dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2022; Collias & Collias, 2014; Hansell, 2000) , as well as the many gaps Englishlanguage Western science has in its knowledge of tropical natural history (e. g. ,  Lees et al. , 2020). Here, we present a detailed database of nesting traits (structure, location, height, materials, sex of builder, building time, and size) for the world's birds. We record intraspecific variation where appropriate, and we note both uncertainty in our coding and where we were unable to find specieslevel information. We also present a phylogenetically corrected summary of major environmental and morphological correlates of key global variation in nest structure and location, as well as an exploration of geographic biases present in our dataset. We hope that this level of precision and broad taxonomic scope will facilitate future studies of the macroecology and macroevolution of avian parental care as well as direct attention to fruitful directions for future behavioural research.
Species were more likely to nest on the ground in cooler locations, and species were more likely to nest in or near water at lower latitudes and/or in places with greater temperature seasonality. Species were more likely to nest on elevated rocks in cooler, drier, and less seasonal (both temperature and precipitation) places. Species were more likely to nest in vegetation in warmer and/or rainier places with greater temperature seasonality and/or less precipitation seasonality. Additionally, both heavier species and species with greater HWI (higher flight ability) were more likely to nest in artificial locations or on elevated rocks, while both lighter species and species with smaller HWI (lower flight ability) were more likely to nest in vegetation (Figure  2 , panels b and d). Finally, to explore potential research biases in the nest dataset by geography, we compared the proportion of species lacking nest information across biogeographical realms.
We anticipated that species from tropical regions would be most likely to be under-represented in the nest dataset, due in part to pervasive inequalities in the global distribution of research funding (e. g. ,  Lees et al. , 2020). Further details on realm scoring and data analysis can be found in the Supplementary Materials. As anticipated, we found that biogeographical realms containing tropical regions generally have more species with missing nest information compared with polar and temperate regions, with species from Oceanian and Neotropical regions particularly under-represented (Figures  S1-S4 ). This is especially notable in the case of builder identity, where the Nearctic realm (i. e. , most of North America, plus Greenland) is the only region where >50% of species have documented data.
Conclusions We here present a species-level dataset of key nest-building traits for a large sample of birds. Our coding system improves on previous attempts with its level of detail, ability to describe intraspecific variation, and lack of taxonomic inference. We have also described basic environmental and morphological correlations between major structure and location categories, demonstrating that the placement of the nest is more closely linked to broad-scale environmental variation than is the structure of the nest itself. One possible interpretation of this is that environmental factors may have driven finer scaled variation in nest features than gross morphological type  (Medina, 2019; Ocampo et al. , 2023) , such as nest dimensions  (Vanadzina et al. , 2023) ; another explanation may be that nest structure is more closely linked to ecological and life history factors not considered here, such as clutch size  (Heenan & Seymour, 2011)  or predation rates  (Collias & Collias, 2014; Hall et al.
, 2015; Mainwaring et al. , 2015; Martin, 1993b; Matysioková & Remeš, 2022). There are many species about whose nesting strategies Western, English-speaking science knows nothing, particularly in the tropics  (Hortal et al. , 2015; Lees et al. , 2020)  (see also Figures  S1-S4 ). We hope, however, that by documenting the variability in nests among well-studied species, we not only provide a useful dataset for future macroecological and macroevolutionary work, but also motivate future detailed documentation of the reproductive biology and behaviour of all the world's birds. Note that some species fall into multiple nest structure/location categories due to intraspecific variation. After correcting for the effects of co-variates and phylogenetic signal, there are significant effects of body mass on the probability that species build dome, excavation, platform, and scrape nests (panel A), as well as nests in artificial, rock, and vegetation locations (panel B), whereas the HWI differences are statistically significant for cup, dome, dome-and-tube, none, and scrape nest structures (panel C), as well as for artificial, earth hole, rock, and vegetation locations (panel D).
