dataesr/scientific-paragraphs-categorization huggingface dataset:
| Delimitation of foldable segments within protein sequences The HCA methodology is described in details in Dataset S1. SEG-HCA  42  was previously developed to automatically delineate regions with high density in hydrophobic clusters, constituting potential "foldable" domains within protein sequences. The new version of SEG-HCA was rewritten for speed and includes new functionalities, including the calculation of HCA score (see below). In addition, we rewrote and packaged in a python module the tools SEG-HCA (in a function named segment), TREMOLO-HCA (Traveling through REMOte homoLOgy)  48  and the HCA drawing, as well as provided some utility scripts to analyze results, adding information on amino acid conservation and taxonomy. These tools can significantly help detection of hidden relationships between sequences, as repeatedly performed using the HCA approach in an expert-based way (see Ref.
Therefore, the HCA score scales with the density of hydrophobic clusters and of strong hydrophobic residues within clusters. As the HCA score calculation motivation is to provide an estimation of the globular character, i. e. , the foldability of a domain, the weight of each residue was optimized within each of the three classes to minimize the overlap between the distributions of the HCA scores calculated on nonredundant disordered foldable segments from DisProt v8. 0. 2 sequences and nonredundant foldable segments of globular proteins from the SCOPe (soluble domains) and OPM databases (transmembrane domains), respectively (see below for details on the datasets). During the optimization step, the possible allowed values of the weights were discrete in the [À10; 10] interval ([À1; 1] scaled to one order of magnitude for better readability).
For a given combination of weights, the distribution overlap (i. e. , the criteria to be minimized) was estimated by histogram intersection as follows: X B i¼1 min H 1 i ð Þ, H 2 i ð Þ ð Þ , with B the number of histogram bins (set to 60), and H1(i) and H2(i) the values in normalized histograms of HCA scores for nonredundant dataset Disprot v8. 0. 2 and nonredundant dataset SCOPe and OPM for bin i, respectively. The optimization was achieved using 10-fold cross-validation, repeated 10 times. We selected the set of parameters that was most often optimal among the 10 iterations, and that allowed to achieve the least overlap between disorder and order. This implementation is an updated version (v2) of a previous one, described in Ref. 49  The optimal HCA score was reached for a minimum overlap of 49% between the score distributions of the globular and disordered sequences, respectively, and with the weights of 9 for strong hydrophobic residues in hydrophobic clusters, 7 for nonstrong hydrophobic residues in hydrophobic clusters and À10 for residues outside of hydrophobic clusters (see Eq.
(  1 ) in Section 2). A vast majority of amino acids of the SCOPe and OPM nonredundant datasets are included in foldable segments (90% and 96%, respectively) (Table  1 ), while non foldable segments in these datasets mostly correspond to large, hydrophilic loops (see for instance the example shown in Figure  S1a ). In contrast, only 50% of amino acids in the DisProt v8. 0. 2 dataset are included in foldable segments (Table  1 ), the remaining ones (i. e. , in nonfoldable segments) can be thus considered as fully disordered (Figure  S1b ). DisProt segments can thus be analyzed after being separated into two distinct categories  Note: Total number of sequences and amino acids (aa) of each dataset, as well as their number (and percentage) of sequences with and without foldable segment(s), and the number (and percentage) of residues found in or outside of foldable segments.
The datasets are those of soluble domains with known 3D structures (SCOPe), transmembrane domains with known 3D structure (OPM) and disordered segments (DisProt v8. 0. ) (see Materials and Methods for details). OPM classes have been shortened to polytopic for alpha-helical polytopic domains, bitopic for alpha-helical bitopic domains and beta for betabarrels. - F I G U R E 1 Distribution of the HCA scores calculated for the foldable segments from disordered protein regions (DisProt) and ordered protein domains (soluble domains from SCOPe and membrane domains from OPM). The considered nonredundant datasets are: DisProt v8. 0. 2 (HCA scores ranging from À6. 95 to 9 and peaking at 0. 5 and 8. 1), SCOPe (HCA scores ranging from À4. 67 to 9 and peaking at 1. 4 and 8. 1), OPM alpha-helical polytopic and bitopic categories (HCA scores ranging from À3.
