dataesr/scientific-paragraphs-categorization huggingface dataset:
Data were first analyzed using Foxtrot and then using the US-SOMO HPLC module 101. This program provided for each SAXS frame the values of the scattering intensity I(0) and of the radius of gyration Rg by applying the Guinier analysis together with a calculation of the approximate molar mass using the Rambo and Tainer approach 102. Identical frames under the main elution peak were selected using Cormap 103 and averaged for further analysis. Both averaged curves corresponding to the two initial concentrations were merged. Data was deposit in the Small-Angle Scattering Biological Data Bank under accession code SASDNW6.
95 objective on a Nikon Eclipse Ti-E microscope (Nikon). The samples were illuminated at 395 nm by a SpectraX light engine (Lumencor) using a quad dichroic filter 310DA/FI/TR/CY5-A and emission filters 450/50 and 525/50 (Semrock). Images were acquired with an EMCCD camera iXon ULTRA 888 (Andor Technologies). In each experiment, a determined number (as provided in the respective figure) of microscopic fields per condition were acquired and 3 independent experiments were performed. Ratiometric analysis of the microscopic field images acquired for emission wavelengths (Em) 447 nm and 520 nm, corresponding to the ratio of cleaved CCF4-AM/intact CCF4-AM, was made using a custom-made ImageJ macro (https://github. com/PaulaSampaio/ALM-i3S_macros) on Fiji software45 (ImageJ version 1. 51n, NIH, USA)  104.
Statistical analysis In each legend, the size of the experiment is indicated. Normality of the data were assessed through the Shapiro-Wilk test. Except for Fig. 4b , one-way ANOVA was used to assess the differences between conditions. Regarding Figs. 2c and 5b, multiple comparisons were evaluated using the Tukey's HSD (honest significant difference) test. Since the data in Fig. 4b  did not pass the normality test, differences between conditions were assessed by Kruskal-Wallis nonparametric test and the adjusted p values for individual comparisons were obtained by Bonferroni correction. Statistical significance was set to p < 0. 05. All statistical analysis was performed with a confidence interval of 95%. Statistical analysis was performed using the IBM SPSS Statistics (v25) software (Armonk, NY).
Acknowledgements This work was supported by  National  funds through  FCT  under the project  UIDB/04293/2020  and by  FEDER  funds through  Programa Operacional Factores de Competitividade -COMPETE  and by national funds through  FCT -Fundação para a Ciência e a Tecnologia  under the project  PTDC/BIA-MIC/29910/2017  to N. M. S. S. A. d. V. was funded by  Portuguese national  funds through the FCT and, when eligible, by  COMPETE 2020 FEDER  funds, under the  Scientific Employment Stimulus-Individual Call   2021. 02251. CEECIND/CP1663/CT0016. We acknowledge access to the HTX crystallization facility (Proposal ID: BIOSTRUCTX_8167) and  SOLEIL ,  ESRF  and  ALBA  synchrotrons for provision of measurement time and thank their staff for help with data collection. The authors acknowledge the support of i3S Scientific Platforms (https://www.
Data availability The final refined coordinates and structure factors generated in this study have been deposited in the Protein Data Bank under PDB entry 7ZPF. The corresponding diffraction images have been deposited at the SBGrid Data Bank (https://doi. org/10. 15785/SBGRID/911). The SAXS data generated in this study have been deposited in the Small-Angle Scattering Biological Data Bank under accession code SASDNW6. The crystallography data of NleC used in this study are available in the Protein Data Bank under PDB entry 4Q3J. Source data are provided with this paper.
Cardiac pump function arises from a series of highly orchestrated events across multiple scales. Computational electromechanics can encode these events in physics-constrained models. However, the large number of parameters in these models has made the systematic study of the link between cellular, tissue, and organ scale parameters to whole heart physiology challenging. A patient-specific anatomical heart model, or digital twin, was created. Cellular ionic dynamics and contraction were simulated with the Courtemanche-Land and the ToR-ORd-Land models for the atria and the ventricles, respectively. Whole heart contraction was coupled with the circulatory system, simulated with CircAdapt, while accounting for the effect of the pericardium on cardiac motion. The four-chamber electromechanics framework resulted in 117 parameters of interest.
