dataesr/scientific-paragraphs-categorization huggingface dataset:
Genotyping, sample demultiplexing, and doublet detection To assign cells to donors of origin in our multiplexed design, we used the genetic demultiplexing tools Freemuxlet and vcf-matchsample-ids, each a part of the Popscle suite of statistical genetics tools (https://github. com/statgen/popscle). Freemuxlet leverages the single-nucleotide polymorphisms (SNPs) present in transcripts and performs unsupervised clustering on the droplet barcodes to assign each to a nameless donor or assign them as doublets between genetically distinct nameless donors. The algorithm takes in a list of candidate loci throughout the genome at which to scan for SNPs and returns droplet barcodes with donor assignments and a set of observed variants per donor. These sets of variants are then matched using genotypic similarity to those from an orthogonal bulk RNAseq assay, done on an individual basis, to determine which donor is which patient.
Bulk RNA sequencing Bulk RNA-seq data were generated to extract genotype information so that single cells could be demultiplexed and matched to single donors. For each donor, RNA was extracted from PBMCs using the Quick-RNA MagBead Kit (Zymo Research) on a KingFisher Flex system (Thermo Fisher Scientific) according to the manufacturer's protocol. RNA integrity was measured with the Fragment Analyzer (Agilent), and library generation was continued when integrity was at least 6. Total RNA-seq libraries were depleted from ribosomal and hemoglobin RNAs and generated using FastSelect (Qiagen) and Universal Plus mRNA-Seq with NuQuant (Tecan) reagents. Pooled libraries were PE100-sequenced on a HiSeq 4000 or PE150sequenced on an Illumina NovaSeq 6000 S4 flow cell at the Chan Zuckerberg Biohub.
Single-cell epitope and RNA-seq preprocessing and alignment CellRanger v3. 1. 0 (run 1 to 7, cDNA library generation of run 6 failed) or v4. 0. 0 (run 8 to 10) software with the default settings was used to demultiplex the sequencing data and generate FASTQ files (Cellranger mkfastq), align the sequencing reads to the hg38 reference genome, and generate a unique molecular identifier (UMI)filtered gene and protein expression count matrix for each lane (Cellranger count for scRNA-seq and CITE-seq data). Count matrices were then concatenated across all 50 lanes to generate two matrices: one mRNA matrix with 971,550 cells and 36,601 genes, and one surface protein matrix with 971,550 cells and 189 proteins.
Single-cell epitope and RNA-seq processing and quality control Resulting gene and protein expression count matrices were further processed in the Python package Scanpy v1. 5. 1  (49). Processing of the concatenated mRNA count matrix was done using a two-step process. We have found empirically that traditional workflows for the processing of droplet-based RNA-seq data for PBMCs can sometimes create unwanted effects in the downstream end points. In particular, filtering of cells with a high percentage of mitochondrial cells may create visual artifacts in UMAP projections, and filtering of the matrix to only a few hundred highly variable genes and reducing the memory footprint of the data can sometimes lead to spurious clustering of cells based on only a few genes. Our iterative process yields a UMAP projection that captures all available heterogeneity with minimal filtering in the first iteration, then removes nontarget cells, and corrects for nonbiological signal in the second iteration.
By doing this, we use a relatively large number of components to inform projections and clustering but observe that the outputs in our dataset match the known biology better (such as proximity of similar cell types and states in UMAP space) and yield higherconfidence annotations. In the first step, the mRNA matrix was filtered to remove doublet droplets, as annotated by Freemuxlet, and very lowly expressed genes with less than 100 UMIs across the nine runs. The matrix was then normalized to yield a constant UMI sum per cell and log-transformed. Matrix values were scaled to yield a mean of zero and SD of 1 per gene. Principal components analysis (PCA) was performed, followed by nearest neighbors, UMAP projection, and Leiden clustering, using an input of the 150 principal components with the highest variance explained and otherwise default Scanpy parameters.
Last, further filtration of a relatively small number of cells with high expression of platelet, red blood cell, and mitochondrial genes was performed, as well as removal of donors that declined study participation. After processing, 600,929 cells and 18,262 genes remained in the mRNA matrix. The surface protein matrix was filtered to the cells found in the mRNA matrix. One protein was removed from the matrix, as it appeared to have very low counts relative to the other surface proteins. The remaining proteins were then normalized using the centeredlog ratio (CLR), computed for each gene independently. The CLR has typically been used for CITE-seq data with the recognition that antibody counts are typically not zero-inflated and flow cytometry-like Gaussian distributions are achievable when treating the data as compositional.
