dataesr/scientific-paragraphs-categorization huggingface dataset:
• Regions with similar developments (e. g. , planar or cylindrical) in which geometric features may yield several values, can be misclassified as falling into the same class. For instance, the analysis of the off-diagonal elements of the confusion matrices suggested that Class 5-Wall' and 'Class 4-Door and Window' are often interchanged with each other, as both are characterized by predominantly planar behaviors. •As the covariance and curvature features are computed in a given local neighborhood, the density of the point cloud influences the classification results. In other words, if two point clouds of a same object have different point densities, feature selection may produce two different results, as seen in the example in Figure26. In order to align feature selection for different surveys of the same dataset, one could then plan to return the point clouds to the same density by means of a subsampling operation.
5. 0) [59]. Due to an imbalanced number of observations across social contexts, contexts with fewer observations were randomly upsampled prior to the specificity calculation. During the upsampling procedure all observations of the minority contexts were kept, and new observations were randomly sampled to match the number of observations in the majority context. This procedure corrects for any bias in the specificity results from an imbalanced dataset (see fig. S1 ). Specificity is the conditional probability of a social context given that an Action Unit combination is observed, and ranges from 0 (when an Action Unit combination is never observed in a context) to 1 (when an Action Unit is only observed in one context). Low specificity values indicate that Action Units were used flexibly across multiple contexts whereas high values indicate that Action Units were used primarily in a single context.
Acknowledgments We thank the  German Primate Center (DPZ)  for permission to collect data on the rhesus macaques,  Uwe Schönmann  for logistical support, and  Julia Ostner  for being our host at the DPZ. We thank  Matt Lowatt  and  Ellen Merz  for permission to collect data on the Barbary macaques at Trentham Monkey Forest. We thank the  Indonesian State Ministry of Research and Technology (RISTEK) , the  Directorate General of Forest Protection and Nature Conservation (PHKA)  and the  Department for the Conservation of Natural Resources (BKSDA) ,  North Sulawesi , for permission to access groups of crested macaques in the  Tangkoko-Batuangus Nature Reserve. We thank  Christof Neumann  for statistical advice. This work was funded by the  Leverhulme Trust  ( RPG2018-334 ).
Experimental design A video description of each task is available online (https://gitlab. com/nfaivre/metaface_scz_public/-/tree/main/videos). All participants were naive to the purpose of the study, gave written informed consent in accordance with institutional guidelines and the Declaration of Helsinki, and received monetary compensation (10€ / h) except those participants under legal protection. The study was approved by the ethical committee Sud Méditerranée II on April the 3d 2020 (217 R01 MS1). Stimuli 4000 copyright-free artificially generated faces were downloaded from the open platform https://generated. photos. Two independent observers screened the stimuli to exclude children's faces, unrealistic faces, and faces with salient features (e. g. sunglasses, hats). The remaining 1700 male and 1700 female adult faces were converted to grayscale and equalized in contrast and luminosity (SHINE Matlab toolbox  15  ).
edge 5. 5 camera (PCO, Kelheim, Germany). A 20 X PlanAPO objective (Nikon, Tokyo, Japan) was used to acquire a minimum of 4 view fields in each well. The acquired images were processed using the open-access R software (https://www. r-project. org, accessed on 12 May 2023) with the use of the freely available packages EBImage (available on the Bioconductor repository https://www. bioconductor. org, accessed on 12 May 2023) and RBioFormats (https://github. com/aoles/RBioFormats, accessed on 12 May 2023), and the custom packages MetaxpR. (https://github. com/asauvat/MetaxpR, accessed on 12 May 2023) and MorphR (https://github. com/kroemerlab/MorphR, accessed on 12 May 2023). Nuclei were first detected using the Hoechst 33342 signal and used as a marker to detect cytoplasmic areas based on GFP signal, allowing for the evaluation of cytoplasmic GFP intensity.
e. , a low concentration and a 10-fold higher concentration, referred to as "low" and "high" doses, as described in the Section 2). The results of the Prestwick library screening at a low drug concentration were plotted. Brefeldin A was used as a positive control for the inhibition of secretion. The compounds causing a cytoplasmic GFP intensity below that observed for the biotin-only treated cells were potential enhancers of protein secretion. Drugs that induced a GFP fluorescence level higher than that measured in the presence of biotin only were potential inhibitors of protein secretion. The biotin-induced decrease of the cellular GFP signal was influenced by a minority of compounds from various chemical libraries, as indicated through the plotting of primary data (Figure  4B ) or Z-score analyses (Figure  4C ).
