dataesr/scientific-paragraphs-categorization huggingface dataset:
We used HI results to build an antigenic map (Figure  2 ) using ACMACS software (https://acmacs-web. antigenic-cartography. org). The antigenic map revealed 2 slightly distinct antigenic profiles for Benin H5N1 viruses. A/poultry/Benin/21-A-09-031-O/2021 was antigenically similar to most of the World Health Organization candidate vaccine viruses used in the HI assay, especially to A/ Sichuan/26221/2014-like (IDCDC-RG42A) and A/duck/Hyogo/1/2016 (NIID-001). In contrast, the other 4 Benin isolates that clustered together in the phylogenetic trees were 2-3 log 2 away from the candidate viruses, suggesting a good but lower level of cross-reactivity. The most antigenically similar candidate virus to these 4 Benin isolates was A/Astrakhan/3212/2020-like (CBER-RG8A). Amino acid sequence analysis showed that the H5N1 viruses from Benin had multiple basic amino acids motif (PLREKRRKR/GLF) at the HA cleavage site, characteristic of HPAI viruses.
Acknowledgments We gratefully acknowledge the authors and submitting laboratories for providing the sequences to the GISAID EpiFlu and Influenza Research databases on which this research is partially based. This study was funded by the  National Institute of Allergy and Infectious Diseases, National Institutes of Health  (CEIRS contract no. HHSN272201400006C ). I. N. S is supported by a PhD scholarship from the  French Embassy in Mali.
Highly Pathogenic Avian Influenza A(H5N1) Clade 2. 3. 4. 4b Virus in Poultry, Benin, 2021 DISPATCHES Idrissa Nonmon Sanogo, Fidelia Djegui, Yao Akpo, Corneille Gnanvi, Gabriel Dupré, Adam Rubrum, Trushar Jeevan, Pamela McKenzie, Richard J. Webby, Mariette F. Ducatez Author affiliations: Université de Ségou, Ségou, Mali (I. N. Sanogo); Institut national de recherche pour l'agriculture, 5 H5N1 virus isolates from which we generated full- genome sequences and designated them A/poultry/ Benin/21-A-08-009-O/2021, A/poultry/Benin/21-A- 09-031-O/2021, A/poultry/Benin/21-A-09-033-O/ 2021, A/poultry/Benin/21-A-09-034-O/2021, and A/ poultry/Benin/21-A-09-035-O/2021; we deposited all sequences into GenBank (accession nos. 2534 Emerging Infectious Diseases • www. cdc. gov/eid • Vol. 28, No. 12, December 2022 l'alimentation et l'environnement, Ecole Nationale Vétérinaire de Toulouse, Toulouse, France (I.
In the following decades, the gradual advancement of molecular genomics allowed the identification of an incredibly high level of polymorphisms responsible for the exceptional variability of this gene cluster, which to date encompasses more than 33,000 different alleles among classical and non-classical HLA genes (IPD-IMGT/ HLA database, v3. 48, July 2022, Figure  1 )  (13). This exceptional variability has also triggered increasing MHC research in a wide range of non-model vertebrate species, which has advanced our understanding of immune system evolution in natural populations  (14, 15). Overall, the intensive research of the last decades has contributed to define how genetic variations within the MHC region may determine predisposition to diseases, and the study of the functional consequences of this immunogenetic diversity is now a paradigm for human and evolutionary genomics  (16, 17).
Such differences in the genomic architecture of the MHC cluster across the phylogenetic tree represent a byproduct of the evolutionary forces shaping vertebrate immune systems. Indeed, mechanisms of genome wide duplication, rearrangements, genetic drift and balancing selection are responsible for the complex hyper-polymorphic mammalian and human MHC systems, whose main feature is the capability to adapt to a wide range of pathogens and develop complex immune responses  (23) (24) (25). The evolutionary step allowing for the introduction of an antigen-based immune specificity is assumed to be the integration of RAG transposable elements in the jawed vertebrates' genome, ancestral transposons at the basis of (D) Representation of the HLA binding motifs of selected epitopes from a HLA-A2 related dataset, extracted from the IEDB (11) an visualized with Weblogo (https://weblogo.
HLA evolutionary divergence calculation and pathophysiological implications. The HED score between two HLA alleles approximates the functional dissimilarity between the encoded molecule variants based on the Grantham distance between the amino acid sequences of their peptide binding domains. This computation is made using a dictionary including all the protein sequences of exons 2 and 3 for class I alleles and exon 2 for class II alleles, assembled from the IPD-IMGT/HLA database. (39) From a pathophysiological point of view, the divergence of HLA binding sites can influence patterns of antigen presentation in different processes, from response to infections to tumor surveillance, with a higher HLA divergence being associated with stronger immunocompetence and vice versa. At the right and the left as an example, A1 and A2 represent two alleles of the same locus.
