dataesr/scientific-paragraphs-categorization huggingface dataset:
Results of the search strategy A total of 10,536 articles were found from the three databases. Subsequently, 983 duplicates were removed and the remaining 9553 articles were screened by title and abstract. Overall, 230 papers were considered suitable for inclusion according to the eligibility criteria and were read in full text. Of these, 23 articles were not retrieved, 131 had a wrong outcome (not considering the strengths or weaknesses of vaccination plans), 32 considered a wrong population, 21 were in a language different from English, Italian, Spanish, or French and 14 had a wrong study design. Of these, nine articles were finally selected for inclusion in this systematic.
The structural parameters were determined from the Rietveld refinement of the XRD patterns using the Maud 2. 9 software  [38]. The morphology of the milled powder was examined using scanning electron microscopy (SEM) (Carl Zeiss GmbH, DSM960A, Jena, Germany) in secondary electron mode operating at a voltage of 15 kV. The SEM was equipped with a Vega©Tescan (Tescan, Brno, Czech Republic) energy dispersive X-ray spectrometry (EDS) analyzer. A superconducting quantum device with a maximum applied field of 20 kOe from Quantum Design ((Caledonia, MI, USA) SQUID MPMS-XL) was used at 300 K to measure the coercive field (Hc) and saturation magnetization (Ms) of the as-milled powders. The milled powders were then sintered by SPS (HPD10, FCT Systeme GmbH, Frankenblick, Germany) in a vacuum environment.
The decrease in the Mn peak located at 2-Theta = 43° occurred as the milling duration increased. This Mn peak seemed to vanish after 25 h of milling. Generally, the disappearance of XRD pattern peaks can be attributed to the formation of a solid solution, lattice distortion, or crystal refinement  [44] [45] [46]. The XRD patterns were analyzed via the Rietveld technique using MAUD 2. 9 software. An example of Rietveld refinement is given in Figure  3  for four experimental XRD patterns: un-milled powders and those milled for 25, 50, and 130 h. The goodness of fit (GOF) values of the refinement mentioned in Figure  3  show that the refinement can be trusted. The Rietveld analysis allowed us to obtain the phases proportion, lattice parameters, size of the crystallites, microstrain, and their error bars.
The XRD patterns were analyzed via the Rietveld technique using MAUD 2. 9 software. An example of Rietveld refinement is given in Figure  3  for four experimental XRD patterns: un-milled powders and those milled for 25, 50, and 130 h. The goodness of fit (GOF) values of the refinement mentioned in Figure  3  show that the refinement can be trusted. The Rietveld analysis allowed us to obtain the phases proportion, lattice parameters, size of the crystallites, microstrain, and their error bars. Figure  4a  shows the monitoring of the evolution of the profile of the peak located at position 43. 5 • , specific to (111) FCC and (110) BCC , as a function of milling time. This allows us to fit and analyze peak asymmetry and shift during the milling process. From 25 h of milling, the result of the fitting procedure shows that this large peak appears asymmetric due to the overlapping of the two peaks specific to two phases of different crystalline structures: the first one is located at a position of 2 theta lower and is specific to the FCC Ni-rich phase, whereas the second is placed 2 Theta higher and results from the presence of the BCC Fe-rich phase.
Second, risk scoring in general faces limitations when it comes to individuals with extreme risk factor levels (eg, familial hypercholesterolemia)  [33]. Conventional risk scoring tools such as ours might underestimate the risk of these persons. This also emphasizes that the HerzFit calculator is not intended to replace a professional health care consultation. Nevertheless, self-assessment tools can be seen as a way to provide information, direct people toward traditional preventive measures, and empower them in their health management. Finally, the HerzFit calculator is only available in German language so far, which could lower user rates in non-native-speaking groups.
Acknowledgments The authors would like to report support from the  Bavarian State Ministry of Health and Care  through the research project  DigiMed   Bayern. Moritz von Scheidt  reports support by the  Clinician Scientist Excellence Programme  of the  German Center for Cardiovascular Research  ( Deutsches Zentrum für Herz-Kreislauf-Forschung eV, DZHK) , the  German Society of Cardiology (Deutsche Gesellschaft für Kardiologie, DGK)  and the  German Heart Foundation (Deutsche Herzstiftung eV). Finally, the authors wish to thank  Anna Hinge  for help with Figure 4.
