dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We would like to thank  Dr. Jean-Luc Dupouey  for his help in statistical analysis and figures preparation. We would like to give many thanks to the editor and the reviewers for their valuable comments and suggestions that have significantly increased the quality of the manuscript. We would also like to acknowledge  Zewdu Eshetu ,  A. Park Williams ,  Marcin Koprowski ,  Tommy H. G. Wils ,  Mergi D. Dinka ,  Agnieszka Piernik ,  Kassahun Tesfaye  and  Ethiopian Institute of Biotechnology , and  Werner Ulrich  for their kind help.
Introduction The International Geomagnetic Reference Field (IGRF) is a set of spherical harmonic coefficients which can be input into a mathematical model in order to describe the large-scale, time-varying portion of Earth's internal magnetic field between epochs 1900 A. D. and the present. The IGRF is produced and maintained by an international task force of scientists under the auspices of the International Association of Geomagnetism and Aeronomy (IAGA) Working Group V-MOD. This thirteenth generation IGRF has been derived from observations recorded by satellites, ground observatories, and magnetic surveys (see Appendix 1 for a list of World Data System data centers and services). IGRF is routinely used by the scientific community to study Earth's core field, space weather, electromagnetic induction, and local magnetic anomalies in the lithosphere.
Open Access *Correspondence: alken@colorado. edu 2 NOAA National Centers for Environmental Information, Boulder, CO, USA Full list of author information is available at the end of the article DGRF models are so labeled because they have been built from the best available data sources of that time period and therefore are unlikely to be improved in future IGRF revisions. Models labeled as IGRF are non-definitive, and will likely be revised in the future as more data are collected. DGRF models have been built only starting in 1945. Details of the history of IGRF can be found in  Barton (1997)  and  Macmillan and Finlay (2011). Past generations of IGRF models are archived at https ://www. ngdc. noaa. gov/IAGA/vmod/igrf_old_model s. html. Since later IGRFs can revise model parameters for past epochs, it is important to record which generation of IGRF was used to process a particular dataset, so that the original data can be recovered and reprocessed with the latest generation of IGRF if needed.
IGRF-13 online data products Further general information about IGRF: https ://www. ngdc. noaa. gov/IAGA/vmod/igrf. html Coefficients of IGRF-13 in ASCII format: https ://www. ngdc. noaa. gov/IAGA/vmod/coeff s/igrf1 3coeff s. txt Fortran software to compute magnetic field components from coefficients: https ://www. ngdc. noaa. gov/ IAGA/vmod/igrf1 3. f Linux C software to compute magnetic field components from coefficients: https ://www. ngdc. noaa. gov/ IAGA/vmod/geoma g70_linux. tar. gz Windows C software to compute magnetic field components from coefficients: https ://www. ngdc. noaa. gov/ IAGA/vmod/geoma g70_windo ws. zip Python software to compute magnetic field components from coefficients: https ://www. ngdc. noaa. gov/ IAGA/vmod/pyIGR F. zip Online calculation of magnetic field components for IGRF-13: https ://www.
Funding The Swarm and Cryosat-2 missions are supported by the  European Space Agency (ESA). The Ørsted mission was made possible by extensive support from the  Danish government ,  NASA ,  ESA ,  CNES, DARA , and the  Thomas B. Thriges Foundation. The CHAMP mission was sponsored by the  Space Agency of the German Aerospace Center (DLR)  through funds of the  Federal Ministry of Economics and Technology, following a decision of the German Federal Parliament  (grant code  50EE0944 ). The SAC-C mission was made possible by a collaboration between the  Comisión Nacional de Actividades Espaciales (CONAE) ,  NASA/JPL , and the  Danish Space Research Institute. The CSES mission is funded by the  China National Space Administration (CNSA)  and the  China Earthquake Administration (CEA). The international network of geomagnetic observatories is supported by numerous institutes, staff, and  INTERMAGNET.
Availability of data and materials Swarm and Cryosat-2 data are available from https ://earth. esa. int/web/guest /swarm /data-acces s. CHAMP data can be obtained from http://isdc. gfz-potsd am. de. Ørsted and SAC-C data are available from https ://www. space. dtu. dk/ engli sh/resea rch/scien tific _data_and_model s/magne tic_satel lites. CSES data is available from http://www. leos. ac. cn. INTERMAGNET data is available from https ://www. inter magne t. org.
