dataesr/scientific-paragraphs-categorization huggingface dataset:
PMF settings and post-PMF data curation Although a rolling PMF is a more convenient methodology with respect to the seasonal PMF as reported in  Via et al. (2022) , in this study, in which the TRs are so different and both datasets include multiple gaps, it would lead to different representation of each subsets because each has different number of points of each. To avoid this issue, PMF was run across the whole period. Constraints were not applied to allow the model to freely adapt the mass proportions of both subsets. The lack of similar studies that couple OA mass spectra, SIA, BC types and metals precludes availability of reference profiles. The application of constraints through anchor profiles would require the a-priori estimation of the mass proportion between LR and HR species for a given source, while this result is in fact one of the expected outputs of the present study newly applied methodology.
Independent PMF SA is performed on the different TR subsets separately, this is, the aforementioned Approach 1 and Approach 2. Their objective is to calculate the scaled residuals histogram overlap of the independent data subsets (F* overlap ) as in  Tong et al. (2022). The Approach 1 results, responding to the HR subset SA, are shortly described hereinunder and in Figure  S4  and briefly here. Regarding the LR data subset SA (Approach 2), the results are not a self-explanatory SA solution since information provided by the species is limited and they only represent a 2% of the bulk PM 1 mass. However, an environmentally reasonable solution was obtained, whose scaled residuals are later used for the F* overlap calculation (Eq. (  3 ). The detailed solution is presented in the SI Section C and Figure  S5.
Base case for MTR-PMF Approach 4 provided a SA of a regular PMF applied to the data subset built with all available species averaged to the lowest TR, i. e. , 24-hours. No C weights (C HR = 1, C LR = 1) were applied during the model running. This solution will be used to evaluate the improvement resulting from the MTR-PMF application with optimised parameters. The mass closure plot (Fig. S7c ) reports only a 9% underestimation of the input mass. The SA resolved 6 PM 1 sources: Aged SOA + AS (31%), Fresh SOA + AN (26%), Heavy oil combustion (15%), Traffic (13%), Biomass Burning + Mineral (12%) and Road Dust + Industry (4%) (Fig. S7d ). The number of factors is the same as that of HR data subset SA, even if the metal species were added. Nevertheless, some mixing in sources was acknowledged. For instance, the Fresh SOA + AN, which also included m/z60, m/z73 and BC sf , markers of biomass burning (Fig.
MTR-PMF sensitivity to R 1 and C values Approach 5 explores the optimal HR data subset TR and the uncertainty weights combination for the MTR-PMF analysis before proceeding with the final SA solution exploration. The number of factors of the solutions is not assessed in the parameter evaluation but in the solution assessment (following section 3. 5), which was not evaluated mathematically but with the aim to provide an environmentally interpretable sense. Hence, MTR-PMF was run for 6-8 factors for each combination of TR and (C HR , C LR ). Although 13 C HR , C LR combinations were set when launching the model, not all of them produced reasonable results (or not for all resolutions) due to the model's inability to process those weighted matrices. This result was found for (C HR , C LR ) = (1,100), (1000,1).
Scaled residuals overlap The first step in this analysis was to examine the scaled residuals of both data subsets when coupled into the MTR-PMF. Figure  S8  shows the means and standard deviations of all C value combinations for HR and LR including all the TR runs. This plot assesses both the magnitude of the scaled residuals and their stability, which is severely compromised in the C combinations of (0. 01,1), (0. 001,1). These combinations enlarging the HR uncertainties result in a LR data subset scaled residuals increase. In terms of centrality, some C combinations present means systematically above zero, especially for the LR subset. Those C value combinations whose medians are less zero-displaced were the (1,1), (0. 1,1), (0. 01), (1,10), (1,1). However, some of their means were out of the range of the plots for the LR data subset, indicating the inclusion of significative outliers.
