dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic classification of microbial rhodopsins. A maximum likelihood phylogenetic tree was built with all the type-I microbial rhodopsins recovered from the pool of marine SAGs, as well as from the metagenomic sequences retrieved from marine and freshwater datasets. As references, we included well-characterized rhodopsin proteins with several functions described so far (H + , Na + , Cl -and sensory rhodopsins). Sequences were first aligned with muscle  (65) , and the resulting alignment trimmed with trimal (66) to remove regions with gaps in more than 80% of the sequences. The phylogenetic tree was built with iqtree (67) with 5000 ultrafast bootstraps and the -m MFP option to find the best model that fitted to our data.
6. 3 (69) was used to predict genes from contigs retrieved from the individual SAGs, as well as from the metagenomic assemblies. Predicted protein-encoded genes were taxonomically and functionally annotated against the NCBI NR database using DIAMOND 0. 9. 15  (70)  and against COG  (71)  and TIGRFAM (72) using HMMscan v3. 3  (64). Domains within proteins were predicted using InterPro  (73). In order to determine the location of predicted proteins within the gene cluster, transmembrane domains and signal peptides were identified with DeepTMHMM (  74 ) and SignalP v6. 0 (75), respectively. In-silico determination of protein structures. We used AlphaFold (41), a protein structure prediction server hosted on Google Colab  (78) , to predict the structures of three FArhodopsins (AG-390-L03 of Pelagibacterales, AG-337-N20 of HIMB59, and AG-313-D08 of Pseudothioglobus) using standard parameters.
We then used PyMOL (https://pymol. org/2/) to align these structures to the structure of a blue-light absorbing proteorhodopsin from Med12 (PDB ID 4JQ6). Since the FArhodopsins and proteorhodopsin shared high overall similarity and the positions of the lysine residues, which are putative binding residues for retinal coincided, we transferred the all-trans retinal molecule from proteorhodopsin (PDB ID 4JQ6) to the FArhodopsins for further analysis of the retinal binding pocket. S4  and within Methods section.
ACKNOWLEDGEMENTS This work was supported by grant " FLEX3GEN "  PID2020-118052GB-I00  (cofounded with  FEDER  funds) from the  Spanish Ministerio de Economía, Industria y Competitividad  to ML-P and FR-V. JMH-M was supported with a PhD fellowship from  Margarita Salas program , cofounded by the  Spanish Ministerio de Universidades  and the  European Union --Next Generation EU  ( 2021/PER/00020 ). RS was supported by the  Simons Foundation  grants  510023  and  827839. The work of AA was supported by funding of the  German Research Foundation  to  Dr. Tobias Moser  via the  Multiscale Bioimaging -Cluster of Excellence  ( EXC 2067/1-390729940 ).
Phylogenetic tree of flotillin sequences. Domain sequences of the SPFH family of proteins were downloaded from the Pfam database (76)  and then used to search for candidates within SAGs with hmmscan. The resulting flotillin sequences were then phylogenetically classified. Reference sequences from eukaryotic flotillins Flot-1 and Flot-2 (Bos Taurus, Homo sapiens, and Rattus norvergicus) together with well-characterized sequences from Bacillus, Staphylococcus and E. coli were downloaded from the Uniprot database. We also included the closest hits determined by a blastp search against the NCBI protein database. Sequences were first aligned with muscle, and the resulting alignment trimmed with trimal to remove regions with gaps in more than 80% of the sequences. A maximum-likelihood phylogenetic tree was built with iqtree with 5000 ultrafast bootstraps and the -m MFP option to find the best model that fitted to our data.
Metagenomic and metatranscriptomic read recruitment. Illumina raw reads fromTara Oceans metagenomes and metatranscriptomes were downloaded from the European Nucleotide Archive (ENA) following the accession numbers PRJEB1787 and PRJEB6608, respectively. Eight additional samples covering a metagenomic depth profile from the Mediterranean Sea during thermal stratification (summer --15, 30, 45, 60, 75, 90, 1000 and 2000m) were downloaded from NCBI BioProjects PRJNA352798 and PRJNA257723. Raw reads were trimmed with Trimmomatic v0. 39 (77). Coverage values were calculated by read alignment (in subsets of 20 million reads) against the genomic islands containing the FArhodopsin using BLASTN v2. 9. 0 (90 % identity, > 50 bp alignment). At least 70% of the length was required to be covered by reads to be considered a recruit.