We therefore opted to retain the RUSH antibody through its heavy chain, in order to confidently monitor the secretion of fully assembled antibodies  [41] [42] [43] [44]. With these constraints considered, the RUSH machinery was added to the C-terminus of the anti-PD-L1 heavy chain. A furin cleavage site was also added between the heavy chain and the RUSH construct for the removal of the SBP and GFP sequences past the Golgi network, which might have affected the functions of the antibody otherwise. Our data showed the efficient detection and retention of the antibody-GFP fusion in the ER of clone #7, and its release and maturation upon addition of biotin in the cell culture medium. ELISA and cell-based assays also demonstrated the specific binding of the released antibody to its PD-L1 target.
Funding: This work was primarily supported by a research contract involving  Sanofi and Institut Gustave Roussy. M. C. is supported by a grant from the  French ANRT  agency (CIFRE No  2020/0106 ). O. K. receives funding from the  Institut National du Cancer (INCa)  and the  DIM ELICIT initiative of the Ile de France. GK is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR) -Projets blancs; AMMICa  US23/CNRS UMS3655 ; Association pour la recherche sur le cancer (ARC);  Cancéropôle Ile-de-France ;  European Research Council Advanced Investigator Grand "ICD-Cancer", Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  Equipex Onco-Pheno-Screen ;  European Joint Programme on Rare Diseases (EJPRD) ;  European Research Council (ICD-Cancer) ,  European Union   Horizon 2020 Projects Oncobiome and Crimson ;  Fondation Carrefour ;  Institut National du Cancer (INCa) ;  Institut Universitaire de France ;  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ); a  Cancer Research ASPIRE Award  from the  Mark Foundation ; the  RHU Immunolife; Seerave Foundation ;  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and  SIRIC Cancer Research and Personalized Medicine (CARPEM).
This study contributes to the  IdEx Université de Paris   ANR-18-IDEX-0001. The work in FP lab was supported by  LabEx CelTisPhyBio  ( ANR-10-LBX-0038  part of the  IDEX PSL  no. ANR-10-IDEX-0001-02 , by the  ANR  grants  ANR-22-CE14-0066-01 ,  ANR-20-CE14-0017-02 ,  Cells 2023 ,  12 ,  1642 16  of  18 ANR-19-CE13-0006-03 ,  ANR-19-CE13-0002-03 ) and by the  Fondation pour la Recherche Médicale  ( EQU201903007925 ). Institutional Review Board Statement: Not applicable. This study did not involve humans or animals. Informed Consent Statement: Not applicable. This study did not involve humans.
Urbanisation generates multiple trait syndromes for terrestrial animal taxa worldwide A list of authors and their affiliations appears at the end of the paper Cities can host significant biological diversity. Yet, urbanisation leads to the loss of habitats, species, and functional groups. Understanding how multiple taxa respond to urbanisation globally is essential to promote and conserve biodiversity in cities. Using a dataset encompassing six terrestrial faunal taxa (amphibians, bats, bees, birds, carabid beetles and reptiles) across 379 cities on 6 continents, we show that urbanisation produces taxon-specific changes in trait composition, with traits related to reproductive strategy showing the strongest response. Our findings suggest that urbanisation results in four trait syndromes (mobile generalists, site specialists, central place foragers, and mobile specialists), with resources associated with reproduction and diet likely driving patterns in traits associated with mobility and body size.
