dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimating historical biogeography We estimated the ancestral areas of origin and geographic range evolution for Cycadales using the ML approach of dispersalextinction-cladogenesis (DEC,  Ree & Smith, 2008)  as implemented in the C++ version  (Beeravolu & Condamine, 2016) , as well as the R package BIOGEOBEARS  (Matzke, 2018). To infer the biogeographic history of a clade, DEC requires a time-calibrated tree (i. e. the consensus tree obtained from the Bayesian TED analysis), the current distribution of each species, a set of geographic areas, and a time-stratified geographic model that is represented by connectivity matrices for specified time intervals spanning the entire evolutionary history of the group. We first defined a set of geographic areas based on paleogeographic knowledge as follows: (1) West Palearctic, defined as Western Europe to the Urals, (2) East Palearctic, defined as east of the Urals, above 3000 m in the Himalayas and north of Sichuan in China, (3) West Nearctic, defined as Western North America including the Rocky Mountains, (4) East Nearctic, defined as North America east of the Rocky Mountains, (5) Central America, going from the northern border of Mexico southward to the border between Panama and Colombia, (6) Caribbean Islands, excluding Trinidad and Tobago, (7) South America, defined as all countries from Colombia to Argentina and including Trinidad and Tobago, (8) Africa, defined as the whole African continent and Arabian Peninsula but excluding the islands in the Indian Ocean, (9) Madagascar, defined as the island of Madagascar and all other Indian Ocean islands in the vicinity, (10) India, defined as the area below 3000 m from NW Pakistan to the border with Myanmar, (11) Indonesia and Wallacea, defined as Myanmar, SE Asia, southern China, western Indonesia to Lydekker's Line; including the Lesser Sunda Islands but excluding Timor, Wetar and associated islands, which are Australasian in origin, and (12) Australasia, defined as everywhere east of Lydekker's Line but including Timor, Wetar and small nearby islands.
Furthermore, there is evidence for the ancient (Triassic) presence of cycads in Antarctica (e. g. Antarcticycas schopfii  Smoot, Taylor, et   (Harris, 1926 (Harris, , 1932)) ). To reflect their increasingly recognized role in global plant biogeography (e. g. Estrella et al. , 2019), we added Antarctica and Greenland to the geographic areas to consider the possibility that cycads colonized these continents. We choose these 14 areas to test biogeographic hypotheses and estimate implications from the breakup of ancient Mesozoic supercontinents, whereas Wallace's bioregions do not capture tectonic and geological changes. Relying on the WCVP  (Govaerts et al. , 2021) , the geographic distribution for all extant cycad species (column 'geographi-c_area' in the database) was categorized by coding the presence or absence of each species in each of the above-defined areas.
We also used data available in the literature (e. g. http://cycadlist. org/, Calonje et al. , 2022). Occurrences of introduced species or marginally entering an area were not considered. Similarly, we coded the 60 fossil species of our dataset. However, fossil data provide evidence on the past geographic presence of taxa, but not about geographic absence due to the incompleteness of the fossil record. In BioGeoBEARS, but not yet in DECX, one can include this information by coding the geographic range of fossils with missing data '?' instead of true absence (useAmbiguities = TRUE option). Coding presence in region A and unknown presence or absence in other regions mean that any geographic range including A will have tip likelihood of 1, and any geographic range excluding A will have a tip likelihood of 0.
We made a custom R script to retrieve the biogeographic events and calculate their timing across the phylogeny (see Data availability). Following previous studies (e. g. Antonelli et al. , 2018; Meseguer & Condamine, 2020) , anagenetic events were dated at middle points of branches and constrained by the time-stratified geographic model to account for appearances and disappearances of regions and connectivities between them through time.
Acknowledgements We thank  Michael R. May ,  Ixchel Gonz alez-Ram ırez ,  Yaowu Xing , and  Jason Hilton  for providing constructive comments that improved the study. MC has been supported by the  Austrian Science Fund (FWF) ,  Lise Meitner  project no. M3168. The visit of MC to the  NRM  and the NHM was funded by the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 226506  ( SYNTHESYS  project  SE-TAF-5774  and project  GB-TAF-5763 ). The computational results presented have been achieved in part using the  Vienna Scientific Cluster (VSC). This project benefited from an  'Investissements d'Avenir' program  managed by the  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ) and the  ANR   GAARAnti  project ( ANR-17-CE31-0009 ). MC acknowledges  Peta Hayes  for the assistance with the collections at the  NHM , and  Christian Pott  for assistance with the collections at the NRM.