For deconvolution of the microarray datasets, we used the previously developed methodology by Adderley and colleagues  [45]. Datasets were mapped onto a network followed by a pathway analysis, which utilizes random walks to identify chains of phosphorylation events occurring more or less frequently than expected  [45]. Rather than focusing solely on the largest fold changes, this analysis identifies cellular pathway interactions to provide an interpretation of the most important pathways that are influenced by exposure to phages This provided us with an overview of the main cellular pathways that were influenced by the presence of phages ( From this analysis, we found 52 hits for the MDCK-I cells, and 150 hits for the A549 cells, which utilized the improved KAM-2000 antibody array. Based on this analysis, we focused our attention on 2 pathways-AKT and CDK1.
Once activated, AKT and its downstream effectors will induce a broad range of cellular responses, including activation of glycolysis, protein synthesis, cell survival and proliferation, glycogen synthesis, fatty acid synthesis, and the inhibition of autophagy. In our MDCK-I datasets, we observed the activation of AKT by both PDK1 through the S473, and SRC through the Y326 phosphorylation sites in the presence of T4 phage (Fig  2A ). Once activated, AKT led to increased inhibition of BAD (also called BCL2) through the S75 phosphorylation site, which is an agonist of cell death that prevents apoptosis and enhances cell survival  [47] [48] [49]. Interestingly, in our A549 dataset (Figs 2B and S3), we observed similar activation of AKT through the S473 phosphorylation site indirectly by PDK1, but also via MAPK14, known as p38α MAPK.
KAM-1325 and KAM-2000 Kinexus antibody microarrays Following the protocol described by Bichet and colleagues  [32] , confluent MDCK-I or A549 cell lines, for KAM-1325 or KAM-2000 microarrays, respectively, were incubated with T4 phages or Filter control samples for 8 hours at 37˚C and 5% CO 2. After incubation, the cells were scraped in lysis buffer before sonication. All samples were treated as chemically lysed proteins and followed the recommended protocol by Kinexus. The MDCK-I and A549 proteins were quantified using the Bradford protein concentration assay (Thermo Fisher Scientific). The MDCK-I samples were then incubated on the KAM-1325 array before sending the array to Kinexus for analysis, while the A549 samples were sent directly to Kinexus for analysis on the KAM-2000 array. Briefly, microarray datasets were filtered to remove low signal intensity and/or relatively high error signals compared to control signals.
Microarray analysis The analysis of the microarray dataset was performed using the MAPPINGS V1. 0 network analysis program developed by Adderley and colleagues  [45]. There was redundancy between the antibodies tested across the KAM-1325 and KAM-2000 arrays with different antibodies targeting the same protein for more precision (S2 and S3 Figs and S1 and S2 Tables). First, the signals were filtered and all signals below 1,000 units were considered as low for the KAM-1325 array and below 500 units for the KAM-2000 array and removed from the assay. Any signal with a high error relative to signal change was disregarded, and any antibody with a higher total signal error across all the arrays compared to the control arrays was disregarded. Next, each unknown substrate effect, where no known biological data wereAU : PleasenotethatasperPLOSstyl found, was considered as an activation effect for this analysis.
All nodes (kinases) without a directed edge toward them were the most probable kinase for the downstream phosphorylation event. This was a consequence of the microarray data not reporting which kinase is responsible for each phosphorylation event. Independent positive and negative network analyses were analyzed, and in the case of parallel phosphorylation, only the ones with the greater magnitude values were selected and appeared on the pathway map. To ensure the proper termination of each pathway, 3 options were chosen: (1) If no other path were available after the last kinase or substrate, then the pathway was stopped. (2) If the last phosphorylation had an inhibitory effect, then the pathway was stopped as well. (3) Finally, for pathways with only one downstream option available but with no changes from the datasets between the cells incubated with the phages and the Filter control, then a percentage of fold change was assigned for each of these single paths, either between 0% and 20% for the experimental dataset and 20% for the control  [45].
The cells were centrifuged for 5 minutes at 300g and resuspended in 500 μL of cold PBS before being transferred to a 5-mL round polystyrene bottom tube (Corning). Cells were left at 4˚C until FACS analysis following the  [66]  protocol. The experiment was performed with triplicate wells for each condition and a control well with no PI (S4 and S5 Figs). Data were generated on a 4-laser Fortessa X-20, manufactured by Becton Dickinson (BD). AAU : PleasenotethatasperPLOSstyle; numeralsarenotallowedatthebeginningofasentence:Ple total of 100,000 cells were analyzed for each assay and assigned to a cell cycle stage: G0/G1, G2, S, or Sub G0.