Investigate whether the proportion and spatial aggregation of urban land and forest cover (see Methods) induce stronger changes in community functional diversity than known latitudinal or climatic trends. In this case, we use urban land cover to represent a gradient of urbanisation filters, and forest cover to represent the amount of tree canopy cover; 3. Investigate the spatial scale at which the proportion of urban land has the strongest effect, and how this differs among functional groups. This study used a collaboratively compiled dataset of 5302 species found in >70000 plots across 379 cities from 48 countries (Fig. 1 ) to investigate how urbanisation shapes the community traitcomposition and diversity of six terrestrial animal taxonomic groups (amphibians, bats, bees, birds, carabid beetles, and reptiles) across the globe.
The data are a collation of empirical studies at the highlyresolved spatial scale of individual sites rather than generalised to city. Only one taxon (birds) was extracted from a global biodiversity dataset (eBird). We acknowledge there are still geographic biases in the data that reflect legacies of studies published prior to 2017  10. We are also aware that there are additional taxa groups that we would have liked to include but lacked the capacity to consider in this project. However, to our knowledge, this is the most comprehensive compilation to date of urban biodiversity data for several terrestrial animal taxa at the site scale. The six taxa represent a broad range of natural histories, ecologies and behaviours and have sufficient occurrence data and trait information to conduct a global study, despite some geographic biases.
Landcover effects were strongest at the largest spatial scales considered (1000 m for birds, 500 m for all other taxa), and the composition of the landscape (% cover) was more important than configuration (agg). These results highlight the importance of landscape-level management of urban biodiversity and the role of spatial context. They also provide additional support for our proposed general urban trait syndromes, which are highly influenced by the distribution and abundance of resources within the landscape. We acknowledge that processes occurring at larger spatial scales than those considered in this study can also be important, especially for species with high mobility. Equally, there may also be finer-scale processes that we were not able to consider due to the resolution of available datasets.
"R 2 obs-pred " is the performance of the model on the independent test dataset where high values indicate that the response variable is well-predicted by urban and forest land cover, climate, and latitude. "R 2 obs-pred" was calculated as R-squared of the relationship between the predicted and the observed values of the independent test dataset. "% inc MSE" is the average increase in squared residuals when the variable is permuted. It represents the specific contribution (or importance) of the percentage of urban land cover (within a 500 m radius for all other taxa except birds for which we used a 1000 m radius) to the overall model performance. High values suggest that urban land cover is an important predictor. mobile generalist traits, and waterbirds could display mobile specialist traits.
Urban biodiversity data To identify potential datasets for our analysis, we conducted a systematic review of the published urban biodiversity literature from 1990 to 2016 to identify studies that met the following criteria: 1) community level data, 2) collected in multiple plots, and 3) across one or multiple cities. The Web of Science literature review was conducted using the following keywords: TOPIC: (urban OR "peri-urban" OR periurban* OR suburban* OR "sub-urban*" OR conurbation OR city OR cities OR town.
? Central place forager Site specialist Fig. 5  | Simplified representation of the four urban trait syndromes. Two types of green habitat patches with different resources are represented in an otherwise mostly unsuitable urban matrix. Grey patches represent green habitats that are unusable for a specific taxon. Red dashed lines show typical movement pattern of taxa among patches. The landscapes of Fig. 5  and their individual elements were created by Bertrand Fournier using the basic set of tools available in the vector graphics open source software "Inkscape 1. 2. 2". No previously-created elements were used. No images from a database were used. The landscapes of Fig. 5  and their individual elements were not published elsewhere. Image credits for taxa silhouettes: Idem Fig. 2. TOPIC: (Bees OR Hymenoptera OR Aculeata OR Pollinator*) TOPIC: (Carabid* OR "ground beetle*" OR Cincidel* OR "tiger beetle*") TOPIC: (Mammal* OR Marsupial*) TOPIC: (Amphib* OR frog* OR toad* OR Anura OR Reptil*) AND Refined by: TOPIC: (Biodiversity OR "species composition" OR "species assembl*" OR "species communit*" OR "species richness" OR "species diversity" OR "Shannon-Weaver" OR "Shannon-Weiner" OR "Shannon-Wiener" OR "Shannon* H" OR "Simpson* index" OR "Simpson* diversity" OR "Simpson* dissimilarity" OR "Simpson* beta" OR evenness OR "alpha diversity" OR "beta diversity" Biodiversity OR composition OR assembl* OR communit* OR richness OR diversity OR Shannon OR Simpson OR evenness) AND TOPIC: (Gradient* OR Grid OR Site OR Map* OR Spatial* OR GIS OR Plot OR Sample OR Area OR Occurrence OR Presence OR distribution) AND WEB OF SCIENCE CATEGORIES: (ECOLOGY OR BIODIVERSITY CONSERVATION OR PLANT SCIENCES OR URBAN STUDIES OR FORESTRY) Indexes=SCI-EXPANDED Timespan=1990-2016 From these studies, we identified those taxa with sufficient number of papers to permit a meta-analysis (e.
