dataesr/scientific-paragraphs-categorization huggingface dataset:
These samples were analyzed using a combined single-cell mass cytometry (Fig. S13 ) and plasma proteomics (Somalogic) approach. Stabl merged all omic datasets into a final model that accurately classified patients with and without SSI (AUROCStabl = 0. 80 [0. 69, 0. 89]). When compared to early and late fusion Lasso, Stabl had comparable predictive performance (Fig. 5b, S14 ), yet superior sparsity (Fig. 5c ) and reliability performance (Fig. 5h, i ). As a result of the frequency-matching procedure, there were no differences in major demographic and clinical variables between the two patient groups, suggesting that model predictions were primarily driven by pre-operative biological differences in patients' susceptibility to develop an SSI. Stabl selected four mass cytometry and 25 plasma proteomic features that were combined into a biologically interpretable immune signature predictive of SSI.
Discussion Stabl is a machine learning method for analysis of high-dimensional omic data designed to unify the biomarker discovery process by identifying sparse and reliable biomarker candidates within a multivariate predictive modeling framework. Application of Stabl to several real-world biomarker discovery tasks demonstrates the versatility of the algorithm across a range of omic technologies, single-and multi-omic datasets, and clinical endpoints. Results from these diverse clinical use cases emphasize the advantage of Stabl's data-driven adaptation to the specificities of each omic dataset, which enables reliable selection of biologically interpretable biomarker candidates conducive to further clinical translation. Stabl builds on previous methods, including Bolasso, SS, and Model-X knockoff.
13, 45, 46  Minimization of the FDP surrogate (FDP+) offers two main benefits. First, it expresses a trade-off between reliability and sparsity, as it is the sum of an increasing and a decreasing function of the threshold. Second, assuming exchangeability between artificial and uninformative features Stabl's procedure guarantees a stochastic upper bound to the FDP using the reliability threshold estimate, which ensures reliability in the optimization procedure. By minimizing this function ex-ante, Stabl objectively defines a model fit from the procedure without requiring prior knowledge of the data. On a synthetic dataset, we experimentally demonstrate that Stabl selects an optimal reliability threshold by minimizing the FDP+ and allows for improved reliability and sparsity compared to Lasso or EN at similar predictivity performance.
When tested on real-world omic studies, Stabl also performed favorably compared to Lasso and EN. For each case study, the identification of a manageable number of reliable biomarkers facilitated the interpretation of the multivariate predictive model. Prior analyses of similar datasets  18, 20, 21, 31  required suboptimal analysis frameworks: either post-hoc analyses were performed using user-defined cut-offs for feature selection after an initial model fit, or features associated with the clinical endpoint were selected before modeling, thus risking overfitting. In contrast, Stabl embeds the discovery of reliable candidate biomarkers within the predictive modeling, alleviating the need for separate analyses. Stabl extended readily to analysis of multi-omic datasets where a predictive model can utilize features from different biological systems.
Here, Stabl offers an alternative that avoids the potential shortcomings of early and late fusion strategies. In the case of early fusion, all omic datasets are first concatenated before applying a statistical learner. This leads to optimization on all omics combined, regardless of the specific properties (e. g. , dimensions, correlation structure, underlying noise) of individual omic datasets. [47] [48] [49] [50]  In contrast, the late fusion method trains the learner on each omic data layer independently and merges the predictions into a final dataset. 19, 21, 31, [51] [52] [53]  In this case, although a model is adapted to each omic, the resulting model does not weigh features from different omics directly against each other. Stabl analyzes each omic data layer independently and fits specific reliability thresholds before selecting the most reliable features to be merged in a final layer, thus combining the advantages of both methods.
Multi-omic data integration with Stabl was particularly useful for analysis of our newly generated dataset in patients undergoing surgery. In this case, the Stabl model comprised several features that were biologically consistent across the plasma and single-cell datasets, revealing a patient-specific immune signature predictive of SSI that appears to be programmed before surgery. Our study has several limitations. Although we demonstrate the validity and performances of Stabl experimentally and theoretically under the assumption of exchangability between artificial and uninformative features, a more general theoretical underpinning of the method will require further guarantee. In addition, our evaluation of Stabl's performance focused on fitting Lasso and EN models as gold standard SRMs. Further development of Stabl will be needed to allow for fitting of any SRM.