Nevertheless, running large numbers of simulations remains a challenge due to numerical instabilities of highly non-linear mechanics, and due to high computational costs. This, combined with the large numbers of parameters of the model, makes performing global sensitivity analysis (GSA) and parameter inference very challenging. In this context, tools from statistics and machine learning can be used to approximate complex and expensive three-dimensional models  [10, 11]. Fast model evaluations offered by these tools allow, firstly, to run a GSA to increase model credibility  [12]  and to identify important model parameters and, secondly, to infer the values of these parameters to fit clinical data at a fraction of the computational cost. This can finally enable the construction of patient-specific four-chamber models, or digital twins, that replicate how a specific subject progresses and responds to therapy.
Clinical data The long-term goal of this work is to construct a digital twin of a specific subject. Therefore, we focused this study on one patient. The clinical data were gathered from a 78 yo female heart failure (HF) patient with atrial fibrillation (AF) selected for cardiac resynchronisation therapy device upgrade from RV pacing to biventricular pacing. Right ventricular (RV) pacing was therefore the baseline rhythm, with a QRS duration of 200 ms measured from a 12-lead electrocardiogram (ECG). The patient underwent ECG-gated CT prior to the upgrade procedure, providing ten CT frames over a cardiac cycle. During the upgrade procedure, the LV pressure was invasively recorded through a pressure wire. After the ectopic baseline beats were removed, 30 beats were left to characterise the patient's LV haemodynamics.
HM in the second column indicates the parameters that were constrained by a history matching procedure on a sub-model (see S2, S3 and S4 Files). Abbreviations/symbols: CV = conduction velocity, FEC = fast endocardial conduction, Na + = sodium, K + = potassium, Ca 2+ = calcium, Cl -= chloride, SR = sarcoplasmic reticulum, DS = diadic space, HM = history matching. endocardium and epicardium set to be +60˚and -60˚  [4] , and -65˚and +25˚  [14] , respectively. Atrial myofibre orientation was assigned by computing universal atrial coordinates on the atria and mapping an ex-vivo diffusion tensor MRI dataset onto the endocardial and the epicardial surfaces (Fig  1 , top right)  [15, 16]. The transmural fibre orientation was then set to be the endocardial and the epicardial orientation for elements below and above 50% of the wall thickness, respectively.
The systemic and pulmonary orifices areas were computed as the average of the cross-sectional area of the superior and inferior vena cava, and of all pulmonary veins, respectively, all computed from the end-diastolic CT. The reference area for the aorta, the pulmonary artery, the systemic veins and the pulmonary veins tubes were set to be the same as the corresponding adjacent valve. The aortic and the pulmonary artery wall area were then computed assuming a circular crosssection (consistent with CircAdapt) and a wall thickness of 2 mm  [35]. The systemic and the pulmonary veins were assigned a wall thickness of 1 mm, because the veins are thinner due to the presence of less smooth muscles compared to arteries  [36]  as they operate at lower pressures. The blue rows in Table  3  show the values of the parameters that were computed from the patient-specific data and mesh.
We also increased the tolerance for the solution of the linearised system to 10 -4 for all beats. In S1 File, we show that these settings have a limited effect on the pressure-volume dynamics simulated by the model, with differences in the pressure and volume features always below 3%, while allowing up to 3 times speedup in the simulation time. Finally, the time step for the ionic models and the mechanics were set to 0. 02 ms and 1. 0 ms, respectively. All simulations were run with the cardiac arrhythmia research package (CARP)  [37]  on a supercomputer on 512 cores. Details about the software implementation is provided in  [17, 29].
