dataesr/scientific-paragraphs-categorization huggingface dataset:
Prediction of readmission within 30 days As presented in section, we implemented resampling methods to train the algorithms on balanced training sets before testing it on unbalanced sets. We used oversampling (random We singled out the most efficient combinations of classifier and resampling techniques for each dataset to perform a grid search with cross validation when considering the combination of F1-score and ROC-AUC. For the five datasets, it appeared that random undersampling was the most efficient technique. The performance is higher when the algorithm considers all information related to the patients' diagnoses, as seen in Table  4. For example the combination of random forest and random undersampling with all diagnoses information provides an ROC-AUC score of 0. 625 with a 95% Confidence Interval (CI) of [0.
Prediction of length of stay Overall, the random forest algorithm performed better than the decision tree, with an improvement of 10 to 30 square days in mean squared error from the decision tree results. This improvement is the most significant when using all available diagnoses (-0. 802 MAE and -30. 462 MSE). These results can not be considered conclusive as the best mean absolute error is still above 5 days, which represents half of the mean length of stay in the database. The raw results are displayed in Table  5.
Discussion In this study we included patients based on the number of diagnoses in different ICD-10 categories, before applying different scores (the Charlson comorbidity score, Calderon-Larrañaga score and HFRS). The mean age of the studied patients is 76 years. In addition, female multimorbid patients tend to be older than male patients. The mean age-adjusted Charlson morbidity score was 5. 65, and the mean Calderon-Larrañaga score was 4. 94. The most prevalent diseases diagnosed in patients were hypertension and type 2 diabetes mellitus. We built 5 versions of our database for each outcome, each taking into account various information on the diagnosis. The raw diagnosis information was compared to 2 aggregation scores: the HFRS, a score built to measure the frailty of patients based on diagnoses information, and the Calderon-Larrañaga score, a multimorbidity score based on groups of diagnoses.
The prediction of medium-term readmission was efficient, with the best score achieved using random forest and all diagnoses information (an accuracy score of 0. 826 [0. 811-0. 840] and a F1-score of 0. 812 [0. 794-0. 829]). Using Calderon-Larrañaga portfolio resulted in a slight decrease in performance for both indicators (-0. 096 and -0. 108 respectively). We believe that the clusters of diseases used in Calderon-Larrañaga portfolio can be used as an efficient substitute of diagnoses information for predicting readmission within 365 days after initial discharge. We tested multiple resampling solutions to account for the imbalance in the rehospitalization within 30 days dataset. We obtained at best an ROC-AUC score of 0. 625, which is acceptable, although it is slightly lower than the recent results in the literature, as  [22]  reported a median AUC of 0.
68 on the studies they identified. The accuracy on the unbalanced testing set is of 0. 603. When using the combination of Calderon-Larrañaga portfolio, random forest and random undersampling, we obtained a mean ROC-AUC score of 0. 594 and a mean accuracy of 0. 556. The use of Calderon-Larrañaga portfolio can be a viable alternative for the prediction of the within 30 day readmission on a medico-administrative database. Overall, the predictive power of our algorithms is quite low for length of stay prediction. The use of a random forest regressor gave the best results for the two metrics used, MAE and MSE. Both the HFRS and the Calderon-Larrañaga score were outperformed by the use of all information on diagnosis. However, the HFRS performed better than the Calderon-Larrañaga score. The HFRS seems to be an acceptable alternative to the use of exhaustive information on diagnosis with a random forest algorithm for predicting length of stay.
Thus, it can be a good alternative to using the raw information on diagnoses for predicting readmission within 365 and 30 days. The main limitations of this study are related to the lack of information on the vital status of patients after their hospitalization. This could represent a bias, as it is possible that patients died between the initial discharge and the end of the period of interest. Thus, the absence of a hospital stay within 30 or 365 days after discharge may be caused by the death of the patient. A key component of multimorbidity according to Barnett et al. [7]  that we could not grasp in this study is the socioeconomic aspect. Similar to  [7] , we can use geographical information as a proxy, but we do not have access in the present database to an evaluation of the socioeconomic status per area.
