dataesr/scientific-paragraphs-categorization huggingface dataset:
22 software  46, 47. The number of ancestries (K) to be retained in admixture (K = 2-24) was evaluated via a fivefold cross-validation (CV) and the model with minimum CV error was selected for further analysis. Results were also summarized per breed for an easier representation. Discriminant analysis. DAPC  48  was applied to assess breed traceability, as implemented in the R package adegenet  36, 49, 50. DAPC replaces the original SNP data with a small set of principal components (PCs) and then applies a linear discriminant analysis on the selected PCs. In this way, DAPC maximizes the differences among groups while overlooking at the variability within groups. The number of PCs to be used in the discriminant analysis is determined via CV and the targeting function can be either the lowest root mean squared error or the highest mean success.
To select the best option both methods were evaluated: In brief, data were randomly sampled in sets starting from 30% and augmenting by 10% up to the complete dataset, one repetition each, having all the breeds represented (stratified sampling), and the overall model assignment accuracy was recorded (Table  S1 ). For each set, a tenfold CV was applied, and repeated 30 times, to select the optimum number of PCs for the discriminant analysis. On average, minimum prediction error slightly outperformed the highest mean success, and this was the option kept in subsequent analysis. It should be noted that according to Jombart  49  this is also the recommended option. The objective of DAPC was to represent real case scenarios, i. e. , to identify an external individual membership to a group (external validation).
In such a case, the discriminant function is developed in a training set (TRN) and then applied on genotypes of an external validation set (VAL). The function predict. dapc was used for this analysis. Two different approaches were applied: • Scenario 1 (semi-supervised learning). Data were randomly (without replacement) split at 80-20% for the TRN-VAL set, and the split was repeated 10 times. Random sampling was conditioned such that all the breeds were present in both TRN and VAL sets (stratified sampling). • Scenario 2 (un-supervised learning). Each breed was analysed separately and consisted of the VAL set. In this scenario, no pigs of the VAL set were present in the TRN set, hence pigs had to be classified in one of the other 23 breeds. The TRN set consisted of pigs from the rest of the 23 remaining breeds, randomly selected (without replacement).
This procedure was repeated 10 times. Scenario 2 can be seen as a method to assess similarity among breeds. In both scenarios, the design of the DAPC analysis included: (i) tenfold CV for the selection of the optimum number of the PCs, (ii) the maximum number of PCs tested was set to 300 and (iii) minimum prediction error as the target function for model selection. Results were summarized over the 10 repetitions. Moreover, to assess the effect of the sample size and the robustness of the model, the complete dataset was split in sets of 10% increase (from 30 up to 100%). The terms (semi/un)-supervised should not be confused with the terminology in machine learning. These terms were used to distinguish between the two scenarios of DAPC, and although they are analogous to same terms used in the statistical field of machine learning they are not identical.
Acknowledgements This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 634476  for the project with acronym TREASURE. The content of this article reflects only the authors' view and the  European Union Agency  is not responsible for any use that may be made of the information it contains. MŠ and MČP received core-financing by the  Slovenian Research Agency  (grant number  P4-0133 ).
Funding This study has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 634476  to  CO  (project acronym TREASURE). The content of this paper reflects only the author's view, and the  European Union Agency  is not responsible for any use that may be made of the information it contains. MČP and MŠ were co-financed by  Slovenian Research Agency  (grant  P4-0133 ).
Data availability The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. The raw genetic datasets generated during the current study are available from the corresponding author on reasonable request. The external Wild Boars sample can be found in Dryad Digital Repository: DOI: 10. 5061/dryad. 30tk6 (https:// doi. org/ 10. 5061/ dryad. 30tk6).
However, there is a dearth of information on its involvement in other metabolic, neuropsychiatric, and infectious diseases. Objective: This study aims to identify significant differentially altered genes in the conversion of Th2 to Th9 cells, and their regulating microRNAs (miRs) from publicly available Gene Expression Omnibus data sets of the mouse model using in silico analysis to unravel various pathogenic pathways involved in disease processes. Methods: Using differentially expressed genes (DEGs) identified from 2 publicly available data sets (GSE99166 and GSE123501) we performed functional enrichment and network analyses to identify pathways, protein-protein interactions, miR-messenger RNA associations, and disease-gene associations related to significant differentially altered genes implicated in the conversion of Th2 to Th9 cells.