Emulators, global sensitivity analysis and history matching GPEs, GSA and HM methods used in this study were based on Longobardi et al. [11] , using code available on GitHub (https://github. com/stelong/Historia. git). Briefly, a GPE provides a statistical model for how a scalar simulator output f(x) varies as a function of D input parameters x = (x 1 ,. , x D ). Under our GPE, the simulator outputs are modelled as being jointly Gaussian with prior mean and covariance E½f ðxÞ� ¼ b 0 þ X D i¼1 b i x i ð3Þ C½f ðxÞ; f ðx 0 Þ� ¼ kðx; x 0 Þ ; ð4Þ where k(x, x 0 ) is a positive-definite kernel, and β 0 ,. , β D are the degrees of freedom of the GPE, which need to be estimated. In this case, we chose the exponentiated quadratic kernel, also known as squared exponential or the Gaussian kernel, because it offers a parsimonious model even in situations where the data is limited and noisy  [43].
The ISE evaluates the uncertainty quantification of the GPEs, and represents the number of data points that fall within two standard deviations away from the average prediction. As it is presented as a percentage over all evaluated points, an ISE close to 100% indicates that, for most points, the distance between the GPE prediction and the corresponding observation is within the GPE uncertainty. Once the GPEs were validated, we trained an additional GPE for each output using all available simulations that was then used for GSA and HM. Using the GPEs to predict the simulator outputs, we performed a Sobol variance based GSA to quantify the importance of each parameter  [44]. We computed the total effects S T using the Saltelli method with the SALib Python library. A Saltelli sampling  [44, 45]  was used to generate a convenient structure of the samples to efficiently approximate the sensitivity indices as described in S7 File.
For these parameters, we performed a Latin hypercube sampling with N = 90906 samples. These were then combined with the plausible regions from the HM on the ToR-ORd-Land model, the Courtemanche-Land model and the electrophysiology tissue model. Having identified the plausible region, we constructed a space filling design in this area on which to run the fully coupled electromechanics simulator and then to train the final GPEs. Because the plausible region had a complicated geometry, we used the function psa_select from the python library diversipy to extract N = 500 uniformly distributed samples. We trained GPEs to predict the following outputs: The trained GPEs were then used to predict the model outputs and to run a GSA on the fully-coupled electromechanics framework, to investigate how the 45 parameters we considered affected whole heart function.
This allowed us to quantify the effect of simulator parameters on whole heart function while remaining within the GPE emulator training space. The code to perform the GSA and the HM is provided at https://github. com/ MarinaStrocchi/Strocchi_etal_2023_GSA. At this link, we also provide an example on how to train the GPEs, perform a GSA and rank the parameters for the ToRORd ionic model (S2 File).
7. Bioconvert v0. 6. 2 (https://github. com/bioconvert/bioconvert) converted CCS files to FastQ files resulting in 40,653 and 29,054 CCS reads for Xen31 and Xen36 genomes, respectively. N 50 values were 9,122 and 9,890 bp, respectively. CCS data had high quality with a Phred score above 40 for 95% of reads. Taxonomic analysis using the Sequanamultitax pipeline  (43)  showed 99. 9% Staphylococcus aureus reads. Assemblies were performed using the LORA pipeline from Sequana  (43). Default parameters were used for all software. Using the Canu v1. 6 assembler (44), four contigs were obtained for Xen31. Circularization was performed with Circlator v1. 5. 5  (45). The main circularized contig was 2,966,802 bp long with a G1C content of 32. 95% (Table  1 ) and exhibited uniform coverage of 98Â. The second circularized 32,188-bp contig had a uniform 105Â coverage and was identified as plasmid CP041009.
