dataesr/scientific-paragraphs-categorization huggingface dataset:
Model Selection, Performance Metrics, Hyperparameter Tuning Prior to model training, we randomly split the ComBat-corrected GE data into a discovery data set (70%) and a validation data set (30%). The discovery set was used to train several classification algorithms, taking GE data related to the IPP genes as input and 30-day mortality as outcome: logistic regression with L1 (lasso), L2 (ridge) and mixed (elastic net) regularization, random forest, support vector machines with linear and radial kernels and partial least squares-discriminant analysis. Mortality was considered as a binary variable because time-to-event data were not available in most public data sets. Hyperparameter optimization was performed to select models with the highest mean area under the receiver operating characteristic (ROC) curve (AUROC) using 5 repetitions of 10fold cross-validation.
Statistical Analysis and Software To compare demographics and clinical features in the discovery and validation data sets, we used the Wilcoxon rank sum test. To compare predictive performance between models, we compared ROC curves computed using the same test set with DeLong's test for correlated data. To compare proportions of dead patients between different risk groups obtained with IPP genes, we used the chi-squared test or Fisher's exact test, as appropriate. Significance levels for p-values were set at 0. 05 and analyses were two-tailed. Statistical analyses were performed using R (v3. 6. 2) with packages from the BioConductor library, the tidyverse collection, caret and COCONUT, as well as on Python 3 with the scikit-learn machine learning library.
As presented in Table  2 , demographics and clinical characteristics were similar in the discovery (n = 1,007) and validation (n = 430) sets obtained after random splitting of the "day 1" data set. In the 7 studies  (43, 45, 46, (51) (52) (53) 58)  with GE data collected at time points 3 to 7, there were 270 arrays sampled on 173 patients, of whom 134 were alive and 39 (22. 5%) deceased at day 30; 122 were used for training and 51 for testing models (Supplementary Tables  2, 3 ). We ran the COCONUT algorithm on the 17 studies selected for analysis and assessed the effect of co-normalization: (1) on patient-level GE data across studies (Figure  1  and Supplementary Figure  1);  (2) at the gene level in controls and cases (Supplementary Figure  2  presenting data for CD3D); (3) for 2 genes in controls and cases, here with CLDN8 (a housekeeping gene, with minimal difference in mean GE between controls and cases and minimal overall GE variance) and CEACAM1, up-regulated during sepsis (Supplementary Figure  3 ).
Predictive Performances of the IPP Gene Set at Day 1 Following Admission First, we sought to determine the performance of the IPP gene set to predict 30-day mortality using GE data sampled on the day of patient admission. As shown in Figures  2, 3 , the highest predictive performance was obtained by training of a random forest classifier, with an AUROC computed on the validation set of 0. 710 (CI 0. 652-0. 768). Next, to determine if better predictive performance could be extracted from other genes, we ran the same models using all the genes common to the 17 selected studies as input. We found that the highest predictive performance of the "all genes" set (n = 7,122 genes) was obtained by training of an L2-penalized logistic regression classifier, with an AUROC computed on the validation set of 0.
DISCUSSION The main finding of our study is that the IPP gene set has good overall performance to predict 30-day mortality, as assessed using microarray data sampled at day 1 following admission in a large and heterogeneous cohort of sepsis patients, with best model showing an AUROC of 0. 710 (95% CI 0. 652-0. 768). IPP was designed using existing knowledge on sepsis immunology and pathophysiology, with the aim to assess the immune system of sepsis patients in a multifaceted manner, and this study demonstrates that the selected immunerelated genes also provide predictive information on all-cause mortality. Furthermore, this information can be captured using retrospective and highly heterogeneous data collected on microarrays, even though the IPP tool is based on a PCR assay. Importantly, predictive performance obtained with all the genes common to all microarrays (>7,000 genes) was not statistically different from that obtained with the IPP genes.
It is still possible that the IPP gene set does not capture all the information available in GE data to predict 30-day mortality, but for important technical reasons (e. g. , the limited multiplexing capabilities of most commercially available PCR-based assays), models including a large feature set would not be easy to implement at the bedside. This would mandate finding the optimal trade-off between statistical performance and technical constraints to identify the best number of features to include in the assay. Furthermore, models with a high number of predictors are prone to overfitting, which could limit the prognostic accuracy of gene sets across different technological platforms or in different clinical settings. Overall, these results demonstrate that the IPP gene set can capture similar information on 30day mortality in sepsis as the total gene pool common to 17 microarrays, but with the potential to deliver actionable results in less than an hour, directly at the point of care.