9 aluations performed based on the studies cited  6, 7  indicate that cancer control strategies have had little impact on reversing the mortality trend. The finding that breast cancer screening has been falling since 2014 in all regions of the country,  10  points to a future scenario of possible worsening. It is therefore urgent to review cancer control policies, taking into account the data available on SUS health information systems and the impact of the actions taken so far, always based on the best scientific evidence. Thus, it is certainly possible to reorganize the planning of early detection actions and the monitoring of the flow in the SUS network. In this sense, the Oncology Panel (PAINEL-Oncologia) stands out as a tool made available by the Ministry of Health in 2019 that gathers information from SUS health information systems on diagnosis and first treatment of malignant neoplasms, enabling health service managers.
METHODS We conducted a follow-up study of breast cancer cases, as described in category C50 of the International Statistical Classification of Diseases and Related Health Problems -Tenth Revision (ICD-10), in females throughout Brazil, diagnosed in 2019 and 2020, with treatment information, using the individualized data available on the Oncology Panel. The data were accessed on the SUS Department of Information Technology (Departamento de Informática do SUS -DATASUS) website on December 9, 2021. The Oncology Panel is a tool for monitoring the time between diagnosis and first treatment based on data from the consolidated SUS health information systems. 11  only took into consideration treatment performed in SUS licensed cancer care facilities. Cases treated surgically in non-licensed hospitals were excluded from the study.
We then adjusted simple models with each exposure variable. Finally, the multiple model was built with the variables that showed significant association in the simple models, using the likelihood-ratio test to compare both models. The fixed effects of the variables were estimated as odds ratios (OR) by exponentiating the respective coefficients, with a 95% confidence interval (95%CI). The fit of the final multiple model was evaluated by analyzing the residuals for overdispersion, normality and outliers using the R program DHARMa (Diagnostics for HierArchical Regression Models) package set of functions (https://cran. r-project. org/package=DHARMa). The tests indicated the adequacy of the model in relation to the data. All the analyses were performed using release 4. 1. 0 of the R program;  16  we used the lme  4  package to adjust the multilevel models via the glmer function, and the Geosphere package to estimate distance via the distHaversine function.
It is possible that not including diagnosed but untreated women in the study group may have influenced the results found. Furthermore, the Oncology Panel only includes cases of SUS users who have a national health card in the "master" category,  11  and the public access tabulator only includes people with identified diagnosis of cancer. These two elements may also limit this study. However, a report on the interval between diagnosis and start of first treatment  27  shows that the proportion of breast cancer cases without diagnostic information has been progressively reduced. Therefore, the volume of people treated but with no diagnosis held on the database available for analysis may have had little influence on the results of this study. Despite the limitations pointed out above, this research demonstrates the need to monitor the stages of breast cancer care in the Brazilian National Health System, aiming, above all, to reduce inequities in access to cancer treatment in Brazil.
We pooled data from the NAMACO study and the neuro-HIV platform participants who underwent lumbar puncture (LP) between 2011 and 2019. Both patient groups had neurocognitive symptoms. CSF HIV-1 escape was defined as the presence of quantifiable CSF HIV-1 RNA when plasma HIV-1 RNA was suppressed or CSF HIV-1 RNA greater than plasma HIV-1 RNA when the latter was detectable.
Data Availability Data not provided in the article because of space limitations may be shared (anonymised) at the request of any qualified investigator for purposes of replicating procedures and results. From March 2011 to April 2019, we assessed a total of 1166 patients across the two patient groups (NAMACO and neuro-HIV platform) of whom we excluded 878 patients who did not undergo LP. Among the remaining 288 patients, 25 (8. 7%) were diagnosed with CSF HIV-1 escape (Figure  1 ). Of these, only three patients of 25 (12%) had CSF HIV-1 RNA <1. 0 log 10 greater than plasma HIV-1 RNA.