The HCA score distribution of foldable segments from the Dis-Prot database is wide, ranging from À7 to 9, due to the structural heterogeneity of the corresponding sequences, reflecting diverse order/ disorder ratio. Some foldable segments have low HCA score values, having hydrophobic cluster densities below those observed for folded domains, which reflect their propensity to fluctuate between disorder and order and/or to interact with partners (labeled a and a 0 in Figure  1 ). This is for instance the case of a foldable segment from the yeast nuclear pore complex NUP2 protein, which contains FxFG repeats (shown with a red circle in Figure  3A , HCA score of À5. 36 [interval a in Figure  1] ). FxFG repeats are known to bind transport factors of the importin-beta/karyopherin-beta family, which function as carriers for many nuclear trafficking processes.
| Leveraging AlphaFold2 predictions with pyHCA package 32] 72  We wanted here to compare the AF2 predictions to those of foldability and structural states, which can be made using pyHCA. Each proteome processed by AF2, provided by the AlphaFold Protein Structure Database (AFDB,  47   (Figure  4A ). Instead, the residues located outside of foldable segments (hereafter described as nonfoldable segments, representing 18. 1% residues of the whole dataset) correspond mostly to low confident predictions (61. 3% and 17. 1% residues with very low and low pLDDT, respectively, Figure  4A ). These trends are also observed for each organism separately, at the exception of Plasmodium falciparum whose proteome is dominated by low confident predictions, even in the foldable segments (39. 7% and 13. 1% residues with very low and low pLDDT, respectively) (Figure  S3  and Table  S1 ).
The methodology developed here allows us to put the AF2 prediction made at the residue level into the context of the foldable segment to which it belongs and of its position in the order/disorder continuum. We thus calculated the distribution of residues in each pLDDT category as a function of the HCA scores of the foldable segments in which they are included (Dataset S4). Overall, we observed differences in the relative proportions of each pLDDT category as regards to the structural states (labeled a to d), previously defined based on the HCA score (Figure  5A ). F I G U R E 4 Distribution of AlphaFold2 per-residue prediction confidence scores (pLDDT) within and outside of foldable segments. (A) Distribution of residues from the AFDB 21 proteomes within foldable segments (top, 81. 9% of the total) and outside of foldable segments (bottom, 18.
pyHCA is in line with the spirit of polymer scaling behavior, which combines hydropathy and charge patterning and is used for characterizing the structural properties of IDPs/IDRs. 83 It is also to be compared to ODiNPred, a sequence order/disorder predictor which uses a deep neural network trained on a database of an experimental, continuous-valued quantification of local disorder based on NMR chemical shifts and considering a large number of sequence features,  33  among which foldable domains as predicted by SEG-HCA. The foldable segments defined by SEG-HCA are expected to fold spontaneously or conditionally into stable 3D structures through the participation in an hydrophobic core, while nonfoldable segments correspond to full disorder, with the exception of regions whose stably fold without the need of a consistent hydrophobic core, but, for example, depending on ion binding.
It allows therefore to unravel the characteristics of the hidden order, as already applied to the identification of hidden actors of the transcription machinery. 98 portantly, the reliability of the "order" prediction by pyHCA can unambiguously be demonstrated in cases where the AF2 prediction fails. Indeed, in a joint study,  99  we have focused on long (>30 amino acids) foldable segments with HCA scores typical of soluble, globular-like domains and that, in the corresponding AF2 models, include only amino acids very low pLDDT values and lack regular secondary structures and folded hydrophobic core (random coils). We showed that some of these sequences are reported in the DisProt database as forming transient regular secondary structures (as determined by various biophysical approaches). 99 Moreover, a specific search of these segments against the PDB sequences that were published after the deposit of the AF2 models highlighted a case of an AF2 structureless coil prediction, which was further investigated by NMR and ESR, combined with Rosetta computations.
