dataesr/scientific-paragraphs-categorization huggingface dataset:
8. 4 • 4 Script sbl-misa-mix. py Overview The sbl-misa-mix. py script allows, based on the individual colored MISA produced by sbl-misa. py, to display together several colored MISA for an easier comparison. Main steps Parse the mix_ifile • Parse the several. html files containing the MISA id / colorings of interest • Combine them into a mixed fig • The third is a matrix showing the number of atoms involved in the calculation of i-RMSD for each pair of chains.
7 Supplemental: formal specification of the method 23 8 Implementation details 24 8. 1 Overview. 24 8. 2 Computing the MISA : script sbl-misa. py, module Multiple_interface_string_alignment. py. 25 8. 3 Coloring the MISA : script sbl-misa. py, modules Colored_MISA. py, MISA_ColorEngines. py. 25 8. 3. 1 Coloring SSE. 28 8. 3. 2 Coloring BSA and ∆_ASA. 28 8. 3. 3 Coloring B-factor. 28 8. 4 Script sbl-misa-mix. py. 28 8. 5 Script sbl-misa-bsa. py. 29 8. 6 Script sbl-misa-diff. py. 29 8. 7 Structural comparison, script sbl-misa. py, module iRMSD. py. 29.
). The quality of the RNA was evaluated by electrophoresis on a 1% agarose gel. The A260/A280 ratio was determined using a Nanodrop spectrophotometer (NanoDrop Technologies, Thermo Fisher Scientific, Inc. ) and was accepted between 1. 8 and 2. 0. RNA integrity was assessed by Agilent 2100 Tape Station analysis (Agilent Technologies, Inc. ) and RIN >7 were found to be acceptable  (33). RNA sequencing was performed on an Illumina X-ten RNA-seq sequence production system (Illumina, Inc. ). Identification of novel ncRNAs. The raw data were first filtered to eliminate low-quality reads, based on the reading mapping to the reference genome, and StringTie was used to assemble clean data. The combined transcripts were annotated using the GFFCompare program. The filtering of the RNA coding for the putative protein was performed using the minimum length and the threshold of the number of exons.
Therefore, ncRNA analyses were performed on protein-coding genes that were located in the 5 kb flanking region of the individual cdNAs. Whilst predicting the genes encoding the target protein of a trans-acting ncRNA, the sequence of each ncRNA was obtained, based on the browser of the human genome, and Gene Ontology (GO; http://www. geneontology. org/) was used to functionally classify these neighboring genes in humans  (37). For GO analysis, the R package was used to separately prepare biological processes, molecular functions and cellular components. Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www. genome. jp/kegg/) pathway analysis was performed to obtain enriched pathways using the predicted target genes. In order to analyze the regulatory network of differentially expressed ncRNAs and target mRNAs, the dIANA-LncBase database (http://carolina.
1% FA, 2% ACN (phase A) and 80% ACN, 0. 1% FA (phase B). Samples were separated with a 120 min gradient at 300 nl/min flow rate. Gradient B: 5% for 3 min, 8-35% for 92 min, 35-45% for 20 min, 45-100% for 2 min, 100% for 2 min, 100-2% for 2 min and 2% for 2 min. Data acquisition and iTRAQ analysis. data-dependent acquisition was performed in profile and positive mode with a Orbitrap analyzer at a resolution of 70,000 (200 m/z) and m/z range of 350-1,600 for MS1. For MS2, the resolution was set to 17,500 (200 m/z) with a fixed first mass of 120 m/z. The automatic gain control (AGC) target for MS1 was set to 3. 0e+06 and 1. 0e+05 for MS2. The top 20 most intense ions were fragmented by Hcd with normalized collision energy (NCE) of 32%, and isolation window of 2 m/z. The dynamic exclusion time window was 30 sec.
Raw MS files were processed with MaxQuant (version 1. 5. 6. 0). The Human protein sequence database (Uniprot_HUMAN_2016_09) was downloaded from UNIPROT. This database and its reverse decoy were used by MaxQuant software. The quantification type was reporter ion MS2 with 6-plex TMT specific to Lys (K) and unmodified N-term; Filter by PIF (0. 75). Trypsin was set as specific enzyme with up to 2 miss cleavage; oxidation [M] and acetyl [protein N-term] were considered as variable modification, Carbamidomethyl [C] was set as fixed modification; min peptide length was 7 and max peptide mass was 4600. Both peptide and protein FdR should be <0. 01. Only unmodified unique peptides were used for quantification. The iBAQ label-free quantification was also measured with log fit checked. All the other parameters were reserved as default.
