dataesr/scientific-paragraphs-categorization huggingface dataset:
Current forecasting methods participating in the SEP Scoreboard include: SAWS-ASPECS, MAG4, RELeASE, SEPMOD, SEPSTER, SEPSTER2D, SPRINTS, SWPC, and UMASEP. The GSU and iPATH models are currently being onboarded. The time series plots also show relevant SWPC alerts and MLSO K-Cor automatic CME detections or CME all-clear. The SEP Scoreboard is being monitored by SRAG operators as a test-case ahead of NASA's planned Artemis missions to the Moon. Table  12  lists each of the operational or real time models, the year that they were first implemented, the location of the model forecasts, and the end users. The model implementation history was collected through personal communication with the model developers and historical literature. About 1/3 of the models summarized in this paper have performed the required work to transition their models to systems external to their home institutions where they have been configured to run continuously and robustly, regardless of data outages, corrupted observations, or other complicating factors.
Validation Validation of SEP models has been non-uniform across the community. Validation efforts have ranged from subjective comparisons between model and data ''by eye" for a few selected events, to thorough efforts that assess a variety of metrics and skill scores for a statistical sample of events. This non-uniformity is generally a consequence of the different computational needs and level of automation of each model. As the SEP modeling field matures, all SEP models should pursue a quantitative comparison to observations. A number of steps can be taken to improve the quality, consistency, and ease of validation within the community. The creation of a standard set of SEP event definitions and the identification of community-accepted metrics would facilitate consistent validation and crossmodel comparison.
The curation of a shared data set of all observations required as input into the models (e. g. , linked flare, CME, radio, and energetic electron measurements) and for comparison with model output (e. g. , in situ particle data) would significantly reduce the workload for individual modelers and minimize duplicated efforts, and also remove disparities between model predictions that are due to the use of different input parameters. Lastly, a service with the ability to validate all types of SEP forecasts could be developed and made available to the community in a system such as CCMC's Comprehensive Assessment of Models and Events using Library Tools (CAMEL) Framework (Rasta ¨tter et al. , 2019). Validation performed with the intent to demonstrate operational performance should aim to reproduce realtime operational scenarios as closely as possible, such as through participation in CCMC's SEP Scoreboard 26.
Model forecasts should be issued without any tuning after the fact, and use only information available in real time as input into the model. It is also important to produce forecasts for time periods when no SEP event was observed, to assess false alarms and the validity of All Clear forecasts. Multi-point validation of 3D models for time periods when multiple spacecraft SEP observations are available at different locations can provide additional constraints and important insight into model performance. Lastly, forecasts should be output in energy ranges that match observational capabilities to ensure a meaningful comparison between model and observational data. High-quality validation requires high-quality observational measurements to provide the ''ground truth", but the current spacecraft instruments that provide important SEP data sets have limitations that degrade their accuracy.
Therefore, near-term validation efforts should be aware of the limitations of the existing data archives, and further work should be done to correct the deficiencies to the greatest extent possible. In conclusion, the SEP modeling community has developed a rich and diverse set of SEP models that exhibit a wide array of capabilities but currently have significant limitations, in particular arising from the gaps in real-time observations. If supported with the necessary observations, and with further developments, for example in computational capabilities and the application of artificial intelligence and machine learning, the field is poised for continued growth with a great potential to contribute significantly both to space weather operations and advances in the understanding of the physics of SEP events.
Acknowledgements The  ISEP  project is supported by the  Advanced Exploration Systems Division  under the  Human Exploration and Operations Mission Directorate of NASA  and performed in support of the  Human Health and Performance Contract for NASA  ( NNJ15HK11B ). The  HESPERIA  project was funded through the  European Union 's  HORIZON 2020 research and Innovation Programme  (Contract No  637324 ) and coordinated by the  National Observatory of Athens in Greece. PARADISE has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  870405  ( EUHFORIA 2. 0 ) and the  ESA  project " Heliospheric modelling techniques '' (Contract No. 4000133080/20/NL/CRS ). The code was developed in the framework of the projects  C14/19/089  ( C1  project  Internal Funds KU Leuven ), G.
