dataesr/scientific-paragraphs-categorization huggingface dataset:
Biochemical analysis This study assesses a comprehensive set of water chemistry data in the Otava river watershed, divided into 14 sub-catchments, covering the period from 2000 to 2022 (Table  S3 ). The data used in this analysis consists of historical monthly records from 2000 to 2020, sourced from the Vltava Basin Authority and Čevak Inc. Additionally, quarterly data from 2021 to 2022, generated by the ongoing DWARF project, are included. The water samples are analyzed for pH, alkalinity, suspended solids (SS), biological oxygen demand (BOD5), chemical oxygen demand (CODMn and CODCr), total-and/or dissolved organic carbon (TOC, DOC), UV absorbency (Abs. @UVλ254), total nitrogen (TOT-N), total phosphorous (TOT-P), chlorophyll A (Chl-a), major cations (Ca 2+ , Mg 2+ , Na + , K + ), major anions (SO4 2-, NO3 -, Cl -), phosphate (PO4 3-), and ammonium (NH4 + ) (Table  S3 ).
It is important to note that the monitored parameters varied between sites and over the years. The analytical methods employed by the Vltava Basin Authority for the historic data can be found in Appendix A (Table  A1 ). The DWARF samples underwent pre-filtration in the field using a 200 μm sieve to remove coarse particles. Subsequently, the samples were stored in the dark at 4 °C until analysis within two days. In the laboratory, membrane filters (0. 45 μm) were used for ions analysis, while glass-fibre filters (0. 4 μm) were used for other analyses. Detailed information regarding the analytical methods employed for the DWARF samples can be found in Appendix A (Table  A2 ). To investigate the fluorescence characteristics of the DOM, fluorescence excitation-emission intensity (Ex I -Em I) spectra (EEM) were determined for the four seasons in 2021 (n = 56) and 2022 (n = 56), and the winter season in 2023 (n = 14).
Derived parameters and statistical analysis DOM characteristics were determined based on spectroscopic indexes and fractionation (Table  S4 ). The sUVa was calculated by normalizing the UVλ254 value to the DOC concentration. Several indices were determined based on the EEM matrix. These include the degree of humification (𝐻𝐼𝑋 = ∑ ∑ λ )  [33] , the biological index (𝐵𝐼𝑋 = λ )  [23, 34]  the Fluorescence index (FI = λ )  [22, 25, 35, 36] , and the spectral ratio (𝑆𝑅 = )  [37]. These indices were derived using the stardom package  [38]  in the R programming environment  [39]. Further, the fluorescence EEM signals were decomposed by the PARAFAC analysis (See Chapter S4). This analysis was also computed using the staRdom package in the R programming environment, following the principles outlined by Murphy et al.
[24]  and Stedmon & Bro  [40]. The fluorescence intensities were expressed in Raman units, and various pre-processing steps were applied, including scattering removal, interpolation, data normalization, and the constraint of nonnegativity. No outliers were identified. Three PARAFAC components were identified that collectively provide a robust description of DOM fluorescence within the dataset, accounting for 98. 6% of the total variance in the EEM data matrix. The maximum (and lower) fluorescence intensities were for the three components found at the following excitation and emission wavelengths (nm): component 1 (C1) had the highest fluorescence intensity at λexcitation/λemission 265 (365)/487; component 2 (C2) exhibited maximum intensity at λexcitation/λemission 250 (305)/413; while component 3 (C3) had a maximum intensity at λexcitation/λemission 280/336.
The characteristics of the underlying fluorophores were found from matches (Tucker's congruence coefficient (TCC) > 0. 95) with literature in the OpenFluor database  [24] : i. e. , C1) high molecular weight humic DOM of allochthonous origin and associated with a high degree of aromaticity  [41] [42] [43] ; C2) medium molecular weight fulvic DOM, likely derived from microbial reprocessing in the water  [41, 44] ; and C3) protein like material. The latter is customary linked to anthropogenic sources  [45, 46]. The relative fluorescence intensity of each component (%Ci), expressed as a percentage of the sum of the three components, was used for further analyses. Model validation procedures, including visual evaluation of spectral loadings, leverages, sample residuals, and split-half analysis (TCC > 0.