Feature selection frequency Hyperparameters Frequency g f Fig. 2  | Synthetic dataset benchmarking. a. A synthetic dataset consisting of N = 50,000 samples x p = 1,000 features was generated. Some features are correlated with the outcome (informative features, light blue), while the others are not (uninformative features, grey). Forty thousand samples are held out for validation. Out of the remaining 10,000, 50 sets ranging of sample sizes n ranging from 30 to 1,000 are drawn randomly. c. Three metrics are used to evaluate performance: sparsity (average number of selected features compared to the number of informative features), reliability (Jaccard Index, JI, comparing the true set of informative features to the selected feature set), and predictivity (mean squared error, MSE). c. The surrogate for the false discovery proportion (FDP+, red line) and the experimental false discovery rate (FDR, dotted line) are shown as a function of the frequency threshold.
S7Comparison of Stabl and selection with fixed frequency threshold sparsity performance on synthetic data. Extended Data Figure S8Comparison of Stabl and selection with fixed frequency threshold reliability performance on synthetic data. Extended Data Figure S9Comparison of Stabl and selection with fixed frequency threshold predictivity performance on synthetic data. Extended Data Figure S10Reliability threshold variation with the number of samples. ExtendedData Figure S11 Performance of Stabl compared to EN on transcriptomic (Preeclampsia, PE) and proteomic (COVID-19) datasets. Extended Data Figure S12 Predictivity of Stabl and Lasso for the training and validation cohort of the COVID-19 dataset.
Overview of transcriptome and proteome analyses of B. subtilis the Rho + strain To identify modifications of transcription presumably caused by rho over-expression, we performed RNAseq on Rho + , WT and Δrho strains grown in LB medium. Two time points corresponding to the mid-exponential and early stationary phase were selected for this comparison (Materials and methods). Exploration of the RNAseq data integrated two levels of analysis. At the gene level, we conducted differential expression (DE) analyses of sense and antisense strands of the native transcription regions (TRs) composed of 4,292 Genbank-annotated genes and 1,583 other TRs annotated as "Segments" (S-numbers) comprising ncRNAs and asRNAs  [15]. The normalized gene expression (fpkm, fragments per kilobase of transcript per million fragments mapped) Cells grown in a defined rich medium SpC to stationary phase were transferred to competence medium (T0) and tested for transformation by homologous genomic DNA over three hours as described in Materials and Methods.
The mutants were analyzed in parallel with the parental strains WT P comK-luc (blue line) and Rho + P comK-luc (red line). For each strain, data acquisition and processing were performed as in (C). Each strain was analyzed at least three times. The results of representative experiments are shown. (E) Inactivation of the known repressors of comK does not rescue competencenegative phenotype of Rho + cells. B. subtilis WT and Rho + strains and their respective derivatives carrying single mutations in the abrB, rok, codY genes or double abrB rok mutation were transformed by donor genomic DNA after two hours of growth in competence medium as described in Materials and Methods. Shown are the mean values with SD (in parentheses) from two independent experiments each incorporating three biological replicas of each strain.
To allow analysis of quantitative variations between WT and Rho + proteomes, we used Protein Abundancy Index (PAI) values calculated from mass spectrometry data as described  [96, 97] : 175 and 193 proteins were found to be differently abundant (0. 5 �PAI Rho + /WT� 2) in the exponential and stationary Rho + cells, respectively, compared to WT. A number of proteins were found exclusively in Rho + (28 and 73 for the exponential and stationary-phase cells, respectively), or in WT cells (76 and 92 for the exponential and stationary-phase cells, respectively) (S1 Table ). ), which mirrored the increase of antisense transcription already described in the Δrho mutant  [15]  and confirmed by the present analysis (S3 Table ). We found that transcription of antisense strand of 338 GenBank-annotated genes was down-regulated (log2 Rho + /WT � -1; q-value � 0.
For 19. 7% (42 out of 243) of S-segments down-regulated in the stationary Rho + cells, expression levels are decreased due to an improved termination of transcription upstream of them, which may be a direct outcome of Rho activity (S4 Table ). The 62% (26 out of 42) of S-segments directly down-regulated by Rho fall into the category of 3' untranslated regions (3'UTR), which includes three types of 3 0 UTRs as defined in  [15] : the proper 3 0 -UTR with clear termination signals; 3 0 -PT with partial termination, and 3 0 -NT without termination signals (S4 Table ). None of the S-segments encoding ncRNAs with assigned physiological functions showed an altered expression level in Rho + compared to WT. To be able to retrace the propagation of the effects into regulatory cascades, we examined the correlation between DE and the known regulons and functional categories as defined in SubtiWiki database (  [99] ; http://subtiwiki.