Statistics n represents the number of samples analyzed; each sample was performed at minimum in duplicate. The datasets were tested for normality before statistical analysis. All the statistics across this article were done using the GraphPad Prism software using one-way ANOVA or two-way ANOVA including Dunnett's multiple comparison tests. The results of the statistics are represented with stars on top of the corresponding data. All the microarray experiments were performed with only one sample; no statistics were applicable to these results.
Acknowledgments We thank the following labs for kindly providing the cell lines;  Hudson Institute of Medical Research  and the  Oncogenic Signaling Lab  for providing the A549 cell line;  Stephane Chappaz  for preparing the mice femurs and extracting the BMDMs. We thank  Rongtuan Lin  ( McGill University, Canada ),  Naoto Ito  ( Gifu University Japan ),  Natalie Borg  ( RMIT, Australia ), and  Takashi Fujita  ( Kyoto University, Japan ) for kindly providing the INF-ß GL3 and NF-κB, pEF-FLAG-RIGI-I(N) and FLAG-MAVS plasmids, respectively. Authors acknowledge and thank the following facilities for kindly providing equipment and guidance:  Monash Micro Imaging Facility  for help with microscopy acquisition, and the  FlowCore  for assistance with flow cytometry analysis. Marion C. Bichet  was supported by  Monash Graduate Scholarship  (MGS).
This work was funded by the  Australian Research Council   DECRA Fellowship  ( DE170100525  to JJB),  National Health and Medical Research Council  ( NHMRC :  1156588  to JJB &  1125704  to GWM), and the  Perpetual Trustees Australia  award ( 2018HIG00007  to JJB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Drosophila live imaging Embryos were prepared as previously described  84  , stage six embryos were collected from flies caged at room temperature (except for overexpression experiments, where flies were caged at 18 °C to allow sufficient maternal GAL4 deposition). Time-lapse images were acquired every 15 s for 30-45 min, on Nikon dual camera Eclipse Ti inverted spinning disk microscope (distributed by Roper) with a X-100/1. 4 oil-immersion objective at ~21-22 °C, controlled by the Metamorph software. In all, 11 z-stacks separated by 0. 5 μm were acquired starting from the most apical plane. 491-nm and 561-nm lasers were used to excite GFP or mNeonGreen and mCherry fluorophores, respectively. Laser powers were measured and kept constant between controls and perturbed experiments. Bright field time-lapse images were acquired using an inverted microscope (Zeiss) and a programmable motorized stage to record multiple positions over time (Mark&Find module from Zeiss), running on AxioVision software (Zeiss).
4b-h ) and Rho1/MyoII in Toll triple RNAi knockdown (Supplementary Fig. 10 ), the Rho1 sensor signal was used to segment the cells. For the 5HT2A and Cirl genetic interaction (Fig. 3e-g ), the average projection of the MyoII signal were used for the segmentation as well as tracking the centroid distance to quantify local extension. The resulting skeletons were dilated by 2 pixels on either side of the one-pixel wide cell-cell interface (5 pixels wide), excluding the vertices and used as junctional masks to extract junctional intensities. For the apical signal quantification, the individual cell masks were shrunk by 4 pixels to exclude the junctional signal using the macro by G. Kale  50. Junctional and medial Rho1 or MyoII values were mean intensities calculated from the above masks. All the data points obtained were plotted using GraphPad Prism 9 software.
All the images were segmented using the ImageJ Tissue Analyzer plugin (Aigouy et al. , available at https://github. com/baigouy/tissue_analyzer)  85. To quantify the amplitude of polarity, the junctions obtained by the above method were categorized into different orientations, (0-15)°were considered as transverse junctions (AP junctions) and (75-90)°as vertical junctions (DV junctions). Mean values of all junctions in the categories were measured and the amplitude of polarity was quantified by taking the ratio of the mean intensity of vertical and transverse junctions to ensure the robustness of the measurement  39, 50, 54. To quantify the 5HT2A::mNeonGreen membrane signal (Supplementary Fig. 3e, f ), the GAP43::mCherry signal was used to segment the cells. For the quantification of the 5HT2B::mCherry signal (Supplementary Fig.
