dataesr/scientific-paragraphs-categorization huggingface dataset:
Secondly, other explanatory variables can better predict overall survival for some cancers. For example,  [30]  recently showed that tumor microbiome diversity influences the tumor outcome for patients with pancreatic cancer. Predicting patient outcome with transcriptomic data if they are poorly correlated to these hidden variables here means a low probability of success. Finally, the existence of sub-types of cancers with different molecular characteristics  [7, 19]  would lead to poor regression performance and prediction, as all of the patient data are treated similarly with the same model. The main goal of penalty methods in Cox models is both to provide significant weight for prognostic genes, and to predict overall survival. Several comparisons of selection methods that include multivariate Cox models have been carried out, but these were applied to smaller datasets and with different metrics to assess the quality of the models.
Conclusions This article investigates Cox model penalty methods (i. e. , lasso, elastic net, adaptive elastic net, ridge), univariate Cox procedure, and pre-filtering of the genes based on their variability, for prediction of overall survival and the selection of a set of prognostic biomarkers. We compare four cancers (i. e. , KIRC, BRCA, LGG, LUSC) using data from TCGA. We first demonstrate that the prediction accuracy is highly cancer dependent, with concordance indices as high as 0. 81 for LGG, to 0. 55 for LUSC, and down to 0. 50 for PRAD. Secondly, we propose a methodology to pre-filter the genes according to their variability, and show that the prediction accuracy varies very slightly while keeping the genes with IQR ≥ 4. This methodology allows for an important dimension reduction. Thirdly, we show that even in the ideal situation with the data simulated using the Cox model, the pool of selected genes is not stable, with many false positive and false negative genes.
17, n = 6) decrease from pH cyt = 7. 17 ± 0. 20 to pH cyt = 7. 05 ± 0. 40 was observed, with a rate of pH decrease lower than with 30 mM KNO 3 (Fig. 2B ). Similar results were obtained when stomata were exposed to KCl only (SI Appendix, Fig. S7 ). As a whole, these data demonstrate that ClopHensor enables us to simultaneously monitor in vivo the variations in [Cl -] cyt or [NO  3  -] cyt and pH cyt at a cellular resolution. In the conditions tested, [Cl -] cyt was below the limit of detection of ClopHensor for Cl -(i. e. , 2 mM) (Fig. 2D ). This suggests that in our experimental setting, ClopHensor was measuring essentially cytosolic NO 3 -variations. Notably, cytosolic NO 3 -and pH changes appear to be concerted, suggesting that they are governed by a common mechanism. AtCLCa Accounts for Cytosolic Acidification in Response to NO 3 -.
Methods Wild-type Arabidopsis plants were Col-0 ecotype. The clca-3 knockout line corresponds to Gabi Kat GK-624E03-022319. Images were acquired with a Leica SP8 upright CLSM. Image analysis was performed with ImageJ. Detailed description of the methods is available in SI Appendix. Data Availability. All data presented in the paper are described in the text and SI Appendix. Biological materials are available from the corresponding author on request.
ACKNOWLEDGMENTS. This work was supported by  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ) and by the  ATIP-AVENIR-2018 program. P. C. -F. was supported by a postdoctoral grant from  Fundacion Alfonso Martin Escudero. This work has benefited from the facilities and the expertise of  Imagerie-Gif  microscopy platform, which is supported by  France-BioImaging  ( ANR-INBS-04  " Investments for the future ") and by  Saclay Plant Science  ( ANR-11 IDEX-0003-02 ). We thank  M. Dauzat  ( Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux ) and  N. Sidibé  ( Institute for Intergrative Biology of the Cell ) for the help with experiments,  Joni Frederick  for reading the manuscript,  D. Arosio  ( Consiglio Nazionale delle Ricerche ) for providing plasmid with ClopHensor and advice,  R.
Understanding how microorganisms adjust their metabolism to maintain their ability to cope with short-term environmental variations constitutes one of the major current challenges in microbial ecology. Here, the best physiologically characterized marine Synechococcus strain, WH7803, was exposed to modulated light/dark cycles or acclimated to continuous high-light (HL) or low-light (LL), then shifted to various stress conditions, including low (LT) or high temperature (HT), HL and ultraviolet (UV) radiations. Physiological responses were analyzed by measuring time courses of photosystem (PS) II quantum yield, PSII repair rate, pigment ratios and global changes in gene expression. Previously published membrane lipid composition were also used for correlation analyses. These data revealed that cells previously acclimated to HL are better prepared than LL-acclimated cells to sustain an additional light or UV stress, but not a LT stress.