Sequences belonging to the new clade of rhodopsins (Flotillin-associated rhodopsin; FArhodopsin) are highlighted in red. Branches within color background contain type-I rhodopsins from the SAG dataset. The yellow circles correspond to the SAGs containing the two type-I rhodopsin paralogs. C) Protein sequence alignment of Helix C and G residues for the two type I rhodopsins. Two reference proteorhodopsin proton pumps at the top. Residues involved in rhodopsin.
These results will need to be further investigated in the future, however they are suggestive that a possible simultaneous inference of different cosmological parameters with LISA standard sirens will indeed be possible. In particular the full LISA cosmological analysis with results obtained from the combination of all classes of LISA standard sirens, namely SOBBHs, EMRIs and MBBHs, should substantially increase the accuracy of the estimates on X m and X k above, thanks especially to the combination of cosmological datasets from different redshift ranges which might break some of the degeneracy between the cosmological parameters. A future combined investigation will thus be needed to thoroughly assess the ability of LISA to constrain parameters beyond H 0.
In all the scenarios studied by  Belgacem et al. (2019c)  (which depend on the BH seeds and on the assumptions about the error on redshift), the GW sources considered are expected not to contribute relevantly to improve the knowledge on w 0 , with respect to what is already known from current cosmological observations. More precisely, the 1r error Dw 0 only goes from Dw 0 ¼ 0:045 (using CMB, type Ia supernovae and BAO data) to Dw 0 ¼ 0:044 adding MBBH standard sirens to the datasets, even in the best scenario where LISA alone can only reach a 20% relative 1r uncertainty on w 0  (Tamanini 2017). It is important to remark, however, that these outcomes are based on a mission duration of 4 years and sources are limited to MBBHs with EM counterparts. Significant improvements are expected by extending the data taking time or by combining with information from other sources, notably EMRIs.
This approach was then applied to third-generation ground based detectors and deci-Hz space detectors to prove that the method would in principle be applicable to DE and modified gravity in a tomographic analysis  (Camera and Nishizawa 2013). More recently, it was shown that a joint analysis of lensing and distance-redshift on a combined dataset of LISA and third generation ground detectors would allow us to jointly constrain geometry parameters (such as H 0 and density) and clustering parameters (r 8 and n s ) at the same time with percent precision (KCDM with curvature, nominal distance errors, and nominal number density)  (Congedo and Taylor 2019; Mpetha et al. 2023). This is because a geometry probe such as distance-redshift can act as a strong prior to break degeneracies that are usually present in lensing.
At the moment, very mild constraints exist on both in the case of SOBBH  (Fishbach et al. 2018; Abbott et al. 2021e ), but their impact-especially of the evolution of the BBH merger rate-can be relevant  (Finke et al. 2021). A public Python code implementing the completion procedures described above, as well as calculation of selection effects, is available at https://github. com/ CosmoStatGW/DarkSirensStat. The code has been applied to the latest data release from the LIGO-Virgo collaboration for constraining H 0 and modified GW propagation and supports the combination with the GLADE, DES and GWENS galaxy catalogues, but it could easily be be adapted for LISA and other galaxy catalogues.
In order to reduce the degeneracies among those parameters and assess the potential of LISA for cosmological parameter inference, in addition to LISA mock catalogues, the analysis in  Belgacem et al. (2019c)  also included further currently available datasets such as CMB measurements from Planck 2015, Type Ia Supernovae (JLA catalog), and a collection of BAO measurements. The likelihood of the LISA mock data given the parameters {H 0 ; X m ; w 0 ; N 0 } was assumed to follow lnðLðH 0 ; X m ; w 0 ; N 0 ÞÞ ¼ À 1 2 X N s i¼1 d GW L ðz i ; H 0 ; X m ; w 0 ; N 0 Þ À d i Â Ã 2 r 2 i ; ð162Þ where an additive constant in the logarithm of the likelihood from normalization is omitted. Here N s is the number of mock sources in the catalogue, d GW L ðz i ; H 0 ; X m ; w 0 ; N 0 Þ is the theoretical value of the GW luminosity distance for the i-th source (at redshift z i ), r i is the error on luminosity distance taking also into account the error on redshift determination, and d i is the "measured" value of the luminosity distance of each event contained in the catalogue (obtained by scattering the fiducial KCDM prediction with a Gaussian distribution with variance r 2 i ).
The MCMC code of  Belgacem et al. (2019c)  explores the cosmological parameter space, accepting or rejecting sampled points following a Metropolis-Hastings algorithm. The total likelihood used in the algorithm is obtained by multiplying the independent contributions from each of the four datasets employed, namely from CMB, Type Ia supernovae, BAO, and LISA. The cosmological evolution, both at the level of the background and perturbations, is computed using the CLASS Boltzmann code. The modified version of CLASS, implementing the GW luminosity distance parametrisation in Eq. (  21 ), is available at https://github. com/enisbelgacem/class_public. The MCMC code is built on MontePython with the inclusion of the LISA mock source catalogues and the likelihood from Eq. (  162 ), which uses the modified CLASS version.