All the identified proteins were annotated and classified by GO and KEGG. The differentially expressed proteins were then processed by dAVId Functional Annotation Tool 6. 8 (http://david. abcc. ncifcrf. gov/) for the analysis of term enrichment. The results were filtered on the basis of a Fisher Exact statistical methodology, as previously described. The GO biological network was evaluated using the clueGO of Cytoscape software (http://www. cytoscape. org/, cytoscape 3. 7. 2). The analysis of the protein-protein interaction was performed by STRING v10. 0 (http://www. string-db. org/), and a high coefficient value of 0. 7 was used as a factor reduction. A cluster analysis was performed to identify the protein expression profiles differentially expressed (fold change ≥1. 2 or ≤0. 8 and P<0. 05) using hcluster (https://pypi.
Integrative analysis of proteome and transcriptome data. Transcriptome data obtained from a previous study  (39)  was used for integrative analysis with proteome data. In order to identify proteins that have been consistently expressed at RNA and protein levels, the differentially expressed proteins were compared with differentially expressed ncRNAs. Through previous studies and the starBase database  (40) , potential target genes of the differently expressed ncRNAs were predicted. The predicted potential target genes of the differently expressed ncRNAs were also compared with the differentially expressed proteins. The interaction network of the corresponding proteins and ncRNAs related genes was constructed by the STRING v10. 0 software and cytoscape software. Reverse transcription-quantitative PCR (RT-qPCR).
Furthermore, cPAT v. 1. 2. 2 was used to predict whether these transcripts are coding or non-coding. The results demonstrated that most of these were protein-coding RNAs, and some were novel transcripts which were not found in the database (Fig. 1 ). For non-coding RNAs, predicted RNAs indicate mRNAs which are near to ncRNAs and exhibit similar functions. The expression value (genes) for each sample is summarized in Table  II.
The regulatory network analyses of differentially expressed ncRNAs and mRNAs were performed through predication on the dIANA-LncBase database (http://carolina. imis. athena-innovation. gr/ diana_tools/web/index. php?r=lncbasev2%2Findex). In the predication analysis, NEAT1 targeted 144 mRNAs through miRNAs (Table  III ). According to the predication results in Circnet  (38) , NEAT1 exhibited an association with 120 circRNAs (Fig. 4 ). These results illustrated the regulatory association between ncRNAs and mRNAs in the mechanisms of eleutheroside B1 in inhibiting the influenza virus.
Proteomic expression in A549 cells infected with the influenza virus. The proteomic expression levels in the A549 cells at 24 h following influenza virus infection were analyzed in order to identify the specific proteomic expression of cells treated with eleutheroside B1. The analysis of the Lc-MS/MS data generated a total of 70,249 peptides. A total of 5,809 proteins were identified with at least one unique peptide with a confidence level of >95%. Through GO analysis, 71 differentially expressed proteins were enriched for the biological process, cellular component and the molecular function (Fig. 5A ). The GO results demonstrated that the majority of the proteins expressed were enriched in these categories of biological processes, such as virus response, type I interferon signaling pathway, cellular response to type I interferon, regulation of viral process and response to type I interferon.
cells group, in which no significance difference was found for the expression of LAGE3 (data not shown). According to GO analysis, LAGE3 was involved in tRNA processing, tRNA metabolic process and lncRNA processing (Fig. 6A ). Through analysis with STRING, LAGE3 was shown to interact with a series of other proteins (Fig. 6B ). Interaction network of integrative proteome and transcriptome analysis. Integrative analysis of the proteome and transcriptomes were performed to determine the mechanisms through which proteins are regulated by eleutheroside B1 at the translational and transcriptional level. LncRNAs can regulate gene expression by acting on miRNAs. Based on prediction, the differentially expressed ncRNA NEAT1 in the PR8+eleu vs. PR8 groups exhibited an interaction with 144 proteins through miRNAs.