(i. e. , foldable and nonfoldable segments). A few instances of poor coverage of folded domains of the SCOPe database by foldable segments are observed. These correspond to sequences rich in alanine (class a) and threonine/serine (class b), which are not included in the hydrophobic alphabet used for hydrophobic cluster definition, but participate in the hydrophobic core (FigureS1c,d). Some other cases of low coverage actually correspond to domains stabilized by a ligand or for which the folding is cooperative or dependent of an oligomeric organization (obligate oligomers) (FigureS1e). The HCA score values range between À7 and 9 (Figure1, Dataset S3). Soluble domains have HCA scores included mostly in the [À1,3. 5] interval (labeled b). As illustrated for the SCOPe b class, the lowest and highest values are mainly associated with the presence.
classes of proteins; the beta-barrel strands, albeit longer, are indeed characterized by a periodicity of 2 in hydrophobic amino acids, as for beta-strands from globular domains, while transmembrane alpha-helices from alpha-helical polytopic segments are associated with larger numbers of strong hydrophobic amino acids dotted with charged/polar amino acids, as well as tiny ones (Gly, Ala), ensuring tight packing (Figure2B). Beta-barrel transmembrane sequences can be distinguished from soluble globular domains by their amino acid composition and hydrophobic cluster characteristics (Figure2B, also see Discussion). Finally, a separate category exists for all reference databases, with HCA scores above a value of 7. 6 (labeled d), including F I G U R E 2 Foldable segments extracted from the SCOPe and OPM databases: HCA scores, HCA 2D plots and experimental 3D structures.
The HCA plots of the sequences extracted from the SCOPe (all beta) (A) and OPM (B) databases are shown, with their foldable segments boxed (dashed lines). The corresponding HCA scores are reported below the boxes. Special symbols used in the HCA representation and the way to read the sequences and regular secondary structures (RSSs) are indicated in the inset. Positions of the RSSs, as experimentally observed from the corresponding 3D structures (ribbon representations), are reported in color below the HCA plots. The position of the 13 aa-long segment which has been removed for the OPM protein sequence in order to only keep membrane domains is highlighted in red (see Section 2 for details) mostly short sequences covered by a single hydrophobic cluster. These correspond entirely to the foldable segments of OPM bitopic sequences (made of only one transmembrane segment, rich in strong hydrophobic amino acids, Figure2Blight blue) or are part of OPM polytopic or SCOPe soluble sequences, which have been fragmented.
(Figure 3E, HCA score of 5. 66). Another example is the flexible C-terminal part of the Escherichia coli antitoxin ParD, which is involved in the binding and neutralization of the ParE toxin 69 and forms upon binding long helices docking into the groove of the partner 70 (DP0033r012, HCA score = 5. 15, 27 amino acids). Finally, HCA score values above 7. 6 (interval d in Figure1) include foldable segments with a single hydrophobic cluster, as for similar segments of the SCOPe and OPM databases. Such segments frequently correspond to preformed secondary structures (MORFs) or short linear motifs (SLIMs), which fold upon binding, as illustrated here with the short linear motif of the C. elegans EGL-1, whose binding to CED-9 initiate cell programmed death 71  (Figure3F, HCA score of 8. 23).
Among this whole dataset, 81. 9% of the residues are part of foldable segments (ranging from 73. 1% to 96. 3% for the proteomes of the parasitic protozoa Leishmania infantum and the autotrophic F I G U R E 3 Foldable segments extracted from the DisProt database: HCA scores, HCA 2D plots and experimental 3D structures. The HCA plots of the sequences extracted from the DisProt database are shown (arrows indicate the N-or C-terminal limits of the DisProt segments). The foldable segments are boxed (dashed lines). The corresponding HCA scores are reported below the boxes. Special symbols used in the HCA representation and the way to read the sequences and regular secondary structures (RSSs) are indicated in the inset. 3D structures (in green, with their pdb identifiers) have been solved for only a few of the DisProt sequences (moreover often limited to only a part of the regions), stabilized by interaction with a partner (gray surfaces for two of them).
(a) disordered sequences included in large loops and/or undergoing conditional folding (AF2 then capturing the structure of the complexed state), (b) long, repetitive structure with a particular abundance I G U R E 6 Principal component analysis (PCA) for the AFDB v1 21 proteomes according to HCA score and AF2 prediction confidence. The 64 variables correspond to the proportion of residues of foldable segments found in each pLDDT category (very high, confident, low, very low) for each of the HCA score intervals (from À7 to 9, in steps of 1) (as represented in Figure4). The plot represents the 21 AFDB proteomes in the factor map formed by the first and second principal components (explaining 59. 24% of the dataset variability). The correlation circle of the variables is represented in FigureS6.