Our goal is to evaluate the organizational impact of redirecting those patients toward a newly created unit, dedicated to multimorbid patients. It would also be of interest to combine the approach of this paper with prospective data at admission, which would provide additional valuable information, such as socioeconomic data and familial context, respecting the anonymity of patients. This could allow us to evaluate if the patient would be fit for the new multimorbid pathway and to track the decision process of the care team when deciding in which unit the patient must be routed. The algorithms were trained on a dataset balanced using the specified resampling technique using a random search with cross validation from the scikit-learn package  [27]. Only the ROC-AUC score on the testing set is displayed here.
Metabolite Profiling of E. exsul Determined via LC-MS/MS LC-HRMS/MS analyses of the crude extract and fractions of E. exsul were performed in a positive ionization mode using the data-dependent acquisition mode. A TIC chromatogram of the crude extract is presented in Figure  1. Next, an auto-MS 2 was carried out in which the most predominant MS 1 ions are selected for MS 2 fragmentation. A manual inspection of the resulted MS/MS spectra led to the putative identification of chemical constituents of the crude extracts and fractions of E. exsul; they are listed in Table  1. Known and unknown compounds were annotated using databases such as: Dictio ary Natural Product (https://dnp. chemnetbase. com/, accessed on 5 May 2022)  [11, 12] , Co puter software review: Scifinder (https://scifinder-n.
cas. org, accessed on 05 May 2022), a PubChem (https://pubchem. ncbi. nlm. nih. gov/compound, accessed on 05 May 2022). shown in Table  1 , a total of 18 compounds were putatively identified. These metaboli were reported to belong to eight (8) families of natural compounds: cyclopeptide alk loids, isoflavonoids and flavonoids (polyphenols), glycerophospholipids, esterified ami acids, steroids and their derivatives, and quinoline alkaloids  The number above each peak corresponds to the peak numbers in Table  1. Known and unknown compounds were annotated using databases such as: Dictionary Natural Product (https://dnp. chemnetbase. com/, accessed on 5 May 2022)  [11, 12] , Computer software review: Scifinder (https://scifinder-n. cas. org, accessed on 5 May 2022), and PubChem (https://pubchem.
MS/MS-Molecular Networking-Based Dereplication In addition, we also performed an analysis of the crude extract and fractions of E. exsul using the molecular network (MN) approach by the GNPS website (http://gnps. ucsd. edu, accessed on 5 May 2022)  [17]. All obtained HRESIMS/MS spectra were preprocessed via MZmine2  [18]  software following the feature-based molecular networking workflow  [19]. These arrayed MS/MS spectra were then searched in an automated batch mode against the spectral libraries hosted by GNPS and the results were annotated as part of this dereplication strategy. A re-examination of the molecular network that had previously been obtained from the organization of the LC-MS/MS data of the crude extract and fractions of E. exsul revealed the presence of a total of 585 precursor ions visualized in  Cytoscape 3.
(2017)  [6]. Indeed, they showed plants of the Vochysiaceae family, o which Erismadelphus is a member, have the ability to live in association (symbiosis) with hyphomycetes. Given the similarity in terms of climate (equatorial climate) between Ga bon and Brazil, it seems important to consider this hypothesis without rejecting the fac that these molecules can come from the plant since the harvests were made on severa sites. In order to assess the likelihood of a possible contamination of the samples, thorough molecular biology investigations will be performed in due course to get more insight into the chemical diversity of this plant. The results show that the combination of molecular networks and LC-HRMS/MS data greatly improves the efficiency of the identification of chemical components in the targeted plant extracts.
Data Dependent LC-HRMS 2 Analyses HPLC-QTOF-HRES analyses were achieved by coupling the LC system to a hybrid quadrupole time-of-flight mass spectrometer Agilent 6530 (Agilent Technologies, Massy, France) equipped with an ESI source, operating in positive ion mode. Source parameters were set as follows: capillary temperature at 320 • C, source voltage at 3500 V, and sheath gas flow rate at 10 L. min -1. The divert valve was set to waste for the first 3 min. MS scans were operated in full-scan mode from m/z 100 to 1700 (0. 1 s scan time) with a mass resolution of 11 000 at m/z 922. A MS 1 scan was followed by MS 2 scans of the three most intense ions above an absolute threshold of 5000 counts. The selected parent ions were fragmented with one collision energy fixed at 45 eV and an isolation window of 1.