Experimental apparatus and data sample The results presented in this article are based on the data set of Pb-Pb collisions at √ s NN = 5. 02 TeV collected in 2018. In total, 230 × 10 6 events were analysed, of which 86. 7 × 10 6 are central trigger events in the 0-10% centrality interval and 74. 3 × 10 6 are semicentral trigger events in the 30-50% centrality interval. The ALICE detector  [66, 67]  has excellent particle identification and vertexing capabilities. The (anti) nuclei were measured using the Inner Tracking System (ITS), the Time Projection Chamber (TPC) and the Time-Of-Flight (TOF) detector. All these detectors are located inside a homogeneous magnetic field with a strength of 0. 5 T and cover the full azimuthal acceptance and the pseudorapidity range |η| < 0. 9 for interactions located in |z| < 10 cm, where z is the distance from the nominal interaction point along the beam direction.
Event and track selection The data were collected using a minimum-bias trigger requiring at least one hit in both the V0 detectors. In addition, a central and a semicentral trigger were used, also determined by the V0 detectors, selecting collisions in the 0-10% and 30-50% centrality intervals, respectively. Moreover, the timing information of the V0 scintillator arrays is used to reject the events triggered by the interactions of the beam with the residual gas in the LHC vacuum pipe. A further selection using the Zero Degree Calorimeter is applied in order to reject the electromagnetic beam-beam interactions and beam-satellite bunch collisions  [73]. These three rejections are done in the offline analysis. The production yield of primary (anti)deuterons, (anti)tritons and (anti) 3 He are measured at midrapidity.
All particles are transported with GEANT4  [75]  through a full simulation of the ALICE detector. The GEANT4 version used in the ALICE software framework was modified to take into account the latest (anti)nuclei hadronic interaction measurements  [24, 76]. For the (anti)deuteron, (anti) 3 He and (anti)triton analyses, the efficiency×acceptance was determined for each centrality interval separately. The input p T distributions of (anti)nuclei in the simulation are Light (anti)nuclei production in Pb-Pb collisions at √ s NN = 5. 02 TeV ALICE Collaboration modified according to a Blast-Wave parametrization using p T -dependent weights. The BW parameters are taken from  [65].
Systematic uncertainties The sources of systematic uncertainties affecting these measurements were studied as follows: 1. the amount of material budget employed in the MC simulation of the ALICE apparatus was varied by ± 4. 5%, corresponding to the uncertainty on the ALICE material budget determination  [67] ; 2. track selection criteria were varied as done for previous analyses  [21] [22] [23] [24] [25] ; 3. the fit functions used for the signal extraction were varied; 4. for the antitriton analysis different functions were used to weight the input spectra in the simulation. A large contribution of the systematic uncertainty is due to the limited knowledge of the interaction of nuclei with the detector material. The main transport code used in ALICE is GEANT4  [75]. This is used to estimate the efficiency×acceptance that is applied as correction to the spectra.
Methods This cross-sectional retrospective study was based on the clinical records of all patients followed by the general practitioners (GPs) referring to the "COMEGEN" (COoperativa di MEdicina GENerale in Italian) Medical Cooperative operating within the urban area of ASL Naples 1 (40°50′N 14°15′E). As of June 1, 2018, the participating GPs selected, among their patients, women who had simultaneously undergone an evaluation of BMD by DXA [International Classification of Diseases-9th revision (ICD9) code 8898] because of clinically suspected of Op according to the Italian Ministry of Health prevention plan and completed a questionnaire that included queries on their smoking behaviour and their cohabiter's between June 1, 2008 and May 31, 2018. In case of more than one contextual assessment of BMD by DXA and of smoking habits questionnaire in the lapse of time considered, only the data referring to the first contextual assessment were collected.