PMF approaches comparison To assess the added value of the MTR-PMF technique, its results were compared to other SA methodologies solutions. SA was also performed on the HR data subset (Approach 1), the LR data subset with only the offline species (Approach 2), a pseudo-conventional data subset (Approach 3), and an unweighted low TR base case (Approach 4) (Fig. 8 ). The SA of Approach 2 and Approach 3 retrieved only 4 sources, although with different fingerprints. This difference was driven by the included species. The source resolution performed by PMF can be substantially different and of more or less environmental feasibility. While the offline LR data subset SA led to the identification of road dust, it was unable to identify a road traffic source corresponding to exhaust emissions, as the data subset lacks the tracer species for that.
For the pseudoconventional data subset SA, the identified sources were mixed in some cases, which is a known limitation of this type of datasets. The clearest example is the source that included biomass burning and AN together, which are two separated factors in Approach 5 (although crossed influence is still noticeable). Additionally, it distributed quite equally the OA and EC in all factors. None of these SA results was able to identify a COA factor due to the lack of COA tracers or diel cycle, as Approach 2 only considers metal species, and Approach 3 includes OA a bulk single species and both present low TR. The base case (Approach 4) comprised all OA m/zs, SIA species, differentiated BC species, all averaged to the LR TR, and metals, naturally at this TR. Even though it merges different instrumentation data, no uncertainty weightings were applied, as we want to use these results to assess the final MTR result, which includes the uncertainty weightings as one of its improvements.
The base case SA resulted in 6 sources, as it includes information from a wider range of species. Aged SOA + AS was differentiated from Aged SOA + heavy oil, as opposed to the results from the HR SA, which was unable to separate them. However, the AN source and fresh SOA were not separated in this approach and neither were the COA and HOA factors, the latters being clearly separated in the HR SA. The capacity of the MTR-PMF SA for identifying and quantifying sources has been found superior to that of any of the other assessed approaches. Hence, it managed to retrieve 8 different sources, which is four more than the LR dataset or the pseudo-conventional data subset, and two more if compared to the HR data subset or the base case. The MTR-PMF merges two major advantages that cannot be applied together otherwise: a) the use of a high number of species analysed by different techniques and/or collected from the atmosphere by different methods and b) the use of the best available TR for a given species.
Note that the TR can be determined by the analytical technique or by the sample collection technique; i. e. filter samples could be collected every 12 h and the dataset time resolution would be improved while applying the same analytical techniques, provided the concentrations were high enough to remain above detection limits. Thus, advantage a) would lead us from solution of Approach 1, of Approach 2 or of Approach 3 to the solution of the base case (Approach 4). Advantage b) applied independently would just give us the solution in Approach 1, where we can use the original time resolution without any restriction. The application of MTR-PMF allows the merging of both advantages and leads us from solution of base case (Approach 4) to the MTR-PMF solution. The intra-day patterns, stemming from the use of HR TR measurements, provided significant insights into the behaviour of the sources and even enabled the identification of some sources such as COA.
Conclusions A comprehensive source apportionment (SA) of the submicron particulate matter (PM 1 ) has been performed coupling Quadrupole Aerosol Chemical Speciation Monitor (Q-ACSM), aethalometer (AE33) and , and Cl -from Q-ACSM at a 30 min time resolution (TR); black carbon (BC) speciated into BC liquid fuel (BC lf ) and BC solid fuel (BC sf ) from aethalometer at 30 min TR; and metal species  (Ca, Al, K, Mg, Na, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, As, Sn, Sb, Pb)  from offline samples at a TR of 24 h every 4th day. The use of the Positive Matrix Factorisation (PMF) model for the ensemble of the socalled high-resolution data subset (HR, 30-minutes TR) and the lowresolution data subset (LR, 24 h every 4th day TR) taken together implied the use of the multi-time resolution (MTR) PMF. The main outcomes from this work are: • The extended practice of averaging the high TR data subsets to reduce the TR difference with the lower TR data subsets or even to match it has been proven disadvantageous for model performance and error minimisation as also reported by  Kuo et al.