3 amu. The calibration solution containined two internal reference masses (purine, C 5 H 4 N 4 , m/z 121. 050873, and HP-921 [hexakis-(1H,1H,3H-tetrafluoropentoxy)phosphazene], C 18 H 18 O 6 N 3 P 3 F 24 , m/z 922. 0098). A permanent MS/MS exclusion list criteria was set to prevent oversampling of the internal calibrant. LC-UV and MS data acquisition and processing were performed using MassHunter Workstation software (Agilent Technologies, Massy, France).
Feature-Based Molecular Networking Parameters The MS 2 data files were converted from the. d (Agilent) standard data-format to. mzXML format using the MSConvert software, which is part of the ProteoWizard package  [35]. All. mzXML were then processed using MZmine 2 v32  [18]. The mass detection was realized by keeping the noise level at 3. 0 E3. The ADAP chromatogram builder was used using a minimum group size of four scans, a group intensity threshold of 3000, a minimum highest intensity of 4000, and m/z tolerance of 0. 01 Da (or 20 ppm)  [36]. The ADAP wavelets deconvolution algorithm was used with the following standard settings: S/N threshold = 8, minimum feature height = 3000, coefficient/area threshold = 8, peak duration range 0. 02-2 min, and RT wavelet range 0. 02-1. 2. MS 2 scans were paired using an m/z tolerance range of 0.
03 Da and RT tolerance range of 1. 0 min. Isotopologues were grouped using the isotopic peaks grouper algorithm with an m/z tolerance of 0. 005 Da (or 15 ppm) and a RT tolerance of 0. 3 min. Peak alignment was performed using the joint aligner module (m/z tolerance = 0. 005 Da (or 15 ppm), weight for m/z = 2, weight for RT = 2. 0, and absolute RT tolerance 0. 3 min). The peak list was gap-filled with the same RT and m/z range gap filler module (m/z tolerance of 0. 005 Da (or 15 ppm)). Next, the. mgf spectral data file and its corresponding. csv metadata file (for RT and areas) were exported using the dedicated "Export to GNPS-FBMN" built-in option.
Molecular Network Analysis A molecular network was created using the online workflow at GNPS (http://gnps. ucsd. edu, accessed on 5 May 2022) with a parent mass tolerance of 0. 02 Da and an MS/MS fragment ion tolerance of 0. 02 Da. A network was then created where edges were filtered to have a cosine score above 0. 65 and more than 6 matched peaks. Further edges between two nodes were kept in the network if and only if each of the nodes appeared in each other's respective top ten most similar nodes. The spectra in the network were then searched against GNPS spectral libraries. All matches kept between network spectra and library spectra were required to have a score above 0. 6 and at least 6 matched peaks. The molecular networking data were analyzed and visualized using Cytoscape software (ver.
(2021)  [40]. The fungal growth was recorded eleven days after microplate inoculation with fungal spore suspension by measuring the perpendicular diameter of each fungal colony. The percentage of inhibition was calculated following the same formula used above for P. infestans. Dose-response curves for both P. infestans and Z. tritici were performed on GraphPad Prism software version 9 (GraphPad Software Inc. , San Diego, CA, USA). The bioassays for both pathogens were performed twice in two independent experiments with three biological repetitions each.
The analyses reported herein focus on the following four biomes (Fig. 1 ): the seasonally stratified North Atlantic subpolar gyre (NASPG); the permanently stratified Atlantic and Pacific subtropical gyres, which were grouped together (STG); the seasonally stratified Southern Ocean (subantarctic); and the Mediterranean Sea, which was added here owing to the abundance of BGC-Argo data and represents a seasonally stratified subtropical biome. Fay and McKinley's definition allows biome boundaries to change from one year to another. Here we analyzed only data from the core of each biome, defined as the grid cells that never changed classification during the 1998-2010 satellite observation period.
BGC-Argo observations The global dataset acquired by the array of BGC-Argo floats was downloaded from the Global Data Assembly Center hosted by Ifremer (ftp://ftp. ifremer. fr/ifremer/argo/dac/, last access: 14 January 2020)  (Argo, 2000). The selected floats were equipped with a Seabird-Wetlabs ECO-Triplet sensor package including a Chl a fluorometer (excitation at 470 nm; emission at 695 nm) and a backscattering sensor at 700 nm (b bp700 ), in addition to the conductivity-temperature-depth (CTD) probe. The downloaded measurements had undergone the standard processing, which includes the application of calibration equations to raw sensor output and the performance of near-real-time quality control to both CTD  (Wong et al. , 2021)  and Chl a measurements  (Schmechtig et al. , 2018). Since no specific quality control procedure has been established yet for b bp700 profiles, the latter were only quality-controlled according to the general criteria  (Schmechtig et al.
