dataesr/scientific-paragraphs-categorization huggingface dataset:
Selecting The Best Performing Methods In The Absence Of Ground Truth In contrast to the simulation tasks performed above, here we decompose real bulk pro les from 9 Peripheral Blood Mononuclear Cells (PBMC)  9  samples, utilising our deconvolution pipeline to assess the results and generate an honest consensus of predictions of cell-type proportions. Apart from the publicly available RNA-seq dataset from the above study, there is also ow cytometry data available for the 9 individuals which can be used as ground truth in our deconvolution pipeline. The researchers have measured the proportions of 8 immune cell types using speci c markers for each cell population (CD4 + T cells, CD8 + T cells, T-regulatory cells, B cells, NK cells, myeloid dendritic cells, monocytes and neutrophils (Supplementary Fig.
8a-b ). For this deconvolution task, we selected a recent single-cell PBMC dataset  51  to use as a reference. We re-analysed the single-cell data and matched the cell types between the single-cell and the bulk dataset so that our reference has the same cell types that had been measured in the bulk (Supplementary Fig. 9a ). We also summarised the proportions of CD4 + positive and T-regulatory cells measured by ow cytometry since our single-cell did not contain T-regulatory cells (Supplementary Fig. 9b ). Before we apply deconvolution on the 9 real bulk samples we rst performed a self-reference deconvolution to evaluate how the reference performs in pseudo-bulk deconvolution. Eight methods yield good results with a Pearson Correlation of more than 0. 92 (Supplementary Fig. 9b ). Next, we applied the 29 deconvolution methods included in the pipeline on the real bulk PMN/PBMC data and obtained the cell-type predictions.
In the self-reference task, with the single-cell PBMC dataset, we con rm that the best-performing methods based on Pearson Correlation with ground truth also cluster together after a pairwise comparison of the results and without the use of ground truth. This suggests that when ground truth is not available, pairwise comparison of the results per cell type and per sample can help us select the methods that give more consistent results. In parallel, we evaluated the scalability of each module of our pipeline(the pseudo-bulk generation method,normalisation and the deconvolution methods) and we report the time(s), memory, mean load and CPU time values for each one of them for both deconvolution tasks described above(Supplementary Fig. 10a-b ).
Results from Smart-seq2 and 10X single-cell datasets that have been used in this study, show that 10X data is consistently performing better as a reference, this is likely because this technology captures more cells of a speci c cell type and as a result, more accurate features are extracted from these data. It should also be noted that in tasks when the bulk and the reference data come from different sources (studies or technologies) -the accuracy score of the deconvolution drops signi cantly. This indicates that batch effects in the expression of genes, across datasets, affect deconvolution heavily. Methods that take into account and successfully minimise these effects (DWLS, MuSiC) seem to also perform better overall in cross-reference tasks. We apply deconvolution in real PBMC samples using a suitable matching single-cell reference and conclude that two methods, DWLS and bseqsc perform best.
All the above steps that should be performed before deconvolution have been implemented as part of the pipeline with a number of parameters that can be de ned by the user. We also propose a novel approach to select suitable deconvolution methods based on pairwise comparison of deconvolution results when ground truth is not available or reliable. The CATD pipeline, developed using snakemake, enables the reproducibility of our results and can be additionally utilised by users to deconvolve new bulk samples by providing only a bulk dataset and a single-cell dataset from the same tissue. When ground truth is provided too, the pipeline will provide evaluation metrics for each method. On the other hand when ground truth is absent the pipeline will perform pairwise comparison of the results and report a heatmap with methods to be selected.
Building Deconvolution Inputs Aside from the pseudo-bulk data and the corresponding proportions, deconvolution methods require different references to process the inputs. Within the pipeline, these references are encoded as C0, C1, C2, phenData and refVar. C0 is a generic single-cell matrix, with rows being genes and columns being cells. C1 is the averaged (arithmetic mean) gene expression matrix by cell types, with rows being genes and columns being cell types. C2 is a list of marker genes obtained from differential gene expression tests through the R package Seurat, speci cally using a combination of NormalizeData and FindAllMarkers functions. phenData is the metadata object that is associated with C0. Finally, refVar is the row standard deviations matrix by cell types, similar in structure to C1.
