dataesr/scientific-paragraphs-categorization huggingface dataset:
Extracting data from narrative reports to instantiate OntoTox Free text extraction. We leveraged QuickUMLS and PyMedExt 1  , a Python library designed to process clinical text, to process the text. PyMedExt includes annotators to detect polarity (negation, affirmation), the experiencer (patient, family) and hypothesis in French. We leveraged the Stanza dependency parser to link toxicity entities and their grades  [14]  Stanza is a Python natural language analysis library that uses the Universal Dependencies formalism  [15]. We processed all the sentences that contained at least one toxicity entity. The dependency parser provided the syntaxical structure of the sentences. We selected recursively all the entities that were under the head of the toxicity entity (see Figure  2 ). We linked the toxicity and grade if a path exists between the two entities.
Enriching OntoTox With comparison use-case, we illustrated that classes and properties of OntoTox may be used to guide the integration of chemotherapy toxicities information of various provenances. In this initial effort, we focused on the extraction and integration of the type and grade of toxicities. Of course, more qualification of the toxicity, such as its associated treatment, and its duration are required to provide a view of the potentially available information. We plan on enriching progressively OntoTox, while we develop scripts adapted to various toxicity attributes.
Conclusion In this article we introduced OntoTox, a simple ontology to represent chemotherapy toxicities. We show that this ontology can guide the integration of information from various data sources. OntoTox is rather small, but aim at being enriched to enable integrating a maximum of information qualifying chemotherapy toxicities and response that can be found in EHRs. OntoTox constitutes the seed of a valuable resource for oncology research and will further serve as a brick of a clinical decision support software.
GC-MS metabolomics The metabolomics study was performed on samples from two independent experiments, each including four biological replicates (Methods S1; Table  S1 ). Semiautomated integration of chromatograms was performed with QUANLYNX software (Waters, Milford, MA, USA) after conversion of the raw data into NetCDF format. A model ion was chosen manually to perform a correct integration for 42 peaks, with the following criteria: Gaussian shape, high relative abundance in the mass spectrum, and the absence of saturation in any sample (Table  S1 ). Metabolites were annotated with confidence levels according to the Chemical Analysis Working Group  (Sumner et al. , 2007) , on the basis of both spectral and retention index matches with public database  (Kopka et al. , 2005). Peak surfaces were then normalized according to the internal standard peak surface (ribitol) and sample fresh weight (FW) (Table  S2 ).
RNA extraction and qRT-PCR analysis Total RNA was extracted from 4-wk-old plants grown on jiffy pellets in plant growth chambers (Percival Scientific) under 16 h : 8 h, light : dark photoperiod at 22°C, 60% humidity, using NUCLEOSPIN Plant RNA (Macherey Nagel, Düren, Germany) kit following the manufacturer's protocol. First-strand cDNA was synthesized from 1. 5 μg of total RNA using Super-Script III First-Strand Synthesis SUPERMIX kit (Invitrogen). The diluted cDNA (10 times) was used to perform quantitative RT-PCR (qRT-PCR) using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad) on StepOnePlus Real-Time PCR System (Applied Biosystems, Waltham, MA, USA). The data were analyzed using Bio-Rad CFX MANAGER software. ACTIN was used as a housekeeping gene for normalization of gene expression levels.
Transcriptome analysis Four-week-old plants were collected for RNA analysis. Microarray analysis was carried out using the CATMAv. 6. 2 array based on Roche-Nimble Gene technology as described  (Frei dit Frey et al. , 2014). The arrays contained 219 684 primers representing all the Arabidopsis thaliana genes: 37 309 probes corresponding to TAIRv. 8 annotation (including 476 probes of mitochondrial and chloroplast genes); 1796 probes corresponding to EUGENE software predictions and 5328 probes corresponding to repeat elements; 1322 probes for miRNA/MIR, 329 probes for other RNAs (rRNA, tRNA, snRNA, and soRNA); and several controls. Three independent biological replicates of the microarray analysis were performed (Table  S4 ). For each biological repetition, 5-wk-old plants grown under 16 h : 8 h, light : dark photoperiod at 22°C, 60% humidity, were collected, and RNA samples were obtained by pooling more than 50 plants.