Five clusters of proteomes were defined using hierarchical clustering on the first four principal components (explaining 83% of the dataset variability). ARATH: Arabidopsis thaliana, CAEEL: Caenorhabditis elegans, CANAL: Candida albicans, DANRE: Danio rerio, DICDI: Dictyostelium discoidum, DROME: Drosophila melanogaster, ECOLI: Escherichia coli, HUMAN: Homo sapiens, LEIIN: Leishmania infantum, MAIZE: Zea mays, METJA: Methanocaldococcus jannaschii, MOUSE: Mus musculus, MYCTU: Mycobacterium tuberculosis, ORYSJ: Oryza sativa, RAT: Rattus norvegicus, SCHPO: Schizosaccharomyces pombe, SOYBN: Glycine max, STAA8: Staphylococcus aureus, TRYCC: Trypanosoma cruzi, YEAST: Saccharomyces cerevisiae of (alpha-forming) alanine or (beta-forming) serine/threonine and sequences with an ion-dependent folding (FigureS8).
However, one can note that such IDPs/ IDRs are generally shorter than typical well-folded globular domains extracted from the SCOPe database, which may explain that they are unable to fold stably in absence of partners. Indeed, only 48. 4% of sequences from this category in DisProt have length greater than 30 amino acids (mean length 81. 6 aa), against 85. 1% in SCOPe (mean length 133. 3 aa). In cases of longer IDPs/IDRs from this category, amino acid composition may help to distinguish them from well-folded globular domains. Indeed, we observed that segments from DisProt of this category (interval b in Figure.
5 F 5 (a) disordered sequences included in large loops and/or undergoing conditional folding (AF2 then capturing the structure of the complexed state), (b) long, repetitive structure with a particular abundance I G U R E 6 Principal component analysis (PCA) for the AFDB v1 21 proteomes according to HCA score and AF2 prediction confidence. The 64 variables correspond to the proportion of residues of foldable segments found in each pLDDT category (very high, confident, low, very low) for each of the HCA score intervals (from À7 to 9, in steps of 1) (as represented in Figure4). The plot represents the 21 AFDB proteomes in the factor map formed by the first and second principal components (explaining 59. 24% of the dataset variability). The correlation circle of the variables is represented in FigureS6.
They are then centrifuged at 2500 rpm for 2 minutes and incubated for 1 minute with trypan blue quenching solution to distinguish cells which have phagocytosed the beads from those simply binding the beads at the surface. Cells are then washed with assay buffer and taken up with 200 µL of PBS in FACS tubes to be analysed by FACS Fortessa X20 (BD Biosciences, San Diego, CA, USA) and FlowJo Software (Tree Star, Ashland, OR, USA).
Subsurface data Within the study area the Delaware basin contains over 6 km of sediments. Stratigraphic information was obtained from DrillingInfo (now Enverus), along with technical details on all wells in the area. The main stratigraphic units of interest are the Upper Permian Ochoan Series evaporites (salt), located between approximately 1. 0 and 1. 5 km depth below the local land surface; the Delaware Mountain Group, the primary wastewater disposal formation, between 1. 5 and 2. 5 km depth; the Bone Springs Formation between 2. 5 and 3. 25 km, which contains a few producing horizontal wells; and the Wolfcamp Formation between 3. 25 and 4. 6 km depth where over 90% of the production wells are located, all bottoming above 3. 6 km below surface. Between 2014 and the end of our observation period in mid-2020, 77 horizontal wells were drilled, fracked, and put into operation (Fig.
Hypocenter solutions The S-P time analysis shows that many of the earthquakes locate at shallow depth, within about 1 km of the base of the Ochoan salt at 1. 5 km. As discussed earlier, horizontal refraction in the salt complicates accurate timing of the initial P wave for such events, and we use only the four closest stations to locate the hypocenters. We further restrict the events to those with at least five phases. This guarantees that at least one S phase is included in the solution. We select program VELEST  (Kissling et al. , 1994)  to determine the hypocenters. VELEST traces rays in 1D models with embedded low-and high-velocity zones. Table  S3  lists the 101 best-determined earthquakes, with the selection criteria including short epicentral distance (minimum station distance ≤ 2 × depth), sufficient observations (≥5 phases), and small root mean square error (≤0.