Analysis of XRD Results of Pre-dephosphorization Final Slag In order to explore the influence of mineral phase structure of the pre-dephosphorization final slag on dephosphorization, the four groups of experimental final slags were tested by XRD, and the obtained data were analyzed by the Search-Match software. The results are shown in Fig. 6. It can be seen from Fig. 6 (a) that the dephosphorization slag has a relatively similar mineral phase structure, all of which contain Fe, 2CaO•SiO 2 , CaO and 3CaO•P 2 O 5 mineral phases. The diffraction peaks related to CaO in the final slag of the first three groups of experiments gradually become sharp, which indicates that the molten CaO not involved in the dephosphorization reaction gradually increases, while in the 4# experiment, the diffraction peaks of CaO obviously decrease.
Analysis of Raman Spectrum Results of Pre- dephosphorization Final Slag In order to further explore the migration behavior of phosphorus between molten iron and slag after adding dedusting ash, the final slag of experiments 1-4 was characterized by Raman spectroscopy. Based on the position of vibration peak concentration in Raman test results, combined with previous studies, and referring to Table  6 , the Raman spectrum is deconvoluted by Origin 9. 0 software. The spectrum In the figure, Q n (Si) (n = 0,1,2,3) represent the asymmetric stretching vibration of Si-O bond in SiO 4 tetrahedron with bridge oxygen numbers of 0, 1, 2 and 3 respectively, and Q n (P) (n = 0,1,2) represent the asymmetric stretching vibration of P-O bond in PO 4 tetrahedron with bridge oxygen numbers of 0, 1 and 2 respectively.
We measured the luminescence activity 30 h post-transfection. Co-transfection with DTA completely abolished NLuc expression within parent cells (less than 3% of control protein synthesis activity), whereas DPHK2KD cells retained more than 75% of protein synthesis activity in the same conditions (Fig. 1B ). This demonstrated that DTA-HA expression efficiently blocked protein synthesis in parent cells, and that DPH2KD cells were DTA resistant. We confirmed these data by co-transfecting parent or DPH2KD cells with plasmids encoding mCherry as a reporter and plasmids expressing either mock or DTA-HA. We assessed protein expression by western blot 36 h post-transfection. The mCherry protein was detected in parent cells when co-transfected with a mock plasmid but was absent from cells co-transfected with DTA-HA (Fig.
EV uptake assay. EVs were obtained from parent or DPH2KD HeLa cells transfected with a plasmid encoding NLuc-Hsp70. Acceptor parent HeLa cells were seeded in 96 well plates the day before EV incubation. The same protein amount of EVs for each condition is incubated with acceptor cells for 24 h. After incubation, acceptor cells are washed twice in DPBS and NanoLuc activity was measured in each well using the Nano-Glo Live Cell Assay System (Promega, Wisconsin, USA) following the manufacturer's instructions using the iD3 Particle metrics. Nanoparticle Tracking Analysis was performed using the ZetaView® QUATT (Particle Metrix, Meerbusch, Germany) and its corresponding software (ZetaView 8. 02. 28). For the size measurements, the 448 nm laser in scatter mode was used. The instrument settings were 25 °C, sensitivity of 80, shutter of 100 at a frame rate of 30 frames per second.
FACS analysis. After treatments, cells were detached from cell culture plates with 0,05% Trypsin-EDTA and washed once in DPBS through centrifugation at 1000 × g for 5 min at 4 °C. Cells were finally resuspended in DPBS and kept on ice (less than one hour) until analyzed on an Attune NxT flow cytometer (Thermo Scientific, Illinois, USA). Each sample was incubated with 10 µg/mL DAPI (Merck Millipore, Massachusetts, U. S. A. ) right before analysis. The gating strategy is depicted in Fig. S1C. Data was analyzed using the FlowJo software (BD Bioscience, New Jersey, U. S. A. ).
Acknowledgements GL thanks  INSERM U932/Institut Curie  (and its members  C. Théry  and  S. Amigorena ), where the project was initiated. This work is supported by  INSERM , the  French National Research Agency  ( ANR-Excelldisc, -BIOEV, -EVfusion ) and  Chaire d'Excellence Idex Université Paris Cité. We thank the IVETh platform (https:// iveth. uparis. fr). We thank  H. Salmon  for initial technical support for imaging with CellInsight-CX7-LZR.