(D) Representation of the HLA binding motifs of selected epitopes from a HLA-A2 related dataset, extracted from the IEDB (11) an visualized with Weblogo (https://weblogo. berkeley. edu ) (E) Crystallographic structure of the antigen binding domains of HLA-A*01:01 with some variable residues highlighted. (F) WebLogo visualization of the variable amino acids within the peptide binding domains (corresponding to exon 2 and 3) of the HLA-A alleles in a representative population of healthy controls (N=960). (12) The height of each letter corresponds to the relative frequency of each amino acid in the population. This graphic represents an attempt to visualize the variability of the antigen binding domain of HLA-A alleles, but does not capture the whole spectrum of heterogeneity, since it represents only a limited population.
1 1 FIGURE 1 Variability of HLA locus. (A) Circle graph capturing the distribution of the number of HLA class I, class II and non-HLA alleles known to date within the human MHC region. Data extracted from the IPD-IMGT/HLA database v. 3. 48, downloaded in July 2022. (B) Bar graphs depicting the distribution of the number of alleles, proteins and null alleles (characterized by the presence of truncating mutations), per locus among classical and non-classical HLA genes. Of note is that HLA-DRB1 is responsible for the greatest variability within the copy number-variable DRB locus. (C) 3D crystallographic structure of TCR ab chains interacting with an MHC class I molecule (allele A*02:01 and b2 microglobulin -b2M). These structures were extracted from the Protein databank (PDB) website and reoriented in order to show MHC-TCR synapsis (ref PDB:4MNQ) (10).
Collected Data 2. 3. 1. Clinical Data After the initial inclusion, the parents of each child were contacted by telephone every 3 months until age 3. A standardized questionnaire on respiratory symptoms was used for every interview. A clinical examination was performed and an immunological profile was determined at age 3 at the Outpatients Department of CHU-Estaing Hospital  [19]. A questionnaire was given asking about respiratory symptoms (day or night cough, discomfort during exercise, use of salbutamol), respiratory treatments (corticosteroid per os or inhaled, long-acting bronchodilator), hospitalization for asthma, wheezing outside viral episodes, personal atopy (presence of atopic dermatitis, eczema, rhino-conjunctivitis, food anaphylaxis from birth), environmental exposure to tobacco smoke (currently or during pregnancy), pets, type of housing, daycare attendance, and number of siblings.
Data also included were sex, weight, height, and precariousness assessed using the Assessment of Precariousness and Health Inequalities in Health Examination Centers (EPICES) questionnaire  [20]. Precariousness was recorded if the score was equal to or greater than 30. Data from the medical examination comprised asthma symptoms, eczema, rhino-conjunctivitis, and chest deformity. Asthma was recorded if the child presented three or more respiratory symptoms at least twice or an episode lasting four weeks or more. We considered the following as respiratory symptoms of asthma: tachypnea, wheezing, expiratory stridor, respiratory chest retraction, doctor-diagnosed wheezes, or the use of anti-asthmatic medication with medical prescription  [21, 22].
Statistical Analysis Statistical analysis was carried out with the Stata v15 software (StataCorp, College Station, TX, USA). The study sample is described by frequency and percentage for categorical data and by means ± standard deviation for continuous data or by median and interquartile range when data were not normal. Comparisons of sRAGE levels were made using Student's t-test for two groups (or ANOVA for three or more groups), or Mann and Whitney's test (or Kruskal-Wallis for three or more groups) when data were not normal. Relationships of sRAGE with continuous data were sought using Pearson's correlation coefficient (or Spearman's in the case of non-normal distribution). All tests were two-sided and a p-value < 5% was considered significant.
Researchers and end users using climate data face a challenge when they analyze the data they need. Data volumes are increasing very rapidly, and the ability to download all needed data is often no longer possible. Most of the climate analysis tools for research and application needs must use very large datasets, often distributed among several data centres and into a large quantity of files. This is especially true when they are stored in a federated architecture like the ESGF. One of these tools is icclim (https://github. com/cerfacs-globc/icclim ), a flexible python software package to calculate climate indices and indicators. This tool adhere as much as possible to metadata conventions such as CF, implementing also provenance information. It also aims at providing increasing support for all FAIR aspects.