The code is available at https://github. com/enisbelgacem/montepython_ public. In addition to the test of the model-independent parameters fN 0 ; ng, similar analyses have been carried out using the modified luminosity distances of specific gravity models, e. g. Horndeski gravity  (Lombriser and Taylor 2016; Baker and Harrison 2021)  or f ðQÞ gravity  (Frusciante 2021). These make use of the modified Boltzmann codes hi CLASS  (Zumalacárregui et al. 2017; Bellini et al. 2020)  and EFTCAMB  (Hu et al. 2014; Raveri et al. 2014) , respectively. A wrapper to interface hi CLASS with the MCMC code Cosmosis  (Zuntz et al. 2015 ) is available at https://github. com/itrharrison/hi_class. Current tools are predominantly specialised to probing the effects of frequencyindependent modifications of the GW propagation, although frequency-dependent effects have been studied in the context of GW oscillations in bigravity  (Belgacem et al.
31  and 33. In a first step, the PrimBholes code will focus on the computation of GW observables without including a likelihood module for LISA. However, it will be made modular so that it can be used in combination with other codes such as the LISA analysis code for the SGWBs or the Botzmann code CLASS  (Blas et al. 2011). Later, likelihood modules for the MontePython code (Brinckmann and Lesgourgues 2019) could be included for the computation of the various astrophysical and cosmological limits on the PBH abundance. 12 Tools/pipelines for the analysis of stochastic gravitational-wave background data The SGWB to be measured by LISA consists mainly of the confusion noise created by the overlapping signals from unresolved astrophysical events, and possibly of a cosmological component due to one or more physical mechanisms taking place at different stages of the late or early universe (e.
Noise modelling The instrumental noise in LISA is expected to be non-stationary, and at the same time we expect noise transients that cause the statistical properties of the noise to depart from Gaussianity. At a first approximation we can assume that the short noise transients (i. e. glitches) will be modelled, identified, and removed from the data streams  (Robson and Cornish 2019). However, slow variations of the noise, like for example the slow decrease of the acceleration noise of the test-masses due to outgassing  (Armano et al. 2018) , are due to known effects that can be properly modelled and considered in the analysis. We can tackle those problems by adopting a piecewise analysis, where we can consider that each data segment can be assumed as stationary and Gaussian. In order to eliminate the dominant noise sources (i.
Acknowledgements This work is partly supported by:  A. G. Leventis Foundation ;  Academy of Finland  Grants  328958  and  345070 ;  Alexander S. Onassis Foundation ,  Scholarship ID :  FZO 059-1/2018-2019 ;  Amaldi Research Center  funded by the  MIUR program  " Dipartimento di Eccellenza " (CUP:  B81I18001170001 ); ASI Grants No. 2016-24-H. 0and  No. 2016-24-H. 1-2018 ; Atracción de Talento  Grant  2019-T1/TIC-15784 ;  Atracción de Talento  contract no. 2019-T1/TIC-13177  granted by the  Comunidad de Madrid ;  Ayuda 'Beatriz Galindo Senior ' by the  Spanish 'Ministerio de Universidades ', Grant  BG20/00228 ;  Basque Government Grant  ( IT-979-16 );  Belgian Francqui Foundation ; Centre national Fig. 44 Cosmological amplitude uncertainty estimates from the  Fisher Information study denoted by solid lines  and from the MCMC by crosses.
Acknowledgments We thank  Christof Beierle  for fruitful discussions and a careful proofread of a first draft of this paper. Jules Baudrin  is funded by  ANR (French National Research Agency)  grant  ANR-20-CE48-0017  (SELECT). This work is partially supported by the  European Research Council (ERC , grant agreement no. 101041545  " ReSCALE ", and  ERC  grant agreement no. 101097056  " SymTrust ") , and by  ANR (French National Research Agency)  grant  ANR-21-CE39-0012  (SWAP). This work was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under Germany's  Excellence Strategy  - EXC 2092 CASA -390781972.