0D07. 19N  ( FWO-Vlaanderen ), SIDC Data Exploitation ( ESA Prodex-12 ), and  Belspo  project  B2/191/P1/SWiM. Predictive Science Inc. was supported by  NASA  grants  80NSSC19K0067  and  80NSSC20K0285 , and  NSF  grant  ICER1854790. The development of the Sadykov et al. model was supported by  NASA ESI  grant  80NSSC20K0302  and  NSF   FDSS grant   1936361. JGL and COL acknowledge support for the SEPMOD model development for  CCMC  under  National Aeronautics and Space Administration (NASA)  Grant/Cooperative Agreement 80NSSC20K1873 from  GSFC  to  UCB/SSL , with additional support to COL from  AFOSR  Grant  FA9550-16-1-0418. SEPSTER was supported by the  NASA   Living With a Star Program  (grants  NNX15AB80G  and  NNG06EO90A ), and by the  CCMC/SRAG ISEP  project. SEPSTER2D was funded by the  NASA/HSR  program  NNH19ZDA001NHSR , the  Goddard Space Flight Center / Internal Scientist Funding Model (ISFM)  grant  HISFM18 , and the  Johnson Space Center / Space Radiation Analysis Group (SRAG)  under the  Integrated Solar Energetic Proton Alert/Warning System (ISEP) project.
SOLPENCO was funded by the  ESA  contract  14098/99/NL/MM  and its validation by the  Spanish Ministerio de Educacion y Ciencia  under the project  AYA2004-03022. SOLPENCO2 was developed under  ESA 's  SEPEM  project (Crosby et al. , 2015) and updated during  ESA 's  SOL2UP  project (Aran et al. , 2017), both projects under  ESA 's contract n. 20162/06/NL/JD  and  4000114116/15/ NL/HK , respectively. SPARX was initially developed as part of the  European Union-funded Seventh Framework Programme  ( FP7 )  COMESEP  project. The development of the SPREADFAST framework has been funded by a contract to the  ESA. SD and CW acknowledge support from  NERC  via grant  NE/V002864/1.
DENV-1 infections associated with recent travel history to Africa This retrospective study was conducted on samples collected by the French National Reference Center for Arboviruses (NRC). The NRC's missions include surveillance, delivery of expertise, and confirmatory diagnosis of arbovirus infections. Various French facilities (e. g. , hospitals, private laboratories, and medical teams temporarily based in Africa) may request confirmatory diagnosis for suspected arboviral cases from the NRC by submitting blood samples in the form of sera, plasma, or dried whole blood spots (DBS). An analysis request form, completed by the medical examiner, provides detailed clinical and travel information, as well as patient consent for medical research. Data from patient records collected and anonymously presented in this report were data used in routine diagnosis by the NRC.
Dengue virus genome sequencing Viral RNA from cell culture supernatant or primary sample was extracted using the EZ1 Advanced XL instrument (Qiagen, Hilden, Germany) and the EZ1 Virus mini kit, in accordance with the manufacturer's recommendations. Two sets of primers were used for full-genome amplification  (20, 21). Next-generation sequencing was attempted as previously described  (20). All complete coding sequences (CDSs) obtained in this study were deposited in GenBank under accession numbers listed in Table  1.
Complete CDS dataset A total of 105 strains representing the major DENV-1 clades from various locations and years were selected and complete CDSs with dates and locations of isolation were retrieved from GenBank. In addition, all sequences available in GenBank (containing data on isolation year and origin) that met one of the following criteria were selected: (i) DENV-1 sequence from an African country; or (ii) DENV-1 sequence sharing over 95% nucleotide identity with sequences obtained in this study. All partial (length < 10,100 bases), ambiguous (presence of continuous N; >0. 2% single ambiguous sites), and redundant sequences (sharing >99. 5% nucleotide identity, with identical epidemiological characteristics, i. e. , identical country and year of isolation) were excluded. The final complete CDS dataset contained 110 sequences (Supplementary Table  1 ).