Epifluorescence microscopy, image processing and cell measurements For all microscopic observations, cells were mounted on a 2% agarose pad and topped with a coverslip. Bacteria were imaged with an inverted microscope (Nikon Ti-E) equipped with an iLas2 laser coupling system from Roper Scientific (150 mW, 488nm and 50 mW, 561 nm), a 100× oil immersion phase objective, an ORCA-R2 camera (Hamamatsu) and controlled by the MetaMorph software package (v 7. 8; Molecular Devices, LLC). The post-acquisition treatment of the images was done with the Fiji software [166-168]. Cultures of B. subtilis were performed as described above. To quantify morphological changes, cells were sampled during exponential growth (OD 600 0. 2) and early stationary phase (OD 600 1. 8), and mixed with the lipophilic fluorescent dye Nile Red (10 μg/ml final concentration), prior to observation by epifluorescence microscopy.
The mean cell lengths were determined with the ChainTracer plugin of the Fiji software [169], on two independent experiments with N >140 (N avg = 290). To determine the frequency of cells entering into the sporulation pathway, cultures were sampled three hours after the induction of sporulation by the resuspension method  [164]. Strains carrying an inducible kinA gene were supplemented with 0. 1 mM IPTG at the time of resuspension. Sampled cells were mixed with the lipophilic fluorescent dye Mitotracker Red (10 μg/ml final concentration) prior to microscopic observation. Asymmetric septa were manually counted in two independent replicas (N > 450 per strain and per replica). To assess the frequency of cells entering the competence state, cells bearing P comGA -gfp transcriptional fusion were grown in competence medium and sampled two hours after entering stationary phase (OD 600 1.
Data was deposited in GEO (accession number GSE195579). Graphical representations of the expression level of a gene in a given strain and condition used the geometrical mean of log2(fpkm+5) values, where FPKM was computed with the DESeq2-estimated library size factors multiplied by the median of sample count sums. To allow interactive exploration of the sense and antisense signal along the genome with bp-resolution, we also implemented in Genoscapist  [95]  the representation of a new data type corresponding to RNAseq coverage. For this purpose, count values are extracted with "bedtools genomecov" (version 2. 27. 0, 156; [174]) and represented as a step function with breakpoints corresponding to extremities of mapped read pairs along the genome sequence. To make these counts comparable between different samples and with gene-level expression values, coverage counts are converted to fpkm, using the formula fpkm_cov(t) = cov(t) � (10 3 /F) � (10 6 / L), where cov(t) is the coverage count for genome position t, L is the library size used to compute gene-level FPKM, and F is the average fragment length (from 178 bp to 200 bp across samples) obtained from the distance between extremities of inward oriented read pairs returned by "samtools stats" (version 1.
injection time = 50 ms, data type = profile. Analyzed charge states were set to 2-5, the dynamic exclusion to 80 s and the intensity threshold was fixed at 5. 103. Protein identification was performed with X!Tandem software X!TandemPipeline (open source software developed by PAPPSO, version 3. 4. 3, http://pappso. inra. fr/bioinfo/xtandempipeline/) against a protein database of B. subtilis (version December 2019, 4188 entries). The proteins identification was run with a precursor mass tolerance of 10 ppm and a fragment mass tolerance of 20 ppm. Enzymatic cleavage rules were set to trypsin digestion ("after Arg and Lys, unless Pro follows directly after") and no semi-enzymatic cleavage rules were allowed. The fix modification was set to cysteine carboxyamidomethylation and methionine oxidation was considered as a potential modification.
Acknowledgments This work has benefited from the facilities and expertise of the high throughput sequencing core facility of I2BC (Research Center of GIF-http://www. i2bc. paris-saclay. fr/. ). We are grateful to the  INRAE MIGALE bioinformatics facility  (doi: 10. 15454/1. 5572390655343293E12) for providing computing and storage resources. We are grateful to  Celine Henry  and members of the  Plateforme d'Analyse Proteomique de Paris Sud-Ouest (PAPPSO- http://pappso. inrae. fr/ en/),  MICALIS ,  Jouy en Josas, France , for mass-spectroscopy analysis.