bacterial sepsis, or in ICU vs. ward patients, remains to be fully investigated to demonstrate the usefulness of this strategy. The IPP prototype has been designed to be run on a dedicated real-time multiplex PCR platform, whereas GE data used in our analysis was collected on microarrays, which raises the question of cross-platform transferability of transcriptomics assays. Given the sometimes weak correlation in expression levels of the same gene target measured on one given sample but different technology, it is highly possible that the real association between our gene signature (as measured with the IPP tool) and 30-day mortality might not be accurately recapitulated in our study. While many gene signatures have been devised for diagnosis and prediction in sepsis, none so far has been proven robust enough to be translated into a clinically usable tool, in part because good statistical performance seen during the conception phase was not reproduced on prospectively collected new patient data, especially if analyzed on a different platform  (60).
In recent studies for instance, a gene signature devised using microarray data did not show major improvement in predictive power compared to usual severity scores (SAPS 3 and APACHE II) when tested on prospectively collected patient samples processed on the NanoString nCounter platform  (60, 61). In line with this, a prospective multicenter study [IMPACCT (62)] is currently enrolling sepsis patients to better evaluate the predictive performance of the IPP gene set when used on its dedicated platform. Independent of the question of cross-platform transferability, transcriptomics-based diagnostic tools in sepsis might fail to take into account all the relevant information available to predict key outcomes. For instance, there are validated and widely-used clinical severity scores that can predict mortality in intensive care patients with moderate discrimination but wide generalizability and at virtually no added cost.
In the same line, it can be argued that for both, clinical and methodological considerations, it would be   FIGURE 5 | Prognostic enrichment with the IPP tool. We used the best IPP models (trained on the "day 1" and "days > 2" discovery sets) and computed a test threshold using the top-left method on corresponding validation sets. This enabled us to divide the validation sets in 2 sub-groups with a low and a high predicted risk of death. Then, we compared the actual proportion of sepsis patients deceased at day 30 in both sub-groups, to assess if IPP could be used for prognostic enrichment at the bedside. interesting to include in our prediction models patient-level data on demographics, clinical characteristics and therapeutics (such a steroids, which are known to influence shock severity and sepsis mortality  (63) , and are also potentially responsible for a change in immune-related GE profile).
CONCLUSION Through multi-cohort analysis using ComBat co-normalization on microarray data in a heterogeneous group of sepsis patients, we found that the IPP gene set, when assayed at day 1 following hospital admission, can reliably predict all-cause 30day mortality. Our data also suggest that more information could be extracted from mRNA data if sampled at later time points, when immunological trajectories begin to diverge between sepsis survivors and patients who will eventually die. Since mortality prediction in sepsis is of limited interest to clinicians if not coupled with specific interventions meant to influence disease trajectory and prognosis, using IPP to identify sepsis endotypes or predict HAI is more likely to have a positive impact on the care of patients with sepsis.
DATA AVAILABILITY STATEMENT The raw and normalized gene expression data sets analyzed in this study, data on patient demographics, clinical characteristics and outcomes have been deposited publicly and are available from referenced studies and from Synapse (doi: 10. 7303/ syn5612563). The code written for co-normalization across studies and assessment of predictive performance of gene signatures is available at: https://github. com/lkreitmann-bmx/ IPP_mortality_2022.
frontiersin. org Dataset accession GSE27131 GSE32707 First author Berdal Dolinay Country Norway United CA vs. HCA** CA CA Time points d1 d6 d7 d1 Age 41. 1 57. 1 Sex (%males) 85. 7 54. 2 Arrays 21 103 Patients Controls 14 7 103 55 Sepsis 7 48 Bacterial 0 NA Viral 7 NA Alive 5 86 Deceased 2 17 Chip Affymetrix Illumina Normalization method RMA Quantile States GSE40586 Lill Estonia CA d1 46. 1 NA 39 39 18 21 21 0 19 2 Affymetrix RMA GSE66099 Wong United CA d1 3. 7 63. 1 276 276 77 199 NA NA 248 28 Affymetrix gcRMA States GSE21802 Bermejo- Canada CA d1 NA NA 15 15 4 11 0 11 7 4 Illumina Quantile Martin GSE54514 Parnell Australia CA d1-d5 59. 8 41. 7 163 54 18 36 36 0 26 10 Illumina Quantile GSE20346 Parnell Australia CA d1-d7 NA NA 55 22 18 4 0 4 22 0 Illumina Cubic spline GSE40012 Parnell Australia CA d1-d5 NA 45.