ACKNOWLEDGMENTS We thank  PerkinElmer  for providing S. aureus strains Xen31 and Xen36 and  Laurence Ma (Biomics Platform, Institut Pasteur)  for strain sequencing. We acknowledge funding from  Institut Pasteur ,  Direction Générale de l'Armement, l'Agence Innovation Défense (AID) , l' Agence Nationale de la Recherche  ( ANR-17-CE18-0011-02  and  ANR-21-ASM1-0001-02 ), and  Université Paris Cité  and  CNRS. O. B. was supported by an  Ecole Polytechnique/AID doctoral fellowship. E. C. was supported by an  Institut Pasteur/AID doctoral fellowship. The Yersinia Research Unit is a member of the  Laboratory of Excellence Integrative Biology of Emerging Infectious Diseases  ( ANR-LBX-62-IBEID ).
e. mean tree height, LAI, and SSCI) were tested on the datasets built for this purpose ( a : 32 plots, b : 54 plots, c : 74 plots, d : 27 plots; see methods). All pathways were fit to data from site A measured in 2019 except for LAI, which was measured in 2014 (see methods and Supplement S3 for details). Species richness and SSCI were log-transformed in all models. Significant directional relationships between variables are shown as solid and nonsignificant relationships as dashed arrows. Significant standardised path coefficients are shown next to each path (*P < 0. 05, **P < 0. 01, and ***P < 0. 001), and path width is scaled according to coefficient size. The explained variation of variables (marginal R 2 ) is given in %. The SEM fit the data well (Fisher's C = 8. 4, df = 6, P = 0. 21).
18. LAI was measured using digital hemispheric photography at five positions within each plot in August by Peng et al. 28  , and SSCI by a single terrestrial laser scan at the centre of each plot under leaf-off conditions of the deciduous tree species (February-March) as described by Perles-Garcia et al. 22  (see these studies for details on the respective methods and datasets). For all forest property variables, we used data collected at site A of the BEF-China experiment in 2019 (where we had the best data coverage), except for LAI, measured in 2014.
Acknowledgements We thank local workers for their help in the field. This research was supported by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ; grant  DFG FOR 891 ), the  International Research Training Group TreeDì jointly  funded by the  DFG  (grant  319936945/GRK2324 ) and the  University of Chinese Academy of Sciences (UCAS). We are grateful for the support of iDiv funded by the  DFG  (grant  DFG-FZT 118 ,  202548816 ). F. S. acknowledges support by a  TreeDì  start-up grant. R. B. acknowledges funding by the  Saxon State Ministry for Science, Culture and Tourism (SMWK ; grant  3-7304/35/6-2021/48880 ), N. E. funding by the  DFG  (grant  Ei 862/29-1 ), H. Y. and N. E. funding by the  DFG  (grant  FOR 5000 ), and B. S. support by the  University Research Priority Program Global Change and Biodiversity of the University of Zurich.
Code availability All R scripts used for this study can be found in our GitHub repository, at https://github. com/remybeugnon/Schnabel-Beugnon-Yang-et-al_tree-diversity-temperaturebuffering Method references 47. Yang, X. et al. Establishment success in a forest biodiversity and ecosystem functioning experiment in subtropical China (BEF-China). Eur. J. Forest. Res. 132, 593-606; 10. 1007/s10342-013-0696-z (2013). 48. Harris, I. , Osborn, T. J. , Jones, P. & Lister, D. Version 4 of the CRU TS monthly highresolution gridded multivariate climate dataset. Scientific data 7, 109; 10. 1038/s41597-020-0453-3 (2020).
Micro-and macroclimate measurements The microclimate air temperature was recorded hourly over six years (January 2015-December 2020) across the VIP plots (32 at each site) using temperature loggers (HOBO Pro v2, U23-001) covered by a radiative shield and installed at 1 m height in the centre of the plots (see Supplement S5). Data were controlled and cleaned to remove unrealistic data due to logger malfunction (e. g. , temperature outliers or time series divergent dynamics; Supplement S5). Plots with incomplete monthly records were excluded from the monthly analyses and incomplete yearly records were excluded from yearly analyses (1 plot of the 64 plots was removed in all analyses; Supplement S5). Macroclimate data-minimum, average, and maximum monthly temperature (°C), monthly precipitation sum (mm) and monthly potential evapotranspiration (mm) sum-were retrieved from the high-resolution gridded dataset of the Climatic Research Unit (CRU) Time-Series (TS) version 4.