UVA and UVB radiations, generated from UVA-351 and UVB-313 fluorescent bulbs (Q-Panel Lab products, Cleveland, OH, United States), were measured between 280-320 and 320-400 nm, respectively using a USB2000 spectroradiometer  (Ocean Optics, EW Duiven, Netherlands). In order to estimate the cell recovery capacities, HL and UV stressed cultures were then shifted back to their initial growth condition and sampled after 1h (R1) and 24h (R24) of recovery. Low (13 • C, hereafter LT) and high temperature (30 • C, hereafter HT) stresses were performed in temperature-controlled chambers (Liebherr-Hausgeräte, Lienz, Austria). All of these experiments were conducted in triplicate. For diel cycle experiments, run at two temperatures (21 and 27 • C), a bell-shaped 12/12 h L/D cycle, triggering a proper synchronization of cell division, was generated using the multicolor LED systems and the Ether software (Alpheus, France).
RNA-Seq Analysis Reads quality check was performed with the FastQC tool  (Andrews, 2015). Reads smaller than 50 nt and with a mean quality score lower than 25 were excluded using Prinseq v0. 20. 4  (Schmieder and Edwards, 2011). Illumina adapters, constituting 23. 5 ± 4. 5% of reads, and rRNAs, constituting 5. 0 ± 5. 9% of all reads, were removed with Cutadapt v1. 8. 3  (Martin, 2011)  and SortMeRNA v2. 1  (Kopylova et al. , 2012) , respectively. The remaining reads were then mapped to the reference genome of Synechococcus sp. WH7803 using Bowtie2 v2. 2. 9 software  (Langmead and Salzberg, 2012)  with '-non-deterministic -endto-end -sensitive' parameters and by selecting only the properly paired reads (i. e. , SAM flagged as  99, 147, 163, or 83). Read count tables were obtained using HTSeq Count v0.
6. 0  (Anders et al. , 2015)  with following parameters: '-stranded = reverse -a 10 -m intersection-nonempty'. Gene expression levels between stress and control conditions as well as adjusted p-values based on the Benjamini-Hochberg procedure  (Benjamini and Hochberg, 1995)  were determined using SARTools v1. 4. 0  (Varet et al. , 2016)  with embedded DESeq2 v1. 14. 1  (Love et al. , 2014). Genes were considered as differentially expressed (DE) if the adjusted p-value was ≤0. 05 and the absolute value of log 2 fold change (log 2 FC) was ≥1. The RNA-seq dataset is available as raw and processed data in the SRA and GEO databases, respectively (see section Data Availability Statement) and can be visualized using JBrowse  (Buels et al. , 2016)  in the Cyanorak v2. 1 database 2.
Co-expression Network Model Analysis Weighted gene correlation network analysis (WGCNA;  Langfelder and Horvath, 2008)  was performed on shift experiments to define gene subnetworks, called modules, based on gene expression patterns. A signed adjacency matrix between genes was calculated and Pearson correlations were weighted by taking their absolute value and raising them to the power β = 12 to optimize the scale-free topology network fit. In order to identify groups of genes, whose expression was correlated to the biological, physical or chemical traits available for this experiment (including previously published lipid data;  Pittera et al. , 2018) , the pairwise Pearson correlation coefficients between the principal component of each module, referred to as the module eigenvalue (ME), and these traits was then calculated using the R package WGCNA  (Langfelder and Horvath, 2008).
Modules were then filtered using a partial least square (PLS) regression, a dimensionality-reduction method that aims at determining predictor combinations with maximum covariance with the response variable. The predictors were ranked according to their value importance in projection (VIP), as previously described  (Guidi et al. , 2016). Furthermore, the UpSet R package was used to count and plot the number of genes per module that were DE in a given set of conditions (e. g. , LLHT and HLLT) by considering only genes significantly DE in at least one time point of each condition for this set of conditions.
Relationship Between Genomic Features and Transcriptomic Responses to Different Stresses Transcriptomic data were synthesized as a circos plot integrating: (i) core genes, as determined based on the comparison of the 81 non-redundant genomes of the Cyanorak v2. 1 database 2 (Doré et al. , under revision), (ii) WGCNA module and submodule membership, (iii) predicted operons as extracted from ProOpDB  (Taboada et al. , 2012) , (iv) 'cyanorons' that we defined as suites of ≥4 adjacent genes on the genome that belong to the same WGCNA module and displayed at least half of all time points for a given stress with a similar expression pattern (e. g. , all 4 adjacent genes belonging to module Blue and all displaying either a Log 2 FC > 1 or < 1).
