time_of_part_min**: Duration of the observed segment (Minutes). 11. **TotalDistance**: Total distance covered in the segment (meters). 12. **vg**: Ground speed during the segment (m/sec). 13. **va**: Airspeed during the segment (m/sec). 14. **tw**: Tailwind component during the segment (m/sec). 15. **sw**: Sidewind component during the segment (m/sec). 16. **meanFlapRate**: Average flap rate of the individual during the segment (Flaps/sec). 17. **percent_time_in_thermals**: Percentage of the segment time spent in thermals (%). 18. **time_in_thermals_sec**: Total time spent in thermals during the segment (Seconds). 19. **mean_thermal_length_sec**: Average duration of thermal events during the segment (Seconds). 20. **mean_climb_rate**: Average climb rate during thermal events (m/s). 21. **mean_max_elevation_above_ground**: Average maximum elevation above ground level for each thermal during the segment (Meters). 22. **Mean_blh**: Mean boundary layer height during the segment (Meters). 23. **Mean_msl**: Mean sea level pressure during the segment (Pa). 24. **Mean_DeltaT**: Mean temperature difference during the segment (Degrees Celsius). 25. **wvel**: Wind velocity during the segment (m/s). 26. **wang**: Wind angle during the segment (Degrees). 27. **number_of_thermals**: Number of thermal events encountered during the segment. 28. **age**: Categorical age of the individual (1= Breeding adult, 2 = Adult with unknown breeding status, 3 = Subadult, 4 = Juvenile). 29. **age_w**: Age class of the individual. 30. **sex**: Sex of the individual (e.g., Male, Female, Unknown). ### **(3) SoarGlide:** NAN indicates not available. 1. **individual**: Identifier for the tracked animal. 2. **datetime_start**: Date and time when the soaring-gliding segment started (DD-MMM-YYYY HH:MM:SS). 3. **lon_start**: Geographic longitude at the start of the observation (Degrees). 4. **lat_start**: Geographic latitude at the start of the observation (Degrees). 5. **time_soaring_sec**: Total time spent soaring during soaring-gliding segment (Seconds). 6. **climb_rate_m_sec**: Average climb rate while soaring (m/s). 7. **falp_rate_climb**: Flap rate during the climb phase (Flaps/sec). 8. **falp_prop_climb**: Proportion of time flapping during the climb phase (%). 9. **type_thermal**: Type of thermal encountered (1 = classic soaring, 2 = spring-like soaring pattern). 10. **start_alt_above_terr**: Starting altitude above terrain at the beginning of the soaring phase (Meters). 11. **exit_alt_above_terr**: Exit altitude above terrain at the end of the soaring phase (Meters). 12. **time_gliding_sec**: Total time spent gliding during the soaring-gliding segment (Seconds). 13. **distance_gliding_m**: Total distance covered while gliding (Meters). 14. **sink_speed_m_sec**: Average sink speed while gliding (m/s). 15. **air_speed_m_sec**: Airspeed during the gliding phase (m/s). 16. **air_speed_calc_m_sec**: airspeed during the gliding phase based on tailwind estimated from thermal drift (m/s). 17. **tail_wind**: tailwind during the gliding phase (m/s). 18. **tail_wind_calc**: estimated tailwind using bird drift in thermals from the horizontal displacement of thermals (m/s). 19. **side_wind**: sidewind during the gliding phase (m/s). 20. **side_wind_calc**: estimated sidewind using bird drift in thermals from the horizontal displacement of thermals (m/s). 21. **falp_rate_glide**: Flap rate during the gliding phase (Flaps/sec). 22. **falp_prop_glide**: Proportion of time flapping during the gliding phase (%). 23. **mean_blh**: Mean boundary layer height during the observation period (Meters). 24. **sea_land**: Indicator of whether the soaring-gliding segment (0=land, 1=Black sea, 2=Mediterranean sea).. 25. **day_time**: Indicator of whether the soaring-gliding segment was during daytime or nighttime (0=night, 1=day). 26. **age**: Categorical age of the individual (1= Breeding adult, 2 = Adult with unknown breeding status, 3 = Subadult, 4 = Juvenile). 27. **age_w**: Age class of the individual. 28. **sex**: Sex of the individual (Male, Female, Unknown). 29. **Vopt**: Optimal flight speed which maximizes bird cross-country speed (m/s). 30. **RAFI**: Risk-Averse Flight Index. 31. **RAFIcalc**: Risk-Averse Flight Index calculated based on estimated tailwind. 32. **Date**: Date of the soaring-gliding segment (DD-MMM-YY). ### **(4) AnnotatedTimePointsMedSea:** NAN indicates not available. 1. **indev**: Identifier for the tracked animal. 2. **Date**: Date of the real sea crossing event (YYYY-MM-DD). 3. **fall**: Flag indicating whether the season of the sea crossing event (0 = spring, 1 = fall). 4. **thermap_presence**: Indicator of thermal presence during the observation (0 = no thermal soring during crossing, 1 = at lest 1 thermal soaring event). 