dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Badihi et al. 2023). Our approach offers a novel framework to test competing hypotheses on the drivers of behavioural diversity across a wide variety of animal cultures. Our ndings have broad implications for understanding the interplay between chimpanzee behavioural and genetic diversity with the environment. Increased data collection efforts on behavioural repertoires in so far unsampled chimpanzee communities, as well as expanding observation times, and increasing the availability of genetic and genomic datasets would enable further tests of the hypotheses we addressed in this manuscript. Further developments on our work may investigate causal relationships between predictor variables for example using structural equation models, and account for covariance among behaviours using a multi-outcome Gaussian process.
Data extraction and preparation We extracted behavioural data from a georeferenced dataset covering 144 communities across 20 countries spanning the entire chimpanzee range (Kuehl et al. 2019, Kalan et al. 2020, Fig. 4 ). Behavioural traits were selected based on their detectability in unhabituated chimpanzee communities with indirect observation methods such as remote camera trapping or identi cation of remains and artefacts, and we assumed that each sampling site represented a single community. For each community, 31 behavioural traits were coded as observed (direct/indirect evidence) or not observed. The original data matrix contained 4464 occurrences (observed/not observed) of 31 behaviours and associated data for 144 communities. Six very rare behavioural traits (Table  S3 ) that were observed in fewer than three chimpanzee communities were excluded from the analysis as it was impossible to t our spatial models with very rare behaviours.
Our nal data set thus included 25 behaviours across 144 chimpanzee communities. We considered the following environmental predictors of behavioural diversity: distance from (Pleistocene) refugia, (ecosystem) productivity, spatial heterogeneity of productivity, temperature and precipitation anomalies in the last 21 kyr, intensity of human footprint on local ecosystems (details below). As genetic predictors, we considered interpolated heterozygosity from whole chromosome 21 sequences and from 14 microsatellite markers in separate models, which we refer to as chr21 and msat models, respectively. Information concerning genetic diversity (heterozygosity) was extracted, for 41 sites (Figure  S8 ), from the datasets published by  Fontsere et al. (2022)  and  Lester et al. (2021)  for genomic data from chromosome 21 (chr21, based on the unfolded site frequency spectrum for 23.
358 Mb of target sequence across 553 individuals that passed quality lters for contamination and relatedness) and microsatellite DNA (14 loci sequenced across 939 individuals), respectively. We used the raw data to calculate observed heterozygosity from the data using ANGSD v. 0. 925  (Korneliussen et al. 2014 ) for chromosome 21 (minimum 5x coverage, see  Fontsere et al. 2022 ) and the "diveRsity" R package  (Keenan et al. 2013)  for microsatellite data. To estimate genetic diversity for all of the 144 communities in the behavioural dataset, we spatially interpolated heterozygosity estimates using an inverse distance-weighting approach (see  Clark & Evans, 1954 , Tobler, 1970)  in the gstat v2. 1. 1 R package, generating continuous surfaces of heterozygosity based on chr21 and msat data (Figure  S8 ) and then extracted the values from the point data for each community without genetic data in our matrix.
We computed the distance from each community to the closest Pleistocene refugium (area of stable suitable climate) as identi ed by  Barratt et al. (2021) , who used a species distribution modelling approach on chimpanzees occurrence data and dynamic paleoclimate models. We used the enhanced vegetation index (EVI) as an indicator of ecosystem productivity. This index is known to be positively related to annual aboveground NPP, as the more used normalised difference vegetation index (NDVI)  (Paruelo et al. 1997 ), but allows reducing the canopy background noise in densely forested areas. Data for productivity was retrieved from the MODIS MOD13Q1 product (Didan 2021; 250 m spatial and 16 days temporal resolutions), and per-cell median productivity over the 2001-2021 period was calculated using Google Earth Engine and the "rgee" R package  (Aybar 2022 ).
Temperature and precipitation anomalies in the last 21 kyr were calculated as the difference (for mean annual temperature) or ratio (for annual precipitation) between current and LGM (Last Glacial Maximum, 21 kyr BP) conditions for each community. Consequently, positive temperature anomalies and precipitation anomalies > 1 re ect local increases between the LGM and the present, and vice versa. Current mean annual temperature and annual precipitation data were obtained from the CHELSA dataset (Karger et al. 2017; 30 arcsec resolution). To reduce single model uncertainties, we built multi-model ensembles for LGM climate by averaging the projections from the whole set of general circulation models available on CHELSA  (Loarie et al. 2009; Sandel et al. 2011 ). Intensity of global human footprint (IGHP, which corresponds to the Human In uence Index -HII -normalised by biome) was obtained at a native resolution of 30 arcsec from Last of the Wild Data v.