Total RNA was extracted using Qiagen RNAeasy according to the supplier's instructions. For each comparison, one technical replicate with fluorochrome reversal was performed for each biological replicate (six hybridizations per comparison, RIN score cutoff = 7). The labeling of cRNAs withCy3-dUTP or Cy5-dUTP (Perkin-Elmer-NEN Life Science Products, Waltham, MA, USA) and the hybridization to the slides were performed as previously described  (Lurin et al. , 2004). Two-micron scanning was performed with InnoScan900 scanner (InnopsysR, Carbonne, France), and raw data were extracted using MAPIXR software (InnopsysR). Transcriptome analysis was performed as described  (Frei dit Frey et al. , 2014). Briefly, for each array, the raw data comprised the logarithm of median feature pixel intensity at wavelengths 635 nm (red) and 532 nm (green).
1b ). C1 and C2 were therefore considered as complementation lines. The cell death phenotype in the OXI1-OE lines consistently started with the darkening of the center of the rosettes, which then spread to the petioles and the leaves. The pattern of cell death development parallels the expression pattern of OXI1 as evidenced in pOXI1::GUS analysis, which showed strong expression along the vascular system (Fig. 1c ) and was also reflected by the localized accumulation of ROS (Fig. 1d ) and cell death (Fig. 1e ) as evidenced by diamine benzidine (DAB) and trypan blue staining, respectively. Bar, 1 mm. (f) GO analysis of significantly upregulated genes (log 2 (FC) (P < 0. 05)) of 4-wk-old OE2 when compared with Col-0 WT plants. Gene ontology term analysis was performed using the AGRIGO database (for a complete list of differentially regulated genes, see Table  S4 ).
These data are consistent with the role of OXI1 as a positive regulator of defense and cell death. Next, we compared oxi1-2 with OE2 (Fig. 2b ). One hundred and forty proteins were enriched in the GO terms defense response, programmed cell death, and SAR (Fig. 2b ). The 37 upregulated proteins in oxi1-2 did not show any significant GO terms. Finally, on comparing oxi1-2 with C1, the 23 downregulated proteins in oxi1-2 did not show any significant GO terms, but the 122 upregulated proteins in oxi1-2 belonged to the GO terms photosynthesis, photosynthesis light reaction, and oxidation-reduction process (Fig. 2c ). To test whether OXI1 regulates the photosynthesis proteome at the level of transcription, we performed qRT-PCR on a number of photosynthesis genes. As shown in Fig. 2d , in comparison with WT and the C1 complementation line, these photosynthesis-related genes show enhanced expression in oxi1-2 but all of them were strongly repressed in OE2.
OXIDATIVE SIGNAL-INDUCIBLE1 GOF lines showed enhanced transcript levels of RbohD and RbohF (Fig. 5a, b ) and accumulation of H 2 O 2 (Fig. 5c ). Moreover, a significant set of genes with roles in ROS homeostasis are identified in OXI1-OE lines, including ascorbate   a-c ) The fold enrichment for the GO terms was calculated based on the frequency of proteins in the deregulated group compared with their frequency in the total proteome (log 2 FC) (P < 0. 05) using the AgriGo database (for complete protein lists see Table  S5 ). (  d   oxidase 1, the Cu/Zn superoxide dismutase CSD1 (Fig. 5a ), the oxidoreductase TXR5, or a number of peroxidases such as PER32 or PER50 (Table  S4 ).
Acknowledgements We would like to thank all members of the  Hirt and Zeier laboratories. The work was supported by grants to HH by the  Laboratory of Excellence Saclay Plant Sciences IPS2  and the  King Abdullah University of Science and Technology (KAUST)  ( BAS/ 1/1062-01-0  and  URF/1/2965-01-01 ), and by a grant to JZ by the  German Research Foundation  ( DFG grant   ZE467/6-2 ). The IPS2 benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). HN was supported by the  Max Planck Society.
Data availability The proteome data that support the findings of this study are openly available in PRIDE under the following project number PXD023853. Microarray data were deposited at CATdb (http://urgv. evry. inra. fr/cgi-bin/projects/CATdb/consult_expce. pl?experiment_id=337) (Project AU2010-01_OXI1) and GEO (Project GSE35941) according to the 'Minimum Information About a Microarray Experiment' standards. Global proteome sample preparation, data acquisition, and analysis by LC-MS/ MS (Methods S2).