While this included review and/or optimisation of CPE score (study period 2011-2019), data from the NAMACO study subsequently demonstrated a non-association between CPE score and NCI  37. Our study has limitations. First, a potential selection bias exists for NAMACO study participants who could be symptomatic or asymptomatic prior to neurocognitive assessment. Neuro-HIV platform patients were all symptomatic or suspected by clinicians of having NCI. Second, the cross-sectional design did not allow us to determine the persistence of CSF HIV-1 escape or the possibility of late-onset NCI development in initially asymptomatic PLWH with CSF HIV-1 escape, nor the clinical and virological outcome after ART modification. Furthermore, because we lacked longitudinal analysis, we described patients as having low levels of viremia at the time of plasma sampling which is not the same as 'low-level viremia', a clinical entity which requires at least two consecutive plasma samples showing detectable HIV-1 RNA.
ACKNOWLEDGEMENTS We thank all the patients participating in the NAMACO study. We thank all the infectious disease physicians and the study nurses working in the centres for their dedicated patient work and contribution to the NAMACO study. We thank the neuropsychologists  Samanta Simioni ,  Severin Fruh ,  Stefanie Clarke  and  Stefania Rossi , for their work in NAMACO. Finally, we thank the late  Dr Kevin Robertson  for his advice regarding the selection of cognitive tests and his encouragement to launch the study, and to  Prof Scott Letendre  for providing support materials for some definitions in our study. The NAMACO study group: director:  Matthias Cavassini ; co-director:  Renaud Du Pasquier ; neuropsychologists:  Melanie Metral ,  Samanta Simioni ,  Peter Brugger ,  Klemens Gutbrod ,  Andreas U.
g. forest and tundra habitats  20, 26  ). Yet, it is unclear whether the strength and expression of climate-trait relationships depend on the thematic resolution (hereafter "narrowness") of the habitat definition. Analysing climate-trait relationships at different levels of habitat narrowness could help us understand whether these relationships are consistent across both levels (resolution-invariant) and habitats (i. e. , habitat-specific). This understanding might be important for more accurate predictions of the effects of a changing climate on the distribution and functioning of natural plant communities. We used >300,000 geo-referenced vegetation plots (Supplementary Fig. S1 ) from the European Vegetation Archive (EVA  27  ), 19 high-resolution bioclimatic variables from the CHELSA database  28, 29  and species-level plant trait information from the TRY database  30, 31.
S3-S6 ). Second, decomposing the analysis into increasingly narrow habitats revealed stronger climate-trait relationships for SLA and SRL but not for plant height and seed mass. Third, in the most narrowly defined habitats, the effects of climate on the expression of all four traits were strongly habitat-specific, regardless of which broad habitat type or climatic gradient was considered. By using increasingly narrow habitat definitions, we were able to reduce the effects of local factors on CWMs and thus better unveil the effects of macroclimate on the functional trait composition of plant communities. The habitat classification we used is based on floristic and biogeographic characteristics. As a result, plant communities were analysed within homogeneous groups that reflect common adaptations to local environmental features that are either independent of global climatic gradients  34  or difficult to capture with global datasets on climate and soil conditions.
These local conditions were increasingly accounted for by including more narrowly defined habitats in the models of climate-SRL relationships. In order to synthesize the effects of macroclimate across a largescale dataset, we had to accept some shortcomings in our analysis. We focused only on the linear effects of climate and ignored interactions among the different dimensions of climate, although we can assume that the distribution of the four plant traits is also shaped by nonlinear and interaction effects (c. f. tree height  50  and leaf traits  20  ). We partially accounted for non-linearity by using log-transformed CWMs. Still, our models identified stronger linear effects of climate on community traits compared to global-scale analyses  13. Whether the classification into narrower habitats might have strengthened or alleviated any nonlinear or interaction effects has, to our knowledge, not been tested yet.
Vegetation data The raw vegetation survey data consisted of 1,741,856 plots with 37,318,600 species records from 107 databases, collated and curated by the European Vegetation Archive (EVA)  27  and accessed on May 12th, 2021. Individual databases are listed in Supplementary Data 1. Species abundances were measured or converted to percentage cover. Before the analysis, we removed all plots (1) with only presence/absence data, (2) without geographic coordinates, (3) situated in Greenland, (4) with latitudinal coordinates lower than the southernmost point of 34°N or higher than the northernmost point of 82°N, (5) with trait information for less than 80% of the total plant cover and (  6 ) for which climatic variables could not be calculated due to empty pixels around the focal plot. To reduce the effects of spatially clustered and repeated censuses, we only retained the most recent census for each unique location and randomly stratified the dataset to include only one plot per     S1 ).