The slightly hydrophobic acids (SHA) or charged hydrophilic (CHA) protein-like material is closer associated to runoff from fishponds than from urban environments, as found in Chapt. A Principal Component Analysis (PCA), also conducted on the quarterly dataset (Figure  8 ), gave a first Principal Component (PC1) representing an allochthonous-autochthonous gradient that accounts for 71. 8% of the data variation. It effectively separates the allochthonous humic DOM from the autochthonous fulvic DOM and the anthropogenic protein-like material. The second Principal Component (PC2), explaining an additional 9. 0% of the variation, represents a DOM gradient. It does not deconvolute the sources of autochthonous fulvic DOM from the anthropogenic protein like material. When plotted on the PC1 vs. PC2 plane, the four clusters identified in the cluster analysis (Figure  7 ) can be recognized within the PCA.
The third Principal Component (PC3), explaining an additional 5. 5% of the variation in the dataset, separates the clusters with fulvic DOM in regions with grassland and urban development, from the cluster with protein like material (Appendix D, Figure  D2 ) from fishponds. In summary, the PCA results demonstrate the presence of distinct gradients in the data, with PC1 representing the distinction between allochthonous and autochthonous DOM, and PC3 capturing the differentiation between autochthonous and anthropogenic DOM from sewage on the one side and anthropogenic DOM from fishponds on the other. The PCA plot confirms the clustering patterns observed in the cluster analysis and highlights the role of fishponds in driving an anthropogenic components of autochthonous DOM.
On the other hand, it was noteworthy that the sUVa values were high, indicating that the DOM in the fishponds exhibits significant aromatic characteristics. The sUVa data are supported by a significant correlation with humification index (HIX) (R 2 = 0. 689). This heightened aromaticity can serve as a useful distinguishing factor, allowing the differentiation of DOM originating from fishponds from other sources of autochthonous and anthropogenic fulvic DOM, which typically exhibit lower levels of aromaticity. In summary, the water samples collected from the streams affected by fishponds exhibited specific chemical and DOM characteristics, including high SR and sUVa values. These features are indicative of low molecular aromatic DOM. This aromatic nature of DOM originating from the fishponds distinguishes the DOM from fishponds from other autochthonous and anthropogenic sources of fulvic DOM.
Finally, DOM from fishponds differ from sewage by possessing a high sUVa, as well as higher fraction of charged hydrophilic and slightly hydrophobic organic acids. Table  C1. List of studies from the OpenFluor database with components matching (> 0. 95) the excitation and emission of the components found in the study. The ten studies presented for each component were selected among the strongest correlating, readily available, and by prioritizing studies that matched more than one of the three components in this study. For component assignment and description, the reader is referred also to the references within the cited studies. K + SO4 2- NO3 - Cl - PO4 3- N-NH4 + # mmol L -1 mg L -1 mg L -1 mg L -1 mg L -1 mg L -1 mg L -1 mg L -1 mg L -1 mg L -1 mg L -1 mg L -1 mg L -1 1 Ref.
Data Availability Statement: The data used in this analysis consists of historical records from 2000 to 2020, sourced from the Vltava Basin Authority and Čevak Inc. , Additionally, quarterly data from 2021 to 2022, generated by the ongoing DWARF project (Drinking WAter Readiness for the Future), are included. All data are stored in the databases of the Biology Centre CAS, Institute of Hydrobiology, 370 05 České Budějovice, Czech Republic.