2 (2005). Spatial predictors (productivity, precipitation and temperature anomalies and human footprint) were extracted from the relevant rasters using the "raster" package  (Hijmans, 2023)  by computing means within a given radius (30 km). The spatial heterogeneity of productivity by computing the standard deviation of productivity within the same radius. Prior to further statistical analyses, we examined correlations between predictor variables, nding no particularly strong correlation. Except for the correlation between heterozygosity at chr21 and microsatellite (which were analysed in separate models), the strongest correlations involved control predictors (refdist and footprint: Pearson's r = -0. 54, footprint and het_chr21: r = -0. 45), with all other r < 0. 4.
1 and 0. 9 quantiles, respectively, of the estimated posterior probability densities. To evaluate the t of mixed models, we calculated the amount of variation explained via pseudo-R 2. Speci cally, pseudo-R 2 were calculated both as the squared Pearson's correlation coe cient between the observed and predicted BD (i. e. , summing the prediction of all traits within each community), and as the proportion of variance explained by the model for single traits within each community on the response scale (i. e. , Var(prediction) / (Var(prediction) + Var(residuals)); Gelman et al. 2019). We estimated variable importance using the principle implemented in the "biomod" R package  (Thuiller et al. 2009 ). The impact each predictor has on the model's predictive ability was computed by shu ing its values n times and computing Pearson's correlation coe cients r between the prediction calculated on the original values and the n post-shu ing predictions.
2022 ) were available as (pairwise) F ST -(1/F ST )  (Rousset et al. 1997 ) based on the unfolded site frequency spectrum for  23. 358  Mb of target sequence across 553 individual samples. To visualise dissimilarity/distance structures among communities based on behavioural and genetic data, we performed Principal Coordinates Analysis (PCoA) using the pcoa function in the "ape" v. 5. 0 R package  (Paradis & Schliep, 2019). To check for correlation between pairwise dissimilarity matrices, we used Mantel tests as implemented in the mantel function in the "vegan" v. 2. 6. 4 R package  (Oksanen et al. 2022) , applying 1000 randomizations.
Discussion In this study, we examined the demographic data from 749,037 participants, across 58 countries and navigation performance from 422,772 from 41 countries. We found no reliable evidence supporting a benefit in spatial ability associated with hand preference, but positive evidence for an association with education and age on handedness prevalence. Here, we discuss handedness first in relation to spatial performance and then in terms of demographics. Our findings challenge previous studies that suggested a significant relationship between an individual's hand preference and their spatial performance in either small-scale  (Krumina et al. , 2018)  or large-scale tasks  (Piper et al. , 2011). There are at least three reasons for this difference in findings: First, previous studies of spatial skill drew conclusions using small sample sizes.
Acknowledgements Sea Hero Quest initiative  funded and supported by  Deutsche Telekom. Research was also supported by the  Leverhulme Trust  award  DS-2017-026. Alzheimer's Research UK  ( ARUK-DT2016-1 ) funded the analysis; Glitchers produced the game; and  Saatchi  and  Saatchi London  managed the creation of the game. generated by the SHQ system according to the criteria and requirements of a specific project. https://seaheroquest. alzheimersresearchuk. org/ Access to the portal will be granted for noncommercial purposes.
Data Availability A dataset with the pre-processed trajectory lengths and demographic information is available at https://osf. io/xfz8w/?view_only=08e221cfbff4416db02b1b2fda1b9539. The dataset with the full trajectories is available on a dedicated server: https://shqdata. z6. web. core. windows. net/. We also set up a portal where researchers can invite a targeted group of participants to play SHQ and generate data about their spatial navigation capabilities. Those invited to play the game will be sent a unique participant key,.
1c ) and to determine a global γ value for all measurement conditions (Supplementary Note 2 and Supplementary Fig. 3 ). For this workflow, participants used custom or publicly available software packages. FRET efficiency histograms for representative experiments on MalE in the apo (no ligand) and the holo state (1 mM maltose) are shown in Fig. 1c  with mean values reported by 16 laboratories. They show very good agreement and reproducibility. It was not possible to extract accurate FRET efficiency values from three laboratories due to, for example, missing or suboptimal laser lines (Supplementary Table  1  and Supplementary Note 2). All laboratories observed the expected changes for MalE-1, MalE-2 and no shift for MalE-3. This indicates that the samples did not degrade during shipment on dry ice and storage in the laboratories at 4 °C.