It is designed with performance and optimisation in mind, because the goal is to provide on-demand calculations for users. It provides the implementation of most of the international standard climate indices such as ECAD, ETCCDI, ET-SCI, including the correct methodology for calculating percentile indices using the bootstrapping method. It has been validated against R. Climdex as well (https://cran. r-project. org/web/packages/climdex. pcic/index. html ). The new 5. x version of icclim is now based on functions from the xclim python library, which was inspired by earlier versions of icclim, but using xarray and dask for data access and processing. icclim is also a candidate as the software to calculate climate indices for the C3S toolbox (https://cds. climate. copernicus. eu/cdsapp#!/toolbox ).
icclim is integrated in the IS-ENES C4I 2. 0 platform (https://climate4impact. eu/ ), using a Jupyter notebook collection in a SWIRRL environment (Software for Interactive Reproducible Research Labs https://gitlab. com/KNMI-OSS/swirrl ). Having access to this type of analysis tool is very useful, and seamless integration with front-ends like C4I enable the use of those tools by a larger number of researchers and end users.
I Impacts of Climate Change https://rebrand. ly/icclimposter The IS-ENES3 project has received funding from the European Union's Horizon 2020 research and innovaAon programme under grant agreement N°824084 : https://is. enes. org/ icclim 5. 0. 0rc2: https://github. com/cerfacs-globc/icclim (pip install icclim) climate4impact 2. 0 beta: https://dev. climate4impact. eu/ 2020 Hurricane Delta causes damage to Louisiana's Gulf Coast 2021 Germany Er<stadt, southwest of Cologne h@ps://dev. climate4impact. eu ➤Urgent needs of impact assessments ➤Identify mitigation solutions ➤Multiple domains: infrastructures, urban, agriculture, transportation, etc. ➤Easy to use tools are needed for very diverse users ➤Climate indices and indicators are widely needed ➤Using xclim climate indices functions as building blocks ➤xclim functions are using xarray, dask, pandas and numpy: optimized and parallel execution ➤icclim v5 implements a specific API very similar to v4 ➤Extended capabilities: userdefined indices, user-specific thresholds, etc.
➤Fix remaining issues in 5. 0. 0rc2 in order to release 5. 0. 0, expected on 31 Jan 2022 ➤Will be integrated in the Copernicus CDS toolbox early 2022 ➤Implement full support of provenance information (PROV-O) ➤Finalize standards for climate indices clix-meta https://github. com/clix-meta/clix-meta ➤Release support tools: testing suite ➤Provide more Jupyter Notebooks to include in C4I https://gitlab. com/is-enes-cdi-c4i/notebooks -Intra-period extreme temperature range [°C] -ETR -Warm days (days with mean temperature > 90th percentile of daily mean temperature) -TG90p -Summer days (days with max temperature ≥ 25 °C) -SU -. ➤Fully compliant to CF and Metadata Standards ➤Validated against climpact & xclim ➤Easy install: pip install icclim ➤Implement the proper percentile indices calculations when calculation period overlaps reference period: bootstrapping method >>> files = ['tasmax_day_CNRM-CM5_historical_r1i1p1_19950101-19991231.
Patient and donor samples B-ALL diagnosis was based on French-American-British and World Health Organization classifications. BM samples (n = 82) from pediatric patients with confirmed HHD-B-ALL were obtained from collaborating hospitals. A number of samples (n = 32; discovery cohort) were available for seq-iFISH and computational modeling (Table  1 ). Among these samples were DX samples (n = 22) obtained at disease presentation from patients who either remained alive in CR, without relapses after a minimum of 5-year follow-up (CR, n = 10), or who relapsed within this timeframe (except one patient who relapsed after 7 years; REL, n = 12). The remaining samples (n = 10) were relapse samples with matched corresponding DX samples (Table  1 , Fig. 1 ). An independent cohort of 50 DX HHD-B-ALL samples (validation cohort) was used for blind validation of computational modeling data (Table  S1 ).
Fluorescence microscopy and nuclei relocalization Automated FISH evaluation was performed using an Olympus BX-61 epifluorescence microscope coupled to SPOT AX software (Applied Imaging, Newcastle, UK), and equipped with a BX-UCB motorized stage (Olympus), a 60× objective and narrow band-pass fluorescence filters specific for DAPI, Aqua, fluorescein isothiocyanate (FITC), and cyanine 3 (Cy3). After the first hybridization round, slides were scanned using nuclei recognition morphometric parameters and FISH signal capture  [28]. Merged images (overlay of DAPI with fluorescence signals) and the exact coordinates of a minimum of 500 nuclei per sample were recorded and evaluated. After the second and third hybridization rounds, slides were evaluated by semi-automated re-localization of previously recorded cells, overall allowing the assessment of the ploidy status of the eight chromosomes at a single-cell level.