7h, i ), the average projection of the 5HT2B::mCherry signal was used for the segmentation. Mask obtained as described above were used to extract the mean values of the signals from the 2D images obtained by average projection of apical z-stacks corresponding to 7 μm. Number of vesicles (in Supplementary Fig. 8f" ) were counted manually and normalized with the number of cells (30-60 cells per embryo) counted. To quantify the co-localization of dextran with 5HT2A::mNeon-Green (Supplementary Fig. 8a' ) or 5HT2B::mCherry (Supplementary Fig. 8b' ), and 5HT2A::mNeonGreen and 5HT2B::mCherry (Supplementary Fig. 8c' ), pixel-pixel intensities were correlated using customwritten Python code. To quantify the F-actin levels, Lifeact::mCherry signal were used to segment the cells and mean intensities were measured from the 2D projected images as described above.
The tracking data were plotted in Python. T1 events and rosettes were detected using the stable T1 tracker and the rosettes tracker, respectively, included in the ImageJ Tissue Analyzer plugin (Aigouy, Benoît et al. , available at https://github. com/baigouy/tissue_analyzer)  85. The tracker automatically detects cells that irreversibly lose contact. Rosettes were detected with the threshold of a minimum distance of ten pixels and contain minimum of five cells. The number of stable T1s or rosettes obtained at each time point was normalized to the number of cells tracked at that time point (on X60 videos, images acquired every 30 s or 1 min for 50 min after the cellularization front passed the nucleus) and plotted as cumulative events over 30 min (T0 is the onset of ventral pulling) in custom written Python code.
A pinhole of the size of an airy unit was used to reduce out-of-focus light. All measurements were performed at room temperature. Data acquisition. For sFCCS, a line scan of 128 × 1 pixels (pixel size 63 nm) was performed perpendicular to the plasma membrane at the cell-cell interface with 403. 20 µs scan time. Typically, 100,000 lines were acquired for each excitation line (total scan time ca. 80 s), alternating the two different excitation wavelengths. The effective time resolution is thus 806. 4 µs, sufficient to reliably detect the diffusion dynamics observed in the samples described in this work (i. e. , diffusion times ∼10-100 ms). Laser powers were adjusted to keep photobleaching below 50% at maximum in both channels (average signal decays were ca. 40% for mNeonGreen and 20% for mCherry).
Typical excitation powers were ca. 8 µW (488 nm) and ca. 12 µW (561 nm). Scanning data were exported as TIFF files, imported and analyzed in MATLAB (The MathWorks, Natick, MA, USA; version R2020a) using custom-written code. Data analysis. sFCCS analysis was performed as previously described  62, 86  with minor modifications. Briefly, all scan lines in each channel were aligned as kymographs and divided into blocks of 2000 lines. In each block, the lines were summed column by column and the lateral position of maximum fluorescence was determined. This position defines the membrane position in each block and was used to align all lines to a common origin. A fit of a function consisting of a Gaussian and a sigmoid component was then fitted to the fluorescence profile in each block. The sigmoid part of the fit was subtracted from all lines in the block, effectively removing background fluorescence.
Chick image acquisition. Optical sections of fixed samples were obtained on a confocal microscope (Zeiss LSM 880 or LSM 700) using ×20 (Plan-Apochromat NA 0. 8) objectives and ZEN software (Zeiss). Fiji software was used for image processing and data analysis. Analysis of tissue flows in chick embryo. Following a common approach to describe the morphogenesis of epithelial sheets  89  , the motion of the planar epiblast was described as a continuous, twodimensional flow field. Time-lapse movies of embryos were analyzed using custom Java software based on the ImageJ API for image processing  35. Briefly, particle image velocimetry (PIV) was used to evaluate the local displacement of the tissue between successive movie frames. The resulting displacement fields were used to reconstruct cell trajectories.