The differential expression of LAGE3 in the 2 groups was regulated in the same manner as its mRNAs in the RNA sequences (data not shown). The potential ncRNA targets were used with proteomics data to construct an interaction network, which indicated that the potential ncRNA targets, such as ATG12, EGFR, cd44, cXcL12 and cdc42 were interacting, whereas there was no direct interaction between these targets and LAGE3 in the database (Fig. 7 ). The interaction between these proteins was also intricated in the GO terms and KEGG pathways, such as the positive regulation of cellular metabolic process (GO:0031325), regulation of cell proliferation (GO:0042127), protein binding (GO:0005515), enzyme binding (GO:0019899), nuclear part (GO:0044428), microRNAs in cancer (05206), FoxO signaling pathway (04068), indicating that eleutheroside B1 can affect cell function by regulating both ncRNA and protein.
The influenza virus recruits variable and complementary host cellular pathways during its replication cycle  (50) (51) (52). Current anti-influenza virus medication often targets one part of the virus life cycle, which potentially influences the development of drug-resistance  (2). The data suggest that the involvement of eleutheroside B1 in altering cellular pathways is necessary for the virus life cycle, such as binding and cytokine-cytokine receptor interaction, consistent with previous studies  (13, 53). cytokine-receptor complexes are ideal drug targets. However, the protein-protein interactions are difficult to block using small molecules  (53, 54). Further proteomics analysis of the function of eleutheroside B1 was performed through iTRAQ assays. Only one protein (LAGE3) was downregulated in the PR8+eleu vs.
We also included micro-distancing by reducing the transmission probability in non-household contacts, reflecting preventive measures taken to reduce the percontact transmission probability, such as keeping a greater distance, hygiene measures, and wearing masks. Under the base-case assumption of persistent immunity, recovered individuals were assumed to be permanently protected against future infection. Four scenarios of waning immunity were also considered by assuming that recovered individuals became susceptible to reinfection after an average duration of six or 24 months, with or without reduced disease severity during repeat SARS-CoV-2 infections (Supplementary Sections 1. 2, 1. 4). The model code is publicly available on Github  36.
Model fitting and simulation phases We fitted the model to observed numbers of confirmed cases, hospitalisations and deaths over time. Seroprevalence data were also included as calibration targets when available (Supplement Section 2. 2). Fitted parameters included those governing transmission, disease severity and the time-variant profiles of case detection and micro-distancing. Our simulations were divided into three successive phases (Figure  8 ). In Phase 1 we modelled the preceding SARS-CoV-2 epidemics and included social distancing measures in place in each country until 31st September 2020 (Figure  S4 ). In Phase 2 the model was run using the same epidemiological parameters and detection profile as during Phase 1, but social mixing interventions were optimised for six or 12 months, before being lifted in Phase 3.
9 Therefore, sarcoma patient selection for immunotherapy with anti-PD1 is challenging and may be based on histotypes classification, somatic mutations, immune infiltrates and possibly other markers. The exploration of other ICP in therapy should also benefit from a systematic analysis of the expression of the different ICP in each tumor type. A better description of the immune landscape of sarcomas is therefore needed to promote the rational development of immunotherapy in these tumors. To study inter-and intra-tumor immunologic heterogeneity across the different subtypes, high throughput transcriptomic data of a series including different sarcoma subtypes were analyzed: 1/by expression levels of 93 genes related to immune checkpoint (ICP) and membrane markers (MM) of immune cells and 2/using the LM22 CIBERSORT signature to distinguish prognostic subgroup.
Gene expression pattern of each sarcoma subtype The differential expression of each gene across sarcoma subgroups is presented in Figure  3. Number values of data represented in Figure  3  are reported in Supplementary Table  S1. Expression level of each of the 93 ICP/MM genes was determined for the 253 sarcoma cases included in the study using either the 25 and 75 percentile of samples included in each sarcoma subtype and the Z-score that gives a numerical measurement of each value's relationship to the mean in the group of values. The significance represents the heterogeneity of each gene expression level across the four subgroups. We observed genes with 1) homogeneous expression patterns across the four groups, 2) heterogeneous patterns of expression across the four groups, and also 3) genes with different expression patterns within a given group.
CIBERSORT signature and prognostic impact CIBERSORT for Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts is a signature providing an estimation of the distribution of immune cell populations based on their transcriptomic profile. It consists of an in silico approach for characterizing 22 immune cell populations of interest based on a bulk transcriptome deconvolution using a pre-defined signature of 547 genes. 11 It reports proportions of immune cell infiltrates within samples and not absolute values. Comparing CIBERSORT profile from a sample or histological subtype to another one reflects the impact of the higher or lower representation of an immune cell type rather than global immunologic potential. This signature was not found correlated to histotypes, tumor site, tumor grade, CINSARC signature or metastatic relapse.