(2014)  and  Liao et al. (2013). High TRs do not represent noise to PMF since stable, robust sources are retrieved profiting the 30 min TR for disentangling sources. For instance, the COA, which was only resolved by using high TR data since its diel cycle is vital for its differentiation. • The use of a greater number of species has enabled the characterisation of specific sources traced by key species not present in all datasets. This is the case, for instance, of the inclusion of industrial markers, which have enabled the characterisation of the industry source, whose description with only OA, SIA and BC species would have been less conclusive. • The MTR-PMF provided an environmentally reasonable PM 1 SA with more speciated and detailed sources with respect to the other approaches and previous studies in the area using simpler SA methodologies, even if they contain longer time series or if they contain a greater number of species.
Financial support The present work was supported by European Union's Horizon 2020 research and innovation programme under grant agreement 101,036,245 (RI-URBANS); the "Agencia Estatal de Investigación" from the Spanish Ministry of Science, Innovation and Universities, and FEDER funds under the projects CAIAC (PID2019-108990RB-I00) and HOUSE (CGL2016-78594-R); and the Generalitat de Catalunya (AGAUR 2017 SGR41) and the Direcció General de Territori.
Quantification of serotypes and bacterial density. The GMD (Geometric Mean Density) for all serotypes and bacterial targets are presented in Supplementary Table  S6 , and summarised in Supplementary Fig. S2 , and the hierarchy of co-colonising pneumococcal serotypes is presented in Supplementary Table  S7. Out of the additional serotypes detected by 'Fluidigm' qPCR, 72. 6% (831/1 144) were co-colonising, or lower density serotypes relative to the primary, or highest density serotype. The GMD of the additional serotypes detected by  There was excellent agreement between the pneumococcal density calculated from the LytA and PiaB assaysets (line of bias: -0. 2) when compared using Bland-Altman plots (Supplementary Fig. S3A ). Further, only 6. 9% (58/836) of the data points were outside of the narrow 95% CI (-1.
Methods: Data on ART-naïve PWH diagnosed with CM from 1994-2012 from Europe/North America were pooled from the COHERE, NA-ACCORD and CNICS HIV cohort collaborations. Follow-up was considered from the date of CM diagnosis to earliest of the following: death, last follow-up or 6 months. We used marginal structural models to mimic an RCT comparing effects of early (within 14 days of CM) with late (14-56 days after CM) ART on all-cause mortality, adjusting for potential confounders. Results: Of 190 participants identified, 33 (17%) died within 6 months. At CM diagnosis, median age was 38 years (interquartile range 33-44); CD4 count was 19 cells/mm 3  ; and HIV viral load was 5. 3 log 10 copies/mL (4. 9-5. 6). Most participants (157, 83%) were males and.
METHODS Data on ART-naïve (no prior ART use) PWH diagnosed with CM between 1994 and 2012 were combined from the COHERE  [9] , NA-ACCORD  [10]  and CNICS  [11]  collaborations of clinical HIV cohorts from Europe and North America. To avoid duplication between NA-ACCORD and CNICS, the record from NA-ACCORD was used. Participants were eligible if, at CM diagnosis: they were ≥16 years old, and CD4+ count and viral load (VL) measures were available. Additionally, follow-up data were required after CM diagnosis (records present regarding lab tests/visits/mortality, bloods were taken every 3 months on average across these settings). CM diagnosis was based on the 1993 Centers for Disease Control clinical case definition  [12]. The outcome was all-cause mortality.
Sensitivity analyses Missing baseline values of CD4+ count or VL were imputed by modelling their trajectories from baseline to 6 months post-diagnosis, for those with and without baseline measures. Imputation used the twofold fully conditional specification algorithm via the twofold command in Stata  [15] ; this imputes missing values at particular time points (baseline, 2, 4 and 6 months, with a window period of +/-4 weeks for each measurement) given data available at that time point and at adjacent time points. CD4 was square-root transformed and VL was log-transformed for the imputations. We fitted the model to generate IPTW and the final marginal structural model for mortality on each of 25 imputed datasets; Rubin's rules  [16]  were used to combine results over datasets.