5. **daysBack**: Number of timescales relative to the autumn real sea crossing event. 6. **lat**: Geographic latitude used for annotation (Degrees). 7. **lon**: Geographic longitude used for annotation (Degrees). 8. **mig_ang**: Migration angle at the time of observation (Degrees). 9. **datetime_utc**: Date and time at the location (DD-MMM-YYYY HH:MM:SS). 10. **datetime**: Date and time UTC used for annotation (DD-MMM-YYYY HH:MM:SS). 11. **t2m**: Temperature at 2 meters above the surface (Degrees Celsius). 12. **sst**: Sea surface temperature (Degrees Celsius). 13. **blh**: Boundary layer height (Meters). 14. **cbh**: Cloud base height (Meters). 15. **cape**: Convective Available Potential Energy (Joules per kilogram). 16. **cin**: Convective Inhibition (Joules per kilogram). 17. **msshf**: Mean Sea Surface Heat Flux (Watts per square meter). 18. **tcc**: Total Cloud Cover (fraction from 0 to 1). 19. **msl**: Mean Sea Level Pressure (Pascals). 20. **u850**: Zonal wind component at 850 hPa (Meters per second). 21. **u925**: Zonal wind component at 925 hPa (Meters per second). 22. **v850**: Meridional wind component at 850 hPa (Meters per second). 23. **v925**: Meridional wind component at 925 hPa (Meters per second). 24. **t850**: Temperature at 850 hPa (Degrees Celsius). 25. **t925**: Temperature at 925 hPa (Degrees Celsius). 26. **sstdiff**: Difference between sst and t2m (Degrees Celsius). 27. **windDir850**: Wind direction at 850 hPa (Degrees). 28. **windSpeed850**: Wind speed at 850 hPa (Meters per second). 29. **windDir925**: Wind direction at 925 hPa (Degrees). 30. **windSpeed925**: Wind speed at 925 hPa (Meters per second). 31. **GPCC**: Precipitation (Millimeters). 32. **time_point**: location during the crossing used for annotation, "sea enter" was used for the analysis (1 = starting point, 2 = sea enter 3 =in sea). 33. **tw**: Tailwind component (Meters per second). 34. **sw**: Sidewind component (Meters per second). ### **(5) TimesOfMigrationFall:** NAN indicates not available. 1. **indev**: Identifier for the tracked animal. 2. **Date**: Date of the sea crossing event (YYYY-MM-DD). 3. **fall**: Flag indicating season (0 = spring, 1 = fall). 4. **Hz1_section_num**: number of 10 minutes 1Hz sections during the sea crossing event. 5. **mean_thermal_prop**: Mean proportion of time spent in thermals during the sea crossing event. 6. **prop_section_with_thermal**: Proportion of 10 minutes sections containing thermal soaring during the sea crossing event. 7. **thermals**: Number of thermal events encountered during the sea crossing event. 8. **lat_start_migration**: Geographic latitude at the start of the sea crossing event (Degrees). 9. **lon_start_migration**: Geographic longitude at the start of the sea crossing event (Degrees). 10. **datetime_start_migration_utc**: Date and time at the start of the sea crossing event in UTC (DD-MMM-YYYY HH:MM:SS). 11. **datetime_enter_sea_utc**: Date and time when the individual enters the sea area in UTC (DD-MMM-YYYY HH:MM:SS). 12. **datetime_exit_sea_utc**: Date and time when the individual exits the sea area in UTC (DD-MMM-YYYY HH:MM:SS). 13. **time_at_sea**: Total time spent at sea during the sea crossing event (Hours). 14. **Days_at_stopover**: Number of days spent at the last stopover site before the sea crossing event. 15. **ang**: Angle of movement at the beginning of the sea crossing event (Degrees). Thermal soaring conditions above the sea have long been assumed absent or too weak for terrestrial migrating birds, forcing large obligate soarers to take long detours and avoid sea crossing, and facultative soarers to cross exclusively by costly flapping flight. Thus, while atmospheric convection does develop at sea and is utilized by some seabirds, it has been largely ignored in avian migration research. Here we provide direct evidence for routine thermal soaring over open sea in the common crane, the heaviest facultative soarer known among terrestrial migrating birds. Using high-resolution biologging from 44 cranes tracked across their transcontinental migration over 4 years, we show that soaring characteristics and performance were no different over sea than over land in mid-latitudes. Sea-soaring occurred predominantly in autumn when large water-air temperature difference followed mid-latitude cyclones. Our findings challenge a fundamental paradigm in avian migration research and suggest that large soaring migrants avoid sea crossing not due to absence or weakness of thermals but due to their uncertainty and the costs of prolonged flapping. Marine cold air outbreaks, imperative to the global energy budget and climate system, may also be important for bird migration, calling for more multidisciplinary research across biological and atmospheric sciences.

