dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS. This work was supported by grants from  Associazione Italiana per la Ricerca sul Cancro  ( IG13074 ) and  Telethon  ( GGP11176 ), l 'Institut National du Cancer  ( INCA-PLBIO17-043 ), the  Project International de Coopération Scientifique-CNRS  ( n°6824 ) and l' Association pour la Recherche contre le Cancer  ( 130607082 ). Confocal analysis was performed at the  Centro Grandi Apparecchiature of the University of Tuscia. We also thank  A. Epanchitsev  for providing the CS cell lines. We thank  Izabela Sumara ,  Manuel Mendoza ,  Evanthia Pangou ,  Emanuel Compe , and  Frederic Coin  for fruitful discussions.
An extensive set of Ref. ANR-10-LABEX-25-01). BPD was supported by the Fundacão de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) (grant number APQ-01574-17). GST was supported by Decit/SCTIE/ MoH. BPD and GST were supported by the Secretaria de Estado de Saude de Minas Gerais (SES-MG)/Secretaria de Estado de Planejamento de Minas Gerais (SEPLAG-MG)/ Fundacão Oswaldo Cruz (FIOCRUZ) (grant Yellow fever). BPD and GST were supported by the National Council for Scientific and Technological Development (CNPq) (grant: Research fellowship). LS was supported by Coordenacão de Aperfeicoamento de Pessoal de Nı ´vel Superior (CAPES)/BRAZIL (grant 0001). NIOS: National Council for Scientific and Technological Development (CNPq) -(grant Graduate Scholarship). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Epizootics in NHP are confirmed for at least one animal at the Probable Place of Infection or by an epidemiological link  [41, 43, 44]. As spatial dynamics changed over the YF outbreaks for both datasets, two periods were considered: July 2016 to December 2017, and January 2018 to October 2018. The human 2016-2017 dataset included 579 confirmed cases from 158 locations, in Minas Gerais (81%), Espı ´rito Santo (10%), São Paulo (4%), Rio de Janeiro (4%), and Tocantins (one location) states. In most locations (84%), the number of cases ranged from 1 to 5, and up to 20 in 3% of the locations. The human 2018 dataset included 837 confirmed cases from 120 locations, in São Paulo (64%), Rio de Janeiro (31%), and Minas Gerais (5%) states. In 73% of the locations, the number of cases ranged from 1 to 5, and up to 20 in 6% of the locations.
The NHP 2016-2017 dataset included 1,054 confirmed epizootics (of Callithrix spp. , Sapajus spp. , Callicebus spp. , and Alouatta spp. ) from 319 locations, in São Paulo (54%), Minas Gerais (19. 5%), Bahia (18%) Espı ´rito Santo (6%), Rio de Janeiro (1. 5%), Distrito Federal (Brası ´lia), Mato Grosso, Tocantins (<1%) states. In 91% of the locations, the number of cases ranged from 1 to 5, and up to 20 in 4% of the locations. The NHP 2018 dataset includes 382 confirmed epizootics from 123 locations, in São Paulo (75%), Minas Gerais (16%), Rio de Janeiro (7%), Bahia (2%), and Tocantins (<1%) states. In 92% of the locations, the number of cases ranged from 1 to 5, and up to 20 in 2% of the locations.
The lower risk in the Amazon basin is likely explained by a smaller extent of anthropogenic drivers and high vaccination coverage since this is a region where yellow fever vaccination is mandatory  [63]. Although a decrease of the geographic extent of the YF at-risk area was observed from 2017 to 2018, the distribution of the favorability values remains similar both in monkeys and humans and for both periods, showing that there are still areas with high risk for YF occurrence. As study limitations, the modelling process relies on successive steps, the limits of which and the ways expected to control the induced biases must be emphasized. First, heterogeneity of diagnostic methods to consider occurrence data may increase the models' omission rate, although we were restricted to data retrieved directly from the public health databases.
BdT was supported by the  RESERVOIRS program  funded by  European (ERDF/FEDER)  and assistance from  CollectiviteTerritoriale de la Guyane and Direction Regionale pour la Recherche et la Technologie , and the  MicroBIOME project granted by Laboratoire d'Excellence CEBA "Investissement d'Avenir " and managed by the  Agence Nationale de la Recherche  (CEBA,.