Longitudinal data on polar bears We live-captured and marked polar bears around Svalbard (Norway, Figure  1 ) in 1992-2019 and 2021-2022 using methods described in  Stirling et al. (1989). Captures occurred between late March and early May when most females with cubsof-the-year (hereafter "cubs"), have emerged from maternity dens  (Wiig, 1998). Age of cubs, yearlings and 2-year-olds was determined with certainty based on size. Age of bears captured for the first time as sub-adults or adults was estimated using an extracted vestigial premolar tooth  (Calvert and Ramsay, 1998; Christensen-Dalsgaard et al. , 2010 ). Mother's straight body length (cm) (hereafter "size"), was measured as the straightline distance between the tip of the nose and the caudal end of the tail bone of bears laying in sternal recumbency  (Derocher, 2005).
Sea-ice We downloaded daily sea-ice concentration grids made available by the university of Hamburg for year 1992-2019  (Kern et al. , 2020). These grids are derived from radiance temperature data measured remotely using the 85 GHz Special Sensor Microwave/Imagers (SSM/Is) and Special Sensor Microwave Imager/sounder (SSMIS) channels and processed using the ARTIST Sea Ice algorithm  (Kern et al. , 2020). The data has a resolution of 12. 5 × 12. 5 km. Nonetheless, as the Hamburg University timeseries does not extend further back than December 1991, we retrieved daily sea-ice concentration grids (with a 25 × 25 km gird cell size) for 1990 and 1991 from the National Snow and Ice Data Center  (Cavalieri et al. , 1996). To define the study area, over which sea-ice covariates were calculated, we used telemetry data from 135 adult female polar bears captured between 1989 through 2021 and fitted with a satellite-linked collar.
Modeling approach First, we used a binomial Generalized Linear Mixed Model (GLMM) to examine the-effect of individual and environmental covariates on the probability of litter production (i. e. , of being with at least one cub at capture) in interaction with age. The covariates were size (Size, with a quadratic effect Size 2 ), DayBreakup, DayFreezeUp, WinterAO, PriorWinterAO, and PriorSpringAO. We also controlled for the date of capture (DateCapture). We included data on n 1 = 441 captures of females that were alone or with ≥1 cub in 2000-2019 and 2021-2022, corresponding to 289 distinct females. Females with yearlings or 2-year-olds were not included in this analysis. We did not take into account the reproductive status of females in the years prior to capture, meaning we considered that females who were alone in spring t-1 and those who had a litter but lost it have the same probability of producing a litter.
Including only females for which past reproductive history was known would have greatly reduced sample size (n = 68 instead of 441). Second, we built another binomial GLMM to investigate the effect of the same individual and environmental covariates on the probability of having a twin or triplet litter, given a litter. In this analysis, we included data on n 2 = 251 captures of females with ≥1 cub in 1992-2019 and 2021-2022 corresponding to 204 distinct females. One female was captured once outside of the study area and was excluded from this analysis. We grouped twin and triplet litters because only 3% of females in our dataset were captured with triplets. Hereafter, we refer to both twin and triplet litters as "large litters. " We accounted for environmental variation not captured by our covariates using a yearly random effect.
Implementation We fitted all models in a Bayesian framework using the nimble package  (de Valpine et al. , 2021). Both GLMMs were ran simultaneously as parts of a single nimble model. All continuous covariates were standardized. We used non-informative normal prior distributions for the regression coefficients and a uniform prior distribution for the standard deviation of the random effect. We ran two MCMC in parallel with different initial values, 135,000 iterations and a burn-in of 10,000 iterations. We kept one out of 10 values from each chain. We used the Gelman and Rubin R-hat diagnostic [R-hat < 1. 1,  (Gelman and Rubin, 1992) ] and visual inspection to assess convergence.
Variable selection procedure We considered age-specific covariates to be significant if the 89% credible interval (CRI) of their posterior distribution did not overlap with 0. We used 89% CRI rather than 95% CRI following recent recommendations  (Kruschke, 2015; McElreath, 2020). Only age-specific covariates that were significant were retained in the final model. Because our dataset was already divided in three or four categories according to female's age with a limited sample size in each category, we did not investigate additional interactions.