3  and Supplementary Tables  1  and 2 )  34. The use Analysis https://doi. org/10. 1038/s41592-023-01807-0 (Fig. 2c , row 1) with an average E = 0. 46 ± 0. 04 (row 2) as expected for the open conformation of the holo state  30, 36. Notably, a fraction of around 15% of ligand-free protein remains in the sample at the RNA concentration used (Supplementary Fig. 7 ). For the apo state, we obtained a similar standard deviation of ±0. 03 as found for MalE, however, a clear outlier was apparent (Supplementary Table  4 ). To test whether user bias affected the reported results, a single person reanalyzed the datasets. This person developed an optimal procedure for determining the correction factors for this challenging sample (Supplementary Note 3) and improved the agreement to a standard deviation of ±0.
We also observed a small positive correlation between detected photon numbers and burst duration (Fig. 3c , Pearson's r = 0. 54 and Supplementary Fig. 10 ). This suggests that larger volumes, in combination with high irradiances, The middle shows the reported mean FRET efficiencies reported by seven laboratories. The mean value from all datasets is 0. 739 ± 0. 029, shown above with the corresponding standard deviation in gray. The bottom shows the extracted mean FRET values after reanalysis of the collected data. After reanalysis, the agreement improved to 0. 742 ± 0. 008. e, SmFRET efficiency histogram comparisons of U2AF2 in the holo state. 5 µM of U9 RNA was used to obtain the holo state. The top shows a representative 1D FRET efficiency histogram of laboratory 1 fitted to two Gaussian distributions to determine the FRET efficiencies of the different subpopulations, yielding mean FRET efficiencies of 0.
44 for RNA-bound and 0. 76 for the RNA-free conformation. The middle shows the mean FRET efficiencies reported by the seven laboratories. The mean values from all seven of the datasets were 0. 45 ± 0. 04 for the RNA-bound conformation (in green) and 0. 78 ± 0. 04 for the RNA-free conformation (in gray). The bottom shows the reanalysis of the holo measurements yielding values of 0. 42 ± 0. 02 and 0. 77 ± 0. 03 for RNA-bound and RNA-free fractions, respectively.
For U2AF2 in the holo state, the ds was assessed only for the low-FRET RNA-bound population. Boxes indicate the median and 25/75% quartiles of the data. Whiskers extend to the lowest or highest data point within 1. 5 times the interquartile range. The gray area indicates the ds obtained for the dsDNA used in a previous study  18  based on measurements performed in laboratory 1 for BVA (ds DNA = 0. 0033 ± 0. 0033) and laboratory 2 for the E-τ plot (ds DNA = 0. 0026 ± 0. 0044). The horizontal red lines indicate the expected ds for a potential conformational exchange between the apo and holo states. We computed the expected change in FRET efficiency using their structural models in the PDB (Supplementary Note 6 and Supplementary Table  9 ). over all dye pairs (Pearson's r = 0. 73), with a stronger correlation when each dye pair is investigated individually (Fig.
SmFRET data acquisition and analysis Data acquisition and correction procedures were performed for confocal measurements as described by Hellenkamp et al. 18. The samples were measured using ALEX or PIE on a confocal microscope as sketched in Supplementary Fig. 2. A description of the experimental procedures of all laboratories is given in Supplementary Note 18. Briefly, the three recorded intensity time traces for each single-molecule event are: donor emission after donor excitation ∶ i I Dem|Dex , acceptor emission after donor excitation (FRET signal) ∶ i I Aem|Dex , and acceptor emission after acceptor excitation ∶ i I Aem|Dex. The apparent (raw) FRET efficiency is computed as: E app = i I Aem|Dex i I Dem|Dex + i I Aem|Dex , (3) Recorded intensities were corrected for background contributions as: ii I Dem|Dex = i I Dem|Dex -i I (BG) Dem|Dex , (4) ii I Aem|Dex = i I Aem|Dex -I (BG) Aem|Dex , (5) ii I Aem|Aex = i I Aem|Aex -I (BG) Aem|Aex , (6) where I (BG).