ISG score method An orthogonal scRNA-seq dataset containing PBMCs stimulated with IFN- and IFN- was used to identify the specific and shared type I and type II ISGs in the cMs (Gene Expression Omnibus accession number GSE181897). The gene list was used to calculate a type I, type II, and shared ISG score based on the average gene expression count of the unique or shared type I and type II ISGs. These ISG scores were calculated for each unique combination of cell type, donor, and time point. Subsequently, ISG scores were averaged over each of the disease categories (COVID-19 + moderatesevere, COVID-19 -moderate-severe, healthy control) and then log 2 -transformed.
SARS-CoV-2 detection by clinical RT quantitative PCR Viral titers were quantified in a subset of the COVID-19 + patients in the UCSF CLIAHUB Clinical Microbiology Laboratory. For this, RNA was extracted from tracheal aspirate and nasopharyngeal swabs and used for RT-qPCR (quantitative PCR) as previously described  (52). In short, viral titers were assessed using primers targeting the SARS-CoV-2 N gene (Ct1) and E gene (Ct2) and human ribonuclease (RNase) P gene (Ct_host, positive control). The Ct value of the viral N or E gene was subtracted from the human RNase P gene (Ct: Ct1 and Ct2) (data file S9), and number signs were reversed to obtain a measurement for normalized viral RNA abundance. As there was an almost perfect correlation between Ct1 and Ct2 values (Pearson R = 0. 99, P = 1.
We then used an LMM for each group using the statsmodels Python package, with formula "<measurement> ~ time_ point," where <measurement> was either type I score or LAIR1 expression. Slope deviations from 0 were considered significant when their P value was below 0. 05. All correlations were calculated using the Pearson R. Significance was then defined as Holm's corrected P value < 0. 05.
TANS, TUMOR ANGIOGENESIS AND IMMUNE EXCLUSION In the tumor mass, neoangiogenesis leads to the development of an immature microvascular network that shapes the TME architecture and composition  27 28  and also contributes to disease progression and resistance to ICI treatment. TANs are considered a major source of growth factors involved in tumor angiogenesis, and their infiltration has been associated with resistance to antivascular endothelial growth factor (VEGF) therapy. 29 The anti-VEGF and anti-angiopoietin-2 bispecific antibody venucizumab has been evaluated in a phase I clinical trial in combination with atezolizumab (anti-PD-L1 antibody) in patients with advanced cancer with acceptable safety profile and promising effect on tumor angiogenesis and cellular density were observed (NCT01688206).
Multitargeted RTK inhibitors, such as pazopanib and sorafenib that inhibit PDGFR and VEGFR1-3 are currently tested in combination with ICIs. Importantly, sunitinib, which inhibits FGFR1-4, RET, PDGFR, KIT, CSF1R, and VEGFR1-2 could overcome neutrophil-mediated immunosuppression together with a simultaneous action on tumor angiogenesis and cancer cell oncogenic signaling. 35 Evaluation of lenvatinib (that inhibits VEGFR1-3, FGFR1-4, KIT, RET, and PDGFR) plus pembrolizumab in a phase Ib/II clinical trial in NSCLC, renal cell carcinoma (RCC), melanoma, head and neck cancer (HNC), and urothelial carcinoma demonstrated good tolerability and promising therapeutic activity of this combination (NCT02501096). 36 imilarly, anlotinib and pazopanib inhibit KIT and FLT3 signaling in addition to VEGFR, and thus, determining if these drugs could have a direct impact on neutrophils became particularly interesting.
Indeed, all these tyrosine kinase inhibitor (TKI) could sensitize tumors to immune checkpoint blockade through their simultaneous action on tumor angiogenesis, cancer cell oncogenic pathways and neutrophils. However, combination of the anti-PD-1 nivolumab with pazopanib or sunitinib revealed the need of a precise dose and drug selection in these combinatory approaches (NCT01472081). 37 udy in the KP mouse model of lung cancer showed that neutrophil depletion (using an anti-GR1 antibody) increases tumor vasculature coverage by alpha-SMA + pericytes, reduces hypoxia, reverts tumor immune exclusion and sensitizes tumor to anti-PD-1 therapy. 24 In the CCR2-deficient mouse model of cervical cancer, tumorassociated macrophages (TAM) proportion is reduced compared with control tumors, and TANs support tumor angiogenesis in the absence of macrophages.