These models are largely supported by data obtained in confinement and untested in other contexts. Traditional cell migration assays are typically performed on glass or polystyrene dishes or hydrogels, which do not confine cells, raising the question of how relevant the mechanisms used in unconfined contexts are to confined environments. ECM proteins deposited into lanes  (27) , microgrooved silicon wafers  (28) , and suspended polystyrene fibers  (12)  replicate the one-dimensional (1D) aligned structures found in these regions  (17). Cell contact guidance along these structures notably increases the directional persistence of cells  (12, 27) , but these cells do not experience mechanical confinement as they would in three-dimensional tissue environments. To overcome these limits, photolithography and polydimethylsiloxane (PDMS) replica molding can create confined channels that also permit microscopybased measurements of intracellular structure and dynamics  (21) (22) (23) (24) (29) (30) (31) (32) (33) (34).
1D CMS The 1D CMS employed in this study is modified from a previously described 2D CMS  (9) (10) (11) (12). A detailed model description can be found in the Supporting Materials and Methods. Simulation parameters and their estimated or measured values are reported in Table  S1. Simulations were coded and run in MATLAB (The MathWorks, Natick, MA). Cell body position (x cell ) was recorded from simulations, and mean-squared displacement (MSD) was computed from x cell using the overlap method  (35).
Microchannel devices Microchannel devices were based on previous designs  (36)  and were drawn using a computer-aided design software (AutoCAD; Autodesk, San Rafael, CA). Quartz-chrome photomasks containing the device patterns were produced from these designs using Minnesota Nano Center facilities and were used to create master molds for device designs on silicon wafers using standard photolithography techniques. Photolithography, PDMS replica molding, and device assembly are described in detail in the Supporting Materials and Methods.
Cell culture and imaging Human U251 glioma cells were cultured at 5% CO 2 and 37 C. Culture media consisted of Dulbecco's modified eagle media/F12 (Gibco, Thermo Fisher Scientific, Waltham, MA) supplemented with 10% (v/v) fetal bovine serum (Gibco), 100 U mL À1 penicillin (Corning, Corning, NY), and 100 mg mL À1 streptomycin (Corning). Cells were passaged and subcultured using 0. 25% trypsin-EDTA (Corning). Fluorescent proteins (eGFP-bactin or end-binding protein 1 (EB1)-eGFP) were transiently transfected into cells as described previously  (10). Nuclei were labeled using NucBlue Live ReadyProbes reagent (Thermo Fisher Scientific) before seeding cells in device inlets. Images of migrating cells were acquired every 5 min at 20Â magnification on a Nikon Eclipse Ti2 or Ti-E microscope (Nikon Instruments, Melville, NY) under control of NIS-Elements Advanced Research software (Nikon).
Human glioma cells move persistently in confined microchannels We next sought an engineered platform that would enable us to observe and track individual cells migrating within confined 1D channels and compare to simulation predictions. Using photolithography and PDMS replica-molding techniques  (36) , we created devices that featured 12-mmwide channels with a height of 5 mm (60-mm 2 rectangular cross-sectional area) emerging from inlet ports (Fig. 2, A  and B ). U251 cells were labeled with a live-cell nucleustracking dye before seeding in devices. Seeded cells spontaneously migrated out of the inlet ports and migrated along the channel axis (Fig. 2 C ). Time-lapse videos were acquired in the phase and DAPI channels every 5 min, and individual nuclei were tracked using a semiautomated script  (11).
ACKNOWLEDGMENTS We thank the members of the  Odde and Piel laboratories  for helpful discussions and  M. Titus  and  C. Cadart  for comments on the manuscript drafts. We thank  R. Attia  and  M. Fisher  for help with photomask design and  G. Doak ,  J. Valdez ,  X. Lu , and  D. Wood  for their assistance with photolithography. We thank  A. -M. Lennon-Dum  enil for use of laboratory equipment and the  Bioimaging Cell and Tissue Core Facility at Institut Curie  for use of microscopes during initial data collection. We thank the  Minnesota Supercomputing Institute  for use of the high-performance computing resources. This work is supported by the  National Science Foundation (NSF)  through the  National Nano Coordinated Infrastructure network  under award number  ECCS-1542202  (to the  Minnesota Nano Center ).