To get a general overview of the predicted components for the Fe-S cluster machinery in T. gondii, we conducted homology searches in the ToxoDB. org database  [37] , using well-characterized proteins from plants (Arabidopsis thaliana) belonging to the SUF, ISC and CIA pathways (S1 Table ). Data from global mapping of protein subcellular location by hyperLOPIT spatial proteomics  [38]  was in general in good accordance with the expected localization of the homologs (with the noticeable exception of members of the NBP35/HCF101 ATP-binding proteins). Overall, our search revealed that T. gondii appears to have a good conservation of all the main components of the three ISC, SUF and CIA Fe-S synthesis pathways (S1 Table  and Fig 1A ). Additional information available on ToxoDB. org such as scores from a CRISPR/Cas9-based genome-wide screening  [39] , highlighted that most components of the three pathways are predicted to be important for parasite fitness.
Cellular downstream pathways or functions may also be affected, while other pathways may be upregulated in compensation. Parasites were treated for two days with ATc (cKD TgISU1-HA) or three days (cKD TgNFS2-HA, as it takes slightly longer to be depleted, Fig  3A ), prior to a global proteomic analysis comparing protein expression with the ATc-treated TATi ΔKu80 control. For each mutant, we selected candidates with a log2(fold change) �-0. 55 or �0. 55 (corresponding to a ~1. 47-fold change in decreased or increased expression) and a p-value <0. 05 (ANOVA, n = 4 biological replicates) (S3 and S4 Tables  and Fig 6A  and 6B ). To get a more exhaustive overview of proteins whose amounts varied drastically, we completed this dataset by selecting some candidates that were consistently and specifically absent from the mutant cell lines or only expressed in these (S3 and S4 Tables).
Data represented are mean ±SEM of n = 5 independent experiments. �� p � 0. 01, ��� p � 0. 001, ANOVA comparison. D) Impact of TgISU1 depletion on the parasite mitochondrial membrane potential was measured by JC-1 labelling. cKD TgISU1-HA parasites were grown or not in the presence of ATc, mechanically released from their host cells and labelled with the JC-1 dye. This dye exhibits potential-dependent accumulation in the mitochondrion, indicated by a switch from green fluorescence for the monomeric form of the probe, to a concentration-dependent formation of red aggregates (inset, DNA is labelled with DAPI and shown in blue, scale = 1μm). Parasites were then analysed by flow cytometry. Unlabelled parasites (no JC-1) was used as a control for gating. Numbers represent the percentage of cells in each of the subpopulations (P1, P2, P3).
IspG and IspH, which are key Fe-S-dependent enzymes of the non-mevalonate isoprenoid synthesis pathway  [60] , were only found marginally less expressed in our quantitative analysis after TgNFS2 depletion. However, our proteomics dataset provided indirect clues that their function may be impacted. Isoprenoid synthesis is vital for T. gondii tachyzoites  [34] , and it has implication beyond the apicoplast, as prenylated proteins or isoprenoid precursors are involved in more general cellular processes including intracellular trafficking or mitochondrial respiration  [85]. Isoprenoids are for instance important for synthesizing ubiquinone/coenzyme Q, and the single predicted mitochondrial candidate that was significantly less expressed upon TgNFS2 depletion is a putative UbiE/ COQ5 methyltransferase, involved in synthesis of this co-factor  [86].
Bioinformatic analyses Sequence alignments were performed using the MUltiple Sequence Comparison by Log-Expectation (MUSCLE) algorithm of the Geneious 6. 1. 8 software suite (http://www. geneious. com). Transit peptide and localization predictions were done using IPSORT (http://ipsort. hgc. jp/), Localizer 1. 0. 4 (http://localizer. csiro. au/), and Deeploc 1. 0 (http://www. cbs. dtu. dk/ services/DeepLoc-1. 0/) algorithms. The putative Fe-S proteome was predicted using the MetalPredator webserver (http:// metalweb. cerm. unifi. it/tools/metalpredator/) [56]. The whole complement of T. gondii annotated proteins was downloaded in FASTA format from the ToxoDB database (https://toxodb. org  [37] , release 45) and used for analysis in the MetalPredator webserver. Additional manual curation included homology searches for known Fe-S proteins from plants (see appendix A in  [114] ), and search for homologues in the Uniprot database (https://www.