Results: We extracted 260 common downregulated, 236 common upregulated, and 634 common DEGs from the expression profiles of data sets GSE99166 and GSE123501. Codifferentially expressed ILs, cytokines, receptors, and transcription factors (TFs) were enriched in 7 crucial Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology. We constructed the protein-protein interaction network and predicted the top regulatory miRs involved in the Th2 to Th9 differentiation pathways. We also identified various metabolic, allergic and pulmonary, neuropsychiatric, autoimmune, and infectious diseases as well as carcinomas where the differentiation of Th2 to Th9 may play a crucial role. Conclusions: This study identified hitherto unexplored possible associations between Th9 and disease states. Some important ILs, including CCL1 (chemokine [C-C motif] ligand 1), CCL20 (chemokine [C-C motif] ligand 20), IL-13, IL-4, IL-12A, and IL-9; receptors, including IL-12RB1, IL-4RA (interleukin 9 receptor alpha), CD53 (cluster of differentiation 53), CD6 (cluster of differentiation 6), CD5 (cluster of differentiation 5), CD83 (cluster of differentiation 83), CD197 (cluster of differentiation 197), IL-1RL1 (interleukin 1 receptor-like 1), CD101 (cluster of differentiation 101), CD96 (cluster of differentiation 96), CD72 (cluster of differentiation 72), CD7 (cluster of differentiation 7), CD152 (cytotoxic T lymphocyte-associated protein 4), CD38 (cluster.
Expression Profiling: Gene Expression Omnibus Assay to Data Mining for Th2 to Th9 Cells Differentiation We performed a search in the Gene Expression Omnibus (GEO) database using several keywords, including "Healthy Control," "Wild Type," "Mice," "Mus musculus," "Th9," "Th2," and "Expression profiling by array" from January 1, 2012, to December 17, 2020, and selected 2 gene series expressions (GSEs) data for further study: GSE99166 and GSE123501. GSE99166 contained 4 samples of Th2 wild-type cells (GSM2634701, GSM2634702, GSM2634711, and GSM2634712) and 5 samples of Th9 wild-type cells (GSM2634695, GSM2634703, GSM2634704, GSM2634713, and GSM2634714) from the spleen. GSE123501 contained 2 samples of Th2 wild-type cells (GSM3505597 and GSM3505602) and another 2 samples of Th9 wild-type cells (GSM350598 and GSM3505603) from the spleen (Figure  1 ).
Assortment and Identification of Codifferentially Expressed Messenger RNAs From the Spleen (2 Different) Data Sets The differentially expressed genes (DEGs) were obtained from the 13 samples of 2 different data sets (GSE99166 and GSE123501) using the GREIN (GEO RNA-seq Experiments Interactive Navigator) platform (BD2K-LINCS Data Coordination and Integration Center). This interactive online web tool analyses GEO RNA-seq data  [18]. The DEGs extracted from the data sets comprised genes from Th2 and Th9 cells. As we wanted to assess the alteration of genes during the conversion of Th2 to Th9 cells, the analysis was performed with DEGs of Th2 cells as the standard to which DEGs of Th9 cells were compared. The workflow for the data processing and analysis is portrayed in Figure  2. The DEGs were considered upregulated when the expression of genes in Th9 cells was higher than that in Th2 cells.
The cutoff for the selection was kept at P<. 05, and overlapping DEGs between 2 data sets (GSE99166 and GSE123501) on comparison of Th2 and Th9 cells were identified by the Venn diagram tool  [19, 20]. In addition, the common upregulated, downregulated, and oppositely regulated DEGs of these 2 data sets (GSE99166 and GSE123501) were identified. The fold change expression distribution was visualized by a heat map and violin plot using the Linear Models for the Microarray Data (limma) Package of R (R Foundation for Statistical Computing) and Orange Data Mining (University of Ljubljana)  [21, 22].
