dataesr/scientific-paragraphs-categorization huggingface dataset:
We extracted gene sequences from genome assemblies of the 229 Z. tritici strains using the ncbi-blast+ software  (Camacho et al. , 2009). Prior to performing the population genetic analysis, we first verified population structure in our dataset, to ascertain that there is no inflation due to population structure. We constructed a protein sequence phylogenetic tree using the RaxML algorithm with the GAMMA JTT model and 100 bootstrap replicates  (Stamatakis, 2014). Phylogenetic trees were visualized using iTOL  (Letunic and Bork, 2016) ; https://itol. embl. de/. We used the R package Popgenome  (Pfeifer et al. , 2014)  to calculate sliding-window analyses of nucleotide diversity (p) of the effector gene, including ~500 bp upstream and downstream of the coding sequence. We used a window length of 20 bp and a step size of 5 bp.
Qzt-I05-6 harbours a previously unannotated effector-like gene in a dynamic region The interval defined by Qzt-I05-6 holds 36 genes annotated in  Grandaubert et al. (2015). There is a 60 Kb-long TE-rich region in the middle of the QTL, with markers that came out as peak markers during the linkage analysis on either side. None of these 36 genes has any predicted functions; we therefore predicted functions with the InterPro database (http://www. ebi. ac. uk/interpro/search/sequence/) (Table  S8 ). We searched for genes that have characteristics of known effectors, as these are often involved in fungal pathogenicity. Out of the 36 annotated genes, two had a signal peptide. The first, named Zt09_6_00095 in the Grandaubert et al. (  2015 ) annotation, comes out as being in the PTHR35523 family in the panther classification system (http://www.
, 2018) , we looked further into the corresponding position. The RNAseq data produced by  Haueisen et al. (2019)  was used to make a read coverage file which was used in the integrative genomics viewer (IGV) software  (Robinson et al. , 2011)  as a means of annotating a previously unidentified gene. On chromosome 6 of IPO323, this gene is positioned at 470,027-470,324 bp. It has two exons with coding sequence (CDS); the first has a predicted signal peptide (https:// services. healthtech. dtu. dk/service. php?SignalP)  (Teufel et al. , 2022) , and there is no predicted function or family for the protein (http://www. ebi. ac. uk/interpro/search/sequence/). It has an intron which is 61 bp long and has canonical splice site combination GT-AG  (Kupfer et al. , 2004; Frey and Pucker, 2020). The CDS is 237 bp AUDPCs are the Area Under the Disease Progress Curve for the green, necrotic and sporulating areas (AUDPCG, AUDPCN and AUDPCS respectively).
S20 and S26 are the sporulating areas (%) at 20 and 26 days post-inoculation respectively. PYC is the pycnidia density. NBS is the number of pycnidiospores per pycnidium. long and encodes a 78-aa protein with 11 cysteines in its sequence (14% of the protein sequence). We identified four mutations between parental strains I05 and I07. In a recent, yet unreleased, annotation of the Z. tritici IPO323 genome sequence, specifically improved to detect genes encoding SSP, this gene was identified and named G_07189 (unpublished data). A BLAST search against the NCBI database (https://blast. ncbi. nlm. nih. gov/Blast. cgi) identified in other Z. tritici strains a second, longer protein, with a potential alternative methionine 32 aa before the predicted one. This longer, alternative protein has a cleavage site between the 47 th and 48 th aa, that is unlikely for a signal peptide.
Acknowledgments We thank  Delphine Hourcade  ( ARVALIS Institut du Veǵetal ) for her critical follow-up of the project. We also thank  Dr. Gabriel Scalliet  ( Syngenta Crop Protection AG, Stein, Switzerland ) for providing the vectors pNOV2114, pNOV_3Gate_SUL and pNOV_3Gate_HYG used to clone the candidate pathogenicity genes. We are grateful to  Martin Willigsecker  and  Beatrice Beauzoone  ( INRAE BIOGER ) for assistance in conducting the disease tests under controlled conditions. We are also grateful to  Delphine Paumier  ( ARVALIS Institut du Veǵetal ) for her help with designing the qPCR experiments.