0. cDNA libraries (n of 3 per group) were generated using RNA using the Tecan Universal Plus mRNA-seq kit. These libraries were then sequenced on the NovaSeq 6000 Sequencing System at a sequencing depth of 25 million reads, with 100 cycle base pair paired-end read resolution, provided by the University of Kansas Genomics Core, as previously stated  (Gunewardena et al. 2022 ). An online PFAS dataset using human spheroids as a model was downloaded using SRA-tools from the GEO database, which was analyzed in parallel to the humanized mice RNA-Seq dataset (GSE144775)  (Rowan-Carroll et al. 2021). Raw fastq files were then aligned to the human genome (GRCh38), and genes were counted using STAR software (Version 2. 3. 1u)  (Dobin et al. 2013)  run on an HPE DL380 Gen10 8SFF CTO high-performance server.
Ingenuity Pathway Analysis Differentially expressed genes (DEGs) from the humanized mice (p-value < 0. 05 and an |foldchange| ≥ 1. 5) for each treatment group were uploaded into the Qiagen Ingenuity Pathway Analysis (IPA) software as previously described  (Gunewardena et al. 2022; Robarts et al. 2022a ). The upstream regulators were then exported from the software using the graphical user interface. Once exported, this dataset was uploaded to RStudio (Version 4. 0. 3, RStudio Team). The R package ggplot2 (Version 3. 3. 3) was used to generate dot plots, where size represents the number of genes changed in that pathway, color represents the -log2(p-value), and the x-axis illustrates the z-score assigned by the IPA.
BaseSpace Correlation Engine Illumina's BaseSpace Correlation Engine (BSCE, Version 2. 0) was used to determine the correlation between the humanized dataset and the human spheroid dataset. Both datasets were uploaded to BSCE using their online interface (https://www. basespace. illumina. com). Briefly, BSCE uses the Running Fisher test to establish positive or negative correlations with corresponding -log(p-values), as previously described in depth  (Kupershmidt et al. 2010 ). These data were imported into RStudio (Version 4. 0. 3, RStudio Team). The -log(p-values) were assigned a direction; for example, if there was a negative correlation, the -log(p-value) was multiplied by -1. The correlation -log(p-values) for GenX-, PFOA-, PFOS-exposed humanized mice were plotted using the R package ggplot2 (Version 3.
To determine specific changes in human hepatocytes and exclude changes occurring in mouse NPCs, all alignments were performed on the human genome. We compared ligand-binding nuclear receptors from human RNA-Seq datasets to humanized mice RNA-Seq using a rank-based method and found that the top expressed receptors were RXR, PPAR, and AR, whereas the least expressed receptor was RXR (Fig. S2A ). Cluster analysis shows that transcriptomic changes following PFOA and PFOS were similar to each other but distinct from those induced by GenX (Fig. S2B ). A global analysis of all differentially expressed genes (DEGs) found that PFOA caused significant downregulation of 154 genes and significant upregulation of 86 genes, with the top altered genes including KRT23, NRN1, RGS2, MRC2, and UCP2 (Fig. 4A, Table 1 ).
4D-E, Table 2 ). In addition, 23 DEGs were commonly downregulated across all exposures, including CYP3A7, SCL7A10, MRC2, and cyclins A2 and B1 (Fig. 4D-E, Table 3 ). To visualize alterations in cytochrome P450s and phase 2 drug metabolism enzymes (DMEs), heatmaps were made using the fold change values when compared to the control group (Fig. S2C-B ). CYP2A7, CYP3A4, CYP2A13, CYP26A1, and UGT1A3 were the most commonly induced DMEs (Fig. S2C-D ). Next, to compare these in vivo data to in vitro data, we compared the humanized mice DEGs of PFOA and PFOS with an RNA-Seq dataset derived from human hepatic spheroids treated with either PFOA or PFOS at multiple concentrations (PFOA: 0. 02, 0. 1, 0. 2, 1, 2, 10, 20, 50, or 100 μM, and PFOS: 0. 02, 0. 1, 0. 2, 1, 2, 10, or 20 μM) and time points (1-, 4-, 10-, or 14-day)  (Rowan-Carroll et al.
2021). We found that our PFOS treated humanized mice were most similar to the 1 μM treatment for 14 days and the most different to the 2 μM treatment for 1 day (Fig. S3A-B ). The PFOA treated mice were most similar to the 14-day spheroid exposure at the 10 μM concentration (Fig. S3A, S2C ). Interestingly, we found that GenX was most similar to the PFOA spheroids treated at the 10 μM for 10 days and most dissimilar to the PFOS 1-day exposure at 2 μM (Fig. S3A ). To provide insight into mechanisms that were contributing to these DEGs altered in PFOA, PFOS, and GenX exposed mice, we uploaded the DEGs into the Ingenuity Pathway Analysis (IPA) software to determine altered upstream regulators. We found that PFOA significantly inhibited androgen receptor (AR), FOXM1, and NR1D1, and induced activation of TP53, PPAR, and the proinflammatory regulators INF and IL1B (Fig.