Among these, median time to starting ART was 31 days (IQR:23-42). Participants in the early ART arm were more likely to initiate a PI-based therapy and to die of an AIDS event than those in the late ART arm. More deaths were from unknown causes in the late ART arm. Supplementary Table  3  compares longitudinal CD4+ count and VL values among participants after imputation of missing values with those with complete data. Whilst baseline values were similar, follow-up VL values were generally higher in the imputed dataset than in the completecase dataset, suggesting those with complete data may represent participants with less advanced HIV. Figure  2  shows Kaplan-Meier survival estimates from mimicking an RCT by cloning and censoring the data. Survival patterns were similar for the two arms, with overlapping confidence intervals (CIs), although by the end of follow-up mortality was slightly worse among the late ART arm.
Cohere The COHERE study group has received unrestricted funding from: the Agence Nationale de Recherches sur le SIDA et les Hépatites Virales (ANRS), France; the HIV Monitoring Foundation, The Netherlands; and the Augustinus Foundation, Denmark. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under EuroCoord grant agreement n° 260694. A list of the funders of the participating cohorts can be found at www. COHERE. org. institution). CS reports payment or honoraria for lectures, presentations, speakers, bureaus, manuscript writing or educational events from Gilead Sciences and ViiV Healthcare (payments to author for preparation of educational materials), participation on a Data Safety Monitoring Board or Advisory Board for Glead Sciences and ViiV Healthcare (paid to author) and is the Vice-chair for the British HIV Association.
Acknowledgements: Dr. William Powderly, M. D. from  Division of Infectious Diseases, Washington University in St. Louis, Missouri  for managing and provindig the data of patients with cryptococal meningitis from the ACTG A5164 clinical trial. To  Dr. David Boulware  from  University of Minnesota, Minneapolis, Minnesota  for providing the data on patients with cryptococal meningitis from the COAT clinical trial. JMM received a personal 80:20 research grant from  Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) ,  Barcelona, Spain , during  2017-23. FV is supported by grants from the  Programa de Intensificación de Investigadores  ( INT20/00031 )-ISCIII and form the  Premi a la Trajectòria Investigadora dels Hospitals de l'ICS 2018.
More specifically, commonly used data-driven modelling techniques, such as state-space models or custom-built machine learning architectures are mostly fitted using time series of the event, preferably collected over representative geographical extents (e. g. ,  Lofton et al. , 2022; Marolla et al. , 2021; Rammer & Seidl et al. , 2019; Morera et al. , 2021). Unfortunately, datasets meeting these requirements are often non-existent or remain temporally or spatially limited for many ecological phenomena. At the same time, the number of biodiversity observation records in public repositories, such as GBIF (gbif. org) or iNaturalist (inaturalist. org), has been rising steeply  (GBIF, 2023). These data, particularly those from citizen-science platforms, are frequently georeferenced with high precision, time-stamped, and accompanied by visual media, including photographs.
e. , the so-called 'realized environmental conditions'  (Post, 2019 ; Figure  1 ). These conditions can be sampled using records with the same geographical coordinates as observation records (guaranteeing that the sampling is made in areas where the event occurs) but with dates drawn randomly. Hereafter, we call these records 'temporal pseudo-absences' as they are conceptually similar to 'pseudo-absence' records used in species distribution modelling for sampling the geographical space available  (Phillips et al. , 2009). In summary, our framework assembles a dataset representing the temporal environmental conditions associated with observation of the phenomenon of interest and with the full set of conditions in places where the phenomenon occurs. Discriminative algorithms are then used to distinguish between these two sets, and predictions can be interpreted as the probability of the represented conditions belonging to the relative phenological niche of the represented phenomenon.
Step 1: Assembly of event observation data We obtained observation records for both species from the Global Biodiversity Information Facility (GBIF; https://www. gbif. org/), which is a leading aggregator of biodiversity observation records, including those from citizen science platforms such as iNaturalist (https://www. inaturalist. org/). For the winter chanterelle, we also included records from Mushroom Observer (https://mushroomobserver. org/), another citizen science platform not included in GBIF. We limited our dataset to records with photographic evidence, full date of observation (i. e. , day, month, and year), and geographic coordinates with a spatial precision greater than 0. 1 decimal degrees (~11 km at the equator). We included records from 2015 to 2021 and, to ensure data quality, we removed GBIF records where the observation date was the first day of the month and the observation time was '00:00:00'.