L. S. P. was supported by a  3M Science & Technology Fellowship  and an  NSF   Graduate Research Fellowship  ( 0039202 ). M. R. S. was supported by the  Undergraduate Research Opportunities Program  through the  University of Minnesota. Other funding support included an  NSF   Graduate Research Opportunities Worldwide grant  and an  STEM Chateaubriand Fellowship  to L. S. P. ,  Association Nationale pour la Recherche  grant  ANR-16-CE13-0009  to P. V. ,  European Research Council  consolidator grant  311205 PROMICO  to M. P. , and  National Institutes of Health  grants  U54 CA210190  and  R01 CA172986  to  D. J. O. P. V. is an INSERM investigator.
Pelletier and Ferraris (2000)  used HAC after running a PCA on the original data to identify métiers within an artisanal Senegalese fishery and Celtic Sea fishery, a methodology that has since proven popular among their contemporaries  (Ulrich and Andersen, 2004; Tzanatos et al. , 2006; Campos et al. , 2007; Ziegler, 2012). Lastly, nonhierarchical clustering methods, including partitioning around medoids (PAM)  (Kaufman and Rousseeuw, 1990) , were used by  Duarte et al. (2009)  to define clusters of vessels in a Portuguese purse-seine fishery, while its variant, Clustering Large Applications (CLARA)  (Kaufman and Rousseeuw, 1990) , was used by  Castro et al. (2010 Castro et al. ( , 2011) )  to identify métiers in the Spanish bottom pair trawl and longline fleets, respectively. The main advantages of non-hierarchical clustering methods, such as PAM, over traditional approaches, such as HAC, are that they are less computationally expensive to run when using a large dataset and less sensitive to outliers  (Reddy and Vinzamuri, 2014).
(2009) , and  Campbell et al. (2017). All analyses were performed using the statistical computing program R (R Core Team, 2019) and WEKA  (Hall et al. , 2009). The list of relevant R packages is provided in the Supplementary Material (see Supplementary Table  S1a ). WEKA was only used for the implementation of feature selection methods (see Supplementary Table  S1b  for a description), which was not available in R.
Ideally, métiers should reflect the fishing intention (e. g. , species targeted)  (Marchal, 2008)  but in the ETBF, fishers often report more than one species as "targeted" in their logbook, with only 35% of all sets reporting a single targeted species. Nevertheless, when reporting a single target species, fishers were very effective at catching this species, particularly when targeting either broadbill swordfish or southern bluefin tuna (Supplementary Table  S2 ). A total of 17 fisher decision variables were derived from the logbook data (Table  1 ). These included date and time of set, lunar phase, location, and gear configuration variables such as length of mainline, number of hooks set, number of hooks per basket, and number of light-sticks. Location variables included latitude and longitude of set.
Compositional Data Catch compositional data that are converted into proportions are often used in métier analysis, with a common approach being to use either a logit transformation or an arcsine squareroot transformation of the data to normalize the distributions, followed by a calculation of the Euclidean distance  (He et al. , 1997; Campbell et al. , 2017). However, the logit transformation is not possible when there are too many zeros present in the dataset (as removing records with zero values leads to significant information loss). The arcsine square-root transformation can   1998 ) suggested that the best practice for compositional data analysis is to use a variant of a logarithmic transformation where the natural logarithm is applied to each component of a compositional vector scaled to its geometric mean.
Replacing Zero Values in the Compositional Data The common practice for the treatment of zeros is replacing them with a small positive value. This replacement can be implemented in various ways, including replacing the zeros by a constant or adding a constant to all values in the dataset. These ad hoc methods are not suitable for compositional data as it may distort the relationship between zero and nonzero observations (Pawlowsky-Glahn and Buccianti, 2011). In a fisheries context, these zeros are either true zeros (i. e. , absence of species in the area fished or selective nature of the fishing gear) or false zeros (i. e. , not recorded, misreported or underreported in logbook by the fisher). While different methods are available for replacing zeros in compositional data, our framework caters for zero observations through the use of a geometric Bayesian-multiplicative estimation method proposed by  Martín-Fernández et al.