Antibodies and reagents Rabbit anti-GAPDH monoclonal antibody (60004-1-Ig), mouse anti-GFP tag monoclonal antibody (66002-1-Ig), rabbit anti-PLSCR1 polyclonal antibody (11582-1-AP), mouse anti-Halo tag monoclonal antibody (28a8), rabbit anti-TMEM41B polyclonal antibody (29270-1-AP) and rabbit anti-IFITM3 polyclonal antibody (11714-1-AP) were obtained from Proteintech. Rabbit anti-Na,K-ATPase polyclonal antibody (3010S), rabbit anti-Flag tag monoclonal antibody (14793S) and rabbit anti-β-tubulin monoclonal antibody (2128S) were obtained from Cell Signaling. Goat anti-ACE2 polyclonal antibody (AF933) was purchased from R&D Systems. Mouse anti-PLSCR1 monoclonal antibody (MABS483), mouse anti-dsRNA monoclonal antibody (MABE1134), rabbit anti-TMEM16F polyclonal antibody (HPA038958), sheep anti-mouse IgG horseradish-peroxidase-conjugated secondary antibody (GENXA931-1ML) and sheep anti-rabbit IgG horseradish-peroxidase-conjugated secondary antibody (GENA934-1ML) were obtained from Sigma.
FACS Cells were fixed and collected in FACS buffer (1× PBS, 1% FBS, 5 mM EDTA) and filtered through a 40-μm cell strainer before FACS sorting using a BD FACSAria. For sorting of Huh7. 5 and A549-ACE2 cells, gates were drawn to separate the cells into mNG low and mNG high populations in both IFNγ-untreated and IFNγ-treated conditions. GFP was excited by a 488-nm laser and detected with a 550-nm filter. Cells were sorted on the basis of their mNG intensity and collected into separate tubes for later processing. Data were analysed with FlowJo (BD Biosciences).
RNA-seq Huh7. 5 cells were treated with 100 U ml -1 IFNγ for 24 h. Total RNA was isolated using the Rneasy Plus Mini Kit (Qiagen). mRNA libraries for sequencing were prepared according to the standard Illumina protocol. Sequencing (100 bp, paired-end) was performed using the Illumina NovaSeq sequencing system at the Genomics Core of Yale Stem Cell Center. The RNA-seq reads were mapped to the human genome (hg38) with STAR in local mode using default settings. The uniquely mapped reads (cut-off: mapping quality score (MAPQ) > 10) were counted to ENCODE gene annotation (v. 24) using FeatureCounts. Differential gene expression was analysed with the R package DESeq2. Transcripts with a log 2 -transformed fold change > 1 and adjusted P < 0. 05 were considered as differentially expressed.
Virus infection For fluorescent reporter assays using SARS-CoV-2-mNG, P3 stocks were used for infection. Cells were seeded at 40% confluency in 96-well plates 2 days before infection. The following day, cells were either left untreated or primed with IFNγ (Huh7. 5: 8 U ml -1 ; A549-ACE2: 70 U ml -1 ) 18 h before infection. IFNγ was kept in the medium during infection. Huh7. 5 cells and A549-ACE2 cells were infected at MOI = 1 and MOI = 0. 2, respectively. Subsequently, at 1 dpi (A549-ACE2) and 2 dpi (Huh7. 5), the medium was removed from the wells and cells were washed once with PBS before being fixed with 4% PFA for 30 min and stained with Hoechst 33342 for an additional 20 min. Then, high-content imaging (Cytation 5, BioTek) of the cells was performed to measure mNG expression. The average intensity of mNG per cell as well as the percentage of infected cells were quantified and analysed by Gen5 software.
LET1 cells were infected with MHV-A59-GFP at MOI = 0. 1 for 48 h. Huh7. 5 cells were infected with DENV-I at MOI = 0. 1 for 24 h. Subsequently, at 1 dpi (A549-ACE2) and 2 dpi (Huh7. 5), the medium was removed from the wells and cells were washed once with PBS before being fixed with 4% PFA for 30 min and stained with Hoechst 33342 for an additional 20 min. Then, high-content imaging (ImageXpress Pico, Molecular Devices) of the cells was performed to measure GFP expression. The average intensity of GFP per cell as well as the percentage of infected cells were quantified and analysed by the CellReporterXpress software. For confocal imaging, A549-ACE2 cells were spinfected with SARS-CoV-2 USA-WA1/2020 at 1,000g, 37 °C for 30 min to synchronize the infection, followed by washing twice with pre-chilled PBS.