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    ZENODO; DRYAD
    Dataset . 2024
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      ZENODO; DRYAD
      Dataset . 2024
      License: CC 0
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lumbierres, Maria; Abecasis, David; Alcaraz-Segura, Domingo; Alison, Jamie; +172 Authors

    The information provided here represents the EBV workflow templates collected during the EuropaBON online workshop on Essential Biodiversity Variable (EBV) workflows from 22–24 February 2023. The templates were designed to capture comprehensive descriptions about the three workflow components (data collection and sampling, data integration, and modelling) that are typical for generating EBVs. Recognising the potential value of those EBV templates for European biodiversity monitoring, our objective is to share them for enhancing transparency, knowledge exchange and collaboration, and promoting the operationalisation of EBVs across Europe. EuropaBON (https://europabon.org/) is a Horizon 2020 research and innovation action funded by the European Commission that seeks to co-design a European Biodiversity Observation Network. This network aims to bridge the gap between the biodiversity data needs of policy-makers and authorities on the one hand and the existing reporting streams and available data sources on the other hand, considering both present obligations and forthcoming policy needs. Essential Biodiversity Variables (EBVs) are a central concept of EuropaBON as they provide a standardised framework for biodiversity monitoring and reporting. In 2023, EuropaBON had identified 70 EBVs (Junker et al., 2023) that are policy-relevant for the EU, and measurable with available and existing technologies and with a proven track record of feasibility in ongoing initiatives. EBVs require workflows to process the raw data (primary observations) through data integration and modelling into spatially-explicit EBV data products (Kissling et al., 2018; Schmeller et al., 2017). These workflows can be broken down into three main components (data collection and sampling, data integration, and modelling), with additional aspects of data interoperability and IT infrastructure being recognised as crucial for transnational data streams (Kissling & Lumbierres, 2023). To capture information about the EBV workflows, an online workshop was held on 22–24 February 2023 with 520 registered participants from 49 countries, covering a large range of expertise (Lumbierres & Kissling, 2023). Participants contributed information on EBV workflow components and advanced monitoring techniques, discussed initiatives, and identified tools and requirements for implementing 70 proposed EBVs. The information from the workshop participants was collected through pre-defined EBV workflow templates (provided as Google Docs). Templates were organised into rows representing the workflow components (‘Data collection and sampling’, ‘Data integration’, and ‘Modelling’) and columns reflecting the levels of maturity ('Current initiatives', 'Emerging tools and projects' or 'Future needs'). Prior to the workshop, some information on existing workflows was pre-filled based on previous EuropaBON deliverables, namely an assessment of the current biodiversity monitoring gaps in the EU (Santana et al., 2023) and an assessment of current EU monitoring workflows and bottlenecks (Morán-Ordóñez et al., 2023). After the workshop, the EBV workflow templates were processed to ensure the accuracy and relevance of the information. Each listed initiative was verified to be part of an active biodiversity monitoring scheme and pertinent to the specific EBV under consideration, cross-referencing with the initiative’s websites and other data collected by the EuropaBON deliverables (Morán-Ordóñez et al., 2023; Santana et al., 2023). Moreover, we ensured correct alignment of each initiative and listed requirements and needs with the appropriate workflow components and maturity levels. The EBV workflow templates provide insights into the current biodiversity monitoring landscape in Europe and how EBV production could be operationalized at the EU level. They offer detailed information about ongoing initiatives and projects, methodologies, and technologies that can be used to generate EBVs at a continental scale. Nevertheless, it is important to note that they do not encompass an exhaustive list of all ongoing or proposed initiatives of biodiversity monitoring in all member states of the EU. It is suggested to use them as a starting point and baseline for the further development of EBVs in a European context.