g. , in 2016 there were only 8 papers investigating snails in urban landscapes that met our criteria), and appropriate for the methods we proposed to use (e. g. , fish are unlikely to be strongly influenced by % tree cover within 100 -500 m). Once we had finalised our taxa groups, we identified a core group of authors from the WoS literature review who had datasets that would be suitable for our proposed analysis, and contacted them to see if they would be interested in collaborating in this research. We also gained additional collaborators who volunteered their datasets after hearing our presentations on this project at the 2017 Ecological Society of America meeting, 2017 Ecological Society of Australia meeting and 2017 International Ecology Congress (INTECOL). Researchers who responded to our invitation were provided with a data collection template to ensure we received all of the required information in a format that could readily be integrated into a larger dataset.
This included a species x site table, species x trait table and site information table, and a link to a metadata form where we could capture additional information about the study used to produce the data. To facilitate this process, we formed taxon-based groups which were coordinated by 2-3 members of the UrBioNet coordinating group. Throughout the project the taxa coordinators were responsible for collating the data into a single dataset, and worked with the data contributors to develop the compiled species x trait tables. Once the data had been compiled the coordinating group populated the standardised site information, analysed the data and created a preliminary results document that was circulated back to the data contributors for discussion and feedback within the taxonomic groups in March 2020.
Feedback from this process was compiled back by the coordinating group and used to update the analysis. The coordinating group then drafted a manuscript that was shared back to the data contributors for feedback. After the feedback on the manuscript had been incorporated, a final version of the manuscript was circulated to ensure all named authors agreed to the submission of the manuscript. As the timing of the circulation of the initial round of results coincided with the onset of the COVID-19 global pandemic, the timeframes for delivering this project were disrupted, and some of the additional feedback rounds were bypassed as a large number of contributors had already met the criteria for authorship. The exception to this process was the bird taxon, where we extracted data from existing global datasets to match the cities where we had information for other taxa groups.
As this research was hosted by the UrBioNet Research Coordination Network, we applied their Authorship Policy (https://sites. rutgers. edu/urbionet/about/authorship-guidelines/), where authorship required a substantial contribution beyond simply providing data or being present at the initial workshop. This is in alignment with Weltzin  et al. (2006)   55  and other publications that seek to ensure authorship reflects a substantial contribution. Our final dataset consisted of information from 72086 plots spread across 379 cities worldwide and retained six taxonomic groups with sufficient data for a global assessment of urbanisation effects (see Fig. 1 , Supplementary Fig. 3 , and Supplementary Tables  1  and 2 ): amphibians (140 species, 1202 plots in 191 cities), bats (84 species, 540 plots in 43 cities), bees (486 species, 471 plots in 25 cities), carabid beetles (327 species, 889 plots in 17 cities), reptiles (98 species, 321 plots in 71 cities) and birds (4167 species, 68558 plots in 177 cities).