Envelope gene dataset Approximately 40% of DENV-1 strains from Africa retrieved from GenBank were excluded from the complete CDS dataset due to insufficient length. Consequently, a partial genome dataset focusing on the Envelope (E) gene was constructed. All sequences from the complete CDS dataset were included, and additional Egene sequences were retrieved from GenBank using identical selection criteria. All partial (length < 1,460 bases), ambiguous (presence of continuous N; >0. 2% single ambiguous sites), and redundant sequences (sharing >99. 5% nucleotide identity, with identical epidemiological characteristics) were excluded. The final E-gene dataset contained 123 sequences (including 13 additional Egene sequences) (Supplementary Table  1 ).
Bioinformatics analysis Sequences obtained in this study were added to both the complete CDS and the E-gene datasets and were aligned using the online version of MAFFT (v7. 182)  (22). All regions containing free-end gaps were trimmed manually from the nucleotide MAFFT output, leaving a final complete CDS alignment length of 10,102 bases and a final E-gene alignment length of 1,464 bases. Using the Galaxy web platform (usegalaxy. org)  (23) , maximum-likelihood phylogenetic trees were constructed with the online version of IQ-Tree (v2. 1. 2)  (24). In terms of model parameters, ModelFinder indicated that substitution model GTR+F+I+G4 (i. e. , a general time reversible model with empirical base frequencies and a rate heterogeneity model allowing discrete g distribution, with four categories and evolutionarily invariant sites) was the best fit for both the E-gene and complete CDS alignments based on the Bayesian information criterion (-merit BIC)  (25).
Discussion Cases from our dataset of travelers have revealed the presence of DENV-1 in countries where the serotype and/or virus had yet to be reported (Figure  3 ). We report two cases of DENV-1 acquired in the Sahel, a region where DENV circulation is rarely documented. The Sahelian climate is characterized by an absence of rainfall for several months of the year, with no natural water surfaces for mosquito breeding. However, Aedes species have been reported across the entire Sahel region  (28) (29) (30) (31). Indeed, anthropophilic mosquitoes such as Aedes species can thrive in anthropogenic water sources, especially during the moderate rainy season, from July to September. Interestingly, some laboratory-confirmed DENV infections have been detected in Sahelian countries, such as Sudan, Mali, and Mauritania, since 1986 (6, 32-37) (Figure  3 ).
Funding This work was supported by (i) the  French Defense Health Service , (ii) the  European Virus Archive goes global  project ( EVAg ;  European Union-Horizon 2020 program  under grant agreement no. 871029 ; http://www. european-virus-archive. com/), and (iii)  SantéPublique France. The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript.
Chagas disease is a parasitical disease caused by Trypanosoma cruzi which affects ∼7 million people worldwide. Per year, ∼10 000 people die from this pathology. Indeed, ∼30% of humans develop severe chronic forms, including cardiac, digestive or neurological disorders, for which there is still no treatment. In order to facilitate research on Chagas disease, a manual curation of all papers corresponding to 'Chagas disease' referenced on PubMed has been performed. All deregulated molecules in hosts (all mammals, humans, mice or others) following T. cruzi infection were retrieved and included in a database, named ChagasDB. A website has been developed to make this database accessible to all. In this article, we detail the construction of this database, its contents and how to use it.
However, there is currently no database containing all this information. It is therefore difficult to make the links between new results and what has already been identified by the community. To facilitate the sharing of knowledge related to Chagas disease and thus research on this disease, we have reviewed 193 articles from 1945 to 2022 and collected information on molecules in hosts associated with T. cruzi infection. The collected information has been summarized in a freely accessible online database, ChagasDB (https://chagasdb. tagc. univamu. fr), and as a downloadable file. Thus, any molecule of interest can easily be found, regardless of the organism, its type or the strain of T. cruzi responsible for its dysregulation. These data can then be used to enrich other studies. In this article, we detail the construction criteria of this database, as well as its content and how to use it.
When selected, the complete article was parsed to review the results. Analyses showing significant associations between T. cruzi infection and host molecules were included in the database. For each molecule of interest, the following information was retrieved: its name as used in the article, its type, its level of variation, the compared phenotypes, the analyzed tissue, the method of analysis, the organism, the strain of T. cruzi (if known), the Digital Object Identifier (DOI) and PubMed reference number (PMID) of the article, its title and its year of publication.