5B-C ). CLOCK protein expression did not change, as expected, because NR1D1 does not regulate CLOCK expression (Fig. 5B-C ). BMAL1 protein showed no significant changes in all treatment groups (Fig. 5B-C ), which was consistent with its mRNA expression (Fig. S4B ). IHC of CLOCK and BMAL1 corroborated the western blot data (Fig. S4A ). However, IHC of NR1D1 showed significant localization in the nucleus in PFOA, PFOS, and GenX exposed humanized mice with little localization in the control group (Fig. 5D ). Further, we performed Western blot analysis of NR1D1, CLOCK, and BMAL1 using cytoplasmic and nuclear fractions, which yielded similar results showing no change in CLOCK and BMAL1 and a significant amount of NR1D1 protein in the nucleus (Fig. 5E ). Due to the strong translocation of NR1D1 into the nucleus and the absence of suppressed BMAL1 expression, we examined whether PFAS directly inhibits NR1D1.
To do this, we performed in silico ligand docking with each PFAS into the crystalized structure of the DNA-binding domain of NR1D1. We found that PFOA, PFOS, and GenX all docked successfully into the DNA-binding pocket, with delta G values of -7. 0, -6. 5, and -5. 4, respectively (Fig. 6A-C ). Altogether, our data utilizing humanized mice indicate that PFAS inhibit NR1D1-mediated regulation of circadian rhythm genes (Fig. 6D ).
2016; Reardon et al. 2021; Robarts et al. 2022b; Rowan-Carroll et al. 2021; Schlezinger et al. 2021 ). One issue with in vitro models is the lack of cell-cell communication and cross-organ communication that are critical contributors to toxicity mechanisms. In that regard, the humanized-mouse model used in these studies can overcome many of these challenges, because the mice possess human hepatocytes and an intact in vivo system, which can potentially reveal more human-relevant AOPs. This was exhibited in which no changes in circadian rhythm were detected in the human spheroid dataset. In addition, PPARα activation in humanized mouse models was less prominent in human hepatocytes compared to rodent hepatocytes utilizing the agonist fenofibrate (de la Rosa  Rodriguez et al. 2018). Our study corroborates this with an induction of rodent PPARα target gene expression, while no change in the expression of the orthologous human gene.
Cholesterol and bile acid alterations induced by PFAS exposure. One of the main findings of epidemiological studies was that PFAS (particularly PFOA and PFOS) exposure has a strong correlation with increasing cholesterol levels in serum  (Andersen et al. 2021; Blake and Fenton 2020; Frisbee et al. 2010; Rogers et al. 2021; Rosato et al. 2022; Steenland et al. 2009). Steenland et al. (2009)  found that with increasing PFOA and PFOS concentrations, the odds ratio of having higher levels of cholesterol increased, particularly in LDL/VLDL but not HDL. Another study showed that in children PFOA and PFOS were significantly associated with increases in serum total cholesterol and LDL levels  (Frisbee et al. 2010). These data suggest that, in humans, PFAS exposure leads to hypercholesterolemia. The effect of GenX exposure on cholesterol levels is unknown.
2014). At 10 μM concentrations, PFOS act as inhibitors of NTCP, displacing bile acids, which in turn increases the serum levels of bile acids  (Zhao et al. 2015). The serum concentrations for PFOS in our model was 8. 96 μM, potentially in the NTCP inhibition range. Further studies are needed to determine the inhibitory effects of GenX on NTCP or other bile acid transporters, such as OATs and OATPs. Our data showed no significant increase in serum bile acids in PFOA treated mice. This could be partly due to the large variability in bile acid levels within the treatment group. As most PFAS studies have been on the effects of bile acid transporters in vitro, more in vivo experiments are needed to clarify the role of bile acid transporters in the deposition of PFAS. Induction of proliferation in response to PFAS exposure.