Funding PhD student CL-P was funded by  ARVALIS Institut du Veǵetal  and the  National Association for Research and Technology  ( CIFRE 2019/0608 ). MGX acknowledges financial support from  France Génomique National  infrastructure, funded as part of " Investissement d'Avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). INRAE BIOGER benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
As mentioned earlier, selecting the optimal number of buffers is critical for implementing this detector. Since the normal period is 100 ns, we need to choose the number of buffers whose delay is close to this value. To determine this, we conducted post-implementation simulations using Vivado software and found that the delay of a single buffer was 0. 450 ns. We then gradually increased the number of buffers until their total delay reached 100 ns. The delay increases linearly with each additional buffer. After extensive testing, we discovered that 194 buffers are required to achieve a delay of 97. 89 ns, which is very close to the normal period. This ensures that the detector remains inactive during normal operation but can detect any attacks that modify the delay of the logic gates. This detector was synthesized using a VHDL RTL description in the Xilinx Vivado design suite and implemented on a Xilinx Artix-7 FPGA using a BASYS-3 board.
To reduce bias due to the number of cells, 1920 cells, close to the lowest number of cells from any group, were randomly selected from each group prior to analysis, resulting in a total of 7680 T cells being used for downstream analysis. following dimensionality reduction using the Uniform Manifold Approximation and Projection (UMAP) algorithm (Fig. 3a , heatmap in Supplementary Fig. 6 )  21. Clusters clearly segregated as CD8 + or CD4 + , the latter containing suppressive Foxp3 + CD4 TREG cells and conventional CD4 + T cells (Fig. 3b ). To deconvolute the major transcriptional states of the T cells, the ProjecTILs computational method was used applying the "tumor-infiltrating T lymphocytes (TIL) atlas" as a reference dataset  22  (Fig. 3c , Supplementary Fig. 7 ). This analysis revealed that more than 90% of all T cells were associated with one of four major transcriptional states: CD8 EX (42%), Th1-like CD4 + T cells (CD4 TH1 , 24%), CD4 TREG , (14%), and CD8 EM (10%) (Fig.
To characterize the ProjecTILs-defined subsets within each Seurat cluster, a differential gene expression analysis was performed (Supplementary Data 2, Supplementary Data 3). We then used the literature to interpret the function of each T cell subset. Among CD8 EX cells (Supplementary Fig. 8 ; Supplementary Data 2; Supplementary Data 3), 57% were found in C0, a cluster defined by differential expression of Gzma, Gzmb, and Nkg7 that encode effector cytotoxic molecules  23  , Pdcd1 that encodes the inhibitory receptor PD1, and Klra5 which is associated with terminally differentiated effectors  24. These cells also differentially express Lgals3 that encodes Galectin 3, which in the tumor microenvironment has been shown attenuate T cell infiltration by capturing Ifng 25. CD8 EX cells in C5 (13%), C7 (3.
T-cell differentiation clusters enriched in the tumor of mice treated with radiation therapy and CTLA4i are associated with survival in patients To assess if the T cell subsets that characterized the T cell landscape in RT+CTLA4i treated 4T1 tumors might be relevant to patients, we tested if the gene signatures that defined T cell clusters CD8 EM /C1, CD8 EA , and CD8 PEX were associated with outcome in patients with TNBC using the METABRIC gene expression dataset  39. Enrichment scores were calculated for each patient and their association with survival was assessed using a cox proportional hazard regression model. While there was an association with survival for CD8 EM /C1 and CD8 PEX using an univariate analysis (Fig. 4b ), in a more robust multivariate analysis that also considered age at diagnosis, CD3E expression (as a marker for T cell infiltration that has been associated with survival  40  ), menopausal state, and Nottingham Prognostic Index (NPI; which reflects the size, number of involved lymph nodes, and grade of the tumor), no association was found (Fig.