The BOUSSOLE observatory consists of a mooring where a buoy, specifically designed for collecting radiometric and bio-optical quantities, collects optical data at high 110 temporal resolution. Oceanographic cruises visit the site monthly to collect a complementary dataset of algal pigment concentrations and IOPs, which include light absorption coefficients of phytoplankton, non-algal particles and CDOM  (Antoine et al. , 2006). Numerous studies combining optics and biochemistry have exploited these two complementary sets of observations collected at the BOUSSOLE site, from the use of fluorescence to infer Chl-a (e. g. ,  Bayle et al. , 2015; Mignot et al. , 2011)  and phytoplankton community composition  (Sauzède et al. , 2015) , inferring phytoplankton community size structure 115 from measured light spectra  (Organelli et al.
Observations The Mediterranean Sea is a semi-enclosed marginal sea. The Ligurian sea, in the NW Mediterranean Sea, is characterized by oligotrophic to moderately eutrophic waters. The BOUSSOLE and the DYFAMED monitoring sites, both in the Ligurian sea, provided bio-optical and physical-chemical measurements, respectively (Sect. 2. 2. 1). In addition, we considered ocean-colour data collected by satellites (Sect. 2. 2. 2) (Fig. 2 ). 330.
In situ data at BOUSSOLE and DYFAMED sites The BOUSSOLE project includes a permanent optical mooring deployed at 7°54'E, 43°22'N, where the depth is 2440 m, collecting optical data at high temporal resolution, and oceanographic cruises visiting the site monthly to collect optical and BGC variables  (Golbol et al. , 2000). Only data from monthly cruises were used in the present work. Seawater is generally collected at 12 discrete depths within the 0-400 m water column  (5, 10, 20, 30, 40, 50, 60, 70, 80, 150, 200, 400 m). Sampling 335 is performed with a SeaBird SBE911+ CTD/rosette system equipped with pressure (Digiquartz Paroscientific), temperature (SBE3+) and conductivity (SBE4) sensors and 12-L Niskin bottles, from which water samples are collected for subsequent absorption measurements and high-performance liquid chromatography (HPLC) analyses.
(2008). The fractions of picophytoplankton, 345 nanophytoplankton and microphytoplankton were calculated following the diagnostic pigments method  (Uitz et al. , 2006). The Dynamique des Flux Atmosphériques en Méditerranée (DYFAMED) time-series station  (Marty, 2002 ) is located at 7. 54°E and 43. 25°N, where data have been collected since 1991. Temperature (T) and salinity (S) data were collected within the 0-2400 m water column with the same CTD/rosette configuration used at BOUSSOLE with nutrients sampled at discrete depths. T and S data from 1994 to 2014 were downloaded from the OceanSITES Global Data Assembly Center and nitrate, nitrite and 350 phosphate concentrations were downloaded from  Coppola et al. (2021).
Data from satellites Satellite retrieved IOPs and Chl-a were obtained from the OCEANCOLOUR_MED_BGC_L3_MY_009_143 product (E. U. Copernicus Marine Service Information, 2022). The product provides multi-sensor RRS(λ) spectra (SeaWiFS, MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS and Sentinel3A-OLCI) merged using the state-of-the-art multi-sensor merging algorithms and 355 shifted to the standard SeaWiFS wavelengths  (412, 443, 490, 555, and 670 nm). IOPs (aPH443 and aDG443) are derived from RRS(λ) via the Quasi-Analytical Algorithm version 6 (QAAv6) model  (Lee et al. , 2002). Chl-a concentration is determined using the Mediterranean regional algorithm: an updated version of MedOC4  (Volpe et al. , 2019)  and AD4  (Berthon and Zibordi, 2004) , merged according to the water type present  (Mélin and Vantrepotte, 2015; Volpe et al.
Acknowledgements This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  101004032  (project  SEAMLESS ) and the  Horizon Europe   research and innovation  action under grant agreement No  101081273  (project  NECCTON ). All simulations were conducted on the GALILEO100 supercomputing facility at the  920 CINECA consortium ; we acknowledge  CINECA  for providing high-performance computing resources and support through the IscrB_3DSBM and the tra21_seamless projects. Satellite products were provided by the  Copernicus Marine Environment Monitoring Service (CMEMS) , implemented by  Mercator Ocean  in the framework of a delegation agreement with the  European Union. The  BOUSSOLE  project was funded by the  Centre National d'Etudes Spatiales (CNES)  and the  European Space Agency (ESA)  and supported by the  French Oceanographic Fleet (FOF).