2b ). Using stand-level basal area measured in 2019 instead of LAI resulted in similar pathways (Supplement S4). To remain consistent with prior knowledge on relationships between species richness and the examined forest properties in our experiment, we fitted direct pathways between species richness and LAI and SSCI using the datasets and model structures from the original studies (refs  22, 28  ). Therefore, the tree species richness-forest properties models were fitted on larger plot sets (n = 32, 54, and 74 plots for mean tree height, LAI, and SSCI, respectively) than the forest properties-temperature buffering models fit for the plots for which we had microclimate data and data on all examined forest properties (n = 27 plots, tree species richness ranging from 1-16 species). To prevent pseudoreplication caused by measuring tree height, LAI and SSCI on an annual basis, relationships between tree species richness and these forest properties were fitted using yearly datasets instead of monthly ones.
Cities can host significant biological diversity. Yet, urbanisation leads to the loss of habitats and, potentially, to local extinctions. Understanding how multiple taxa respond to urbanisation globally is essential to promote and conserve biodiversity in cities and surrounding landscapes. Using a dataset with site-level occurrence and trait data of 5302 species from six terrestrial fauna taxonomic groups across 379 cities on 6 continents, we show that urbanisation produces taxon-specific changes in trait composition, with traits related to reproductive strategy consistently showing the strongest response. The effect of urbanisation on community trait composition is strongest at the largest spatial scale considered, and more closely linked to landscape composition (% urban) than arrangement (aggregation), although latitude and climatic variables remain a stronger influence.
This study sets out to: 1) Test our hypothesis by evaluating evidence against the current understanding of an 'urban syndrome' related to average community traits and/or functional diversity; 2) 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 trait-composition and diversity of six terrestrial animal taxonomic groups (amphibians, bats, bees, birds, carabid (ground) beetles, and reptiles) across the globe. The data are a collation of empirical studies at the highly-resolved spatial scale of individual sites rather than generalised to city. Only one taxa (birds) was extracted from a global biodiversity dataset (eBird). We acknowledge there are still geographic biases in the data which 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. The traits we considered were body size, diet, mobility and reproductive strategy, as these are all important for an individuals' survival, growth and reproduction  18. Functional diversity metrics captured key facets of trait diversity (functional richness -FRic, functional evenness -FEve, functional dispersion -FDis), to investigate whether there was evidence to support a contraction of trait space associated with the urban syndrome. Further details can be found in the Methods and Supplementary Materials.
Figures Average variable importance values weighted by the R 2 of the test set of each individual model were computed to estimate urban land cover variable importance for each metric of communityweighted means and variability of traits. Longer bars indicate traits or functional diversity measures that are better predicted by urban land cover within the surrounding landscape. Table  1. Performance of models predicting traits and diversity metrics. Summary statistics of random forests models of community-weighted means of traits and functional diversity metrics. "% explained" is the performance of the model where high values indicate that the response variable is well-predicted by urban and forest land cover, climate, and latitude. "% explained" was calculated as R-squared of the relationship between the predicted and the observed values of the independent test dataset.
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. Further details about the systematic review are available in Supplementary Notes 1. 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  and Supplementary Tables  S1  and S2   55  , 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 traveling 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  56, 57  , the signals are likely to be dampened in this study by including data points across a large number of globally distributed cities. Within our study a plot is defined as an individual location where a survey was conducted. Therefore, while we were unable to explicitly quantify a regional species pool for each taxon and city due to limitations of the available data, we were able to quantify the level of urbanisation in the surrounding landscape for each site and confirm that our data covered the full range of values (See Fig.