4e ). We performed a similar survival analysis in patients with melanoma using the Cancer Genome Atlas skin cutaneous melanoma dataset (TCGA SKCM). We found an association of the combined gene signature with survival when using a cox proportional hazard regression model that corrected for age, AJCC tumor stage, CD3E expression in patients with melanoma (p < 0. 01) (Fig. 4f ). Similarly, results obtained using a log-rank statistical model showed that patients with a high enrichment score for the combined signature had a better prognosis (Fig. 4g ). Altogether, these results suggest that the CD8 + T cells phenotypes that were uniquely expanded in the 4T1 tumor of mice treated with RT+CTLA4i share similarity with T cell functional states present within human tumors in patients who develop spontaneous anti-tumor immune responses.
Green boxes below heatmap indicate that genes were statistically significantly differentially expressed for a specific functional state in the single-cell sequencing data. Blue boxes indicate genes used in the combined gene signature in (d-g). b Univariate and (c) multivariate cox proportional hazard regression models were used to assess the association between gene enrichment score and survival in patients with triplenegative breast cancer (TNBC) using the METABRIC dataset (n = 299 biologically independent samples). d Multivariate cox proportional hazard regression model and (e) Kaplan-Meier curves of survival of patients with TNBC from the METABRIC dataset by enrichment of a combined gene signature that incorporated differentially expressed genes from CD8 EA/C1, CD8 EM/C1, and CD8 Tpex/C6.
Difference in survival between the two groups was assessed using the log rank test. f Multivariate cox proportional hazard regression model and (g) Kaplan-Meier curves of survival of patients with skin cutaneous melanoma from TCGA SKCM dataset (n = 417 biologically independent samples) and enrichment of a combined gene signature that incorporated differentially expressed genes from CD8 EA/C1, CD8 EM/C1, and CD8 Tpex/C6, p-value was based on the log rank test. NPI Nottingham Prognostic Index, CD3E expression of CD3E in tumors, AJCC American Joint Committee on Cancer. Bars represent the 95% confidence interval for the hazard ratio (b-d, f). Source data are provided as a Source Data file. activation and migration to dLN where they cross-present tumor antigens to CD8 T cells, whereas RT reduces the presence of immunosuppressive macrophages in the tumor.
Illumina bcl2fastq2 v2. 20 conversion software was used to demultiplex samples into individual sample and converted per-cycle BCL base call files into FASTQ files for downstream data analysis. The sequencing reads were cleaned by trimming adapter sequences and low quality bases cutadapt v1. 9. 1  73  , and were aligned to the mouse reference genome (GRCm38) using STAR v2. 5. 2b  74. Raw read counts per gene were extracted using HTSeq-count v0. 11. 2  75. Differential expression analysis was performed using DESeq2 v1. 22. 2  76  , with significance cutoffs: pvalue < 0. 01 and |fold change| > 2. Bulk tumor TCR sequencing. For TCRα and TCRβ CDR3 region sequencing, libraries were amplified from an average of 120 ng mRNA using the commercially available amplicon rescued multiplex polymerase chain reaction (arm-PCR) technology (iRepertoire), according to manufacturer's instructions  77.
Calculations of TCR repertoire statistics and comparisons were performed using a combination of functions in the divo, tcR, and immunarch packages in R. Data handling and post-processing and production of figures were performed using the tidyverse package and accompanying packages. Single-cell sequencing and analysis. For in-depth profiling 4T1 infiltrating T cells, we utilized the 10x Genomics 5′ gene expression, V(D)J, and dCODE dextramer single-cell sequencing platform. Tumors were digested on a gentleMACS Dissociator using the mouse Tumor Dissociation Kit (Miltenyi Biotec), according to the manufacturer's instructions. To reduce batch effects, each digestion run always included tumors from all treatment groups. Directly after washing step following tumor digestion, cells were stained with fluorochromeconjugated antibodies against CD3E (BV-650, clone 17A2, Biolegend), CD8a (BV-785, clone 53.