Global gridded climatologies (3D approach) The global dataset acquired between 2010 and 2019 was used to produce global gridded monthly and seasonal climatologies for b bp700 and Chl a. The measurements were binned onto the ORCA2_L31 grid used for NEMO-PISCES simulations (see Sect. 2. 4. 1), which has a horizontal resolution of about 2 • that increases to 0. 5 • in the meridional direction in the equatorial domain, and 30 oceanic vertical levels between the surface and the ocean bottom. The thickness of the vertical bins increases progressively from 10 m at the surface to 339 m in the 22nd bin (870-1209 m), the deepest one containing BGC-Argo data. In each grid element, the average, median, range, and data counts were computed. Profiles from the CSIRO and INCOIS data assembly centers were not used because, at the time of download, they had not taken into consideration the new calibration files provided by the manufacturer.
Profile time series for individual floats sampling at higher resolution (1D approach) A subset of the floats, deployed mostly by the projects NAOS, remOCEAN, and Bio-Argo France (model NKE PROVOR CTS-4), were programmed to sample at higher temporal and vertical resolution than the Argo defaults (10 d and 10 m). These floats made vertical profiles between 1000 m and the surface every 2, 5, or 10 d with a vertical resolution of 10 m between 1000 and 250 m (or 350), 1 m between 250 (or 350) and 10 m, and 0. 2 m between 10 m and the sea surface. We processed this dataset with a dedicated pipeline to extract additional information on POC size fractions and their dynamics. Along each vertical profile we computed depth, conservative temperature, absolute salinity, σ θ , and spiciness  (Flament, 2002)  from the calibrated pressure, temperature, and salinity using the R package oce  (Kelley, 2011).
(2) The exponential decrease prescribed by Eq. (  1 ) is similar to that proposed by  Bol et al. (2018) , except for the inclusion of the constant term c that prevents the ratio from becoming 0 at depth. The slope of the exponential decrease (b = -6. 57) is constant in all biomes and based on our fit to the  Cetinić et al. (2012)  dataset, using the same depth bins as  Bol et al. (2018)  but additionally forcing the curve towards c at 1000 m. The uncertainty of regional b bp700 -POC conversion factors in the epipelagic is typically < 10 % according to the standard error of the POC vs. b bp700 linear regression slopes (Table  A1 ). The few available measurements in the mesopelagic suggest a POC / b bp700 uncertainty lower than a factor of 2. Through this study, we will assume that model / observation ratios larger (smaller) than 2 (0.
The best model grid cell was selected based on model-observation correlation, root-mean-square error, and time lag in the onset date of summer stratification. An example of the metrics used to match NEMO dynamical fields and BGC-Argo MLD is provided in Figs. S1-S4. The associated model configuration and datasets are available in public repositories  (Galí et al. , 2021a and b). PISCES 1D offline simulations were forced using the dynamical fields from the selected model grid cells. The same annual forcing, corresponding to the year of the BGC-Argo observations, was repeated over 5 simulation years. After 4 years of spin-up, the output from year 5 at a 5 d resolution was used for the comparison to the BGC-Argo CATS. Initial conditions (climatological fields of inorganic nutrients and carbon chemistry variables) and boundary conditions (atmospheric deposition) were the same as used for PISCES 3D  (Aumont et al.
2. BGC-Argo Teff usually exceeded PISCES Teff and spanned a wider range. The best agreement was generally found in the STG and the subantarctic biomes, and the poorest agreement occurred in the Mediterranean where BGC-Argo Teff was typically twice the modeled Teff. 3. Distinct patterns were found for four CATS in the Labrador Sea (NASPG) characterized by high epipelagic TPOC (> 440 mmol C m -2 ). In this subset, PISCES and BGC-Argo Teff were in good agreement for SPOC (median 0. 40 for both), whereas, for LPOC, PISCES Teff (0. 36-0. 45) doubled BGC-Argo Teff (0. 12-0. 28), which had some of the lowest values of the dataset. These CATS showed a LPOC minimum at 600-800 m in fall, which PISCES could not reproduce (Sect. 3. 2. 1). 4. PISCES and BGC-Argo Teff showed a weak positive correlation for SPOC (r = 0.