5 129 42 31 11 3 11 42 0 Illumina Quantile GSE57065 Cazalis France CA d1 d2 62. 7 67. 9 107 53 25 28 28 0 22 6 Affymetrix RMA HCA d3 GSE60244 Suarez United CA d1 62. 1 41. 5 158 158 40 118 47 96 158 0 Illumina Quantile* States GSE65682 Scicluna Netherland CA d1 61 56. 8 521 521 42 479 NA NA 365 114 Affymetrix RMA + quantile HCA GSE95233 Tabone France CA d1 d2 62. 1 64. 7 124 71 20 51 NA NA 56 17 Illumina Quantile d3 E-MEXP- Almansa Spain CA d1 NA 50 16 16 4 12 5 3 16 0 Agilent Normexp 3589 E-MTAB- Almansa Spain HCA d1 69. 2 67. 1 155 155 73 82 NA NA 138 17 Agilent Normexp 1548 E-MTAB- Burnham United CA d1 d3 65. 4 53 337 253 10 243 NA NA 204 39 Illumina VSN 5273/5274 Kingdom d5 GSE13015 Planka Thailand CA d1 53. 7 54. 7 92 92 29 63 63 0 52 20 Illumina Quantile* HCA GSE25504 Smith United CA d1 0.
Model simulations Model simulations were performed using reconstructed climate, atmospheric carbon dioxide concentration and different scenarios of nitrogen and phosphorus inputs via atmospheric deposition (Fig. 1 ; Table  S1 ). The climatic forcing was extracted from 6 hourly global Climatic Research Unit -Japanese Reanalysis data (CRUJRA) (v. 2. 1) spanning the period 1901-2019 at the location of the Yoko site from which most evaluation data originate from. For the period 1750-1900, we recycled the climatic forcing of the years 1901-1920. The nutrient deposition scenarios are based on site observation and model-based reconstructions of past changes (as described later). The biogeochemical cycles in ORCHIDEE-CNP were equilibrated to boundary conditions of the year 1750 by recycling the conditions for the period 1750-1769 for 10 k yr.
Data needs The lack of ground-based estimates of ecosystem greenhouse gas balance and plant productivity, as well as the limited data on the amount and composition of atmospheric deposition, hampers the validation of our finding. Besides, there is not a single longterm nutrient addition experiment in tropical forests in Africa  (Wright, 2019; Hou et al. , 2020) , which severely hampers our understanding of nutrient constraints on these ecosystems. The first dedicated databases, like for example the TROPSOC soil property database  (Doetterl et al. , 2021) , and the installation of an eddy-covariance tower for monitoring of greenhouse gas fluxes at a site  (White et al. , 2021)  are promising developments. Repeated measurements of quality and quantity of nutrient deposition for a network of sites are needed to quantify spatial and temporal variation in deposition and to indicate the representativeness of the study site.
Acknowledgements DSG and PC acknowledge support from the  ANR   CLAND Convergence Institute (ANR :  ANR-16-CONV-0003 ), HV from the  Fonds Wetenschappelijk Onderzoek (FWO ; grant no. G018319N ) and the  UGent special research fund  ( BOF16/STA/ 008 ), HZ from the ' Lateral-CNP ' project (no. 34823748 ) supported by the  Fonds de la Recherche Scientifique-FNRS. We acknowledge the use of data provided by the  ESA CCI Land Cover  project. We thank  Marie Combe  for handing over her discontinued initial modelling analysis to us.
The first set of theoretical cross sections for propylene oxide (CH 3 CHCH 2 O) colliding with cold He atoms has been obtained at the full quantum level using a high-accuracy potential energy surface. By scaling the collision reduced mass, rotational rate coefficients for collisions with para-H 2 are deduced in the temperature range 5-30 K. These collisional coefficients are combined with radiative data in a non-LTE radiative transfer model in order to reproduce observations of propylene oxide made towards the Sagittarius B2(N) molecular cloud with the Green Bank and Parkes radio telescopes. The three detected absorption lines are found to probe the cold (∼ 10 K) and translucent (n H ∼ 2000 cm -3 ) gas in the outer edges of the extended Sgr B2(N) envelope. The derived column density for propylene oxide is N tot ∼ 3 × 10 12 cm -2 , corresponding to a fractional abundance relative to total hydrogen of ∼ 2.