Taxonomy We harmonized species names from the European Vegetation Archive using the Taxonomic Name Resolution Service 5. 0 (tnrs. biendata. org). Subspecies and varieties were merged at the species level. Species names were matched to the taxonomic backbone 3. 0 of the sPlot global vegetation database  53  to maximize compliance with species names from the TRY plant trait database  30. Fungi, bryophytes, algae, and lichens were removed from the data, resulting in 13,758 vascular plant species.
Community-weighted trait means We extracted species-and genus-level average values for plant height, SLA, seed mass and SRL from the TRY plant trait database  30  version 5, which covered species-level mean values of 33 traits for 50,404 species, derived with Bayesian Hierarchical Probabilistic Matrix Factorization. This algorithm predicts individual-level trait values based on the observed trait records, observed trait-trait correlations, and the taxonomic hierarchy reflecting phylogeny  54, 55. The root-mean-square error of the z-transformed predicted versus available trait values ranged from 0. 07 (seed mass) to 0. 2 (SLA) (see Supplementary Fig. S14 ). From this gap-filled dataset, we assigned species-level mean trait values to 6,025 species from our dataset and to an additional 690 species, we assigned genus-level mean trait values from the gapfilled dataset.
The relationships between the four focal traits and 24 traits from the gap-filled dataset are shown in Supplementary Fig. S15. The original publications for the data used are listed in Supplementary Table  S2. Without imputed values, we were able to assign trait values to 45% (plant height), 28. 4% (SLA), 34. 4% (seed mass) and 5. 4% (SRL) of all species in the dataset used for the analysis, which accounted for an average of 95. 6%, 91. 4%, 90. 8% and 47. 4% of plant cover at the plot level, respectively. For each plot, we calculated the CWM of the four plant traits according to the equation in  13.
Climatic data For each plot, we extracted the 19 bioclimatic variables from the CHELSA Climatologies version 1. 2  28, 29  at a pixel resolution of 30 arc sec (~1 km) using the cloud-based Google Earth Engine platform. Bioclimatic values were calculated as the average value from all pixels within 500 m from the plot coordinates. Plots with missing climate data in adjacent pixels were omitted from further analyses. Covariation among the 19 bioclimatic variables was removed by a principal component analysis. The first four principal components (PCs) jointly explained 88. 4% of the climatic variation (Supplementary Fig. S1 ). We used the loadings of the 19 bioclimatic variables to extract the positions of all plots along these four PCs, which were used as predictors in the subsequent analyses.
Model 2 included the fixed-effects of the four climatic PCs plus the random effects of the broad level 1 habitat types. Model 3 included the fixed-effects of the four climatic PCs, the random effects of the broad level 1 habitat types plus random interaction terms between the fixed effects of the climatic PCs and the broad level 1 habitat types. Models 4-5 were similar to model 3 except that the broad level 1 habitat types and their random interaction terms were replaced with the intermediate level 2 or the narrow level 3 habitat types and their respective interactions with the climatic PCs. The implementation of all models is documented on GitHub. Model residuals were visually checked and are shown in Supplementary Figs. S16-S20. For models 3-5, we quantified the effects of climate on trait CWMs, both across and within habitats, with predicted marginal mean regression slopes.
All regression slopes were assigned an approximate confidence interval (slope estimate ± 1. 96 * standard error) and confidence intervals that did not include zero were considered significant at approximately p < 0. 05. We did not test regression slopes that were based on fewer than 100 plot observations. For each model, we calculated the proportion of variation in CWMs that could be explained by climatic gradients (fixed effects of climatic PCs and interactions between climatic PCs and habitat types) and habitat type (random effects). The proportion of explained variation was quantified using the marginal and conditional R²  59  , whose calculation for random-slope models was based on the variance components of the fixed and random effects and whose implementation we adopted from the R script provided by  60.