Correlation Between Gene Expression and Biophysical Parameters In order to explore transcriptome-wide responses of WH7803 in all shift conditions, we used a WGCNA approach  (Langfelder and Horvath, 2008; Guidi et al. , 2016). This analysis delimited three modules and 13 submodules of coexpressed genes within the global co-expression network. Each (sub)module thus contains a subset of DE genes, whose pairwise expression was highly correlated, i. e. , expression of genes gathered within a given (sub)module had a high probability of varying similarly over all stress experiments (Figure  4 ). To further reduce the complexity of these transcriptomic data, we first focused on (i) genes with VIP scores values higher than 1 (hereafter VIP genes), i. e. , the minimum set of genes of a module to obtain an association (correlation or anti-correlation) with a given trait similar to that of all genes of the module (Supplementary Table  S2 ), on (ii) genes in each module that were significantly up or down-regulated in specific sets of conditions (hereafter called UpSet genes; Supplementary Figure  S5 ).
VIP genes of this submodule also included several chaperones and proteases (GroL1, GroL2, ClpB1, ClpC, DnaK3 and a small heat shock protein, HSP20) as well as three genes coding for sigma factors (rpoD4, 6 and 7), all three latter genes also showing a notable diel expression (Supplementary Table  S2 ). When looking at the 1,039 UpSet genes of this module, one can see that 149 were induced (brown submodule, 110 genes) or repressed (turquoise submodule, 39 genes) specifically in LLUV and 60 more were mostly induced in LLUV and LLHT, the latter gene set being upregulated mainly at the last data point of the high temperature stress, corresponding to cells in stationary phase (Supplementary Figure  S5 ). These categories notably included genes coding for five ATP-dependent Clp proteases (ClpP1-4, ClpS), the stress-inducible DNA-binding protein DpsA, as well as seven out the 20 subunits of the NADH dehydrogenase I (Supplementary Table  S2 ).
Furthermore 42 UpSet genes were DE in all stress conditions except HT treatments, while 84 others were DE in all but HT and HLUV, the latter conditions previously shown to be the least stressful conditions in this experimental setup (Figure  3A ). Both sets of genes were mainly downregulated and essentially encompassed genes involved in the abovementioned photosynthetic light reactions, but also a few genes involved in heme/vitamin biosynthesis (five genes), cell division (four genes) as well as the universal stress family protein UspG. The blue module, gathering 827 genes, exhibited a mirrored correlation pattern compared to the yellow one, except that a significant correlation was found for several parameters, which were anticorrelated with the yellow module but not significantly (Figure  4 ).
Although the WGCNA analysis resulted in four submodules within the blue module, their analysis did not reveal any clear trend with regard to the different treatments. Among the 135 VIP genes of this module, most of them were upregulated and numerous genes were involved in central metabolism, including DNA replication and repair, protein fate, oxidative stress response, fatty acid biosynthesis as well as sugar catabolism (Figure  6 ). Also notable in this module were several twocomponent systems, three genes of the SUF (sulfur utilization factor) system  (Banerjee et al. , 2017) , as well as several members of the DNA/RNA helicase family, which by rearranging the secondary structure of nucleic acids, could play a role in the response to variations of environmental conditions (see e. g. ,  Chamot et al.
, 1999; Chamot and Owttrim, 2000; Owttrim, 2006). This includes CrhR, an ortholog of Slr0083, known to be upregulated by cold stress in Synechocystis sp. PCC 6803  (Georg et al. , 2019) , which was indeed also the case for HLacclimated WH7803 cells. Consistently with the fact that the blue module was strongly anticorrelated to temperature, UpSet genes of this module gathered 55 genes seemingly specific to LT stresses (LLLT and HLLT), while 41 more genes were found only in LLLT and 49 only in HLLT conditions (Supplementary Figure  S5  and Supplementary Table  S2 ). Genes among these categories were mostly upregulated and notably included ctpA, encoding the carboxyl-terminal processing protease involved in the maturation of the PSII core subunit D1, the lycopene β-cyclase encoding gene crtL-b, several genes involved in DNA replication recombination and repair, numerous glycosyltransferases, potentially involved in cell wall biosynthesis as well as genes involved in biosynthesis and regulation of vitamins, including biotin (B7;  Rodionov et al.
VIP genes notably include two genes involved in the response to oxidative stress, two coding for subunits of the DNA-directed RNA polymerase (the other two subunits being also clustered in the magenta module) as well as rbcL, encoding the large subunit of the RuBisCO, the latter being upregulated in response to the LLHL treatment but downregulated in LLUV, LLLT, and HLLT. Additionally, a large number of UpSet genes (24), mainly found in the pink submodule, seemed to be upregulated significantly in LLHL but non-significantly in other conditions (Supplementary Table  S2 ). This included genes coding for 19 out of the 53 ribosomal proteins, while 19 others were also in the pink submodule, all these genes constituting the second largest 'cyanoron' of the WH7803 genome (Figure  5 , section A). In contrast, the magenta submodule essentially gathered genes upregulated in LLHL but downregulated or not DE in most other shift conditions.