6. 7, Biolegend), CD4 (FITC, GK1. Downstream single-cell analysis was performed using Seurat v3. 0. 2 20 in R v3. 5. 2  79. Briefly, for each condition, a Seurat object was created from the 5′ gene expression and AH1-dextramer (feature barcoding) data, and the VDJ information was added as metadata. After pre-processing of the data according to the Seurat workflow, only Cd3 + Cd4 + CD8 -and Cd3 + Cd4 -Cd8 + cells (as determined by transcriptional levels) with >200 and <5000 features and <5% mitochondrial gene content were included for downstream analysis. Then, datasets corresponding to the different conditions were integrated using functions FindIntegrationAnchors and IntegrateData in Seurat 20. To reduce bias, 1920 cells were randomly selected from each condition prior analysis (original numbers 1920, 2317, 3363 and 2447 cells for untreated, CTLA4, RT, and RT + CTLA4 groups, respectively).
Then, the dimensionality was reduced by calculating principal components and eventually reduced to 2 dimensions using the Uniform Manifold Approximation and Projection (UMAP) algorithm  21. Finally, clustering of cells was performed. Furthermore, the functional state of each single cell was estimated using the ProjecTILs computational method by applying the "tumor-infiltrating T lymphocytes (TIL) atlas" as a reference dataset (16,803 single-cell transcriptomes of TILs from 25 B16 melanoma and MC38 colon adenocarcinoma tumors curated from 6 studies)  22. Differentially expressed genes were calculated between all Seurat clusters and/or ProjecTILs functional states. Differential expression of genes was considered statistically significant if the log2 fold change > 1, the adjusted p-value > 0.
01, and the percent in the target single cell population > 60%. Enrichment of functional states or clusters was determined using Fisher Exact test on a 2 × 2 contingency table; p-values were adjusted for multiple comparison using False Discovery Rate method using the function p. adjust in R. Enrichment was considered statistically significant if p < 0. 05 and odds ratio > 1. 5 or < -1. 5. Enrichment of single cell clusters in public gene expression datasets. The publicly available breast cancer METABRIC  39  and TCGA skin cutaneous melanoma (SKCM) datasets were used to determine the association between transcriptional activity of the different T cells clusters defined in the single-cell experiments and patient survival. Both datasets were downloaded from http://www. cbioportal. org and loaded into R v3.
5. 2, which was used for all subsequent analyses. For the METABRIC dataset, only data from patients with triple-negative breast cancers (patients with "ER_STATUS", "PR_STATUS" and "HER2_STATUS" set to "-". n = 499) was used. Single-cell cluster-specific enrichment scores was calculated for each sample using genes that i) was upregulated > 2 0. 5 fold change with adjusted p-value < 0. 01 in the single-cell experiments and ii) had > 1. 3 fold change with adjusted pvalue < 0. 01 increased gene expression levels in 4T1 tumors following RT+CTLA4i blockade combination treatment as measured by bulk RNA-seq (see method "Bulk tumor RNA sequencing"). For each patient and cluster, an enrichment score (ES) was calculated as: ES = P n i = 1 GE i × log 2 FC i n ð5Þ where i is the gene, n the number of genes in signature, GE the gene expression of gene i for each patient, and FC the fold change of gene i within each single cell cluster.
As shown in the equation, single cell cluster-specific gene expression log 2 fold changes were used as weighting factors prior to averaging the expression of genes. Survival v2. 44 proportional hazard models from scaled enrichment scores and logrank survival models using scores as ordinal data (50% percentile; low and high). For the SKCM dataset, all patients with complete TNM data were included (n = 417). AJCC pathologic tumor stage was transformed to numeric values prior to analysis (stage I, IA, IB: 1; stage II, IIA, IIB, IIC: 2; stage III, IIIA, IIIB, IIIC: 3; stage IV: 4). For all analysis, patients alive or who died of other causes were censored. Assessment of the anti-CD40 activated cDC1s gene signature in mouse tumor bulk RNA sequencing data. A gene signature of anti-CD40 activated cDC1s has been previously defined in MC38 mouse tumors  52.