Even if beneficial to a small proportion of patients with advanced sarcoma, targeting PD1 pathway is most often inefficient and predictive factors of response to immunotherapy must be identified and tested prospectively in the clinic. From the clinical trials data reported so far, exploring the efficacy of immunotherapy in sarcoma, few responses in the most common subtypes (UPS, DDLPS, LMS, GIST) and frequent responses in very rare subtypes (angiosarcoma, ASPS, chordoma) were reported. No consistent biomarkers have been identified so far across sarcoma histotypes: it remains challenging to know if the selection of patients that could benefit from immunotherapy should be based on sarcoma histotypes or biomarkers. Few studies identified biomarkers of response to immunotherapy in sarcoma. Correlation between somatic mutations load and sensitivity to immunotherapy is well documented for a variety of cancers,  12  but a high mutational burden is rarely observed in sarcoma.
13 5] [16] [17]  It can however be concluded that sarcoma tumor cells generally express low levels of PD-L1, and PD-L1 expression is more frequently observed on lymphocytes or monocytes infiltrating the tumor. Immune infiltrates are well documented to be correlated with response to immunotherapy in melanoma  18  and carcinoma. 19 Few series have assessed the presence of immune infiltrates in sarcoma, showing a wide variety of infiltrating immune cells, varying across and within histotypes. 1] [22] [23]  Recently, re-interpretation of the SARC028 results through immune-based classification suggests a higher  response rate to Pembrolizumab in a subgroup SCG enriched in immune cells and tertiary lymphoid structures (TLS). 24 This biomarker is currently tested prospectively (Pembrosarc clinical trial; NCT02406781).
In GIST, our data highlighted high expression levels for ICOS gene and its ligand that is correlated with a poor prognosis. The poor prognosis of Treg has previously been reported in a cohort of various STS. 27 hus, targeting Treg expansion through anti-ICOS antagonists as currently developed in clinics (MEDI-570 NCT025250791) may be an interesting strategy in GIST. Of note, in contrast to most ICP, B7H2 (ICOSL) was expressed at the highest level in SS. In this context, it is surprising to note a low expression of FoxP3 gene across all sarcoma subtypes. This low level of FoxP3 expression had never been reported. Interestingly, Tnfrsf18 (GITR) is expressed in all four subtypes studied and the gene expression of its ligand is associated with a poor prognosis in GIST. Several anti-GITR antibodies are currently assessed alone or in combination with other immunotherapies in advanced malignancies (e.
g. NCT01239134, NCT03277352) and thus, should be further explored in GIST. A radically different immune profile from the three other subgroups is observed for SS. The high CD8 level detected in SS in our study is in contradiction with low T-cell infiltration in SS without prognosis impact, reported by Pollack et al. 27  LTA gene expression correlates with poor prognosis in SS and good prognosis in GIST. Sorbye et al. reported that IHC detection of CD20 + B cells positively impacts disease-specific survival in non-GIST STS  28  but when focusing specifically on SS, peritumoral B cell detection appears of poor prognosis value, a result consistent with our observations. Co-detection of CD8, MS4A1 (CD20), and LTA could reflect the presence of TLS structures close to lymphoid organization promoting the differentiation of effector and memory B and T cells directly within the tumor environment.
Targeting CSF-1 receptor to restore the differentiation mechanism of M0-macrophages into dendritic cells could be an interesting therapeutic approach. 38 nalyzing the individual role of each cell types remains therefore challenging. The exact role of individual cell type requires further analysis. However, taking together the individual genes and CIBERSORT analyses, and consistently with the literature, we can propose that new strategy in the development of immunotherapy in sarcoma could be represented by Treg targeting through ICOS antagonist and GITR antibody in GIST, targeting M0 macrophages differentiation through CSF-1 receptor in all four sarcoma subtypes, OX40 inhibition in SS, CD40 agonists in GIST and SS. This work has two main limitations: 1) A confirmation of these results on an independent data set would be of interest.