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    ZENODO
    Other literature type . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Other literature type . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Other literature type . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Other literature type . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Lampert, Adam;

    # Software for optimizing treatment to slow the spatial propagation of invasive species: code and results ## ## Description: The complete code and simulation results for finding the optimal treatment of a population front, to slow its propagation to a speed v. The algorithm is described in the paper "Optimizing strategies for slowing the spread of invasive species" by Adam Lampert (PLOS Computational Biology, DOI: 10.1371/journal.pcbi.1011996). Some of the results are demonstrated in Figs. 2-5 in that paper. ## Authorship: The code was written by Adam Lampert, Institute of Environmental Sciences, Robert H. Smith Faculty of Agriculture, Food and Environment, the Hebrew University of Jerusalem, Israel. ## Installation: Running the Matlab code requires the installation of Matlab 2021b for Windows (or a similar version of Matlab). ## Running the code – general model: 1\. Extract all files from "general_model_code.zip" into a single folder. 2\. Open "main.m" and "calc_cost.m" using Matlab. 3\. Change the parameter values, run "main.m," and wait until Matlab completes the execution. ## Running the code – spongy moth model: 1\. Extract all files from "spongy_moth_model_code.zip" into a single folder. 2\. Open "main_F2.m" using Matlab. 3\. Change the parameter values run the code, and wait until Matlab completes the execution. ## Description of the data files: The results for the general model's simulations are given as raw data in the folder "general_model_simulation_results.zip". The data files can be accessed with Matlab. Some of these results are demonstrated in the main article, Fig. 4. The results for the spongy moth model simulations are given as raw data in the folder "spongy_moth_model_simulation_results.zip". The data files can be accessed with Matlab. Some of these results are demonstrated in the main article, Fig. 5. Each data file in "general_model_simulation_results.zip" and in "spongy_moth_model_simulation_results.zip" includes the simulation results for a given set of parameters. The name of the file specifies the parameter values used. Specifically, for the general model, the file name indicates the values of α and v used for the simulation. For the spongy moth model, the file name indicates first the value of (kλ₀) and then the values of v used for the simulation. Each data file includes the following variables: * *n_front:* an array that includes the value of the population front (n-opt) as a function of the location (x). * *treatment:* an array that includes the value of the optimal treatment (A-opt) as a function of the location (x). * *Nx:* size of the n_front and the treatment arrays. The general model data files also cinclude the following parameter value: * *Dt:* time resolution (Δt) Spongy moth model data files also include the following parameter values: * *num_moves:* number of spatial steps the front moves per time unit (equivalent to v). * *delta:* the spatial resolution (σ). * *lambda:* the parameter λ₀. Slowing the spread of invasive species is a major challenge. How can we achieve this goal in the most cost-effective manner? This package includes the complete code and simulation results that help finding the optimal, most cost-effective treatment to slow the spread of a propagating species. This package accompanies the paper "Optimizing strategies for slowing the spread of invasive species" by Adam Lampert (PLOS Computational Biology, DOI: 10.1371/journal.pcbi.1011996). The file general_model_code.zip contains the code for the general model; the file spongy_moth_model_code.zip contains the code for the spongy moth model; and the file general_model_simulation_results.zip contains the results for the general model; and the file spongy_moth_model_simulation_results.zip contains the results for the spongy moth model. The code for the simulations was written in Matlab and the simulation results were obtained by running the code.  Opening the code and results requires an installation of Matlab (2021b for Windows or a similar version).