Analysis of cub and litter survival Capture-recapture model We estimated individual cub and litter survival in singleton and large litters using CR data collected between 1992 and 2019. We assessed cub and litter survival between the age of 3-4 months and 15-16 months (i. e. , between the cubs' first spring and the following one) depending on maternal age. Due to the limited sample size, we could not determine the optimal number of age groups and cut-offs. We therefore used the same age groups as in the analysis of litter size (5-9, 10-15, and ≥16 years) as they require fewer old individuals than those used in the analysis of litter production. To perform this analysis, we adapted a CR model which accounts for the multiple-year dependency between the demographic parameters of individuals belonging to the same family unit as well as the influence of past-reproductive history on female survival and reproductive performance (see  Cubaynes et al.
Litter production and litter size Our dataset for the analysis of litter production included 441 captures (of 289 females), among which 167 (38%) were females with cubs. Of those 441 captures, 170 (38%) were females aged 5-9 (mean age ± SD: 6. 96 ± 1. 42 years), 172 (39%) were females aged 10-15 (x = 12. 3 ± 1. 70 years), 63 (14%) were females aged 16-20 (x = 17. 8 ± 1. 44 years), and 36 (8%) were females aged ≥21 (x = 23. 4 ± 2. 38 years). Our dataset for the analysis of litter size consisted in 251 captures (of 201 females), 167 (66%) of which were females with a large litter. Among these 251 captures of mothers, 99 (39%) were females aged 5-9 (x = 7. 25 ± 1. 18 years), 107 (43%) were females aged 10-15 (x = 12. 4 ± 1. 75 years), and 45 (18%) were females aged ≥16 (x = 19 ± 2. 23 years).
None of the 10 females aged 25 or more were accompanied with cubs at capture. The oldest female in our dataset was 29 years old. A more detailed overview of the dataset is provided in Appendix 2 (Supplementary Table  1 ; Supplementary Figure  5 ). Below we present the results from the models that included the date of sea-ice break-up in the year prior to capture (DateBreakUp) as the sea-ice covariate. A full account of the variables included in the final models is provided in Supplementary Table  2. The models that included the date of sea-ice freeze-up in the year prior to capture (DateFreezeUp) produced similar results that we report in Appendix 3.
Cub and litter survival Our dataset consisted in 158 capture histories corresponding to 57 captures of independent juveniles and subadults, 444 captures of adult females, 63 singleton cub litters and 84 large cub litters, 73 yearling litters and 19 2-year-old litters. Cub survival (from the cubs' first spring to the following one) in singleton litters increased with mother's age with a mean of 0. 24, 0. 58, and 0. 75 for females aged 5-9, 10-15, and ≥16, respectively (Figure  8 ). Cub survival in twin litters also increased with mother age from a mean of 0. 43 for females aged 5-9, to 0. 54 for females aged 10-15, before reaching a plateau at 0. 56 for females aged ≥16. Litter survival followed a similar pattern.
24 (twin litters) and 0. 46 (singleton litters)  (Cubaynes et al. , 2021). Thus the presence of females who lost their litter in year t-1 in our dataset may have led to underestimating the probability of producing a litter. According to our findings, young females were more likely to lose their litter (Figure  8 ), suggesting their probability of producing a litter could have been biased low. However, young females may also be more likely to be nulliparous in year t-1, so it is unclear whether overall, their probability of producing a litter was under-or overestimated. Second, since the reproductive cycle of polar bears extends over several years, the number of females who are available to breed (including those who lose their offspring in time to breed) is influenced by environmental conditions experienced over the previous years.
, 2008). Due to the relatively low recapture rate in our dataset, we could not include individual identity as a random effect in the GLMMs nor investigate the covariation between survival and parameters related to reproduction in each female in the CR model. However, we found evidence of strong senescence in litter production and litter size, which suggests that viability selection might not be strong enough to mask senescence in our dataset. In addition, we included female structural size in the GLMMs to account for individual quality and longevity as larger individuals usually live longer  (Gaillard et al. , 2000a)  and have higher reproductive success  (Derocher and Wiig, 2002; Folio et al. , 2019). Importantly, not including size in the GLMMs yielded very similar results (see Appendix 4 for the model without size), further suggesting that the influence of selective disappearance on the observed senescence patterns is limited.