Detection of protein dynamics In this work, we used the following two approaches to detect conformational dynamics: BVA. In BVA, the presence of dynamics is determined by looking for excess variance in the FRET efficiency data beyond the shot-noise limit. The standard deviation (σ E app ) of the apparent FRET efficiency (E app ) is calculated using a fixed photon window of n = 5 over the time period of the individual bursts given by: LabView-based software (ref. 15.
Acknowledgements Work in the laboratory of T. C. was financed by a  European Research Council (ERC)   Starting grant  (no. ERC-StG 638536-SM-IMPORT ),  German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)  within grant nos. GRK2062  (project  C03 ) and  SFB863  (project A13) and an  Alexander von Humboldt postdoctoral fellowship  (to N. Z. ). T. C. , P. T. and D. C. L. acknowledge the support of the  Center for integrated protein science Munich  and the  Center for NanoScience. D. C. L. acknowledges the support of the  Nanosystems Initiative Munich and LMUinnovative program BioImaging Analysis  https://doi. org /10. 1038/s41592-023-01807-0  Network. We also acknowledge support via the  SFB1035  (DFG,  Sonderforschungsbereich 1035  project no. 201302640 , project no. A11  to D.
T. D. C. was supported by the  Biotechnology and Biological Sciences Research Council (BBSRC)  (grant no. BB/ T008032/1 ) and  Engineering and Physical Sciences Research Council (EPSRC)  (grant no. EP/V034804/1 ), B. A. was supported by an  EPSRC   Prize Fellowship. BMBF  grant nos. 03Z2EN11  and  03Z22E511  as well as  DFG  grant no. SCHL 1896/4-1  (to M. Schlierf). SFB960  project  A7  (to D. G. )  US National Institutes of Health  grant no. GM122569  (to T. Ha). J. H. acknowledges the  Research Foundation Flanders (FWO)  (project nos. G0B4915 ,  G0B9922N  and  G0H3716N ) and is indebted to  Johan Hofkens  at  KU Leuven  for the used smFRET infrastructure. ERC grant agreement no. 681891  (Prosint) and  DFG under Germany's Excellence Strategy  ( CIBSS EXC-2189  project  ID 390939984 ) and the  SFB1381 program  (project  ID 403222702 ) (to T.
Hugel). Royal Society Dorothy Hodgkin Research  Fellowship  DKR00620  and a  Research Grant for Research Fellows  no. RGF\R1\180054  (to N. C. R. ), by the  Wellcome Trust  (grant no. 110164/Z/15/Z  to A. N. K. ). The  Israel Science Foundation  (grant nos. 556/22  to E. L. ,  3565/20  to E. L. , within the  KillCorona -Curbing Coronavirus Research Program ), the  National Institutes of Health  (grant no. R01 GM130942  to S. W. and to E. L. as a subaward), by the  Milner Fund  (to E. L. ) and by the  Hebrew University of Jerusalem  (start-up funds to E. L. ). Agence Nationale pour la Recherche  (grant nos. ANR 18-CE11-0004-02 ,  ANR-19-CE44-0009-02 ,  ANR-21-CE11-0034-01 ,  ANR-21-CE11-0026-03  and  ANR-10-INBS-04 , ' Investments  for the future' to E. M. ). E. A. L. acknowledges funding by the  ERC ADG MultiOrganelleDesign  and the  SFB1551  (project  ID 464588647 ).
Data availability The data for all figures, all supplementary figures, the raw data for MalE measurements from all laboratories (with the exception of one mutant from one laboratory) and the raw data for all U2AF2 measurements have been uploaded to Zenodo (https://doi. org/10. 5281/zenodo. 7472900). PDB IDs used are 1OMP, 1ANF, 2YHO and 2YH1. Source data are provided with this paper.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability The data for all figures, all supplementary figures, the raw data for all MalE measurements (with the exception of one mutant from one laboratory) and the raw data for all U2AF2 measurements have been uploaded to Zenodo (https://doi. org/10. 5281/zenodo. ). WͲ/ ŽĨ ƉƌŽƚĞŝŶ ƐƚƌƵĐƚƵƌĞƐ͗ ϭKDW͕ ϭ E&͕ Ϯz,Ϭ͕ Ϯz,ϭ.