4Pi-SMS nanoscopy Two-colour 4Pi-SMS  41  was performed on a custom-built microscope with two opposing objectives in 4Pi configuration  54. A549 cells were seeded on 30-mm-diameter no. 1. 5H round coverslips (Thorlabs) and grown for 1 day before infection. Infected cells were fixed with 4% PFA for 30 min and permeabilized with 0. 2% Triton X-100 for 3 min. Cells were blocked with 10% goat serum in PBS for 1 h and subsequently incubated with mouse anti-PLSCR1 and rabbit anti-spike antibodies overnight at 4 °C. Primary antibodies were detected by goat anti-mouse Fab AF647 ( Jackson ImmunoResearch) and goat anti-rabbit IgG CF660C (Biotium) at a 1:200 dilution (2 h at room temperature). Samples were post-fixed in 3% PFA + 0. 1% glutaraldehyde for 10 min and stored in PBS at 4 °C. Sample mounting, image acquisition and data processing were mostly performed as previously described  54  except that imaging speed was 200 Hz with a 642-nm laser intensity of around 12.
Image processing and analysis Images were processed in LAS X (Leica), SoftWoRx (v. 7. 0), Fiji or Imaris 9. 8 (Oxford Instruments) software. Deconvolution of fluorescent images was performed in LAS X using the default settings. Fluorescence colocalization analysis was performed using Imaris software. The percentage of PLSCR1 fluorescent signal colocalized with LAMP1 or CD63 as well as the Mander's overlap coefficient were calculated by setting proper thresholds for both channels to avoid background signal. The PLSCR1-positive foci were automatically detected by surface reconstitution using Imaris. The average number or fluorescent intensity of PLSCR1-positive foci per cell was calculated by dividing the total number or fluorescent intensity of PLSCR1-positive foci by the total number of cells in each randomly selected image, respectively.
MDS analysis A coarse-grained simulation using the AlphaFold 57 structure of PLSCR1 with the N-terminal region truncated was assembled using the insane. py Python script  58  , memembed (ref. 59) and martinize2 (ref. 60) in an asymmetric membrane comprised of phosphatidylcholine (PC):phosphatidylethanolamine (PE) (8:2) in the lower leaflet and PC:PE:PS:PtdIns(4,5)P 2 (2:5:2:1) in the upper leaflet. A 5-μs simulation was performed to equilibrate the system. This was then back-mapped to atomistic resolution using the cg2at tool  61. The mutation H262Y was made at this point using PyMOL, followed by an energy minimization step using the steepest descents algorithm. Separately, the atomistic system with the palmitoyl tails was assembled using CHARMM-GUI (refs. 62,63) in the same membrane as previously described.
Our home-built optical tweezers setup is combined with a spinningdisc confocal system  74  which allowed us to verify that wild-type PLSCR1-GFP was localized to the GPMV membranes whereas the 5CA mutant was distributed diffusely inside the GMPVs. The trap stiffness was calibrated using a hydrodynamic flow method  75  and was 283 pN μm -1 for the polystyrene beads (diameter = 3. 15 μm, PP-30-10, Spherotech). For micropipette aspiration, we used a micropipette holder attached to a programmable three-axis piezo stage (100 μm range, P-611. 3 NanoCube, with controller E-727 and Mikromove software, Physik Intrumente), mounted on a manual manipulator for coarse movement (Newport M-462, Newport). Both the micropipette and the glass chamber were coated with 1% BSA to minimize GPMV adhesion and facilitate the free flow of the GPMV membrane inside the micropipette.