The most computationally efficient HCA (bottom-up) requires O n 3 time (where n is the number of records). This algorithm groups the data using a sequence of nested partitions from singleton clusters to a cluster including all individual records. In contrast, our choice of algorithm only requires O n 2 k 2 time (where k is the number of clusters, ranging from five to 10 in our study). The k-means (MacQueen, 1967) and k-medoids algorithms  (Kaufman and Rousseeuw, 1987)  are well-known non-hierarchical algorithms for determining métiers that cluster the dataset of n objects into k clusters with k known a priori. k-means clustering finds the k centroids iteratively and assigns every point to the closest centroid, where the coordinate of each centroid is the mean of the coordinates of the points in the cluster.
, 2010 Castro et al. , , 2011;; Cambiè et al. , 2020). However, due to the high dimensionality and inherent disorder of our fisheries dataset and the fact that the optimal solution may still exhibit strong biases toward one or the other metrics, we were cautious to not solely select the k value that resulted in the maximum average silhouette width across all métiers. Instead, we examined a range of values close to the proposed k guided by the maximum average silhouette width to choose the final k and subset of variables that satisfy our internal cluster validation requirements. In our iterative internal métier validation process, we used a bootstrapping method and the Jaccard coefficient concept  (Jaccard, 1901) , which measures the similarities between sets, as a measure of cluster stability.
Moreover, we encourage the use of a simple method of mapping cyclical data (e. g. , time of set) into a two-dimensional Euclidean space to overcome the closeness of such data, which is not often captured in other métier analyses that treat the data as either ordinal or categorical variables (see, e. g. ,  Pelletier and Ferraris, 2000; Deporte et al. , 2012). Our use of 11 different (feature) selection methods, to rank the fisher decision and catch variables according to their contribution to the métiers after each clustering attempt, reduces the biases of choosing variables using a selection method. The variable selection in métier analysis is usually undertaken by mapping the dataset to a new space with lower dimensions using a linear or non-linear transformation, such as PCA and extracting a subset of the principal components that explain most of variability in the data (e.
In our internal validation method, we iteratively resample the dataset at random, re-clustering them and verifying that the same clusters are found for different subsets. In contrast, most studies that use non-hierarchical clustering frameworks  (Duarte et al. , 2009; Castro et al. , 2010 Castro et al. , , 2011;; Winker et al. , 2013)  determine the final number of valid métiers based on the maximum average silhouette width across clusters. While this facilitates a more objective selection of the most appropriate number of clusters compared to simple visual inspection  (Rousseeuw, 1987) , we examined a range of values close to the proposed k guided by the maximum average silhouette width through the use of an internal validation method taken from the machine learning literature  (Hennig, 2007).
However, the fishing practices in relation to set start times, lunar phase, and seasonality were divergent. Métier 1 had no obvious patterns in relation to set start time or lunar phase, with the majority of sets occurring in winter. While in contrast, métier 2 was characterized by early morning set start times, around the new moon occurring in all seasons. These fishing practices were similar to the Hawaiian-based U. S. longline fishery targeting yellowfin tuna  (He et al. , 1997)  and are again a reflection of the daily availability of yellowfin tuna, which is highest in depths less than 50 m during the day and lowest at night  (Campbell and Young, 2012). Although métier 2 was fished all year round in our dataset, the nominal CPUE for yellowfin tuna is highest in the late autumn to early spring and higher in the week preceding and following the new moon  (Campbell, unpublished) , which may explain the majority of sets being deployed in the autumn months around the new moon during the period 2010-2017.
, 2012). Nevertheless, we contend that our clustering framework offers several advantages, but particularly regarding data validation. In the absence of objective benchmarks for métier analysis  (Holley and Marchal, 2004)  and subsequent extensive involvement of stakeholders to determine true fishing intentions on a set basis  (Boonstra and Hentati-Sundberg, 2016) , we advocate for the use of internal validation methods (e. g. ,  Hennig, 2007). By iteratively resampling the dataset at random, reclustering and verifying that the same clusters are found for the different subsets, the level of subjectivity in métier selection often associated with visual exploration is reduced and is more robust than the standard non-hierarchical clustering approach of using simply the maximum average silhouette width  (O'Farrell et al.