Kyoto Encyclopedia of Genes and Genomes Pathway Analysis of Top Ranked Significant, Common, Regulated DEGs We searched the functionally significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for top ranked significantly altered DEGs using the STRING and WikiPathways databases. We identified important genes participating in each pathway, and selected the top 7 pathways based on negative log10 (false discovery rate) and P values (<. 05) that were important for further study.
Genes Assortment and Construction of a Protein-Protein Interaction Network of the Top Enriched Pathways We downloaded the complete gene list of the top ranked 7 individual pathways with an interaction network from the KEGG database. We revisualized and constructed the pathway with the help of Cytoscape (Cytoscape Team/Institute for Systems Biology; an open-source software platform for visualizing complex networks and integrating these with any type of attribute data)  [23]  and marked the DEGs that play a significant role in the differentiation of Th2 to Th9 cells.
Identification of Top Regulatory MicroRNAs Involved in the Th2 to Th9 Differentiation Pathways The top 10 microRNAs (miRs) that targeted the hub genes were predicted by the well-established miR target prediction database miRNet version 22. 0  [24] , with special emphasis on the selected organism. Default values for the degree of interaction and betweenness were selected. Common miRs and their targeted messenger RNAs (mRNAs) of all groups were sorted by the Venn diagram.
Assortment of Significant DEGs in the Differentiation of Th2 to Th9 Cells The Mus musculus (C57BL/6) mRNA expression profiles of GSE99167 and GSE123501, which were selected for this study, included the expression profiles of Th2 and Th9 cells obtained from the spleen. We extracted and compared mice spleen samples from 2 different studies to identify genes that are involved in the differentiation of Th2 to Th9 cells. In both groups, 254 common mRNAs were identified, and 634 common DEGs were identified, of which 236 were downregulated and 260 were upregulated. We performed a quality assessment of the selected samples for our expression profiles (Figures  3A-3I ; see Tables  S1  and S2  in Multimedia Appendix 1, and Multimedia Appendix 2 for larger version of figures). (cluster of differentiation 197), IL-1RL1 (interleukin 1 receptor-like 1), CD101 (cluster of differentiation 101), CD96 (cluster of differentiation 96), CD72 (cluster of differentiation 72), CD7 (cluster of differentiation 7), CD152 (cytotoxic T lymphocyte-associated protein 4), CD38 (cluster of differentiation 38), CX3CR1 (chemokine [C-X3-C motif] receptor 1), CTLA2A (cytotoxic T lymphocyte-associated protein 2 alpha), CTLA28, and CD196 (cluster of differentiation 196).
Construction of the Protein-Protein Interaction Network of DEGs Involved in the Transition of Th2 to Th9 Cells We downloaded the complete protein-protein interaction (PPI) network of the identified KEGG pathways from the KEGG database. The Cytoscape software was used for the construction of the network. The significantly altered DEGs of cytokines, chemokines, receptors, and TFs were highlighted in the respective networks. Our analysis of the KEGG pathway enrichment and PPI network demonstrated that the genes that had a significantly altered expression in Th9 cells when compared with Th2 cells also played a significant role in other immune regulating pathways. These affected pathways were mainly involved in cytokines-cytokines interaction, Th1 and Th2 differentiation, CTLA4 (cytotoxic T lymphocyte-associated protein 4) regulation, T-cell receptor signaling, Fc epsilon signaling, Th17 cell differentiation, IBD, and cancer.