6-phosphogluconolactone; 6PG, 6-phosphogluconate; KDPG, 2-keto-3-deoxygluconate-6-phosphate aldolase; X5P, xylulose-5-phosphate; R5P, ribose-5P; S7P, sedoheptulose-7-phosphate; E4P, erythrose-4-phosphate; 1,3 BPG, 1,3, bisphosphoglycerate; 3PG, 3-phosphoglycerate; 2PG, 2-phosphoglycerate; PEP, phosphoenolpyruvate. Adapted from  Osanai et al. (2007) ,  Chen et al. (2016) , and the KEGG database (www. genome. jp/kegg/pathway. html). previous study of the oxidative stress response of WH7803 cells after a pre-acclimation at LL or HL  (Blot et al. , 2011) , a synergic effect seems to occur between light history and low temperature stress. While this behavior was attributed to the generation of reactive oxygen species (ROS), inducing both direct damages to the reaction centers and inhibition of their repair cycles, the deleterious effect of ROS could be even amplified by the slowing down of the metabolism, and thus of the D1 turnover, at LT.
DATA AVAILABILITY STATEMENT Transcriptomic data supporting the conclusions of this article are available as raw and processed data in the SRA (SRP251300) and GEO (GSE146246) databases, respectively, and sample descriptions and accession numbers per biological replicate are available in Supplementary Table  S3. Differential expression levels and associated statistics as well as genomic features of the Synechococcus sp. WH7803 strain, such as core genes and predicted cyanorons, are available in Supplementary Table  S2. LG interpreted the results. All the authors contributed to the preparation of the manuscript, read and approved the final manuscript.
ACKNOWLEDGMENTS We would like to thank  Nathalie Marsaud  and  Marie Gislard  from the  GeT-PlaGe  as well as the ABiMS platforms for building and sequencing the RNAseq libraries and providing computational support for this work, respectively. We warmly thank  Adriana Alberti  for useful discussions about ribosomal depletion and RNAseq library preparation,  Léna Gouhier ,  Martin Gachenot , and  Priscillia Gourvil  from the  Roscoff Culture Collection  for providing the WH7803 strain,  Garance Monier  and  Théo Sciandra  for participating in the curation of the Cyanorak v2. 1 database, as well as  Théophile Grébert  for his help with sampling.
m, L. s) ← SampleFull(G, A l , max(c -1, 0)) (R. m, R. s) ← SampleFull(G, A r , max(c -1, 0)) if c = 0 then return (multiset: randomly select L. m or R. m with equal probabilities, set: L. s ∪ R. s) else return (multiset: L. m R. m, set: L. s ∪ R. s) procedure SampleExtra(G, A) if |α G (A)| = |A| then return multiset [Ω(G) repeated once] else A l ← ∅ and A r ← ∅ G A ← G A (degree-2 nodes removed) for a ∈ A do p ← the highest ancestor of a in G A tagged as a duplication node (or ∅ if it doesn't exist) if (p = ∅) ∧ (a is to the left of p) then A l ← A l ∪ {a} if (p = ∅) ∧ (a is to the right of p) then A r ← A r ∪ {a} B l ← {x : x ∈ A l , α G (x) ∈ α G (A l ) \ α G (A r )} B r ← {x : x ∈ A r , α G (x) ∈ α G (A r ) \ α G (A l )} G L ← G ((L G \ A r ) ∪ B r ) (degree-2 nodes removed) G R ← G ((L G \ A l ) ∪ B l ) (degree-2 nodes removed) for internal node u of G L where L G (u) ⊂ B r do B u ← {one leaf node arbitrarily chosen from {x : α G (x) = s, x ∈ L G (u)} : s ∈ α G (u)} replace u with a star tree consisting of leaves from the set B u for internal node u of G R where L G (u) ⊂ B l do B u ← {one leaf node arbitrarily chosen from {x : α G (x) = s, x ∈ L G (u)} : s ∈ α G (u)} replace u with a star tree consisting of leaves from the set B u R = SampleExtra(G L , A l ) SampleExtra(G R , A r ) return R Table  1 : Simulation settings for S25 dataset.