The expression of the genes that define this signature (Relb, Etv3, Batf3, Aebp2, Nfkb2, Ccl22, Ccl5, Il15, Ccr7, Il15ra, Plxnc1, Pmp, Cd40, Birc2, Fscn1, Anxa3, Cacnb3, Nudt17, Socs2, Tspan3, Serpinb6b) was investigated to assess the impact of CD40 on the tumor microenvironment. Volcano plots of the genes in this DC1 gene signature were generated using the package EnhancedVolcano package in R. The average expression (generated via DESeq2) of the genes was determined as the arithmetic mean of the log scale gene expression data. AH1-specific TCR repertoire. Using the TCRB CDR3 sequences of AH1specific CD8 + T cells previously published  9  and from the 10X single cell VDJ experiments from this study, we constructed a signature comprised of the TCRB sequences of T-cells that are reactive towards the AH1-antigen.
The list and specifications of antibodies used for each panel are detailed in Supplementary Tables  3, 4 , and 5. CountBright absolute counting beads (Thermo Fisher Scientific) were added to the tumor samples before acquisition. All three panels were optimized for use on a Cytek Aurora spectral flow cytometry platform with a 5 lasers (16UV-16V-14B-10YG-8R) fixed configuration. Data were analyzed using FlowJo v10 (Supplementary Figs. 24, 25) software, DownSample (v3. 3. 1) and FlowSOM (v3. 0. 18) plugins and cloud-based analysis platform OMIQ (https://omiq. ai). High dimensional analysis was conducted on PD1 + antigenexperienced (CD44 + CD62L -) CD8 + T cells. Data were downsampled to 1000 T cells per sample from the RT + CTLA4i group; 8 out of 10 samples met the 1000 cells criteria and were concatenated for the rest of the analysis.
Acknowledgements This work was supported by  NIH  ( R01CA198533  and  R01CA201246 ), to S. D. , and  BCRF-23-053  to S. D. and S. C. F. We acknowledge the use of the Genomics, the Epigenomics, the  CLC Flow Cytometry and the Human Immune Monitoring Core facilities of Weill Cornell Medicine. We also acknowledge the use of the  Integrated Genomics Operation Core , funded by the  NCI Cancer Center Support Grant  ( CCSG ,  P30 CA08748 ),  Cycle for Survival , and the  Marie-Josée and Henry R. Kravis Center for Molecular Oncology  at the  Memorial Sloan Kettering Cancer Center. In addition, we acknowledge the use of data generated by the TCGA Research Network: https://www. cancer. gov/tcga. We thank  Dr. Roberta Zappasodi  for helpful suggestions about the use of anti-GITR antibodies.
b The average expression (generated via DESeq2) of the genes in the signature was determined as the arithmetic mean (red dot) of the log scale gene expression data in all treatment groups. A two-sided t test was used to evaluate statistical significance. c The Jaccard overlap index of the anti-tumor AH1-specific TCR repertoire between paired tumor and tdLN for each mouse was calculated to assess overlap between the two compartments. A Wilcoxon two-sided test was used to evaluate statistical significance. In (b, c) ggplot2::geom_jitter t was used to visualize individual values within each group. Boxplots display median (center line), 25th and 75th percentile (hinges) and minimum and maximum values (whiskers). Source data are provided as a Source Data file.
Methods Eighty participants (healthy adults, 18-55 years-old) were sequentially assigned to receive two (28 days apart) intramuscular doses of 25-µg (n=60) or 50-µg (n=20) of a Gamma RBD-based subunit vaccine adjuvanted with aluminium hydroxide. The primary endpoint was safety. The secondary objective was to describe the neutralising antibody response against the SARS-CoV-2 Ancestral strain and several variants of concern (Gamma, Delta, Omicron BA. 1 and Omicron BA. 5) measured by a live virus-based neutralisation assay. Cellular immune responses were studied as an exploratory objective by an enzyme-linked immunospot (ELISpot) assay. This trial is registered in ClinicalTrials. gov (NCT05656508).