, 2021). This fitness cost could be sizeable even for females aged ≥16 because the residual reproductive value of females aged 16 is substantial. Indeed, if a female survived to 24 years of age (our dataset includes 3% of adult females aged ≥24), she could wean up to six litters, three of which after age 16. Nonetheless, according to our findings, the mean probability of a female producing a litter in a given year and of at least one cub reaching its second spring was 0. 16 for females aged 5-9, 0. 41 for females aged 10-15, 0. 37 for females aged 16-20 and 0. 18 for females aged ≥21. Thus, the increased survival of cubs of females aged ≥16 did not offset their reduced litter production and litter size, and reproductive output did decline at old age.
Patient Population We collected data on patients with wild mushroom poisoning admitted to 5 county hospitals in Enshi, Hubei Province, China, between January 2010 and May 2022. Critical illness was defined as the occurrence of an admission to an intensive care unit, hemodialysis therapy, referral to a higher-level hospital, or death. We collected 24-hour data from 567 patients from 5 hospitals. We used data from 322 patients from 2 of the hospitals as the training set, of which 56 were critically ill and 245 were noncritically ill. Data from 245 patients from 3 other hospitals were used as the test set to assess the performance of our model, of which 43 were critically ill and 202 were noncritically ill. We used the following inclusion criteria to develop the condition assessment model: (1) patients older than 14 years of age and (2) definite consumption of wild mushrooms before the onset of the disease.
Development of Machine Learning Model Four popular machine learning classification algorithms, including extreme gradient boosting (XGBoost)  [32] , random forest, support vector machine, and logistic regression, were applied in this study to build the classification models. We implemented machine learning algorithms using Python (version 3. 9; Python Software Foundation) and several Python modules (panda, numpy, scipy, sklearn, xgboost, shap, and matplotlib). Hyperparameter tuning was performed by a grid search based on 5-fold cross-validation to select the best area under the receiver operating characteristic curve (AUC) value for the classification models. A proper model interpretation must be supplied for the machine learning model. Model predictions were interpreted using Shapley additive explanations (SHAP)  [33, 34].
Patient Characteristics The patient cohort participating in this study included data from 567 cases of mushroom poisoning of patients admitted to 5 county hospitals in the Enshi area. The case data included the following types of mushroom poisoning: gastroenteritis, neuropsychiatric symptoms, acute liver damage, acute renal failure, myocardial injury, and combined types  [7]. For all data, the length of stay was 4. 45 (95% CI, 3. 45-5. 44) and 3. 52 (95% CI 3. 21-3. 82) days for critically ill and noncritically ill patients, respectively, and the cost of hospitalization was ¥11,113. 09 (95% CI ¥8059. 92-¥14,166. 27; ¥1=US $0. 14) and ¥2392. 08 (95% CI ¥2188. 34-¥2595. 82), respectively. Table  1  shows the baseline data of patients. The majority of the indicators did not differ significantly between the training set and the test set, and the remaining indicators (P<.
The application software created by our current model is available on GitHub  [38]. The study identified 14 clinical variables, of which AST, ALT, LDH, INR, PT, TBiL, IBiL, urea, creatinine, and CK-MB were consistent with current clinical evidence. These factors were classified as hepatic (AST, ALT  [39] [40] [41] , LDH  [42] , TBiL  [39] , and IBiL  [43] ), coagulation (INR  [39, 41]  and PT  [43] ), renal (urea  [43]  and creatinine  [40, 44] ), and cardiac impairment (CK-MB  [42] ). Using the SHAP method, we found that the indicators characterizing liver function impairment contributed 57. 5% to the prediction results of the model and had the greatest impact. Interestingly, the study also identified several clinical indicators that lack relevant clinical evidence, such as serum sodium, hemoglobin, and heart rate, highlighting the potential for machine learning to identify novel relationships between clinical factors and patient outcomes.
Conclusions Using routinely accessible data at the time of triage, we found that machine learning has good predictive performance in the triage of patients with mushroom poisoning. Compared with other machine learning algorithms, clinical guidelines, and clinician assessment, the XGBoost model has better diagnostic performance and can be used to select core indicators for a triage model. Machine learning may be a powerful prognostic indicator for early warning in critically ill patients, which has a significant impact on the triage of patients, formulation of treatment options, and the allocation of medical resources.