uniprot. org) that were annotated as containing a Fe-S cofactor. For proteomics candidates, annotations were inferred from ToxoDB, KEGG (https://www. genome. jp/kegg/) and the Liverpool Library of Apicomplexan Metabolic Pathways (http://www. llamp. net/ [115] ). N-glycosylation predictions were done with the GlycoEP webserver (http://crdd. osdd. net/ raghava/glycoep/index. html). GPI anchor predictions were done with the PredGPI (http:// gpcr. biocomp. unibo. it/predgpi/) and GPI-SOM (http://gpi. unibe. ch/) webservers. Candidate variant proteins identified by quantitative proteomics were mapped on the graphic representation of the high-resolution spatial proteome map of T. gondii using the hyperLOPIT dataset  [38]  (https://proteome. shinyapps. io/toxolopittzex/).
Rat monoclonal anti-HA antibody (clone 3F10, Roche) was used to detect epitope-tagged proteins. Staining of DNA was performed on fixed cells by incubating them for 5 min in a 1 μg/ml 4,6-diamidino-2-phenylindole (DAPI) solution. All images were acquired at the Montpellier RIO imaging facility from a Zeiss AXIO Imager Z1 epifluorescence microscope driven by the ZEN software v2. 3 (Zeiss). Z-stack acquisition and maximal intensity projection was performed to visualize larger structures such as in vitro cysts. Adjustments for brightness and contrast were applied uniformly on the entire image.
Egress assay T. gondii tachyzoites were grown for 40 (without ATc) or 120 (with ATc) hours on HFF cells with coverslips in 24-well plates. The infected host cells were incubated for 7 min at 37˚C with DMEM containing 3 μM of calcium ionophore A23187 (C7522, Sigma-Aldrich) prior to fixation with 4% PFA. Immunofluorescence assays were performed as previously described  [124] : the parasites and the parasitophorous vacuole membrane were labelled with anti-GAP45 and anti-GRA3, respectively. The proportion of egressed and non-egressed vacuoles was calculated by counting 250 vacuoles in three independent experiments. Data are presented as mean values ± SEM.
Mitochondrial membrane potential measurement Parasites grown for the indicated time with or without ATc were mechanically released from their host cells, purified on a glass wool fiber column, washed and adjusted to 10 7 parasites/ml in phenol red-free medium, and incubated in with 1. 5 μM of the JC-1 dye (5,5',6,6'-tetrachloro-1,1',3,3'-tetraethylbenzimidazolylcarbocyanine Iodide, T3168, Invitrogen) for 30 min at 37˚C, washed phenol red-free medium and analyzed by flow cytometry or microscopy. Flow cytometry analysis was performed on a FACSAria III flow cytometer (Becton Dickinson). An unstained control was used to define gates for analysis. 50,000 events per condition were collected and data were analysed using the FlowJo Software.
01 and a protein E value smaller than 0. 01 were required. Using the above criteria, the rate of false peptide sequence assignment and false protein identification were lower than 1%. Peptide ion intensity values derived from MaxQuant were subjected for label-free quantitation. Unique and razor peptides were considered  [126]. Statistical analyses were carried out using R package software. ANOVA test with threshold of 0. 05 was applied to identify the significant differences in the protein abundance. Hits were retained if they were quantified in at least three of the four replicates in at least one experiment. Additional candidates that consistently showed absence or presence of LFQ values versus the control, and mean LFQ was only considered if peptides were detected in at least three out of the four biological replicates.
Acknowledgments We are grateful to  L. Sheiner ,  P. Bradley ,  B. Striepen ,  V. Carruthers ,  S. Lourido ,  S. Angel  and  D. Soldati-Favre  for providing cell lines, antibodies and plasmids. We thank the developers and the managers of the VeupathDB. org/ToxoDB. orgdatabases, as well as scientists who contributed datasets. We also thank the  MRI imaging facility  for providing access to their microscopes and flow cytometers, and the  Mass Spectrometry Proteomics Platform (MSPP) of the BPMP laboratory. Thanks to  F. Vignols  for insights into the biochemistry of Fe-S proteins.