Principal Findings In this study, we compared 2 different data sets (GSE99166 and GSE123501) that have compared the mRNA expression in Th2 and Th9 cells. We identified common DEGs that have significantly altered expression between Th2 and Th9 cells from these 2 data sets. Sequential assessment of the DEGs and miRs that had significantly altered expression between Th2 and Th9 cells allows to identify disease states that affect the differentiation process. Although this analysis does not answer whether differentiation of Th2 to Th9 is the cause or the effect of the disease state, it does unravel the possibility of hitherto unknown associations between various diseases and the process of differentiation of Th2 to Th9 cells. Our analysis indicates that differentiation of Th2 to Th9 may play a crucial role via the alteration of DEGs (Table  1 ) and miRs (Table  2 ) in various metabolic diseases, allergic and pulmonary diseases, carcinomas, neuropsychiatric disorders, autoimmune diseases, and infectious diseases.
Acknowledgments: We acknowledge the following individuals for their assistance with the study, none of whom have been compensated for his or her contribution: Participating investigators:  Berthe-Marie Imbert ,  Thomas Drumel ,  Béatrice Mahé ,  Viviane Dubruille ,  Nicolas Blin ,  Anne Lok ,  Cyrille Touzeau ,  Thomas Gastinne ,  Benoît Tessoulin ,  Sophie Vantyghem ,  Philippe Moreau ,  Steven Le Gouill , for collecting data, providing and caring for study patients. Hematology Department nurses  Patricia Lespart ,  Ghislaine Francois  and  Katia Godart  for administrating vaccines and for their help in collecting samples and questionnaires; the paramedical staff who participated to the study as controls. We thank  Nantes Cytometry Facilty Cytocell  for expert technical assistance,  Nantes University Hospital CIMNA core facility  and  GTI platform (Gene Therapy Immunology core)  for ELISPOT counting.
Data analysis The software EpiData was used to collect and manage data. Categorical variables are specified as proportions, with percent and confidence intervals (95%). Quantitative variables are represented by means with IQR. Factors associated with having any difficulty to perform HIVST were assessed using binary logistic regression. Variables with a p-value lower than 0. 20 in the univariable analysis were considered eligible to enter the multivariable model. In the multivariable analysis. A backward procedure based on the Likelihood Ratio Chi-2 test was used to select variables for the final model. Statistical analysis was carried out using Stata/SE 14. 0 software (StataCorp LP, College Station, USA). Given the small number of transgender people (2 persons), they are not included in this analysis.
eHealth tool An eHealth learning tool baptized "MyFIT" (My Functional Insulin Therapy) was developed between June 2018 and June 2020 by the team of investigators in collaboration with five T1D patients (3 women) as partners, from four different age categories according to the use of Internet (<25 years, 25-39 years, 40-69 years, > 70 years)  (Statista Research Department, 2021). Development was carried out based on the principles of FIT  (Bendik et al. , 2009; Grimaldi et al. , 2008)  and the conventional learning module provided in a hospital setting  (Flaus-Furmaniuk et al. , 2017). Learning content was adapted to online education  (Arbaugh and Hornik, 2006). User is guided by a mascot according to his/her age category (child/adolescent, adult, elderly), gender and socio-professional activities.
Statistical analysis All quantitative variables are described as medians and percentiles (IQR, 25-75th percentile), all proportions as percentages with 95 % confidence intervals. The final scores on the MyFIT knowledge tests (Modules 1 and 2) were calculated as a percentage of the overall score for all exercises. The maximum score that could be obtained was 100 %. More than 75 % users should answer the questions correctly. The expected success rate was of 88 % ± 12 %. A total of 30 subjects was necessary, taking into account 5 % lost to follow-up. Correlations were measured, using a Spearman's rank correlation, between the dimensions of the self-administered questionnaire and the scores on the knowledge tests. Non parametric comparisons were two-sided. Alpha risk was set at 0. 05. Free form answers were coded and analyzed thematically by two investigators and any discrepancies were resolved by consensus with two other investigators  (Fitzpatrick and Boulton, 1994).
SoccerNet dataset Giancola et al. [14]  introduced SoccerNet in 2018. The objective was to share a large-scale dataset for reproducible research in soccer video understanding and to define a new task of action spotting for the temporal localization of sports activities defined with single timestamps. Originally, the dataset contained 500 videos of complete broadcast soccer games, totalling almost 800 hours of videos from the six major European championships (Seria A, La Liga, Premier League, Ligue 1, Bundesliga, and Champion's League) from 2014 to 2017. The first annotations cover temporal timestamps of three main actions in soccer: goal, cards, and substitutions. The annotations were scrapped from websites with a one minute resolution and later manually refined to a one second precision. Later, Deliège et al.