Our RNA-Seq data revealed significant activation of p53 in all PFAS exposed mice as compared to the control group, indicating a balance between cell proliferation and cell death. This suggests that PFAS could act as a promoter in hepatocellular carcinomas in hepatocytes possessing loss of function mutations in p53. Implications of changes in NR1D1 activity after PFAS exposure. One of the most intriguing findings of our studies is the induction of NR1D1, a nuclear receptor involved in the regulation of circadian rhythm, by all three PFAS. In general, light is the signal that starts clock synchronization across organs. The signal from the optic nerve travels to the suprachiasmatic nucleus (SCN) in the hypothalamus, which then communicates with the organs utilizing a variety of cellular and molecular cues, within the endocrine and nervous systems  (Mukherji et al.
This study has been subjected to review by the  Center for Computational Toxicology and Exposure, Office of Research and Development, EPA  and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use. Financial Support: These studies were supported by  NIH-COBRE  ( P20 RR021940-03 ,  P30 GM118247 ,  P30 GM122731 ),  NIH   S10 OD021743 ,  NIH   R01 DK0198414 , and  NIH   R56 DK112768.
A second Cox regression model was built including only baseline parameters that were not time dependent. To build a multivariable Cox model, univariate CoxR were performed on each predictor variable separately to assess the unadjusted hazard ratios with 95% CI and corresponding p-value. Next, all variables with a univariate p-value B 0. 20 were added into a multivariable model. Finally, a stepwise Cox or logistic regression was performed on this selection, in which variables with adjusted p-value B 0. 35 were sequentially entered into the model, whilst being retained if they demonstrated an adjusted p-value B 0. 20 in the subsequent steps of the stepwise selection. All analyses were carried out using SAS software, version 9Á4 (The SAS Institute, Cary NC). All statistical tests were 2-tailed.
Data collection, imaging time intervals and follow-up were not standardized across the studies, and patient populations varied between the study sites, which may all compromise the predictive power in the CoxR models. Tumour response was measured according to mRECIST in 4 studies and RECIST1. 1 in one study (which provided 50 patients), which may have influenced the response analysis. The survival analysis may have been biased by the inclusion of 35 patients who were treated with thermal ablation or percutaneous ethanol injection for persistent tumour. Another limitation of the study was incompleteness of data that did not allow for time dependent analysis of survival predictors. Despite these limitations, we found it important to evaluate tumour response as predictor in the landmark model and the obtained results suggest that it can be used as surrogate predictor of survival in similar studies  [25, 26].
Patients were identified using the national hospital discharge database. In both centers, initial dosing regimens were determined by treating physicians, with possible adaptations to dose according to renal function based on the current international guidelines  [1 , 2]. Patients under hemodialysis, or receiving extracorporeal membrane oxygenation, and those admitted for > 7 days in an intensive care unit were excluded because these conditions strongly interfere with antibiotic pharmacokinetics (PK)  [16] [17] [18]. The study was approved by the national review board (CER-MIT n °2022-0704), and data collection was authorized (CNIL n °2205295v0). Patients were retrospectively informed of the study and could refuse to participate, but individual consent was waived in accordance with French legal standards.
Data collection and definitions Data for clinical, microbiological, imaging, and pharmacological variables, as well as treatment and follow-up, were collected from medical charts, through a standardized questionnaire, by two investigators (MD and RA). Plasma creatinine clearance was estimated according to the eGFR equation developed by the CKD Epidemiology Collaboration (CKD-EPI)  [19]. The Kidney Disease Improving Global Outcome (KDIGO) classification was used to assess renal function. Acute kidney injury (AKI) was defined as an increase in serum creatinine (SCr) by ≥ 0. 3 mg/dL or ≥ 1. 5 times the baseline value within 48 h  [20]. Intracardiac devices were defined as permanent pacemakers or cardioverter defibrillators. Antibacterial treatments that included gentamicin or ceftriaxone for at least 5 days in association with amoxicillin were categorized as combination therapy.
Accurate snow cover modeling is a high stake for mountain regions. Alpine snow evolution and spatial variability result from a multitude of complex processes including interactions between wind and snow. The SnowPappus blowing snow model was designed to add blowing snow modeling capabilities to the SURFEX/Crocus simulation system for applications across large spatial and temporal extents. This paper presents the very first spatialized evaluation of this simulation system over a 902 km 2 domain in the French Alps. Here we compare snow cover simulations to the spatial distribution of snow height obtained from Pleiades satellites stereo-imagery and to Snow Melt-Out Dates from Sentinel-2 time series over three snow seasons. We analyzed the sensitivity of the simulations to three different precipitation datasets and two horizontal resolutions.