To check whether the obtained results depended on the proportion of species with actual trait measurements, we repeated all analyses with models in which the contribution of each plot was weighted according to the summed cover of species with trait values divided by the total plant cover. The results of these weighted analyses were qualitatively similar to the results of the unweighted analyses and are shown in Figs. Supplementary S21-S25. The code implemented to calculate the marginal and conditional R² for generalized additive mixed-effects models is documented on GitHub. All analyses were conducted in R 61 using the package mgcv for generalized additive mixed-effects models  57, 58  , emmeans and ggeffects for marginal mean regression slopes  62, 63  , dismo for geographical stratification of plots  64  and ggplot2 for data visualization  65.
Effects of climate on plant traits in narrowly defined habitats. The graphs show the number of slope estimates between the first and second principal components (PCs) of the 19 CHELSA bioclimatic variables and the community-weighted mean of four functional traits, calculated with generalized additive mixed-effects models. Colouring indicates the expression and significance of the relationship. Significance was determined at p < 0. 05 (based on separate two-sided t tests). Slope estimates from habitats with fewer than 100 plot observations were omitted. SLA specific leaf area, SRL specific root length. 0. 01°grid cell (approximately 1. 11 × 0. 69 km in Central Europe). The resulting dataset contained 300,021 vegetation plots with 7,320,239 individual species occurrence observations that were conducted between 1873 and 2020 and distributed between 34.
Acknowledgements The 2019-2020 BiodivERsA joint call for research proposals, under the  BiodivClim ERA-Net COFUND program , and with the funding organisations  Swiss National Science Foundation SNF  (project: FeedBaCks, 193907),  Agence nationale de la recherche  ( ANR-20-EBI5-0001-05 ), the  German Research Foundation  ( DFG BR 1698/21-1, DFG HI 1538/16-1 ), and the  Technology Agency of the Czech Republic  ( SS70010002 ) funded this research and the position of S. K. and G. M. The  Basque Government  ( IT1487-22 ) supported the work of I. B. The  Slovenian Research Agency  ( ARRS P1-0236 ) supported the work of A. Č. The  University of Latvia  (grant nr. AAP2016/B041//Zd2016/AZ03 ) supported the work of S. R. Financial support within the  Rita-Levi Montalcini  ( 2019 ) programme, funded by the  Italian Ministry of University , supported the work of F.
M. S. The  Slovak Research and Development Agency  (grant nr. APVV 16-0431 ) supported the work of J. Š. The  VIL-LUM FONDEN  (grant  16549 ) funded the  VILLUM Investigator  project " Biodiversity Dynamics in a Changing World " which supported the work of J. C. S. J. C. S. also considers this work a contribution to  Center for Ecological Dynamics in a Novel Biosphere (ECONOVO) , funded by  Danish National Research Foundation  (grant  DNRF173 ). S. K. acknowledges financial support from the  Open Access Publication Fund of the Martin Luther University Halle-Wittenberg  and the  iDiv Open Science Publication Fund.
Data availability The data generated in this study (plot-level information on plot coordinates, survey year, CWMs and climatic PCs) were deposited in the data repository of the German Centre for Integrated Biodiversity Research (iDiv) Halle-Jena-Leipzig (https://doi. org/10. 25829/idiv. 3527-g89efk). Raw vegetation data are available under restricted access because they belong to the owners of each vegetation database, but can be requested at euroveg. org/eva-database-obtaining-data. The bioclimatic and plant trait data used in this study can be downloaded from chelsa-climate. org/bioclim and www. try-db. org/TryWeb/dp. php, respectively.
This work presents a gridded dataset on real estate and transportation in 192 worldwide urban areas, obtained from the Google Maps API and the web scraping of real estate websites. For each city of the sample, these data have been associated with the corresponding population density and land cover data, extracted from the GHS POP and ESA CCI data respectively, and aggregated on a 1 km resolution grid, allowing for an integrated analysis. This dataset is the first to include spatialized real estate and transportation data in a large sample of cities covering 800 million people in both developed and developing countries. These data can be used as inputs for urban modeling purposes, transport modeling, or between-city comparisons in urban forms and transportation networks, and allow further analyses on e.