From all the medical records, other than DXA results and answers to smoking questionnaire, data regarding weight, height, waist circumference, body mass index (BMI), age, sex, estimated glomerular filtration rate (eGFR, estimated by a standard formula  [17] ), and any pharmacological treatments were collected as part of clinical practice. Since that, data regarding the number of daily cigarettes smoked were not available for this study. Informed consent was obtained from all individual participants included in the study, also as part of clinical practice. All data were collected in an electronic file. This study is part of the SIMON (metabolic syndrome, osteoporosis, and nephrolithiasis; SIndrome Metabolica, Osteoporosi e Nefrolitiasi in Italian) protocol  [18] [19] [20] , which was approved by the ASL Naples 1 Ethical Committee, protocol number 0018508/2018.
Statistical analysis Statistical analysis was performed using IBM SPSS (Statistical Package for Social Science), version 25 (IBM, Armonk, NY, USA). In univariate analyses, statistical comparisons were based on the Student's t test for continuous variables and on the Chi-squared test for dichotomous variables. The binary logistic regression analysis was used to estimate the role of SHS on the risk of Op, adjusting for age and BMI. The collinearity among variables included in the models was assessed. The analysis did not detect any collinearity among variables (tolerance: 0. 94, variance inflation factor 1. 0). All statistical tests were two-tailed. The results are reported as mean (standard deviation -SD), or absolute, or percentages or as odds ratio (OR) and 95% confidence interval (95% CI).
The low dose and nonmonotonicity of some chemical compounds from side smoke in PS can be one reason for the different result observed in PS and CS  [46]. It must also be considered that side stream smoke from passive smoking is a low-temperature oxygen-poor environment that contains higher concentrations of ammonia and nitric oxides, which are smaller than the ones produced during the act of smoking  [47]. Further studies will be needed to better clarify these observations. Some strengths and limits of this study are strictly related to the use of administrative databases  [34]. Their use warrants a high sample size with reasonable costs, and, in effect, we enrolled a large and very homogeneous population. In addition, the sensitivity and specificity of Op diagnosis based on administrative health database are very affordable, especially when the prescription of anti-osteoporotic drugs is used as diagnostic criteria.
Finally, the study protocol excludes the more prevalent causes of secondary Op. All data collected for this study are integral part of the usual clinical management of the adult subjects registered in the Italian National Health System. On the other hand, the use of administrative data does not allow evaluating if the anatomical site of DXA examination showing pathological BMD finding (i. e. lumbar, femoral or ulnar site) influences the study results, or the number of cigarette smoked per day. We are also not able to obtain data regarding some factors that can influence the BMD (such as vitamin D status, physical activity, dietary habits and socio-economics factors) of enrolled subjects, or the duration of the exposure to secondor third-hand smoke. Moreover, we used data obtained with different types of DXA machines, albeit all measurements were carried out in clinical centres affiliated with the Italian Health National System (in Italian Sistema Sanitario Nazionale) and therefore each clinical centre is subjected to unique quality controls (International Organization for Standardization-ISO 9001).
The flow rate was set to 0. 3 mL min -1. A sample injection volume of 4 µL was used. The capillary and auxiliary gas heater temperatures were set to 320 scan range of mass-to-charge ratio (m/z) of 50 to 750, with a mass resolution of 140 000. Tandem mass spectrometry was performed using higher-energy collision dissociation with a stepped normalized collision energy of 10, 45, and 60. The isolation window was set to m/z 2. 0 with a loop count of 10, selecting the 10 most abundant species for fragmentation in each scan. A mass spectral library was built using the compound database function in TraceFinder 4. 1 General Quan software (Thermo Fisher Scientific, USA). To build the library, compounds from previous studies  (Chan et al. , 2011; Nestorowicz et al. , 2018; Ng et al. , 2008; Riva et al.