These records generally only provide the month and year and are assigned the first day of the month by default  (Belitz et al. , 2023). We then assessed the photographic evidence supporting each remaining record. For the Japanese beetle, we only retained records where the photograph showed an adult life stage and no signs of the specimen being dead. For the winter chanterelle, we kept records supported by photographs of fruiting bodies that showed no signs of significant deterioration. Step 2: Environmental data The timing of ecological events can be influenced by a multitude of biotic and abiotic factors. However, for the two phenomena being modelled, weather-related factors are believed to be the main drivers of their seasonality, as evidenced by previous studies  (Diez et al. , 2013; EFSA et al.
, 2019). Therefore, we used daily spatial time series of minimum temperature, mean temperature, maximum temperature, total precipitation, snow depth, and wind speed to capture the environmental conditions associated with the occurrence of these events. We sourced these data from the AgERA5 dataset  (Boogaard et al. , 2020) , which provides daily weather maps at a spatial resolution of 0. 1° (approximately 11 km at the equator). The data was collected for the period from 2014 to 2021, but its availability has a delay of approximately one month after the last day represented. Hence, to exemplify the implementation of the framework for near-real time prediction, we collected the same set of weather variables from the Global Forecast System (GFS), a weather forecast model from the National Centers for Environmental Prediction (https://www.
ncei. noaa. gov). The GFS provides forecasts of weather conditions at intervals of up to 3 hours and runs four times a day at 00:00, 06:00, 12:00, and 18:00 UTC. To ensure consistency between the two data sources, we extracted the forecasted conditions for the first six hours of each model run and aggregated them to a daily resolution. We also resampled AgERA5 data to 0. 25º cell size (c. 28 km at the equator), the spatial resolution of GFS data. The processing of spatial weather data was performed in R (R Core Team) using functions provided by the 'raster' and 'terra' packages  (Hijmans et al. , 2023). Step 3: Addressing spatial and temporal recording bias  (optional)  Biodiversity observation data are often geographically and temporally biased, particularly opportunistically collected records  (Isaac and Pocock, 2015).
Given the high class imbalance in our datasets (i. e. , 12 temporal pseudo-absences per event record), we took steps to prevent model fitting problems such as overclassification of the majority class  (Valavi et al. , 2022). For RF training, we used the event observation records and an equal number of randomly selected temporal pseudo-absences for fitting each tree, as allowed by the 'sampsize' parameter. In BRT, we assigned a relative weight of 1/12 (i. e. , 0. 83%) to each temporal pseudo-absence, as allowed by the 'site. weights' parameter. Before each model training event, we also measured the Pearson correlation coefficient among environmental variables, retaining only the minimum set of predictors with an absolute correlation value lower than 0. 8  (Valavi et al. , 2022)  using the 'findCorrelation' function from the 'caret' package  (Kuhn et al.
Step 6: Evaluation and validation of predictive performances To evaluate the predictive performance of models, we first measured their capacity to correctly classify event observation records and pseudo-absences. This was evaluated for each year independently, where model training used data for the remaining years (i. e. , temporally independent data). We used the area under the receiver operating characteristic curve (AUC) to measure the agreement between predictions and the actual record (i. e. , observation, or temporal pseudo-absence). In the context of this work, the AUC measures the probability that observation events receive higher probability values than records generated randomly over time. AUC values range from 0 to 1, where a score of 0. 7 or above is considered an acceptable level of discrimination (e.
However, the lower AUC values achieved for the winter chanterelle may be partly attributable to its longer season of suitable conditions, which extends up to approximately 5 months, compared to the 2. 5 to 3 months for the Japanese beetle. This longer season results in the generation of a higher number of temporal pseudo-absences during periods that are environmentally suitable, thereby increasing the misclassification of these records in the evaluation datasets  (Philips et al. , 2009). Relevantly, the predictions were still robust when made to new areas, i. e. , under spatial transferability, a gold benchmark for spatial prediction in ecology  (Roberts et al. , 2017). This approach could be used to determine the optimal timing for surveillance efforts, particularly in the case of the Japanese beetle, a species that has not become established in most of Europe yet.