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    DRYAD; ZENODO
    Dataset . 2024
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2024
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    Authors: Aylin Ulman; Abdel Fattah Nazmi Abd Rabou; Sara Al Mabruk; Michel Bariche; +13 Authors

    The silver-cheeked toadfish Lagocephalus sceleratus (Gmelin 1789), and to a lesser degree the orange spotted toadfish Torquigener hypselogeneion (Bleeker, 1852), pose threats to human health from physical attacks and poisonings in the Eastern Mediterranean Sea. This study reviewed human health-related impacts resulting from these pufferfish, compiling and assessing records from online sources, the peer-reviewed literature, medical records, personal interviews, and observations across the Eastern Mediterranean in the years 2004 to 2023. A total of 198 events impacting human health were documented: 28 records of physical attacks, at least 144 non-lethal poisoning episodes, and 27 human fatalities resulting from consumption. The majority of the reported incidences occurred in Syria, Türkiye, and Lebanon. Most physical attacks occurred in summer, while most poisoning events occurred during winter. The number of recorded incidents greatly increased after 2019, especially with regard to poisonings, yet whether this is related to greater media attention, or to increased fish abundance is unclear. This is the first comprehensive study to collate findings on attacks, poisonings and fatalities caused by these pufferfish in the Mediterranean Sea, and may help in improving national health policies. We urge the continuation of national campaigns to caution residents and tourists of these species’ high toxicities and potential aggressiveness.

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    Biology
    Article . 2024 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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      Biology
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    Authors: Ismael Soto; Paride Balzani; Laís Carneiro; Ross N. Cuthbert; +81 Authors

    ABSTRACTStandardised terminology in science is important for clarity of interpretation and communication. In invasion science – a dynamic and rapidly evolving discipline – the proliferation of technical terminology has lacked a standardised framework for its development. The result is a convoluted and inconsistent usage of terminology, with various discrepancies in descriptions of damage and interventions. A standardised framework is therefore needed for a clear, universally applicable, and consistent terminology to promote more effective communication across researchers, stakeholders, and policymakers. Inconsistencies in terminology stem from the exponential increase in scientific publications on the patterns and processes of biological invasions authored by experts from various disciplines and countries since the 1990s, as well as publications by legislators and policymakers focusing on practical applications, regulations, and management of resources. Aligning and standardising terminology across stakeholders remains a challenge in invasion science. Here, we review and evaluate the multiple terms used in invasion science (e.g. ‘non‐native’, ‘alien’, ‘invasive’ or ‘invader’, ‘exotic’, ‘non‐indigenous’, ‘naturalised’, ‘pest’) to propose a more simplified and standardised terminology. The streamlined framework we propose and translate into 28 other languages is based on the terms (i) ‘non‐native’, denoting species transported beyond their natural biogeographic range, (ii) ‘established non‐native’, i.e. those non‐native species that have established self‐sustaining populations in their new location(s) in the wild, and (iii) ‘invasive non‐native’ – populations of established non‐native species that have recently spread or are spreading rapidly in their invaded range actively or passively with or without human mediation. We also highlight the importance of conceptualising ‘spread’ for classifying invasiveness and ‘impact’ for management. Finally, we propose a protocol for classifying populations based on (i) dispersal mechanism, (ii) species origin, (iii) population status, and (iv) impact. Collectively and without introducing new terminology, the framework that we present aims to facilitate effective communication and collaboration in invasion science and management of non‐native species.

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/