, 2001) , altogether resulting in lower POC content per unit b bp700  (Cetinić et al. , 2012; Oubelkheir et al. , 2005). The proportions of different autotrophic and heterotrophic organisms and detritus are also likely to vary with upper-ocean productivity (see Sect. 4. 3). If the mass-specific backscattering coefficients of these components were better known, their systematic variation patterns could be used to develop a continuous formulation for POC / b bp700 rather than the regionalized conversion factors used here. However, POC / b bp700 is influenced by other seawater constituents whose occurrence is less predictable, foremost, biogenic calcite (e. g. , from coccolithophores) and desert dust  (Claustre, 2002; Loisel et al. , 2011) , both of which enhance b bp700. In our dataset, we detected a CATS (float WMO 6901647, year 2016) that was strongly affected by coccolith backscattering in the Iceland Basin, an area known for its massive coccolithophore blooms  (Moore et al.
Bellacicco et al. (2019)  analyzed the covariation between b bp700 and Chl a in the (much larger) BGC-Argo dataset. They concluded that the background b bp700 , defined as the b bp700 that does not covary with Chl a, decreases with productivity from > 80 % in subtropical gyres to < 20 % in the NASPG, with a mean contribution of 65 % in oligotrophic areas. Although the background b bp700 fraction cannot be entirely assigned to non-phytoplanktonic material, the biogeographic patterns found by  Bellacicco et al. (2019)  are hardly compatible with those found by  Graff et al. (2015)  or with a nearly constant detrital fraction across biomes. The detrital and nonphytoplanktonic POC fractions in PISCES near the sea surface range, respectively, between 15 %-31 % and 42 %-59 % (annual mean values by biome; Table  3 ).
, 2022)  and model development. Our cross-scale evaluation is also informative as to the spatiotemporal scales that can be addressed with a given model setup. This matter was recently tackled by  Bisson et al. (2019)  using the export production model of  Siegel et al. (2014) , which is forced by satellite-derived primary production. In particular, they showed that this diagnostic model could be optimized to reproduce global climatological patterns but exhibited poor skill when faced with non-climatological datasets, which reflect local snapshots of ecosystem functioning. Model evaluation at climatological scales provides an incomplete picture, especially in productive regions where much POC export can take place during intense but short-lived events  (Briggs et al. , 2020). Such events are smoothed out when climatologies are computed, and their coherence with the physical forcing is lost.
The addition of transmissometers may enable more accurate POC quantification  (Bishop, 1999)  and particle characterization  (Boss et al. , 2015). -The inclusion of new types of sensors  (Claustre et al. , 2020)  and the extension of measurements into the bathypelagic (Deep-Argo;  Roemmich et al. , 2019)  hold high potential for advancing BCP research. -Further developments in BGC-Argo data processing are needed, with the final goal of supplying a wide public with user-friendly data products in near real time. -Models -Evaluating model skill at resolving POC stocks, in addition to fluxes, is key to ensure that models reproduce observed fluxes for the right reasons. -Evaluation against globally consistent datasets is critical to avoid model overtuning towards small, sparse datasets, such as vertical POC fluxes.
Our approach essentially follows the literature compilation made by  Cetinić et al. (2012). The linear regressions generally yielded small and non-significant y intercepts for sea-surface data. Therefore, we assumed the slopes were equal to the POC / b bp700 ratio. When the b bp measurements were made at another wavelength, we converted them to b bp700 assuming an exponent η = 0. 41  (Cetinić et al. , 2012) , according to the following equation: b bp (λ 1 ) = b bp (λ 0 ) • (λ 1 /λ 0 ) η. (A1) For measurements taken at 555 nm, which was the wavelength used in two studies (Table  A1 ), this resulted in 10 % lower b bp700 compared to b bp555 and thus higher POC / b bp700 ratios. The dataset of  Cetinić et al. (2012)  was the only one showing a significant negative intercept of the POC vs. b bp700 linear regression.