(2016)  for the CO-H 2 system. Third, due to internal rotation (methyl torsion), rotational levels of propylene oxide are split by tunneling into doublets, the nondegenerate A levels and the doubly degenerate E levels  (Herschbach & Swalen 1958). In the ground torsional state, however, the splitting could not be resolved in the astronomical observations because the barrier to internal rotation is very high  (Stahl et al. 2021). As a result, we used a single rigid-rotor asymmetric top hamiltonian with experimental ground-state rotational constants A = 0. 6012 cm -1 , B=0. 2229 cm -1 and C=0. 1985 cm -1 taken from the Cologne Database for Molecular Spectroscopy (CDMS)  (Müller et al. 2005). Cross section calculations were performed using the Hibridon code  (Alexander et al. 2021) , which is based on solving the time-independent close-coupling (CC) equations.
NON-LTE MODELLING The non-LTE radiative transfer calculations were performed with Radex (van der Tak et al. 2007) using the escape probability approximation for a uniform sphere. The model incorporated both the calculated collisional rate coefficients among 100 lowest rotational levels (a total of 4950 de-excitation transitions) and Einstein coefficients for radiative transitions obtained from the CDMS database  (Müller et al. 2005). The only ambient radiation field assumed is the cosmic microwave background (CMB) and we omitted, in particular, the diluted infrared emission from the hot cores Sgr B2(M) and Sgr B2(N), as well as the extended microwave continuum from the Galactic Center region. These neglected sources of radiative pumping (absorption and stimulated emission) can possibly compete with collisional processes and they will be investigated in future works.
Based on mapping information for the reads (BAM files processed with NuDup to remove PCR duplicates), a read count table for all annotated porcine genes was generated using QuasR Qcount. This count table was filtered to remove sequences with negligible read counts by using counts per million (CPM) per sample filtering tool  [47]. The mean library size and potential CPM cutoff (Counttable statistics, custom Galaxy tool) was calculated and the cutoff set to 10 CPM (corresponding to an average of 15 reads per library) for at least 3 out of 15 libraries. This count table was the basis for the subsequent statistical analysis. The analysis of differential gene expression was performed using BioConductor package EdgeR  [48]. Data normalization was performed on library size (TMM normalization)  [49]  and with the GLM robust (estima-teGLMRobustDisp)  [50]  function.
For comparison of the experimental groups, the following contrasts were set: VIT vs. CO; IVC vs. CO and VIT vs. IVC. An adjusted P-value (false discovery rate, FDR) of 0. 001% was used as threshold for significance of differential gene expression for VIT vs. CO. Because of the lower number of DEGs obtained for the other two comparisons, the likelihood ratio (LR) of 13. 80 corresponding to FDR 0. 0012% in the IVC vs. CO comparison and to FDR 0. 0058% in the VIT vs. IVC was used as a threshold for the other two comparisons to achieve a comparable stringency and sensitivity of the significance analysis  [45, 51]. RNA-Seq data have been deposited at NCBI's Sequence Read Archive (SRA) under the BioProject accession PRJNA697877 and is available at https://www. ncbi. nlm. nih. gov/sra/PRJNA697877.
Data mining and bioinformatics analysis of RNA-seq data Gene symbols and Entrez Gene IDs (porcine and putative human orthologs) were mapped for all transcripts, using bioinformatics custom tools integrated in a local Galaxy installation. Custom database tools (NCBI annotation mapper, Mammalian Annotation database, MADb, https://madb. ethz. ch [52] ) were used to assign known or putative human orthologous genes. Human gene identifiers or symbols were used for subsequent functional annotation. To compare mRNAs altered due to VIT, IVC technologies compared to CO, or VIT vs. IVC Jvenn an integrative tool for comparing lists of genes with Venn Diagrams was used  [53]. To obtain information about overrepresented biological functions Metascape tool was used [http://metascape. org] [54].
Potential canonical pathways, biofunctions, and upstream regulators affecting embryo development in embryos subjected to VIT and IVC Ingenuity Pathway Analysis software core analysis was performed for the VIT vs. IVC as well as for VIT vs. CO and IVC vs. CO datasets and results were compared using Comparison Analysis. The IPA regulation Z-score was used to identify activated and inhibited significantly enriched (P-value < 0. 01) canonical pathways, diseases and biofunctions, and upstream regulators. Selected canonical pathways, biofunctions, and upstream regulators are illustrated in Fig. 4A, B  and C , respectively. Based on these results, for the VIT vs. IVC comparison, significant inhibited enriched canonical pathways were identified: oxydative phosphorylation, cyclins and cell cycle regulations and similar to VIT vs.