46 47 Therefore, PI3Kα/β inhibitors (duvelisib and GSK2636771) that target PTEN mutated cancer cells might also remodel the TME. 47 48 Furthermore, the MET inhibitor capmatinib increases ICI efficacy in various solid cancer mouse models through alteration of neutrophil recruitment at sites with T cell-induced inflammation. 49 A phase II clinical trial (NCT04139317) is currently testing the efficacy of pembrolizumab in NSCLC combined or not with capmatinib, irrespectively of the cancer MET mutation status. Finally, clinical innovations may come from cabozantinib, a MET, RET, AXL, VEGFR2, FLT3, and KIT inhibitor that reprograms TANs toward antitumor activity. 50 51 There are currently 48 and 47 ongoing clinical trials in which the pan-TKI cabozantinib is combined with PD-1/PD-L1 or CTLA-4 blockade, respectively.
COMBINING IMMUNE CHECKPOINT BLOCKADE AND INHIBITION OF NEUTROPHIL RECRUITMENT IN TUMORS 0] [91] [92]  In agreement, CXCR1/2 inhibitors (SX-682 and navarixin) significantly inhibit TAN accumulation in tumor tissues and increase the efficacy of anti-PD-1 immunotherapy in mice. 93 These results might be translated into the clinic thanks to a newly developed Open access IL8/CXCL8 antibody (BMS-986253) currently tested in a phase II clinical trial (NCT04050462) in combination with nivolumab (anti-PD-1 antibody) in patients with advanced hepatocellular carcinoma (HCC). More recently, the expression of the CCR5 ligands CCL3/ MIP1α, CCL4/MIP1β, CCL3L1, and CCL5/RANTES also has been implicated in neutrophil recruitment to tumor tissues in patients with melanoma and mouse models as well as in patients with NSCLC.
94 Therefore, several trials are assessing whether the combination of the CCR5 inhibitor vicriviroc with pembrolizumab (NCT03631407) or of the CCR2/5 inhibitor BMS-813160 with nivolumab (NCT03184870) could be more effective than immune checkpoint blockade alone by inhibiting TAN-mediated cancer promotion and immunosuppression. TAN accumulation might be reduced also through inhibition of CXCR4 because it has been suggested that it directly contributes to neutrophil recruitment in the premetastatic niches in mice. 95 Furthermore, CXCR4 inhibition could impair hematopoietic niches and neutrophil biogenesis by blocking CXCL12-mediated hematopoietic stem cell homing. 96 Interestingly, the phase II clinical trial NCT02826486 recently demonstrated that the combination of BL-8040/BKT140 (a CXCR4 inhibitor) and pembrolizumab in patients with pancreatic cancer reduces TAN infiltration and increases cytotoxic T-cell function.
97 1] [102]  Although inhibiting IL-6 or GM-CSF signaling might interfere with IL-17Arelated neutropoiesis and TAN infiltration, there is currently no therapeutic approach specifically targeting IL-17 signaling in combination with immune checkpoint blockade. Similarly to IL-17A secretion that links antibacterial immunity to the anti-cancer immune response in mice and human,  98 101  release of the complement component 5-a (C5a) in the TME is another potent attractant for neutrophils and macrophages in mice and on purified human neutrophils in vitro. 104 105 lthough the impact of C5a receptor (C5aR) signaling on TAN plasticity and function remains unclear in cancer, the anti-C5aR antagonist antibody IPH5401 is currently tested in the combination with durvalumab anti-PD-L1 antibody (NCT03665129).
Finally, based on observations made in mice and humans, artificial activation of apolipoprotein E signaling using the LXR agonist RGX-104 is considered as one of the most promising approaches to inhibit neutrophil accumulation in tumors and in the periphery. This approach reduces TAN survival in mice and sensitize tumors to anti-PD-1 blocking antibodies. 106 Furthermore, a phase I clinical trial (NCT02922764) showed that RGX-104 reduces the fraction of circulating neutrophils in CRC, RCC, sarcoma, uterine cancer, and melanoma. 106 In addition to the direct effect on neutrophil survival, mouse models suggest that RGX-104 also alters neutrophil biogenesis through modulation of the hematopoietic niche function  107  (figure  3 , table  1 ).