AC was supported by a fellowship from the  Fondation pour la Recherche Medicale (Equipe FRM   EQ20170336725 ), https://www. frm. org/;SB acknowlegdes support from the  Labex Parafrap  ( ANR-11-LABX-0024 ), https://www. labex-parafrap. fr/en/, and the  Agence Nationale de la Recherche  ( ANR-19-CE15-0023 ), https://anr. fr/en/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Introduction The modelling of brain networks, called connectomics, has shaped our understanding of the structure and function of the brain across a variety of organisms and scales over the last decade  [1] [2] [3] [4] [5] [6]. In humans, these wiring diagrams are obtained in vivo through Magnetic Resonance Imaging (MRI), and show promise towards identifying biomarkers of disease. This can not only improve understanding of so-called "connectopathies", such as Alzheimer's Disease and Schizophrenia, but potentially pave the way for therapeutics  [7] [8] [9] [10] [11]. However, the analysis of brain imaging data relies on complex computational methods and software. Tools are trusted to perform everything from pre-processing tasks to downstream statistical evaluation. While these tools undoubtedly undergo rigorous evaluation on bespoke datasets, in the absence of ground-truth this is often evaluated through measures of reliability  [12] [13] [14] [15] [16] , proxy outcome statistics, or agreement with existing theory.
Importantly, this means that tools and datasets are not necessarily of known or consistent quality, and it is not uncommon that equivalent experiments may lead to diverging conclusions  [17] [18] [19] [20] [21] [22] [23]. While many scientific disciplines suffer from a lack of reproducibility  [24] , this was recently explored in brain imaging by a 70 team consortium which performed equivalent analyses and found widely inconsistent results  [17] , and it is likely that software instabilities played a role. This study does not broach dataset differences, but there are considerable works which demonstrate that data selection may compound these effects(e. g. [14, 16] ). The present study approached evaluating reproducibility from a computational perspective in which a series of brain imaging studies were numerically perturbed in such a way that the plausibility of results was not affected, and the implications of the observed instabilities on downstream analyses were quantified.
We accomplished this through the use of Monte Carlo Arithmetic (MCA)  [25, 26] , a technique which enables characterization of the sensitivity of a system to small numerical perturbations. This is importantly distinct from data perturbation experiments where the underlying datasets are manipulated or pathologies may be simulated, and allows for the evaluation of experimental uncertainty in real-world settings. We explored the impact of numerical perturbations through the direct comparision of structural connectomes, the consistency of their features, and their eventual application in a neuroscience study. We also characterized the consequences of instability in these pipelines on the reliability of derived datasets, and discuss how the induced variability may be harnessed to increase the discriminability of datasets, in an approach akin to ensemble learning.
Graphs vary widely with perturbations Prior to exploring the analytic impact of instabilities, a direct understanding of the induced variability was required. A subset of the Nathan Kline Institute Rockland Sample (NKIRS) dataset  [27]  was randomly selected to contain 25 individuals with two sessions of imaging data, each of which was subsampled into two components, resulting in four samples per individual and 100 samples total (25 × 2 × 2 samples). Structural connectomes were generated with canonical deterministic and probabilistic pipelines  [28, 29]  which were instrumented with MCA, replicating computational noise either sparsely or densely throughout the pipelines  [19, 26]. In the sparse case, a small subset of the libraries were instrumented with MCA, allowing for the evaluation of the cascading effects of numerical instabilities that may arise.
Sparse perturbations reduce off-target signal We assessed the reproducibility of the dataset through mimicking and extending a typical testretest experiment  [14]  in which the similarity of samples across sessions were compared to distinct samples in the dataset (Table  1 , with additional experiments and explanation of the measure and its scaling in S2 Section in S1 File). The ability to discriminate connectomes across subjects (Hypothesis 1) is an essential prerequisite for the application of brain imaging towards identifying individual differences  [6]. In testing hypothesis 1, we observe that the dataset is discriminable with a scaled score of 0. 82 (p < 0. 001; optimal score: 1. 0; chance: 0. 04) for both pipelines in the absence of MCA. We can see that inducing instabilities through MCA preserves the discriminability in the dense perturbtion setting, and and discriminability decreased slightly but remained above the unscaled reference value of 0.