1C ). Amongst these, we identified thick ascending limb (TAL) cells with expression of SLC12A1, EPCAM, CDH1 and keratin 7 (KRT7), consistent with previous renal scRNA-seq datasets  (Lake et al. , 2019; Muto et al. , 2021; Young et al. , 2018). After merging cancer and NAT samples, we identified populations enriched in the tumour sample comprising TAMs (tumour-associated macrophages) and 2 clusters of cells harbouring an epithelial mesenchymal transition (EMT) signature which we identified to be the RMC tumour and CAF (cancer-associated fibroblast) cells (Fig. 1B ). All three clusters expressed specific markers (LYZ, MMP7 and POSTN respectively) with cytokeratin expression in RMC cells (Fig. 1C ). Further analyses of RMC and CAFs showed that each expressed overlapping as well as distinct sets of EMT markers (Fig.
S1A  and Dataset S1A). The UMAP plot revealed that RMC cells were located close to the TAL population, consistent with a putative cell of origin located in the distal part of the nephron. We interrogated all renal epithelial populations for shared transcriptional signatures with RMC cells and found the best correlation with TAL cells of the kidney medulla (Fig. 1D ). Differential gene expression analysis of a pseudo-bulk reconstitution of the RMC versus the CAF populations identified about 150 signature genes for RMC and 50 genes for CAF (Fig. 1E ). RMC cells showed a specific oncogenic program, but retained many genes associated with TAL and more broadly epithelial identities (Fig. 1E ). RMC and CAF cells did however commonly express EMT genes such as VIM and FN1, in contrast to TAL cells (Fig.
Among 3372 captured cells, a large group of RMC cells was identified along with TAMs and other CD45-expressing immune cells (Natural killers, neutrophils and T-cells), POSTN-expressing CAFs, and an unexpected population of tumour-associated TAL cells (Fig. 1K-L ). Both the RMC and TAL cells, that segregated into two closely located groups on the UMAP plot, expressed EPCAM as well as a cytokeratin signature (Fig. 1L-M ). The TAL3 population could be distinguished from TAL2 cells by the lowered expression of the SLC12A1, HOXB9 and PAX8 renal identity markers (Fig. 2A  and Dataset S1B). The RMC3 and RMC4 populations were highly similar with the smaller RMC4 cluster displaying an additional G2/M phase cell cycle signature designating them as mitotic RMC3 cells (Fig. 2A ). The SWNE trajectory representation of the TAL and RMC populations illustrated the progressive loss of TAL identity markers from the most differentiated TAL2 to TAL3 with some TAL3 cells closely related to the RMC group that retained an epithelial-like signature (Fig.
2F ). We further performed multi-region tumour RNA-seq on a cohort of four patients, for which single region transcriptome sequencing was previously reported along with that of 7 additional cases (  (Msaouel et al. , 2020a) , designated as the MDACC cohort; and Dataset S2). Overall, we generated an additional 25 bulk RNA-seq from multiple regions of these primary tumours and the corresponding regional lymph nodes as well as 3 NATs. We then analysed intra-and inter-tumour heterogeneity using CIBERSORTx deconvolution to infer RMC1-3 composition of these sections (Fig. S1C-D ). Primary tumour sections showed varying composition, some more enriched in the epithelial-like signature, others with epithelial-like and intermediate signatures, and a third group with all 3 signatures. In contrast, the lymph node metastases sections were strongly enriched in the mesenchymal-like signature.
These data unravelled intra-tumour heterogeneity in RMC and the importance of tumour cells with a mesenchymal signature to metastatic progression. We then used SCENIC regulon analyses software to identify transcriptional regulatory networks underlying the above signatures  (Van de Sande et al. , 2020). Comparison of the TAL and RMC populations from the treated tumour revealed a transcriptional switch from high HOXB9 and TFCP2L1 activity in TAL cells, to high MYC, HIF1A, YY1 and NFE2L2 activity in RMC cells (Fig. 2G ). These data were consistent with the known MYC activity of RMC cells, whereas TFCP2L1 is a previously described determinant of the distal portion of the nephron  (Werth et al. , 2017). Top TAL regulons were progressively lost upon transformation into RMC1 and RMC2 populations exemplified by TFCP2L1, PPARGC1A, perhaps contributing to the OXPHOS signature  (LeBleu et al.
, 2006) , epithelial-like (OVOL2, ELF3) in RMC3 cells  (Sengez et al. , 2019; Watanabe et al. , 2019)  and mesenchymal-like (HES1, FOSL2) in RMC2  (Liu et al. , 2015; Yin et al. , 2019). Notably, activity of the PAX8 renal identity marker was strongly reduced in the RMC1 and RMC2 populations compared to RMC3 (Fig. S1E ). The role of TFCP2L1 in driving expression of epithelial genes was reinforced by analyses of the Cancer Cell Line Encyclopaedia  (CCLE)  showing positive correlation between TFCP2L1 (and also OVOL2) and EPCAM (Fig. S1F ). Similarly, TFCP2L1 correlated with epithelia markers and anti-correlated with mesenchymal markers (Fig. S1G ). In the TCGA chromophobe renal cell carcinoma dataset, originating also from distal tubules, TFCP2L1 and MITF expression correlated with that of CDH1 (Fig.