Cells with no hybridization signals in one or more channels were excluded from further study. Analysis of all nuclei was performed using strict criteria relating to intensity, size, and distribution of FISH signals, as previously described by our group  [28]. Only nuclei with at least one chromosome gain (hyperdiploid blasts) were used for further analysis. A minimum of 200 hyperdiploid nuclei with informative data for all hybridization rounds were analyzed. Cut-off levels for FISH signal positivity were determined by Binomial distribution in the three diploid control samples described above using GRAPHPAD PRISM (San Diego, CA, USA). In patient samples, aneuploidy combinations were considered relevant if each aneusomy level proved to be beyond the assigned cut-off value.
Stress tests for prognostic predictors We performed a stress test for each of the three models derived by Random Forest analysis. Random deviates from a uniform distribution between 0 and each noise level are randomly added or subtracted from the original input data 100 times and predicted again their CR or REL condition. The real conditions were compared with the predictions on the noisy data. The robustness of each model was evaluated by assessing the average number of patients correctly or erroneously predicted along the 100 repetitions and after increasing noise levels.
Validation analyses The prognostic predictor value obtained by computational modeling of seq-iFISH data was validated in a blind analysis using an independent cohort of 50 BM samples obtained at disease presentation from the corresponding number of patients with HHD-B-ALL. Validation analysis was performed by threecolor FISH using CEP10 (Spectrum Orange), CEP18 (Spectrum Aqua), and the LSI21 (Spectrum Green) probes, following standard FISH protocols. Nuclei with no gains for any probed chromosome were considered normal/healthy hematopoietic cells. A minimum of 200 hyperdiploid nuclei were analyzed per sample.
Acknowledgements The authors thank  Anthony V Moorman  ( University of Newcastle, UK ) and  Francesc Solé  ( Josep Carreras Leukemia Research Institute ) for critical discussions. Special thanks to  Francisco Gutierrez-Ag üera ,  Paola Romecıń  and  Talıá Velasco-Hernández  ( PM's lab ) for technical help. We thank the  CERCA program  ( Generalitat de Catalunya ) and the  Josep Carreras Foundation-Obra Social la Caixa  for institutional support. This work is supported by the  Spanish Ministry of Science and Innovation  ( PID2019-108160RB-I00/AEI/ 10. 13039/501100011033 , the  European Research Council  ( CoG-2014-646903 ), the  Spanish Ministry of Economy and Competitiveness  ( SAF2016-80481-R  and  SAF-2019-108160-R ), the  Leo Messi Foundation , the  Health Institute Carlos III (ISCIII)  (RICORS,  RD21/ 0017/0029 ) within the  Next Generation EU program  (plan de recuperaci ón, transformaci ón y resilencia), and the  Fundaci ón Uno entre Cienmil (PM).
Additional funding was provided by the  ISCIII  ( FEDER PI17/ 01028  and  PI20/00822 ) (CB), the  Generalitat de Catalunya  ( 2017/SGR-503 ) and the  Universitat Aut ònoma de Barcelona  ( UAB/CF-180034 ) (JB) and the  Blood Cancer UK (CJH). JLT was supported by a  Juan de la Cierva  fellowship by the  Spanish Ministry of Science and Innovation  ( FJC2019-040868-I ), BL-M was supported by the  Asociaci ón Espa ñola Contra el Cáncer (AECC ;  INVES20011L   ÓPE ). AB was supported by the  Fundaci ón Espa ñola de Hematologıá y Hemoterapia (FEHH). OM was supported by a  Beatriu de Pin ós Postdoctoral Fellowship  ( BP2016-00048 ) from  AGAUR  ( Generalitat de Catalunya ), a 2017  Lady Tata Memorial Trust International Award , and the  AECC  ( INVES211226MOLI ). PM is an investigator of the Spanish Cell Therapy cooperative network (TERCEL).
Microarray Network games have the potential to accurately depict the interactions among genes as their founding premise is to consider the interactions among players governed by a network structure  [19] [20] [21]. In this study, the novel Game-Theoretic-Link Relevance Index (LRI) methodology  21  is studied and applied to analyze the underlying salient genes and the associate functional annotations in CRC gene expression datasets. The resulting salient genes have an excellent potential in diapeutics, exhibiting characteristics essential for both diagnosis and therapeutics to mitigate this global complex human health condition. This work presents an opportunity to explore these salient genes further by experimental, preclinical, and clinical investigation to establish these as biomarkers.