The latter was collected from the eBird global community-science program (https://ebird. org)  56  , and covers the period from 1 January 2002 to 31 December 2018 from across the globe. We retained eBird checklists for analysis that were located within 1. 5 km of the center of each city and were conducted using the P20, P21, P22, P23, P48, and P62 sampling protocols. We retained travelling surveys that were <1 km and area surveys that were <1 km2. We only considered observations that were identified as valid by the eBird review process, and we combined observations in grouped checklists into single checklists. While there are documented biases within this dataset  57, 58  , the signals are likely to be dampened in this study by including data points across a large number of globally distributed cities.
Urban environment characterisation We quantified the landscape context for each plot using data from the Global Human Settlement (GHS) images analytics framework (http:// ghsl. jrc. ec. europa. eu/ghs_bu_s1. php) and the Global Forest Change database  59. These data estimate urban extents during 2016 and forest cover during the period 2000 to 2019, thus providing a reasonable estimate of land cover, as the time ranges overlap with that of the selected studies. We included the forest cover to provide an alternative landscape to the built urban land cover, in recognition that vegetation cover can be important in driving species distributions, yet different types of vegetation offer different potential resources and habitats. We recognise that for cities in more arid landscapes, forest may not reflect the natural vegetation communities, but we consider it to still be a useful landscape type given the emphasis of urban forest strategies on increasing tree canopy cover.
We calculated the percent cover and level of aggregation of urban and forest land cover within a radius of 100 m and 500 m centred on each plot for all taxa except birds, for which we use a 1000 m radius centred on each eBird checklist. We calculated the percent urban land cover around a site as the percent cover of 30 m x 30 m cells dominated by urban features (including all built-up features) using GHS. We calculated the percent forest land cover around a site with the same method, using the Global Forest Change database. To account for landscape configuration, we calculated an aggregation index  60  , which is defined as the ratio of "actual shared edges" versus "maximal possible shared edges" of the 30 m x 30 m cells. Because map units do not affect the calculation, the aggregation index can be compared among classes from the same or different landscapes and even the same landscape under different buffer sizes.
We included latitude and climate data in our analyses since the composition of functional traits have been shown to vary with latitude and climate  61, 62. Latitude was based on the geographic coordinate of the sampling plot. The main statistical trends in climatic conditions were characterised using the 19 Bioclim variables of the CHELSA database  63  , which provides information about biologically relevant aspects of climate for a period ranging from 1979 to 2013. We reduced the dimensionality of this dataset to limit the number of climate variables and avoid their correlations. Specifically, we ran a PCA with 100000 randomly sampled cells. We then projected the remaining cells onto the PCA. The first four PCA axes represented the main statistical trends in climate, that is, gradients in mean temperature (PC1), diurnal range (PC2), temperature seasonality (PC3) and precipitation seasonality (PC4).
Functional composition of animal communities We assessed the functional composition of the species assemblage of each taxonomic group separately. This was done by calculating the community-level mean values of each trait in each plot for each taxon or, in the case of categorical traits, the proportion of species in each category. We also calculated 10 indices capturing complementary aspects of functional trait variation: functional dispersion, functional richness, and functional evenness. To do so, we first imputed missing trait values using the K-nearest neighbours (function "preProcess" in the R package "caret"). We then calculated the Gower functional distance among species based on centered and scaled trait values. We computed a Principal Coordinates Analysis (PcoA) using the resulting functional distance matrices.
The quality of trait spaces was evaluated as the absolute deviation between trait-based distance and distance in the PcoA-based space  64. The number of axes producing the lower deviation was retained for further analyses. In the case of amphibian and reptile, many sites had 3 species or less. As a result, we retained 2 PcoA axes to be able to compute functional diversity metrics for a maximum number of sites. This represented a fair compromise between the quality of the trait space (third-best option for amphibians and second-best option for reptiles) and the number of sites to be included in the analyses. Overall, we included between 48% and 61% of the total variation in trait data in our analyses (Amphibian = 48% over 2 PcoA axes, Bat = 59% over three PcoA axes, Bee = 60% over three PcoA axes, Bird = 61% over three PcoA axes, Carabid beetles = 61% over three PcoA axes, Reptile = 53 % over two PcoA axes).