E apo Fig. 1 | Experimental design of MalE as a protein model system for smFRET studies. a, Crystal structure of MalE in its D87C-A186C A134C-A186C (MalE-2) (MalE-3) Laboratories 0 0. 2 0. 4 0. 6 0. 8 1. 0 0 0. 2 0. 4 0. 6 0. 8 1. 0 0 0. 2 0. 4 0. 6 0. 8 1. 0 FRET e iciency, E FRET e iciency, E FRET e iciency, E d Laboratories -0. 2 -0. 1 0 0. 1 0. 2 -0. 2 -0. 1 0 0. 1 0. 2 -0. 2 -0. 1 0 0. 1 0. 2 E holo -E apo E holo -ligand-free apo state (PDB ID 1OMP) E holo -E apo absence and presence of 1 mM maltose (bottom, green) for one exemplary dataset measured in laboratory 1. The distribution is fitted to a Gaussian with domains D1 and D2 linked by flexible beta sheets (highlighted in blue). b, The distribution. The reported mean FRET efficiencies for 16 laboratories are shown crystal structure of MalE (rotated by 90° as compared to a in the apo (gray, PDB ID below (due to experimental difficulties, the results of three laboratories were 1OMP) and holo (green, PDB ID 1ANF) states with mutations at K29C-S352C excluded; Supplementary Table 1).
Background: Lung lobar ventilation and perfusion (V/Q) quantification is generally obtained by generating planar scintigraphy images and then imposing three equally sized regions of interest on the data of each lung. This method is fast but not as accurate as SPECT/CT imaging, which provides three-dimensional data and therefore allows more precise lobar quantification. However, the manual delineation of each lobe is time-consuming, which makes SPECT/CT incompatible with the clinical workflow for V/Q estimation. An alternative may be to use artificial intelligence-based autosegmentation tools such as AutoLung3D (Siemens Healthineers, Knoxville, USA), which automatically delineate the lung lobes on the CT data acquired with the SPECT data. The present study assessed the clinical validity of this approach relative to planar scintigraphy and manual quantification in SPECT/CT.
Methods: The Autolung3D software was tested on the retrospective SPECT/CT data of 43 patients who underwent V/Q scintigraphy with 99m Tc-macroaggregated albumin and 99m Tc-labeled aerosol. It was compared to planar scintigraphy and SPECT/CT using the manual quantification method in terms of relative lobar V/Q quantification values and interobserver variability.
In fact, it requires the division of the left lung into three parts despite the fact that it bears only two lobes; as a result, the counts in the middle part must be halved and each half then added to the counts in the inferior and superior lobes. By contrast, 3D SPECT/CT scanning permits a more precise definition of the lobar volume and integrates attenuation correction, thus resulting in more accurate lobar quantification  [8]. However, it can be very time-consuming to manually delineate each lobe on the CT dataset, which means that this approach is not suitable for use in the routine clinical workflow. To improve this, artificial intelligence (AI) and the latest deep-learning techniques have been used to automatically segment the lung lobes on the CT data of SPECT/CT lung scans, thus quickly providing V/Q estimates.
Theoretically, such methods should provide more precise and accurate estimates than the conventional planar estimation but within a similar time frame. Indeed, evaluations of some of these algorithms (i. e. , the HERMES hybrid 3D lung lobe quantification algorithm from Hermes Medical Solutions and the adaptive iterative reconstruction algorithm from GE Healthcare) show they are sufficiently robust for clinical application and improve lobar quantitation relative to the conventional planar method  [7, 9]. Another AI-based auto-segmentation algorithm is AutoLung3D, which was developed by Siemens Healthineers (Knoxville, USA). To validate the usefulness of AutoLung3D for lung lobe segmentation in lung SPECT/CT and estimations of V/Q, we conducted the present investigation. Specifically, we compared this method to planar estimations and manual segmentation of the SPECT/CT data in terms of accuracy and interobserver variability.
This kind of collimator, which is the most common collimator used in France, provides good spatial resolution, but its sensitivity is half that of a general-purpose collimator  [11]. Therefore, the administered activities are twice those recommended in the international guidelines, which specifically apply to general-purpose collimators  [12]. To correct the SPECT images for attenuation, a freebreathing CT acquisition was also performed using the following parameters: X-ray tube voltage = 110 kV, the X-ray tube current modulated to compensate for patient attenuation (CareDose mAs ref = 110 mAs), pitch = 0. 8, and collimation = 16 × 1. 2 mm. The CT data used for lung segmentation were reconstructed with the sharp lung kernel (B70s) and a slice thickness of 1 mm. The SPECT reconstruction was performed with an iterative algorithm (proprietary algorithm Flash3D: 3D ordered-subset expectation maximization) using four iterations and eight subsets, collimator-detector response compensation, scatter compensation, and attenuation correction driven by the CT data.