Trial design and oversight This trial is registered in ClinicalTrials. gov (NCT05656508). The trial was conducted at Clinical The study protocol was initiated on April 10, 2022. Twenty seven out of 107 volunteers (13 males, 14 females) were excluded for not complying with eligibility criteria. Participants were recruited between April 28 and June 23, 2022, and sequentially assigned to one of two vaccine groups, one receiving a 25-µg dose of ARVAC-CG (Group A) and the other a 50-µg dose (Group B). Of the 80 volunteers who received at least one intramuscular dose of ARVAC-CG in the deltoid, three were excluded 28 days after the first dose of the vaccine due to impossibility to complete the protocol for personal reasons. Of the 77 remaining, 59 were inoculated with two 25-μg vaccine doses, and 18 with two 50-μg vaccine doses.
Investigators, and laboratory personnel involved in assays were blind to assignment until the end of the follow-up period (Figure  1 ). Demographic characteristics of the participants are presented in Table  1. Missing data or deviations from original protocol were infrequent and inconsequential. Safety was assessed according to the scheme established in the Guidance for Industry, Toxicity Grading Scale for Healthy Adult and Adolescent Volunteers Enrolled in Preventive Vaccine Clinical Trials. 14  Solicited local and systemic adverse events (AE) were recorded during the first 7 days after each dose of vaccine received, unsolicited events were recorded during the first 28 days after vaccination, laboratory tests were carried out after 7 and 28 days of each dose. The following symptom grading was used for local and systemic AE: grade 1 (mild) to grade 4 (potentially life-threatening).
Acknowledgments We thank the trial volunteers for their contribution and commitment to vaccine research. We are thankful to the staff of  Laboratorio Pablo Cassará  for their essential contribution to the development, scaling, manufacture, control and stability study of the clinical batches of ARVAC-CG antigen and vaccine:  Krum ,  Valeria ;  Drehe ,  Ignacio ;  Zurvarra ,  Francisco ;  Baque ,  Jonathan ;  Payes ,  Cristian ;  Heinrich ,  Brenda ;  Gambone ,  Melisa ;  Descoins ,  Horacio ;  De Nichilo ,  Analía ;  Sidabra ,  Johanna ;  Licausi ,  Mariana ;  Cortez ,  Christian ;  Roman ,  María Victoria ;  Villar ,  Alejandra ;  Diaz ,  Sebastián ;  Frattolillo ,  Matías ;  Mestre ,  Diego ;  Gonzalo ,  Javier ;  Maluvini ,  Ernesto ;  Sperandini ,  Cecilia ;  Farre ,  Paola ;  Trovato ,  Fernando ;  Strada ,  Ariel ;  Cocciolo ,  Giovanna ; and  Privitera ,  Anabella.
Except for the kidney, histological quantifications were performed for each tissue (left), namely the mature spermatids area (n WT = 10 fish, n tert -/-no Cre = 8 fish and n tert -/-+ Cre = 9 fish), adipocyte area (n WT = 9 fish, n tert -/-no Cre = 9 fish and n tert -/-+ Cre = 9 fish), muscle fiber thickness (n = 8 fish per group), retinal pigmented epithelium (RPE) and photoreceptor layer (PRL) (n WT = 7 fish, n tert -/-no Cre = 8 fish and n tert -/-+ Cre = 8 fish), respectively. Scale bar, 20 µm. All data are presented as the mean ± s. e. m. ; *P < 0. 05; **P < 0. 01, ***P < 0. 001, using a one-way ANOVA and post hoc Tukey test; *P < 0. 05, using a Kruskal-Wallis test and post hoc Dunn test. sufficient to fully rescue life expectancy to WT levels, suggesting that telomere shortening in other organs may be limiting in later stages.