Data and Resources The seismic data are from the Texas Seismological Network (DOI: https://doi. org/10. 7914/SN/TX) and is publicly available on Incorporated Research Institutions for Seismology (IRIS). The well data are obtained from DrillingInfo (now Enverus, https://www. enverus. com, last accessed July 2020). The supplemental material for this article includes descriptions on estimating the V P =V S ratio, tables for the seismic stations, the 1D velocity profile, the hypocentral solutions, and TexNet earthquake catalog, and figures for the V P =V S ratio analysis and the moment tensor solutions.
Study design and data patients We conducted an observational, retrospective multicenter ancillary study of the randomized ELVIS trial, which assessed the prevention of RRT catheter infections with ethanol lock  [16]. The study was approved by the "Est IV" personal protection committee. (IDRCB No. 2017-A01133-50) and declared to the National Commission for Computing and Freedoms (No. 2008449 v 0). It followed the STROBE (STrengthening the Reporting of Observational studies in Epidemiology) recommendations for good clinical research practice (Additional file 2: Appendix 1). Eligible patients were aged 18 years or over, enrolled inthe ELVIS trial, hospitalized in an ICU, had AKI requiring RRT with the placement of a double-lumen dialysis catheter, discharged from the hospital after 28 days and were alive 3 months after RRT initiation.
The study population consisted of patients with at least one serum creatinine value available between 3 months and 5 years after RRT initiation. The exclusion criteria were CKD requiring chronic RRT at inclusion in the ELVIS trial and refusal to participate in the study. Additional data (serum creatinine level and mortality) to those collected prospectively in the ELVIS trial were retrieved from computerized medical records when possible. Otherwise, patients were contacted by telephone. Five contact tests were performed. If the patient could not be contacted, five attempts to contact the family orthe attending physician were made. The patient was considered lost to follow-up if none of the information could be collected after five attempted contact. Patients were informed at the beginning of the interview of the aim and nature of the study and were told they could opt not to take part.
If they agreed to participate, their responses were collected for analysis and anonymization. When possible, a picture of the blood test with creatinine level was sent by the patient. The data collected were the serum creatinine level available at 3, 6 and 12 months and at 3 and 5 years, the date of the start of dialysis, and the date of kidney transplantation. If there were several serum creatinine levels available at the same timepoint, the lowest level was recorded. The family or the attending physician was contacted for missing data and, when appropriate, the date of death. The clinical variables were those collected during the ELVIS trial. The RRT period was calculated to run from initiation in the ICU to the last day of treatment, which could be in the ICU or after discharge. The estimated glomerular filtration rate (eGFR) was measured at each timepoint (3, 6 and 12 months, 3 and 5 years) from the serum creatinine level, age, sex and ethnicity (13 patients were of African race/ethnicity) according to the CPK-EPI formula  [17].
However, almost all of the patients included in our study had a 4-point renal SOFA criterion, which corresponds to less than 200 ml/24 h of diuresis or serum creatinine > 440 μmol/L, whereas mean serum creatinine level in the trial participants at the initiation of RRT was less than 300 μmol/L  [28] [29] [30]. It is therefore unlikely that our study included patients who could have avoided RRT. Similarly, the impact of modifying RRT methods is unlikely, given the absence of any major publications leading to new recommendations for changing practices. Second, the design of our study did not make it possible to ensure that all creatinine levels were measured at each timepoint. Twenty-two patients had a serum creatinine value available between 3 months and 5 years and were alive at 5 years. However, our choice of missing data imputation did not adversely affect the risk of severe CKD because the imputed CKD stage was equal to the more severe of the two stages before or after the timepoint for which data were missing.
Thus, our imputation methodology could have identified severe CKD before it actually occurred. Some elderly patients improved their renal function between 3 and 6 months or even up to 1 year and hence changed CKD stage accordingly. We assume that the renal function of the elderly patients took several months to be fully recovered because of the structural and functional changes associated with the aging kidney  [31]. Moreover, the baseline of serum creatinine level before ICU admission was not collected in the ELVIS database. Only the variable eGFR ≤ 60 ml/ min/1. 73 m2 was collected, which may have been estimated with means other than CPK-EPI, and this could explain why some of these patients were in the no/mild group. We assessed the occurrence of CKD after AKI requiring RRT and not the renal recovery of patients.