(2020)  using an ozone analyser (49i, Thermo Scientific). SO 2 was measured using a 43i SO 2 analyser (Thermo Scientific). High-resolution aerosol mass spectrometry measurements were conducted as outlined in  Cash et al. (2021). Ion chromatography measurements were undertaken by the experimental approach outlined by  Xu et al. (2020)  as part of an intercomparison study. Briefly, filter cuttings were taken from the filter and extracted ultrasonically for 30 min in 10 mL of ultrapure water and then filtered before analysis  (Xu et al. , 2020). Meteorology data were downloaded from the NOAA Integrated Surface Database via the worldmet R package for the Indira Gandhi International Airport (code: 421810-99999)  (Carslaw, 2021). The planetary boundary layer height (PBLH) was obtained from the ERA5 (ECMWF Reanalysis 5) data product at 0.
Conclusion Isoprene-and monoterpene-derived organosulfate (OS) and nitrooxy organosulfate (NOS) species were quantified during pre-and post-monsoon measurement periods in the Indian megacity of Delhi. An extensive dataset of supplementary measurements was obtained alongside filter samples, including isoprene and speciated monoterpenes. Isoprene and monoterpene emissions were found to be highly influenced by anthropogenic sources, with strong correlations to anthropogenic tracers at night across both campaigns. High nocturnal concentrations of pollutants were observed due to a low boundary layer height and stagnant conditions, especially during the post-monsoon period. Isoprene OS markers (OSi) were observed in higher concentrations during the post-monsoon ((557 ± 423) ng m -3 ) compared to the pre-monsoon campaign ((238 ± 118) ng m -3 ).
The authors acknowledge  Tuhin Mandal  at  CSIR National Physical Laboratory  for his support in facilitating the measurement sites used in this project and  Gareth Stewart  for the VOC measurements. This work was supported by the  Newton Bhabha  fund administered by the  UK Natural Environment Research Council  through the  DelhiFlux and ASAP projects of the Atmospheric Pollution and Human Health in an Indian Megacity ( APHH-India) programme. The authors gratefully acknowledge the financial support provided by the  UK Natural Environment Research Council  and the  Earth System Science Organization, Ministry of Earth Sciences, Government of India , under the  Indo-UK Joint Collaboration (DelhiFlux). Daniel J. Bryant  and  Beth S. Nelson  acknowledge the  NERC   SPHERES doctoral training programme  for studentships.
James M. Cash is supported by a  NERC   E3 DTP studentship. Financial support. This research has been supported by the  Natural Environment Research Council  (grant nos. NE/P016502/1  and  NE/P01643X/1 ) and the  Government of India, Ministry of Earth Sciences (DelhiFlux  (grant no. MoES/16/19/2017/APHH )). Review statement. This paper was edited by  Ivan Kourtchev  and reviewed by two anonymous referees.
Characteristics of the simulated farm household The primary data used in this study were taken from a survey that included 85 farm households on the Saïss plain  (El Ansari et al. , 2020). The survey was conducted in 2014 by the International Centre for Agricultural Research in Dry Areas (ICARDA), the Institut National de la Recherche Agronomique (INRA)-Morocco, and the CIHEAM-IAMM Montpellier  (El Ansari et al. , 2020). Both purposive and stratified random sampling techniques were used to select the villages and households to survey within villages. Interviews with the household heads were conducted using a set of three structured questionnaires. The first questionnaire investigated the socio-economic and nutritional characteristics of the household, such as questions on gender and age composition, farming activities, sources of income, consumption of different food products both from on-farm production and market purchases.
Objective function To simulate the scenarios, we adapted the Dynamic Agricultural Household Bioeconomic Simulation Model called DAHBSIM to the contexts in our study region. The DAHBSIM model is an optimization model for the simulation of scenarios related to policies and economic incentives, to improve the livelihoods of farm households where households consume varying levels of their own agricultural produce. The model was initially used in Malawi to assess fertilizer prices  (Komarek et al. , 2017)  and crop productivity  (Komarek and Msangi, 2019). In the current study, we adapted the model to the context of the semi-arid areas of Morocco. This adaptation was mostly related to adapting the water budget module, as a subcomponent of the DAHBSIM summary crop module, to better reflect drier climates (Supporting Information Section 2).