65 in the sparse case. This lack of significant decrease in discriminability across MCA perturbations suggests its utility for capturing variance within datasets without compromising the robustness and reliability of the dataset as a whole, and possibly suggests this technique as a cost effective and context-agnostic method for dataset augmentation. While the discriminability of individuals is essential for the identification of individual brain networks, it is similarly reliant on network similarity-or lack of discriminabilityacross equivalent acquisitions (Hypothesis 2). In this case, connectomes were grouped based upon session, rather than subject, and the ability to distinguish one session from another based on subsamples was computed within-individual and aggregated. Both the unperturbed and dense perturbation settings perfectly preserved differences between sessions with a score of 1.
0 (p < 0. 005; optimal score: 0. 5; chance: 0. 5), indicating a dominant session-dependent The performance is reported as mean discriminability. While a perfectly discriminable dataset would be represented by a score of 1. 0, the chance performance, indicating minimal discriminability, is 1/the number of classes. H 3 could not be tested using the reference executions due to too few possible comparisons. The alternative hypothesis, indicating significant discrimination, was accepted for all experiments, with p < 0. 005 after correcting for multiple comparisons. https://doi. org/10. 1371/journal. pone. 0250755. t001 signal for all individuals despite no intended biological differences. However, while still significant relative to chance (score: 0. 85 and 0. 88; p < 0. 005 for both), sparse perturbations lead to significantly lower discriminability of the dataset (p < 0.
Distributions of graph statistics are reliable, but individual statistics are not Exploring the stability of topological features of structural connectomes is relevant for typical analyses, as low dimensional features are often more suitable than full connectomes for many analytical methods in practice  [5]. A separate subset of the NKIRS dataset was randomly selected to contain a single non-subsampled session for 100 individuals (100 × 1 × 1) using the pipelines and instrumentation methods to generate connectomes as above. Connectomes were generated 20 times each, resulting in a dataset which also contained 8,400 connectomes with the MCA simulations serving as the only source of repeated measurements. The stability of several commonly-used multivariate graph features  [31]  were explored and are presented in Fig 2.
The analysis was perturbed through distinct samplings of the dataset across both pipelines and perturbation methods. The accuracy and F1 score for the perturbed models varied from 0. 520-0. 716 and 0. 510-0. 725, respectively, ranging from at or below random performance to outperforming performance on the reference dataset. This large variability illustrates a previously uncharacterized margin of uncertainty in the modelling of this relationship, and limits confidence in reported accuracy scores on singly processed datasets. The portion of explained variance in these samples ranged from 88. 6% --97. 8%, similar to the reference of 90. 3%, suggesting that the range in performance was not due to a gain or loss of meaningful signal, but rather the reduction of bias towards specific outcome. Importantly, this finding does not suggest that modelling brain-phenotype relationships is not possible, but rather it sheds light on impactful uncertainty that must be accounted for in this process, and supports the use of ensemble modeling techniques.
One distinction between the results presented here and the previous is that while networks derived from dense perturbations had been shown to exhibit less dramatic instabilities in general, the results here show similar variability in classification performance across the two methods. This consistency suggests that the desired method of instrumentation may vary across experiments. While sparse perturbations result in considerably more variability in networks directly, the two techniques capture similar variability when relating networks to this phenotypic variable. Given the dramatic reduction in computational overhead, a sparse instrumentation may be preferred when processing datasets for eventual application in modelling brainphenotype relationships.
Cost-effective data augmentation The evaluation of reliability in brain imaging has historically relied upon the expensive collection of repeated measurements choreographed by massive cross-institutional consortia  [35, 36]. The finding that perturbing experiments using MCA both preserved the discriminability of the dataset due to biological signal and decreased the discriminability due to off-target differences across acquisitions and subsamples opens the door for a promising paradigm shift. Given that MCA is data-agnostic, this technique could be used effectively in conjunction with, or in lieu of, realistic noise models to augment existing datasets. While this of course would not replace the need for repeated measurements when exploring the effect of data collection paradigm or study longitudinal progressions of development or disease, it could be used in conjunction with these efforts to decrease the bias of each distinct sample within a dataset.