Acknowledgements This work benefited greatly from the stimulating discussions in the Lecuit team and we thank all the lab members for their support during the course of the project. We are grateful to  Leslie B. Vosshall  ( Rockefeller University, USA ),  Yi Rao  ( Peking University, China ),  David Krantz Lab  ( UCLA, USA ), and the  Drosophila Genetic Resource Center  and the  Bloomington Stock Center  for the gift of flies. We thank  Cedric Maurange  lab for providing lab space for chick experiments. We also thank  Pierre-françois Lenne  for access to infrastructure and lab space. The authors thank  Salvatore Chiantia  ( University of Potsdam, Germany ) for technical discussions about the sFCCS analysis. This work was supported by the  ERC AdvGrant Biomecamorph  ( 323027 ), the  Ligue Nationale Contre le Cancer  (Equipe labellisée 2018).
S. Ka. was initially supported by a Ph. D. fellowship from the  LabEx INFORM  ( ANR-11-LABX-0054 ) and of the  A*MIDEX  project ( ANR-11-IDEX-0001-02 ), funded by the " Investissements d'Avenir French Government program " and from  Ligue Nationale Contre le Cancer, 4ème année de thèse , ( TDQD22642 ). V. D. acknowledges support by  HFSP   long-term postdoctoral fellowship  ( HFSP LT0058/2022-L ). We acknowledge the  France-BioImaging  infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04-01 ,  Investments  for the future.
DATABASE Through literature searches and contact with the responsible scientists, aerosol data from 27 research sites and 23 research cruises have been collected in the data base of the present study. The data have been taken from the literature cited in Table  T1. Substantial exceptions are the stations Cape Point, South Africa, and the most recent (> 2019) data from Kennaook Cape Grim, Tasmania, G. v. Neumayer, Antarctica, King Sejong, Antarctica, Syowa, Antarctica, and SHIRASE cruises. The data comprise coastal and island stations from South Africa to Antarctica and cruises from in Atlantic, Australia/New Zealand, Indian, and Pacific sectors of the Southern Ocean to and around Antarctica. Being situated some 200 km inland the Belgian Antarctic station Princess Elisabeth is the only non-coastal site, which is considered in this study because of its relatively low elevation of 1390 m a.
Mass concentrations of the nine most major ions most frequently analyzed by ion chromatography were collected in the data base (sodium, ammonium, potassium, magnesium, calcium, MSA, chloride, nitrate, and sulfate). 1 They were complemented with the non-sea salt (nss) parts of sulfate, potassium, magnesium, and calcium when sodium values were available as seawater reference ion  (Stumm and Morgan, 1981). AMS data were averaged over one-hour periods. Submicrometer or PM2. 5-inlet derived data were combined with parallel super-micrometer samples. Nevertheless, there are remaining inherent uncertainties when comparing strongly wind-speed dependent sea salt components taken at different wind speeds and heights over the sea surface or distances from the shoreline with different samplers. For the interpretation of the aerosol data hourly tenday air-mass back trajectories were calculated arriving at 300 m over the sampling points.
DISCUSSION OF RESULTS The discussion of results proceeds from latitudinal to seasonal distributions and from there to extrapolated data in the form of maps. Despite our efforts to select comparable measurements the content of our database is highly inhomogeneous. Concerning particle number concentrations inlet losses, sensor principles and sensitivities, evaluation algorithms, and particle size limits varied. Concerning particle composition again inlet characteristics and sampling conditions and efficiencies, particle size limits, and analytical procedures varied. Consequently, we consider thorough statistical tests inadequate for the discussion. Instead most results are presented in the form of arithmetic means and medians, the latter complemented by Median Absolute Deviations (MAD) from the medians.
, 1999)  as well as at coastal and inland Antarctica  (Maenhaut et al. , 1979; Legrand et al. , 2017). Until the advent of in situ aerosol mass spectroscopy (e. g. ,  McMurry, 2000)  with temporal resolutions of less than one hour aerosol sampling in pristine environments for chemical analyses often required sampling times of more than 100 hours. Under such conditions at the non-Antarctic coastal (Cape Point, South Africa) or island stations near populated continental regions (Kennaook Cape Grim, Tasmania) very few samples were taken without any influence from the nearest continent, i. e. without any hourly back trajectory hitting the nearest continent during a sampling period. At Cape Point we thus excluded the sulfate results from our study. For the very long chemical time series at Kennaook Cape Grim, MSA data were available.