For the IVC_CO comparison, enriched canonical pathways significantly activated (Z-score > 2) were also involved in Integrin signalling. While pathways significantly inhibited (negative Z-score > 2) were BAG2 signalling pathway and Sirtuin Signalling Pathways. Additionally, diseases and biofunctions analysis (Fig. 4B ) based on the same Z-score was performed for the Fig. 4  Heatmaps illustrating selected canonical pathways A, diseases and biofunctions B, and upstream regulators C identified across the three DEG lists: vitrification versus control (VIT-CO), in vitro culture versus control (IVC-CO) and vitrification versus in vitro culture (VIT-IVC), using Ingenuity Pathway Analysis software core analysis and Comparison Analysis tool three DEG lists (VIT vs. CO, IVC vs. CO and VIT vs. IVC) (filtering all cancer terms).
Particularly, the VIT vs. CO comparison allowed to evaluate the global gene expression changes induced by vitrification and in vitro embryo recovery and embryo development. The IVC vs. CO comparison allowed to have an overview of gene expression changes induced by in vitro culture itself and also by the embryo development progression. The PCA and differential gene expression results clearly showed that there is a strong effect of both IVC and VIT on the embryonic gene expression when compared to CO. The comparison of datasets revealed the IVC as the main factor affecting embryonic gene expression and a much lower number of DEG but with high expression differences for many of the DEG for VIT vs. IVC comparison, which is representing the specific VIT alterations. Moreover, the large overlap in gene alterations between VIT vs.
Towards improving VIT-related embryonic alterations: biomarkers of VIT-damage and VIT-healing processes and clues for intervention The embryonic gene expression alterations due to VIT procedure reported in our study might reflect the activation of genes as a part of the repair and protection processes and stress adaptation response of the embryo to successfully overcome the VIT process impact. Thus, the identification of specific genes among these datasets that could be used as potential biomarkers for embryo quality or targets for intervention represents a valuable tool to evaluate the efficiency of the VIT procedures. With this aim, we focused on 8 genes showing the greatest changes (based on log2FC) among the 321 DEGs in VIT vs. IVC and with GO terms associated to wound healing among others.
Furthermore, these genes were further validated by qPCR to confirm the RNA-seq data and further support the potential use of these as biomarkers. The gene EMP1 (epithelial membrane protein 1), was found as upregulated in VIT vs. IVC and also in VIT vs. CO (log2 FC = 2. 8 and 4. 1 respectively; cluster 3). Similar results were reported for porcine SCNT vitrified embryos  [40] , rabbit vitrified embryos  [67] , and also slow frozen rabbit embryos  [68]  compared to control embryos. These alterations have been linked to fetus development failure  [67]. EMP1 has key roles in cell proliferation, differentiation, cell adhesion and epithelial junction formation  [69]. Moreover, it has been attributed a role in tissue repair and wound healing  [70] , which could be linked to the healing ability of the embryo to overcome the VIT cryo-damage.
Acknowledgments We would like to thank  Susanne E. Ulbrich  ( ETH Zurich, Animal Physiology ) for kindly allowing us to use her labs to prepare the RNA-seq libraries. We thank  Shuqin Zeng  for helping with RNA quality assessment,  Siyka Bozukova  for helping with qPCR validation experiments and the  Functional Genomics Center Zurich (FGCZ)  for performing Illumina sequencing. We would like also to thank  Cristina Cuello  for her valuable help for setting up the vitrification protocol and pig embryo collection. Last, we would like to thank  Juliette Cognie  for her contribution to the animal surgeries for the embryo collection and the embryo transfer.
Although the 571 DEGs were functionally diverse, a number of similarities to transcriptomic datasets from other species were identified (Table  S5 ). For example, homologues encoding the ethylene biosynthetic enzyme ACC SYNTHASE (Bradi5g19100; cluster 5) were also reported to be KAR 1 -responsive in lettuce  (So os et al. , 2012)  and Lotus  (Carbonnel et al. , 2020a)  and kai2 mutant of Arabidopsis  (Li et al. , 2017). Likewise, FLAVA-NONE 3-DIOXYGENASE 2 (Bradi1g77040) was previously reported as a KAI2-related or KAR 1 -responsive transcript in Arabidopsis  (Nelson et al. , 2010; Li et al. , 2017) , lettuce  (So os et al. , 2012)  and Lotus  (Carbonnel et al. , 2020a). Furthermore, six members of the WRKY transcription factor superfamily were induced by KAR 1 or KAR 2. Homologues of WRKY28 (Bradi1g30870), WRKY31 (Bradi2g08620) and WRKY71 (Bradi3g06070; all cluster 3) were also upregulated by KAR 1 in lettuce  (So os et al.