Database annotation The collected features were annotated using different reference databases, depending on the species. For all genes-related features (protein, methylation site, mutation…), the name, description and ID of the associated genes were reported. In that case, for all of them, Ensembl (6) database was used. For Homo sapiens specifically, HUGO Gene Nomenclature Committee  (7)  and GeneCards (8) helped to identify some alias. microRNADataBase (miRBase)  (9)  and LNCipedia  (10)  were used for noncoding RNA. The Mouse Genome Informatics  (11)  and Rat Genome Database  (12)  were employed for Mus musculus and Rattus norvegicus, respectively.
Website development The platform has been developed using a Python (v3. 9. 13) environment with the Django (v. 4. 0. 7) framework coupled to a SQLite (v3. 39. 3) database. The web interface makes use of Jquery (v3. 6. 0), HTML5 and the framework TailwindCSS (v3. 2. 4). ChagasDB was deployed with Docker (v20. 10. 12) and Docker Compose (v1. 25. 0). A CLI (command-line interface) tool using Typer (v0. 4. 1) was developed to ensure an easy and fast update of the platform. All the scripts used to develop the website are available at https://github. com/TAGC-ComplexDisease/ChagasDB.
ChagasDB features analysis To get an overview of the information contained in the Cha-gasDB, a functional enrichment analysis was applied using R language. Due to the low number of features identified in R. norvegicus, Canis lupus familiaris and Ovis aries, only human and mouse features were considered for Gene Ontology (GO) biological process enrichment. Deregulated genes and proteins were analyzed using gProfileR (v0. 7. 0)  (13) , and top GOs were recovered using rrvigo (v1. 8. 0)  (14). Finally, the results were illustrated using ggplot2. To compare genes or proteins between different species, orthologous genes were found using biomaRt R package (v2. 52. 0)  (15).
Results and discussion Among the 21 121 articles listed in PubMed as 'Chagas disease', we have retained 193 articles (Figure  1 ) that correspond to our selection criteria: experimental or computational analysis that found significant deregulation at molecular levels on host infected by T. cruzi. Although the oldest paper referenced in PubMed dates from 1945, the 193 articles in ChagasDB were published between 1995 and 2022, due to the lack of statistical test before 1995 (Supplementary Table  S1 ). Information for each feature in the database was gathered, comprising a set of 25 distinct categories. These categories included details related to the feature itself, such as its name, description, ID and type, as well as information regarding the study conducted, including tissue, phenotypes, analysis, organism, population and T.
cruzi strains were included in the database, corresponding to three discrete typing units, TcI, TcII and TcVI. For TcI were used Colombia, G, Sylvio, SylvioX10/4, AC, CA-I, Col1. 8G2, K98 and Type 1 strains. TcII include ABC, Berenice-78, Y, JG, Lucky and SGO-Z12 strains. Finally, TcVI comprise CL-14, CL-Brener, Cvd, Q501/3, Tulahuén, Tehuantepec, RA and VD strains. From the 193 articles in ChagasDB, 174 present experimental analysis of molecules associated with T. cruzi infection, while 33 present computational analysis. Most of them were interested in H. sapiens response to T. cruzi (142), but some animal models were also employed: M. musculus (40), R. norvegicus  (12) , O. aries (1) and C. lupus familiaris  (1). As a result, a greater number and diversity of molecules have been identified in humans compared to other mammals (Table  1 ).
While acute and asymptomatic forms of studies represent, respectively, 51 (26%) and 77 (40%) articles, cardiac forms totalize 141 articles (73%) of the whole 193 articles, illustrating the interest for this specific disease presentation. There is an urgent need to understand why it develops in order to find a treatment. The development of databases makes it possible to gather a large amount of information that is directly and easily accessible, thus facilitating research. Moreover, the pooling of results, which are sometimes difficult to  access, makes it possible to at least confirm one's own results and at best save time and resources. For this purpose, we have developed a website allowing not only to explore ChagasDB though queries but also to download the whole database. Briefly, users can enter a feature name, ID or description in the 'Search' bar tool, and the different corresponding features will appear.