Aspiration pressure was controlled by vertically moving a water reservoir connected to the micropipette. Before aspirating a GPMV, pressure was zeroed by bringing a bead near the tip of the micropipette (3-6 μm) and adjusting the reservoir height until the bead stopped moving. Subsequently, a bead was trapped, and its zero-force position (x 0 , y 0 ) was recorded. Then, the GPMV was brought in contact with the bead briefly (around 1-3 seconds) before being pulled away to form a membrane tether. Bead positions (x, y) were determined from analyses of image stacks. The presence of a tether was confirmed either visually or by releasing the trap and observing the retraction of the bead toward the GPMV. The tether force was calculated from the deviation of the bead's position from its load-free value and the trap stiffness using a custom-written MATLAB program  74.
Statistics and reproducibility Data were subjected to statistical analysis and plotted using Microsoft Excel 2010 or GraphPad Prism v. 9. 0. D'Agostino and Pearson omnibus normality test or Kolmogorov-Smirnov test were used to determine the normal distribution of data. For data with a normal distribution, single comparisons were performed using the two-sided Student's t-test for groups with equal variances, and Welch's correction was used for groups with unequal variances. Datasets that did not follow a normal distribution were analysed using a nonparametric test (Mann-Whitney test). Multiple comparisons were assessed by one-way ANOVA with Tukey's post-hoc test or two-way ANOVA with Tukey's post-hoc test, whereas Brown-Forsythe and Welch ANOVA with Dunnett's post-hoc test was used for groups with unequal variances.
Acknowledgements We thank all members of the  J. D. M. laboratory  for discussions;  Yale Environmental Health and Safety's Biological Safety Team  for the assistance with BSL-3 training;  A. D. Yadavalli  for the suggestion on data analysis; and  M. Yamamoto  for sharing plasmids and materials. This work was supported by  Fast Grants  ( Emergent Ventures ,  Mercatus Institute, George Mason University ) to E. F. F. ; an  MRC studentship  ( MR/N014294/1 ) to C. M. B. ;  Wellcome  ( 208361/Z/17/Z ),  MRC  ( MR/S009213/1 ) and  BBSRC  ( BB/P01948X/1 ,  BB/R002517/1  and  BB/S003339/1 ) grants to P. J. S. ;  NIH  grant  R01AI163395  to W. M. ; and  NIH  grant  R01 NS113236  to E. K. J. D. M. is an Investigator of the  Howard Hughes Medical Institute. Schematic graphs were created with BioRender (https://biorender.
Data availability The data supporting the findings of this study are available within the paper and its Supplementary Information files. Sequences for all the sgRNAs used in this study are provided in Supplementary Table  1. Genome-wide CRISPR screening data are provided in Supplementary Tables  2  and 3. RNA-seq data are provided in Supplementary Tables  4  and 5. The raw RNA-seq data have been deposited and made publicly available in the NCBI Gene Expression Omnibus with accession number GSE233548. Full versions of all blots are provided in Supplementary Fig. 1. The gating strategies of flow cytometry are provided in Supplementary Fig. 2. Gene expression data derived from nasopharyngeal swab samples is publicly available in dbGaP (https://dbgap. ncbi. nlm. nih. gov/aa/wga. cgi?login=&page=login) with accession no.
phs002433. v1. p1. The human genome reference (hg38) used in the RNA-seq analysis is available in the NCBI genome assembly with accession number GCF_000001405. 39. The protein expression profiles are available in the web-based Human Protein Atlas database (https://www. proteinatlas. org/). The transcriptional-factor binding profiles are available in the web-based JASPAR database (https://jaspar. genereg. net/). The materials, reagents and other experimental data are available from the corresponding author upon request. Source data are provided with this paper.
Properties of Fontainebleau sand In order to interpret these experiments, the following data was considered for the median grain size 𝐷 , uniformity coefficient 𝐶 , minimum and maximum void ratios 𝑒 min and 𝑒 max , and grain density 𝜌 for Fontainebleau sand. The data is taken from  [11]. Small-strain moduli were determined on dry Fontainebleau sand specimens. The detail of this process is explained in  [12]. Fitting the experimental data provides the following estimations of the small-strain shear modulus 𝐺 and a Poisson ratio 𝜈 of 0. 18 (with 𝑝 the effective stress and 𝑃 atmospheric pressure): 𝐺 = 99. 98 𝑝 𝑃. (8) In order to interpret 𝐵 measurements, the small-strain moduli mentioned previously did not seem relevant as the strain range is completely different. Therefore, drained isotropic consolidation tests were carried out on saturated Fontainebleau sand.