Patient samples All patients included in this study had STS and were managed in expert centers of the French Sarcoma Group (FSG) between July 1991 and November 2011. Approval of the patients to participate in the study was obtained according to the French national laws and European laws at the time of study initiation. The CNIL ("Commission Nationale Informatique et Liberté" or French Data Protection Authority) authorization for the study is referenced as CNIL No. 909510 obtained on 5 February 2010. Tumor samples were obtained from the frozen tissue of the primary tumor. Each case was reviewed by the group of pathologists associated with the FSG, members of the RRePPs network. The pathological diagnosis was confirmed according to the World Health Organization classifications based on histology, immunohistochemistry and molecular genetics when needed (molecular biology for GIST, FISH for MLPS and SS, for example).
1 Histological grading was established according to the Fédération Nationale des Centers de Lutte Contre le Cancer (FNCLCC) system,  39  whereas GIST grading was established according to the AFIP system. 40 The cellularity of the samples was not available, as well as the ratio between cancer and immune cells. Clinical characteristics of patients, of tumors and disease outcome were extracted from the Conticabase and Conticagist databases (European databases for STS and GIST, respectively). With respect to regulatory procedures, these databases received authorizations from the Advisory Committee on Information Processing in Material Research in the Field of Health (CCTIRS) and the CNIL (CCTIRS obtained 9 Sept 2010 and CNIL No. 910390 15 Jul 2013). These databases include only patients for whom the entire management, from initial biopsy to follow-up was performed in one of the NETSARC centers.
41 Expression data are available as indicated in a previous study published by our group  42  and can be accessible together with clinical annotations into the ATG-Sarc database (http://atg-sarc. sarcomabcb. org/). Total RNA was extracted from frozen tumor samples by using the miRNeasy Mini Kit (Qiagen, Germantown, MD) and was purified by using the RNeasy Min Elute TM Cleanup Kit (Qiagen), according to the manufacturer's instructions. RNA quality was checked on an Agilent 2100 bioanalyzer (Agilent Technologies). Gene expression analysis was carried out by using Agilent Whole Human 44 K Genome Oligo Array (Agilent Technologies), according to the manufacturer's protocol.
Funding This work was supported by grants from  Association pour la Recherche sur le Cancer (ARC) ,  La Ligue Contre le Cancer -Comité de l'Ain  ( Canopée Project ),  LabEx DEVweCAN   ANR-10-LABX-0061  of  Lyon University, SIRIC-LYriCAN INCa-DGOS-Inserm_12563 , the  French cooperative network INTERSARC , and  Info Sarcomes  and DAM'S patients associations.
Spatio-temporal data on vector abundance The vector abundance in space and time was derived from the predictions of a mechanistic model of mosquito population dynamics developed by  Tran et al. (2019). This model provides the abundance of host-seeking female mosquitoes for the three vector species and the two regions of interest. Mosquito abundance is driven by rainfall, temperature, location of waterbodies, and the surface dynamics of ponds throughout the year. This model uses satellite-derived meteorological data and multispectral images to assess the habitat suitability for vectors. Tropical Applications of Meteorology using SATellite data (TAM-SAT) daily rainfall estimates are used (http://www. tamsat. org. uk/cgi-bi n/data/index. cgi), along with the European Centre for Medium Range Weather Forecasts (ECMWF) 10-daily minimum and maximum temperatures (https://confluence.
Spatial distribution of livestock For livestock host densities, we used the Gridded Livestock of the World database (GLW 3, downloaded from https://dataverse. harvard. edu/dataverse/glw_3,  Gilbert et al. , 2018) , which provides subnational livestock distribution data for 2010, at a spatial resolution of 0, 083333 • (approximately 10 km at the equator). We used the distributions of cattle and small ruminants (goats and sheep) based on  Gilbert et al. (2018)  dasymetric product, which disaggregates census data according to weights established by statistical models using high resolution spatial covariates (land use, climate, vegetation, topography, human presence). This dataset was downscaled to match  Tran et al. (2019)  model pixel size by homogeneously distributing animals in smaller space units.
Acknowledgments We thank  Cyril Caminade ,  Véronique Chevalier ,  Benoît Durand  and  Maxime Ratinier  for helpful discussions. We thank  Annelise Tran  for providing input data and help for its use. We are grateful to the  INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility , doi:  10. 15454/1. 5572390655343293E12 )for providing computing and storage resources.