Tumour cell state of a patient derived RMC xenograft. We further analysed a patient derived xenograft (IC-PDX-132) from an RMC tumour treated with 6 cycles of cisplatin, gemcitabine and bevacizumab that had undergone 4 passages of subcutaneous injections on immunocompromised mice. Around 10,000 cells were captured and the sequences aligned to a human-mouse hybrid genome. A large group of human RMC tumour cells were identified with high expression of EPCAM and the bulk RMC signature as well as a group of murine cells corresponding to CAFs and pericytes, TAMs and monocytes, and a smaller number of other immune cells (Fig. S2A-C  and Dataset S1C). A third group that we tagged 'LQ' (low-quality) comprised cells with high levels of mitochondrial genes and potential doublets, that were removed from the subsequent analyses.
4A , Dataset S3). After 48 hours, a larger number of up and down-regulated genes were observed in both cell lines (Fig. 4B ). Despite the differences in kinetics and numbers of affected genes, GSEA analyses revealed that in both lines, genes down-regulated were involved in oncogenic functions such as cell cycle and proliferation, designated by the GSEA terms MYC or E2F-targets, whereas up-regulated genes were designated by epithelial-like program terms such as cell adhesion, apical junction and apical surface (Fig. 4C ). Comparison with bulk-RNAseq from RMC patients relative to their NAT from both MDACC and Institut Curie cohorts showed the opposite profile with genes up-regulated in the SMARCB1-deficient tumours enriched in proliferation, cell cycle and JAK-STAT3 pathway, whereas those down-regulated associated with apical surface (Fig.
, 2012)  were down-regulated in both lines  (Fig. 5A and Fig 3H). On the other hand, Transferrin (TF) and transferrin receptor (TFRC) regulating iron uptake were both rapidly induced in RMC219 and RMC2C cells (Fig. 4A-B,  and 5A ). Following these acute events, at 48 hours we observed increased expression of a subset of genes involved in lipid peroxidation namely DPP4, LOX, LPCAT paralogs and ACSL4 (Fig. 5A and Fig 3H). These data suggested that SMARCB1 re-expression induced an acute increase in iron uptake followed by increased lipid peroxidation and ferroptosis. Complementary observations were made from our scRNA-seq dataset where SCENIC showed that RMC tumour cells were characterized by the activation of the NFE2L2/3 regulon a major regulator of ferroptosis (Figs. 2G, 2I )  (Chen et al.
S4A-B ). Read density profiles at the non-redundant MYC sites identified those with gained (I2/I8, Fig. S4E ) or diminished (I3/I9, Fig. S4E ) occupancy located at both TSS proximal and distal regions. Notably, integration with BRG1 and H3K27ac datasets revealed that MYC occupancy was increased at TSS proximal sites marked by H3K27ac, but characterized by diminished BRG1 occupancy (D1/E1, Fig. 6D ). In contrast, a large set of distal located sites were lost upon SMARCB1 re-expression (D4/F3, Fig. 6D ) with a smaller number showing increased occupancy (D3/F2). Global analyses confirmed that BRG1 flanking a subset of MYC bound sites in the mCherry control cells was diminished following SMARCB1 re-expression, whereas H3K27ac was unchanged (Fig. S5C ). RSAT analysis of the top 1000 MYC peaks confirmed a strong enrichment of the cognate E-box motif (Fig.
Although there are clear limitations in assigning distal binding sites to regulation of a given gene, a large set of genes showed increased MYC binding at the promoter and diminished binding at distal sites suggesting the importance of enhancer-promoter communication in their activation. More importantly however, BRG1 occupancy was strongly reduced at these promoters showing that MYC cooperated with SWI/SNF lacking SMARCB1 to activate the oncogenic program and that BRG1 eviction and not MYC loss reversed the oncogenic process. Integrating patient and in cellulo-derived data converged to show that pre-tumoral TAL cells displayed a hypoxia/stress state activating MYC and NFE2L2 to drive ferroptosis resistance allowing survival under conditions favourable to SMARCB1 loss (Fig. 6F ). Subsequently, SMARCB1 loss led to BRG1 recruitment at promoters of MYC occupied oncogenic genes and inhibition of the TFCP2L1/HOXB9/MITF-driven TAL epithelial program.