They can download the information relative to the features of interest. Users can apply some filters on different fields (like organism, T. cruzi strain…) to have access to a restricted number of results that correspond to their needs. Finally, users can also submit their own research results to improve the ChagasDB content. To conclude, ChagasDB is a database gathering all the molecules identified in the literature as deregulated in hosts due to T. cruzi infection. These data are easily accessible via a website, to be integrated in different analyses, or to be used as a confirmation. To our knowledge, this is the first database related to molecular response to Chagas disease. Currently, ChagasDB is limited to host molecules, but in the future, it could be extended to other types of data: cellular deregulations, responses to stimuli or treatments.
Clustering metrics and packages for simulations We identified the number of clusters along the niche axes using the KmeansGap clustering algorithm developed by D'Andrea et al. (  2019 ) (https://github. com/ rafae ldand rea/Clust ering -metric). We used the logtransformed abundances of prey or predator species and their niche location (μ i , ν i or μ k ), considering species as extinct if their abundance was below an extinction threshold of 0. 00001 at time step 500 (applied after the simulation). The KmeansGap algorithm finds the number of clusters that minimizes withincluster trait dispersion compared to null expectations, defined by permuting observed abundances across observed traits  (D'Andrea et al. , 2019). Once clusters were identified, we calculated the number of clusters and within cluster richness (average species number per cluster).
All clustering metrics (i. e. number of clusters and average within cluster richness) presented in figures are based on the significant clustering results. For the longer simulations, we identified the number of clusters every 10 generations from generation 1-100, and then every 100 generations from generation 100 to 5. 000, resulting in 59 clustering estimates with time for each simulation. We used R version 3. 5. 1 © for simulations and statistical analysis. Competition coefficients were calculated using a customized version of the dwrpnorm() function in the CircState library, and model simulations using lsoda() in the deSolve package with a 0. 01 time step. The simulation analysis scripts are available at GitHub (https://github. com/Matil daHar aldss on/Emerg ing-niche -clust ering_2023).
Reanalysis Data We analysed climatological data to investigate why the break-out occurred during the 2021/2022 austral summer season and more specifically, on 18 to 20 January 2022. We used ERA-5 Reanalysis data, a climate reanalysis product developed by the Copernicus Climate Change Service (C3S), a part of the European Center for Medium-Range Weather Forecast (ECMWF)  (Hersbach et al. , 2020) , at both monthly and hourly temporal resolution to assess significant trends that occurred in 2017 to 2022 and temperature and precipitation anomalies, as well as foehn wind occurrence in the months prior to January 2022. To calculate monthly anomalies, we averaged that month's climatology (1979 to 2022) then differenced the average temperature of the month in question with the climatology. To investigate the presence of foehn winds in January 2022, we followed Laffin   2022 ), who determined that foehn winds that produce surface melt require a temperature > 0°C, a wind speed of > 2.
Passive Microwave Data To investigate changing sea ice concentrations and melt extent since 2011, we used passive microwave data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) on the Aqua Satellite and its successor the Advanced Microwave Scanning Radiometer 2 (AMSR-2) on the 'Shizuku' (GCOM-W1) satellite. Combined, these passive microwave sensors provide nearly continuous daily data from 2002 until present. The daily sea ice concentration data product was downloaded from the University of Bremen for 19 January on all years from 2010 to 2022 inclusive (apart from 2012, which is not available;  Spreen et al. , 2008)  to assess sea ice concentration on the same day for the 12-year period. We also used the AMSR-E/2 data to investigate the presence of melt on the fast ice for each melt season (October 1 to March 31) from 2011 to 2022.
Optical Imagery and Synthetic Aperture Radar We used several optical satellite and Synthetic Aperture Radar (SAR) imagery to assess glacier characteristics, flow speeds, melt patterns, and elevation changes. We used MODIS, Landsat 8 and 9, and Worldview (WV) -1, 2, and 3 to investigate changes in glacier morphology, meltwater lakes, and frontal positions. The MODIS sensor, on the Aqua and Terra satellites, has a data archive from 2002 to present and was used to determine the dates of break-up and retreat extents. The Landsat 8 and 9 Operational Land Imager product was used to assess melt patterns during the 2021/2022 austral season and to determine ice flow speeds using a python-based image correlation software; PyCorr  (Fahnestock et al. , 2016). WV-1, 2, and 3 satellite images have a very high resolution of less than 0.