109 Here again, combining the anti-IL1β antibody canakinumab or the IL1R antagonist anakinra with ICIs in clinical trials might affect both IL1β-mediated cancer cell proliferation and TANs (table  1 ). Similarly, tumor-necrosis factor (TNF) signaling inhibition could synergize with immune checkpoint blockade by directly influencing T cells and also by reducing IL-17 secretion and TAN recruitment. Indeed, RIP1 and/or RIP3 inhibition, which abolishes necrosome formation and TNF signaling, increases the antitumor activity of macrophages and reduces TAN recruitment in mouse models of pancreatic cancer. 110 111 GSK3145095, a RIP1/ RIP3 kinase inhibitor that might damp TAN-mediated tumor promotion, is currently evaluated in combination with pembrolizumab (anti-PD-1 antibody) in patients with pancreatic cancer (NCT03681951) (table  1 ).
Funding JF is supported by a postdoctoral fellowship from the fondation pour la recherche médicale, aide au retour en France,  ARF201809007001. This work is supported by the  Institute National de la Santé et de la Recherche Médicale ;  Université de Montpellier ; the  Institut Régional du Cancer de Montpellier ; the  Bristol-Myers Squibb Foundation for Research in Immuno-Oncology  and the  French National Research Agency  under the program ' Investisse-ments d'avenir' Grant Agreement LabEx MAbImprove :  ANR-10-LABX-53.
Drivers of reversible and irreversible slope deformations in a paraglacial environment Retreating glaciers around the world lead to rapid and profound changes in the surrounding landscapes. In the Alps, many glaciers are rapidly retreating and downwasting, substantially modifying stresses and hydro-thermal boundary conditions on the adjacent slopes. There is an increase in observations of bedrock responses and the formation of large-scale instabilities in paraglacial environments, but still a little knowledge about the underlying preparatory factors and drivers. This presentation is linked to the one from Hugentobler et al. in the same session. Both studies take place in the same catchment and address the same questions at different spatial scales, with other techniques and datasets. We analyse surface deformation data monitored in a crystalline bedrock catchment, on the recently deglaciated slopes of the Great Aletsch Glacier (Valais, Switzerland).
Our monitoring system has been in operation for six years and comprises 93 reflectors, 2 robotic TPS, and 4 cGPS stations distributed on both sides of the glacier tongue. This unique dataset allows studying the main processes involved at relevant spatial and temporal scales. The response of potential drivers for reversible and irreversible deformation is evaluated through combined multivariate (vbICA) and cross-correlation statistical analysis. We found that the variability in deformation near the glacier tongue is primarily controlled by glacier unloading through melting and seasonal groundwater fluctuations. At the catchment scale, the later effect is poroelastic and hence reversible, but we argue that it could also induce hydromechanical fatigue. By investigating the deformation's spatial pattern, we observed that the reversible deformation is mostly controlled by discrete structures such as hectometer-scale brittle-ductile shear zones striking subparallel to the valley axes and the main Alpine foliation.
Statistical analysis Continuous variables were expressed as means and medians. Categorical variables were summarized into proportions. Differences in categorical variables between groups were assessed using chi-square test or the chi-square likelihood ratio as appropriate. Differences in means were assessed by the student's t test. P values less or equal to 0. 05 were interpreted as statistically significant. Statistical analysis was performed using SPSS Statistics software version 22 (IBM, Armonk, USA).
| PK and PK-PD modelling The M3 method for handling of data below the LLOQ  21  was used for CXCL9 because concentrations in 21% of samples were below the LLOQ. After base model development leading to a two-compartment model in healthy volunteers, with additional nonlinear clearance in patients, the influence of potential covariates was examined and incorporated in the final popPK models, as described in Supporting Information Table  S3. The popPK-PD model was an indirect response model. The final models were qualified by evaluating the agreement between observed versus predicted concentrations, distribution of individual parameters and distribution of residuals. The predictive performance of the final models was evaluated using visual predictive checks on observed concentration-time data.
Serum emapalumab concentrations were simulated 500 times for the healthy population and 1000 times for the HLH population using the dose and covariate data from patients used in the PK and PD model development dataset. The 95% confidence intervals for the median and 5th/95th and 2. 5th/97. 5th percentiles of the simulated PK and PD data, respectively, were then compared visually to the corresponding percentiles of observed data from the phase 2/3 study.