[9]  introduced SoccerNet-v2, which significantly increased the number of annotations for the action spotting task by completing the set with common actions in soccer such as penalties, clearances, ball out of play, etc. , for a total of 110,458 actions split into 17 classes. In addition to these extended annotations, SoccerNet-v2 integrates annotations for all camera changes among 13 camera classes and three transition classes: abrupt, smooth, or logo. Finally, each camera shot is annotated by specifying if it contains a replay of an action, and if so, links it to its corresponding action timestamp during the live feed. Three tasks were proposed with this dataset: action spotting, camera shot segmentation and boundary detection, and replay grounding. Challenges were organized in 2021 for action spotting and replay grounding, the two tasks focusing on retrieving specific moments in videos, using 50 extra games with segregated annotation as challenge set.
As the latest release, SoccerNet-Tracking  [6]  introduces spatiotemporal annotations on a new set of 12 complete games captured from a single main camera. The dataset includes 200 30-seconds long clips extracted around key actions and a complete 45-minute halftime for long-term tracking with all objects annotated with bounding boxes, tracklet IDs, jersey numbers, and team tags. SoccerNet-Tracking is one of the largest multi-object tracking dataset and the largest one related to soccer, accounting for more than 3. 6 M bounding boxes and more than 5,000 unique tracklets.
SoccerNet challenges In the 2022 edition of the SoccerNet challenges, we proposed 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. The data for each challenge is split in 4 sets: a training set and a validation set for training models, a public test set for benchmarking in scientific publications, and a private challenge set for ranking participants, whose annotations are kept segregated to avoid any cheating.
To help participants get started with these challenges, we provide sample code on different SoccerNet GitHub repositories (https: //github. com/SoccerNet) to download the data, run task-specific baselines, and evaluate their performance. To facilitate interactions between participants, we created a Discord server, gathering more than 300 researchers during the 2022 challenges. Furthermore, we organized several live tutorials with Q&As and published explanatory videos on YouTube (https://www. youtube. com/c/acadresearch) to further attract the interest of the community. In total, 67 teams competed on the 6 proposed tasks and submitted 637 results files. We offered prizes for the winner of each task sponsored by Sportradar (2,000 $ for tasks 1-2&5), EVS Broadcast Equipment (1,000 $ for tasks 3-4), and Baidu Research (1,000 $ for task 6).
Furthermore, in its lowest level of granularity, it can support the generation of extended statistics for players and teams. In this year's challenge, we leveraged the videos and annotations from SoccerNet-v2  [9]. The data consists of 500 games, each of them split into two half-time videos of 45 min plus eventual extra time. The annotations amount to 110,458 actions from 17 classes, anchored with a single timestamp. In addition to these annotated data, we reserved extra 50 games for the scope of this challenge, with segregated annotations to impede any participant team to train or overfit on this set.
REPLAY GROUNDING 3. 1 Task description Replays of salient moments are regularly shown in broadcast soccer games to emphasis the importance of an action, visualized under a more informative angle. Being able to link replays with their corresponding actions is thus a great tool for ranking actions by their impact on the game, which may be used to generate highlights of the game. Given a replay clip, the goal of the replay grounding task is to spot the same action during the live game. The action timestamp correspond to the ones of the action spotting task, and thus follow the same annotation format. Thus, the dataset consists of the same 500 broadcast games from the action spotting task from which all.
The soccer field elements are the set of soccer field line or circle markings, and the three posts constituting each goal. Note that the extremity of an element is defined as either its true end, or the intersection of the object with the border of the image. The dataset has been annotated with polylines, a sequential list of 2D points that fits any soccer field element of rectilinear or circular nature. In this task, the objective is to retrieve the first and the last element, i. e. the extremities, of each annotated polyline.