New simulated dataset (S25) We perform a set of simulations using SimPhy  [50]  starting from a default model condition and adjusting five parameters (Table  1 ). We simulate 50 replicates per condition, and each replicate draws its parameters from prior distributions. Exact commands are given in Appendix B. Default model: The species tree, simulated under the Yule process with birth rate 5 × 10 -9 and the number of generations sampled from a log-normal distribution (mean 2. 9 × 10 9 ), has 25 ingroup and an outgroup species. Each replicate has 1000 true gene trees simulated under DLCoal with fixed haploid population size N e = 4. 7 × 10 8. Gene trees have mean ILS level in [60%, 80%] range (mean 70%) across replicates (Fig. S2 ). The duplication rate λ + = 4. 9×10 -10 ; when there is no loss, gene trees on average include 145 leaves (≈ 5 extra copies per species).
Existing simulations (S100) We also used an existing dataset that Molloy and Warnow simulated  [54]  based on a real fungal dataset  [55]. The simulation protocol of this dataset is similar to that of S25 dataset, with some notable differences. (i) The dataset included 100 species (no outgroup); species tree height, speciation rate, and mutation rates all differed from S25. (ii) Shorter gene alignments were also used, resulting in higher MGTE (25bp: 67%, 50bp: 52%, 100bp: 35%, 500bp: 19%). (iii) The duplication rate λ + was set to 1×10 -10 , 2×10 -10 , or 5×10 -10 (named 1, 2, and 5, respectively), and the duplication rate equaled the loss rate for all model conditions. (iv) ILS was much lower than S25; two conditions were simulated with N e set to 1 × 10 7 and 5 × 10 7 (named 1 and 5, respectively), which result in 2% and 12% RF between true gene trees and the species tree.
Methods compared We implemented A-Pro by extending ASTRAL-MP  [36]  and implementing Algorithms 1 and 2 as part of its native C++ library. We compare A-Pro to the following methods. Another method, STAG  [57] , is not included because of its poor performance on the S100 dataset  [54] , including that it fails to run on some model conditions (Fig. S8 ). DupTree  [10]  infers a species tree from rooted or unrooted gene trees minimizing the duplication reconciliation cost  [1]  under the duplication-only model, but it does not model ILS. We Loss/Dup rate Species tree error (NRF) Species tree error (NRF) Controlling the duplication rate (columns; labelled by C) and the ILS level (x-axis; NRF between true gene trees and the species tree for λ + = 0). A-Pro and ASTRAL-multi are identical with λ + = 0.
S25 dataset 5. 1. 1 Controlling duplication and loss rates and the level of ILS We start by experiments that change the duplication and loss rates (λ + , λ -) from the default condition (Fig. 2a ). On true gene trees, A-Pro and DupTree are essentially tied in terms of accuracy, except for the case with no duplication and loss where A-Pro is perhaps slightly more accurate. Overall, the accuracy of A-Pro and DupTree is statistically indistinguishable under these conditions (p-value = 0. 79 according to a multi-variate ANOVA test). Increasing λ + reduces error (p < 10 -5 ), perhaps because additional copies provide more information, akin to increasing the number of loci. Despite statistically significant increases (p = 0. 006) in error as λ -increases, both methods are quite robust to loss rates, losing at most 1.
When λ + = λ -= 0, DupTree has on average an 11. 5% error whereas A-Pro has only a 4. 5% error for 500bp. Adding duplications helps both methods but A-Pro remains more accurate. For example, with 100bp input gene trees, DupTree has an error between 50% to 260% higher than A-Pro. With low-error gene trees, differences are statistically significant (p < 10 -5 ) but are more modest in magnitude (the error increase from DupTree to A-Pro across conditions by a median of 28%). The relative accuracy of A-Pro and DupTree is not a function of λ -(p =0. 8) but may depend on λ + (p =0. 05). In terms of running time, on the default model condition, we observe that A-Pro is the fastest method, taking less than a minute on this dataset, followed closely by DupTree (Fig. S9 ). We will revisit running time of A-Pro on larger datasets below.
S100 dataset Patterns of performance on the S100 dataset are consistent with the S25 dataset (Fig. 4 ). DupTree is highly accurate with true gene trees and gene trees with low estimation error but quickly degrades in accuracy as gene tree error increases. MulRF is less sensitive to gene tree error but is more sensitive to the ILS level (which is always moderate or low on this dataset). As in S25, here, we see that using ASTRAL-multi to handle duplication and loss is not beneficial. A-Pro works the best overall, ranking first in terms of mean error (rounded to two significant digits) in 105 out of 120 test conditions (Table  S2 ). The second best method is MulRF on this dataset, which is not surprising given the low ILS levels in this dataset. Figure  5 : ASTRAL and ASTRAL-Pro on biological plant paper.