Materials and methods Colorectal cancer (CRC) patients dataset. For this study we used meta-dataset (E-MTAB-6698) from Arrayexpress database which comprises of 15 independent GEO datasets (GSE13067, GSE13294, GSE14333, GSE15960, GSE17536, GSE17537, GSE18088, GSE18105, GSE20916, GSE23878, GSE26682, GSE33113, GSE4107, GSE4183, GSE9348). All the GEO datasets of this meta-dataset were built using a common platform (GPL570; Affymetrix Human Genome U133 Plus 2. 0 Array) to prevent deviations across different platforms. In brief, a total of 1566 underlying colorectal tissue samples microarray datasets, from tumor-free (control) and primary tumors (case), were preprocessed with RMA normalization, merged and ComBat-correction for batch effect correction. This large (meta-) dataset offers very high classifying accuracy (0.
997) to test on TCGA (The Cancer Genome Atlas) dataset  22  and serves as unparalleled cohort data for discovering salient genes crucial for disease phenotype development  23. Estimation of differentially expressed genes (DEG) in the dataset. We assessed the microarray dataset using the conventional method of analysis. The 'limma' package  24  from Bioconductor  25  in R environment  26, 27  was utilized to identify DEGs as genes associated with CRC on a pre-normalized dataset (E-MTAB-6698). Linear models were applied and empirical Bayes statistics were calculated to assess DEGs between CRC samples and healthy control samples as defined by the designed experiments  28. The genes with Benjamini-Hochberg False Discovery Rate (FDR) controlled adjusted p-value of ≤ 0. 05 with Log2 Fold Change (LFC) ≥ 2 (two) were considered as DEG in CRC dataset  29.
Game-theoretic Link relevance Index (LRI) for co-expression network analysis. Herein, we utilized the CRC dataset and evaluated each gene using the LRI method  21  , as detailed in this section. Let (N, g E ) be a gene co-expression network where N represents a set of genes and g E be the set of links with respect to the Microarray Experiment Situation (MES) E =< N; S D ; S R ; A S D ; A S R >. Herein, S D and S R be the sets of samples from diseased and normal tissues, A S D and A S R be their expression matrices respectively. The link between i and j are in g E if two genes in N are co-expressed. The set of all links or edges, g N = {ij : i, j ∈ N, i � = j} is called the complete network. Let G = {g : g ⊆ g N } denote the set of all possible networks on N. Let N(g E ) be the set of players who have at least one link in g E i.
Identification of the salient genes associated with CRC. We created an in-house script for calculating the LRI to the 4th decimal point. The genome-wide expression dataset with genes in rows and samples in columns was provided as the input matrix. With a large meta-dataset as input and the downstream analysis of (1) v(g) = 1 n(g E ) i∈N(g E ) u g E i (g) (2) u g E i (g) = 1 g E i ⊆ g 0 otherwise F i (g, v, g E ) = 1 n(g E ) i∈N(g ′ ) g ′ ⊆g |g ′ i | 2|g ′ | α g ′ (v) (3) F i (g, v, g E ) = 1 n(g E ) j∈N(g E i ) |g ′ i | 2|g E j | (4) F i (g E , v, g E ) = 1 2n(g E )   1 + � j∈N i (g E ) 1 n j (g E )   (5) v(g) = 1 4 {u {12,13,14} + u {12,23} + u {13,23} + u {14} }(g) (6) v(g) =        0 if g = {12}, {13}, {23}, {12, 13} 1 4 if g = {14}, {12, 14}, {12, 23}, {13, 14}, {13, 23}, {14, 23} 1 2 if g = {12, 13, 14}, {12, 13, 23}, {12, 14, 23}, {13, 14, 23} 1 if g = g E Functional analysis of salient genes for CRC: biological network construction and enrichment analysis.
The enrichment was performed for Chromosome location with 2025 terms/pathways with 61570 available unique genes (with the latest updated data of 17. 02. 2020) as reference data. Right-sided Hypergeometric (enrichment) test with a cutoff p-value at 0. 05, Benjamini-Hochberg p-value corrections, Kappa Score of 0. 4 and minimum of three (3) genes per cluster threshold was set to ascertain the enrichment. To further evaluate the role of 126 salient genes in terms of the affected biochemical processes and identification of the critical reactions and pathways, enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Reactome pathways and Reactome reactions (database updated latest on 08. 05. 2020) was performed with right-sided Hypergeometric (enrichment) test threshold p-value set at 0.