| Software The popPK and popPK-PD models were developed using NONMEM  Furthermore, emapalumab would be expected to reduce IFNγ-mediated biochemical activity, which could be monitored by measuring serum concentrations of CXCL9. 24, 25 Accordingly, reduced IFNγ activity would also be expected to translate into observable improvements in the clinical and laboratory parameters of disease activity. 17, 19 wever, all components of the model in Figure  1B  (ie, free IFNγ, free emapalumab, IFNγ-emapalumab complex and CXCL9) have their own intrinsic clearance and their concentrations correspond to an equilibrium between production and elimination. Of note, the formation of the IFNγ-emapalumab complex results in an increased rate of emapalumab clearance that is proportional to IFNγ production.
emapalumab (Study NI-0501-03, NCT01459562) and from paediatric patients with primary HLH participating in a multicentre, open-label, single-arm, phase 2/3 study (Study NI-0501-04, NCT01818492 17 ; see study description below) and its long-term, follow-up study (Study NI-0501-05, NCT02069899). Study NI-0501-05 included patients from the NI-0501-04 study and patients with HLH who were not eligible to participate in the phase 2/3 study, but had exhausted all possible treatment options and were previously treated with emapalumab on a compassionate use basis. Full details of all studies are provided in Supporting Information Table.
ACKNOWLEDGEMENTS The authors would like to thank  Geneviève Lapeyre , formerly of  NovImmune , for her contribution to the protocol development and safety data analysis and the  Bioanalysis team at NovImmune/ Swedish Orphan Biovitrum AB (publ) (Sobi). Funding for this study was provided by  Sobi , and by a grant from the  European Commission  through the  First Targeted Therapy to Fight Hemophagocytic Lymphohistiocytosis (FIGHT HLH) program. Medical writing support was provided by  Blair Hesp PhD CMPP of Kainic Medical Communications Ltd (Dunedin, New Zealand ), which was funded by  Sobi.
Activity was recorded by two rows of infrared photocell sensors and processed with the Actitrack software (Bioseb). The total distance covered (in cm), the duration of inactivity (resting time, in seconds) and the mean speed (cm/sec) of each mouse were measured. -Visual recognition memory The novel object recognition (NOR) test is based on the tendency of mice to explore a novel object rather than a familiar one. Mice were habituated to a square arena (50 x 50 x 25 cm) for 10 minutes on the first day. On day 2, mice were exposed to the arena containing two identical objects. In a first phase ("sample phase"), on day 3, mice were presented two novel and identical objects for 15 minutes. Animals returned in their cages. After one hour, they were placed back in the testing arena for 5 minutes ("test phase"), but one of the familiar object had been previously replaced by a novel object.
The libraries were next sequenced for 50 bases in a single-read format (Genome Analyzer IIX, Illumina, San Diego, CA, USA). Quality control of the reads was performed using FastQC software (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/, accessed on 19 May 2021). Reads with lengths between 15 and 35 bp were withheld. Cutadapt v1. 2. 1 (http://journal. embnet. org/index. php/embnetjournal/article/view/200, accessed on 19 May 2021) was used for adapter sequence removal. Genome alignment was performed using Bowtie 0. 12. 7 (Johns Hopkins University, Baltimore, MD, USA) and Samtools 0. 1. 18 (http:// samtools. sourceforge. net, accessed on 19 May 2021). Subsequently, HTSeq v0. 5. 3p9 (https: //pypi. org/project/HTSeq/, accessed on 19 May 2021) using the miRbase v20 annotation for hg19 was used for determining raw counts.
96 Dynamic Arrays. A BioMark HD reader (Fluidigm, South San Francisco, CA, USA) was next used to perform qRT-PCR. Using the Fluidigm qRT-PCR Analysis software (4. 1. 3) (Fluidigm, South San Francisco, CA, USA), mRNA expression levels (threshold cycle values) were calculated. Beta-actin expression levels were used for data normalization.
05. Next, we searched three known miRNA-mRNA interaction databases for miRNAs potentially targeting HIF-2α, VEGFR1 and/or VEGFR2 (miRDB (http://mirdb. org/, accessed on 19 May 2021) and miRabel (http://bioinfo. univ-rouen. fr/mirabel/index. php?page=mir, accessed on 19 May 2021), databases for prediction of functional miRNA targets  [22, 23] , and DIANA TarBase v8 (https://carolina. imis. athena-innovation. gr/diana_tools/web/index. php?r=tarbasev8 %2Findex, accessed on 19 May 2021), a database of experimentally validated miRNAtarget pairs  [24] ). For miRabel, the cutoff of 0. 05 for the predicted RRA score was used, as recommended by the authors  [23]. miRNAs that were both correlated to one of the mRNAs and for which this interaction was listed in one of previously mentioned databases were withheld for further analysis.