Data for model calibration and scenario simulation 2. 4. 1. Input date for crop module A critical component of the database used in DAHBSIM is input data for the crop module. The parameters used in the crop module are as follows (as summarised in Table  3 ): • Soil parameters: soil density (1. 2 m 3 kg -1 ), soil water holding capacity (133 mm m -1 ), soil depth (1. 2 m), organic matter (1%), mineralization rate (1. 3), initial soil water (80 mm m -1 ) and nitrate (10 kg N ha -1 ) contents were obtained from the experimental INRA station in Meknes  (Touhtouh et al. , 2015)  • Rainfall and temperature: average monthly rainfall, as well as daily maximum and minimum temperatures and solar radiation were obtained from the nearest meteorological station of the Institute National de la Recherche Agronomique de Meknès (INRA-Meknès) to the study region.
Activity, farm household, consumption, and market database The DAHBSIM database was divided into two main sections to capture household decisions: the first one describes the structure of the farm households including age and gender structure of the family, and agricultural input use; the second one describes the agricultural activity at plot level for each farm household. The agricultural activities in our study are defined based on the  Hammouda et al. (2018)  methodology, where activities are based on a combination of a crop, a cultivar (local or improved), a soil type (organic matter content), and an intensification level based on application rates for seed, fertilizer, labour, mechanisation cost, and irrigation water. There are two intensification levels for crop production in the current study: (1) extensive and (2) intensive.
However, WSI are not yet extensively used outside the pathology community for two primary reasons. First, the size and complexity of WSI require special skills and equipment for their analysis. A single WSI may contain billions of pixels and thousands of cells, complicating storage, processing, and analysis. Second, while pathology labs are generating ever increasing WSI datasets, annotated WSI datasets are often scarce, in particular for rare diseases, specific molecular subtypes or in the context of clinical trials. Training current deep learning models on such datasets often leads to underperforming models with poor generalization capability. Self-supervised learning offers a promising approach for addressing these chalenges. This training paradigm leverages unlabeled datasets to pretrain neural networks which then demonstrate improved performance when fine-tuned on smaller, annotated datasets.
Numerous studies in the computational pathology field have already adopted self-supervised learning, but only at the tile level  ([3, 7-9] ). They used such pretrained networks to encode the small images that compose the WSI-the tiles-, effectively reducing them from billions of pixels to a few thousand feature vectors. These feature vectors then serve as the basis for training multiple instance learning (MIL) algorithms (  [9] [10] [11] [12] ). Nonetheless, the sheer volume of tiles per WSIs still makes MIL models both computationally intensive to train and susceptible to overfitting. While  [13]  is a fair effort toward training without supervision wide histopathological images -up to 4096 pixel squared-, they do not succeed in training WSI representations. Here, we introduce Giga-SSL, a novel self-supervised method that utilizes large, unlabeled WSI datasets to learn compact and highly discriminative WSI-level features.
We gradually reduced the training dataset size through stratified subsampling down to 50 WSIs and assessed the performance of models trained on these subsets (see Fig. 1B ). We compared Giga-SSL to the CLAM-SB algorithm  [10]  operating on the same tile representations as the one used by the Giga-SSL model. Fig. 2  A. shows the performance difference between the Giga-SSL and CLAM models as a function of the training set size. Each point above the red line indicates superior performance of Giga-SSL. Across all tasks, using 100% of the training data, Giga-SSL consistently achieves state-of-the-art performance. The advantage of Giga-SSL over CLAM grows as the training set size shrinks. With only 50 WSIs, Giga-SSL provides an average gain of 7 AUC points over CLAM. Giga-SSL transfers well to other datasets.
To confirm these findings, we performed external validation experiments to explore the generalization capabilities of our representations. For this we applied Giga-SSL to two in-house WSI datasets for two different cancer types: • Breast cancer (BC): 788 in-house H&E stained WSIs from BC patients with known Homologous Recombination status. We conducted two classification tasks: HRD prediction and subtype prediction (TNBC/luminal)  [7]. • Uveal Melanoma (UM): 516 in-house H&E stained WSIs from UM patients. The objective here was to predict chromosome 3 status (disomy 3 or monosomy 3, which represent two major UM subtypes with contrasted prognosis)  [14]. the data and 5. 7% using 50 WSIs: Giga-SSL weights maintain their performance and label efficiency when applied to unseen datasets. Giga-SSL can predict more mutations and genetic signatures than MIL.