Code availability The GOTM code is available at https://www. github. com/gotm-model. FABM code is available at https://github. com/fabm-890 model/fabm. git. The BFM code adapted to work under the FABM convention is available at https://github. com/inogs/bfmforfabm. git (branch dev_1D, accessible on request) and the bio-optical model in https://github. com/BIOPTIMOD/Forward_Adjoint. git. ParSAC is available from PyPI, can be installed by executing the command: pip install parsac <--user>, and it requires Parallel Python. The 1D setup where the simulations presented can be run and the configuration files for the optimization can be obtained from https://github. com/ealvarez-895 s/setup_BFM1D_CDOM. git.
Data Availability The gridded product of temperature and salinity at DYFAMED was obtained from the OceanSITES Global Data Assembly Center via ftp (ftp://ftp. ifremer. fr/ifremer/oceansites, accessed 22/11/2021). Nitrate, nitrite and phosphate concentrations at DYFAMED were obtained from https://doi. org/10. 17882/43749 (Coppola et al. , 2021). Chl-a and HPLC data at BOUSSOLE 900 are available from http://www. obs-vlfr. fr/Boussole/html/boussole_data/other_useful_files. php (login as guest required). IOPs data at BOUSSOLE are available upon request to IMEV. Satellite data are available from the E. U. Copernicus Marine Service Information at https://doi. org/10. 48670/moi-00299. The results of the numerical simulations can be found in https://doi. org/10. 5281/zenodo. 7628060 and scripts for data analysis and visualization are available through https://github.
The model was incorporated into the PRO/II® commercial software using a user-added subroutine  (Lee et al. , 2019). Another study utilized 2D mathematical modeling with the finite difference method (FDM) to explore the impact of different ionic liquids, their concentrations, and liquid and gas flow rates on the CO 2 absorption process in membrane contactors  (Darabi and Pahlavanzadeh, 2020). Gas mixture adsorption investigations involving metal-organic frameworks (MOFs) were conducted through molecular simulations. In this research, artificial neural networks (ANNs) were employed to predict the separation factor of CO 2 and CH 4 , gas adsorption performance, and heat of adsorption  (Yulia et al. , 2021). To predict CO 2 solubility data in three types of aqueous amine blends, a new simplified Kent-Eisenberg model was developed.
Data collection and analysis In this study, the data on the solubility of CO 2 were gathered from the experiments done by other groups. The solubility data were obtained under different conditions. The 413 data points were separated into training and test sets. Due to several reasons such as unreliable data or unconsidered parameters in tests such as humidity and purity or grades of amines, these data could not correlate well by ML models. The former set contains around 80% of the available experimental data, while the rest forms the test set. The solutions corresponding to each set are the same. The operating conditions are dissimilar. The test set assesses the performance of the algorithm based on any given inputs. One significant step is determining the independent parameters related to the correlation, which occurs before the training phase.
S1  (supplementary information). For the training phase, the number of epochs was selected as 2000. Also, in the beginning, hybrid learning was used to estimate the parameters. The PSO-ANFIS model is based on finding the optimum parameters of ANFIS, which can be obtained using the PSO algorithm  (Kennedy and Eberhart, 1995). MATLAB software was used to set up the PSO-ANFIS algorithm. This algorithm uses PSO to help the hybrid model better correlate the inputs and output. This model can have high accuracy, especially in the case of non-linearity, and therefore can improve the algorithm's efficiency. As mentioned earlier, the particle swarm optimization parameters should be properly selected. The trial and error method was applied (Table  1 ). Fig. S2  (supplementary information) demonstrates the PSO-ANFIS membership functions associated with the input parameters.
Numerical results are reported in Section 5: the proposed method is validated first on realistic simulated spatial patterns for assessing the resolution achieved, also in comparison to state-of-the art approaches. Next, reconstructions computed from real data of a commercial phantom acquired on standard microscopes and of the Ostreopsis cf Ovata alga are reported. The proposed approach achieves notable improvement in resolution and appears much more robust to noise perturbations and modelling errors, being effective also on challenging scenarios.