This limits the influence of any other existing wave components on the calculated frequency. Finally, data points are grouped by frequency range. The size of the frequency ranges is chosen in a way to ensure there are always a few signals per range. It also gives an indication of the precision of the frequency estimation. The considered P-wave velocity for each frequency range is its median velocity. The shear velocity is also measured at different frequencies for each stage of the experiment. As 𝑣 is not frequency dependant, the adopted value is the median of all calculated shear wave velocities.
Metabolic changes associated with response to the OXPHOS inhibitor IACS-010759 To identify metabolic changes associated with response or resistance to IACS-010759, we performed a global metabolomic analysis of untreated and treated xenografts from an IACS responder (HBCx-124) and an IACS resistant PDX (HBCx-137). The heatmaps of the top differentially expressed metabolites are shown in Fig. 5A. Statistical analysis revealed 199 and 163 differentially expressed metabolites between treated and control xenografts for the HBCx-124 and HBCx-137 PDX, respectively (Supplementary Data 2). The levels of 549 metabolites were significantly different between the HBCx-124 and HBCx-137 at baseline. Metabolite set enrichment analyses (MSEA) is shown in Fig. S2A : fatty acids (FA) metabolism, and branched-chain amino acids (Leucine, Isoleucine and Valine) were altered after IACS treatment in both PDX.
Targeted sequencing of PDX Next generation targeted sequencing (NGS) of PDX tumour samples was performed with a 500-1000X coverage as described in detail elsewhere  13, 52. Briefly, NGS was performed on an Illumina HiSeq2500 sequencer and the genomic variants were annotated with COSMIC and 1000 genome databases  53. Reads were aligned using the Burrows-Wheeler Aligner (BWA) software, allowing up to 4% of mismatches with the reference. Variants with low allelic frequency (<5%) or low coverage (<100x) were excluded from the analysis. PDX HBCx-124, HBCx-134, HBCx-169, HBCx-180, HBCx-219, HBCx-202 and HBCx-227, were sequenced with a targeted NGS panel composed of 571 genes of interest in oncology  13. PDX HBCx-3, HBCx-118, HBCx-139, HBCx-137, HBCx-131 and BC1215 were analyzed by targeted NGS of 95 genes, chosen among the most frequently mutated genes in breast cancer (>1%)  13, 52.
Gene expression analysis For gene expression microarray analyses, GeneChip Human 1. 1 ST arrays were hybridised according to Affymetrix recommendations, using the Ambion WT Expression Kit protocol (Life Technologies) and Affymetrix labelling and hybridisation kits, as previously described in ref. 13. Gene set enrichment analysis (GSEA) software and MsigDB database  55  were used to identify overrepresented biological functions for differentially expressed genes between PDX and primary breast tumours.
After qPCR quantification using the KAPA library quantification kit (Roche), Sequencing was carried out on the NovaSeq 6000 instrument from Illumina based on a 2 × 100 cycle mode (paired-end reads, 100 bases) to obtain around 100 million clusters (200 million raw paired-end reads) per sample. For sequenced PDX transcriptomes, reads were aligned to human genome reference (hg19) and annotated with the human GENCODE (v19) database using the STAR tool (v. 2. 7. 5a)  56. In order to filter mouse ARN contamination, alignment and annotation were also performed on mouse genome reference (mm10) and GENCODE (vM22) features, and both human and mouse results were used as input to XenofilteR R package  57. Read counts per human GENCODE features were obtained by HTSeq-count  58. DESeq2 was used to correct batch effect, get normalised data and make paired differential expressions between all conditions.