ACKNOWLEDGMENTS We would like to acknowledge the  Australian Fisheries Management Authority (AFMA)  for providing the commercial catch and effort data for the  Eastern Tuna and Billfish Fishery (ETBF). We would also like to thank  Don Bromhead  (AFMA) and  James Larcombe  ( ABARES ) for fruitful discussions on the analysis and manuscript. We appreciate the scoping work for this study undertaken by  Macgregor Todd  ( CSIRO ) during his summer internship at ABARES. Lastly, we thank  Rupert Summerson  ( ABARES ) for producing the map of the fishery,  Patricia Hobsbawn  ( ABARES ) for data curation, and  John Sims  ( ABARES ) for facilitating the computational resources for this study.
Statistical analysis All the tests were assayed leastways three times. Furthermore, all statistical analysis was accomplished using GraphPad InStat software, version 5. 01 (GraphPad Software, CA, USA). The means comparison among groups was made using one-way analysis of variance (ANOVA) followed by Tukey's post-hoc test. Differences were evaluated significant at *: P<0. 05, **: P<0. 01 and ***: P<0. 001.
The effect of the HB1 NDV on protein levels of cytochrome-C and survivin Protein levels of cytochrome-C and survivin were evaluated in the HB1 NDV-treated TC-1 cell line by ELISA method. As shown in Figure  6 , a significant increase in cytochrome-C protein level was showed in TC-1 cell line inoculated with MOI 15 as compared to the control group (P<0. 01). Moreover, a significant decrease in survivin level was detected in the TC-1 cell line inoculated with MOI 15, compared to the control group (P<0. 001) (Figure  7 ). These data suggest that oncolytic NDV induces the release of DAMPs during lysis of TC-1 tumor cells, and reduce the level of survivin as anti-apoptosis marker in MOI-dependent manner.
As the capacity for generating large scale data continues to grow the ability to extract meaningful biological knowledge from it remains a limitation. Here we describe the development of a new fixed repertoire of transcriptional modules. It is meant to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome profiling data. It is supported by customized resources, which include analysis workflows, fingerprint grid plots data visualizations, interactive web applications. These provide access to a vast number of module-specific functional profiling reports, reference transcriptional profiles, and give users the ability to visualize changes in transcript abundance across the modular repertoire at different granularity levels. A use case focusing on a set of six modules comprising interferon-inducible genes is also provided.
With the construction of this new repertoire we aimed first at increasing the range of immunological states upon which the repertoire is based, which was limited to 7 in our earlier attempt. Secondly, considerable efforts were deployed to improve resources to support analysis and interpretation. Thus, the range of disease and physiological states employed for the construction of this new modular repertoire has been expended considerably compared to the first and second generation repertoires. Specifically, 16 input datasets were employed, comprising 985 unique blood transcriptome profiles from: patients with autoimmune, infectious or inflammatory diseases; cancer patients; liver transplant recipients; and pregnant mothers. We describe here how we developed the analysis workflows for group comparison and individual molecular "fingerprinting".
Ad hoc data visualization strategies are presented as well, namely the use of module fingerprint grids and heatmaps to represent the data. We also provide access to extensive functional annotations for each of the 382 modules comprising the repertoire via an interactive web application. Reference blood transcriptome fingerprints are also made available via a web application that enables users to explore the changes observed across diseases/studies and among individuals. A focused effort aiming at the characterization of a set of six interferon modules is presented to illustrate the manner in which such resources may be deployed to help unravel the biology underlying blood transcriptome signatures.
We also added six new conditions to our framework: inflammatory conditions of the skin (juvenile dermatomyositis), lung [chronic obstructive pulmonary disease (COPD)] and circulation (Kawasaki disease); multiple sclerosis (MS); primary immune (B-cell) deficiency; and pregnancy (serving as a physiological variant). Absent from this repertoire (but represented earlier) are patients with type 1 diabetes or with an Escherichia coli infection. In summary, the sixteen datasets that have been assembled capture a wide breadth of immunological responses. This should permit the construction of a transcriptional module repertoire that will prove useful as a generic framework for blood transcriptome data analyses. Implementation of a step-wise approach to blood transcriptional module repertoire construction.