dataesr/scientific-paragraphs-categorization huggingface dataset:
How did the virus spread in Europe? We then aimed to get a finer view of SARS-CoV-2 transmission events between France and other European countries with the same strategy as the one used at the continental scale (Figure  4 ). Here, we only focused on European countries associated with a high incidence and without under-sampling due to a lack of data on GISAID. While the number of intra-country transmission events reached a plateau in early-June 2020 across nearly all Europe, Russia and Romania were associated with an increased number of cases from April which coincided with a similar increase from Asian countries bordering Russia. The number of intra-country transmission events in Russia and Romania remained high until the end of December 2020. In early September 2020, Spain was the first country associated with a drastic increase of intra-territory transmissions, then followed by France, the United   4E ).
, 2016). Since the GISAID database stores several hundred thousand SARS-CoV-2 sequences (n = 638,704 on the 8th May 2020 for the year 2020 alone), we believe that one phylogeny of a few thousand sequences only may not always accurately reflect transmission events. In order to obtain more comprehensive trends drawing conclusions from a maximum of SARS-CoV-2 sequences, we constructed 100 independent maximum likelihood phylogenies of relatively limited size (from 462 to 999 sequences depending on the geographic scale and time period investigated) and inferred ancestral state reconstruction by a maximum likelihood discrete trait phylogeographic method. Bayesian approach seems not to be more accurate than maximum likelihood for the reconstruction of ancestral states  (Hanson-Smith et al. , 2010) , and we gain a lot in terms of sampling, computational time, and trends through distinct tens of replicates.
Some countries and French regions had to be discarded because of their under-representation in the GISAID database. When a set was constituted, all the sequences were replaced in the whole dataset for the random selection of the next one. For the first time period investigated (January to mid-July 2020), an average of 935, 999 and 462 SARS-CoV-2 sequences were included in each set at the worldwide, European and French scales, respectively (Table  1 ). When considering the second time period (end-July to December 2020), we used about 300 sequences from the first wave to anchor phylogenies with the previously acquired viral diversity. In addition to those sequences, we added worldwide, European and French scale sequence dataset containing 882, 961 and 483 genomic sequences covering July to December 2020 (Table  1 ).
Multiple sequence alignment and maximum likelihood phylogenetic tree For each set, genome sequences were each independently aligned against the Wuhan-Hu-1 reference genome (GenBank accession: NC_045512. 2) using MAFFT v7. 450 then merged altogether  (Katoh and Standley, 2013). From each alignment, we inferred a maximum likelihood phylogenetic tree using FastTree v. 2. 1. 11 under a general time-reversible (GTR) model with a discrete gamma distribution to model inter-site rate variation and 1,000 bootstraps to compute support values  (Price et al. , 2010). The choice of FastTree was mainly due to its short runtime while giving results very close to IQTree 2. 1. 2 (Minh et al. , 2020). Each phylogenetic tree was dated using TreeTime v. 0. 8. 5  (Sagulenko et al. , 2018)  with a molecular clock rate fixed at 6.
Ethics statement This study was carried out in accordance with the Declaration of Helsinki. It is a retrospective non-interventional study based on data from the public database repository GISAID (https://www. gisaid. org/). The Institutional Review Board -IRB 00006477of HUPNVS, Université Paris Cité (AP-HP), has reviewed and approved the research project. Laboratories and organisms that participated in depositing SARS-CoV-2 sequences used in this study can be found on the GISAID database from the identifiers listed in Supplementary Dataset 1.
Sequence acquisition and curation, and dataset production A phylodynamics study (which aims to depict with accuracy transmission events between locations; Figure  6 ), requires a large number of sequences  (Attwood et al. , 2022). SARS-CoV-2 genome sequences were downloaded from the GISAID database on the 8th May 2022. The following inclusion and exclusion criteria were applied to select samples for analysis: (1) only complete viral genomes from infected individuals (more than 29 kb in length) were included; (2) the maximal proportion of undetermined nucleotide bases was fixed to 5%; (3) sequences with unknown or incomplete sampling dates, or unknow geographical location, were excluded; (4) sampling dates were comprised between January 1st 2020 and December 31th 2020; and (5) sequences with host listed as anything but were excluded.
The final dataset was constituted of 638,704 sequences. We studied the epidemic at three different geographic scales: (1) worldwide, to identify SARS-CoV-2 transmissions from continents to France and conversely; (2) at the European scale, to determine more precisely which European countries were mostly involved in transmission flows with France; and finally (3) at the French administrative region level, to get a better understanding of virus spread inside the country. For each geographic scale, the analyses were independently run for the two European epidemic waves (January to mid-July 2020 (weeks 1 to 30), and end-July to December 2020 (weeks 31 to 52); Figure  1 ).
Between May 2020 and January 2021, 254 PCR samples were collected in Koutiala and a total of 32 positive patients reported. At the time of the study, few data were available on the level of exposure of the population of Koutiala district to SARS-CoV-2. As diagnosis of infection was limited and underreporting of cases likely. Less than 15,000 cases had been reported in Mali at the time of the survey and 23 cases in Koutiala, corresponding to attack rates of less than 0. 1% and 0. 002%, respectively. A seroprevalence study was proposed to help assess the magnitude of the epidemic and to guide the health response. In collaboration with the MOH, MSF managed the blood bank of the Reference Health Centre (CSRef) in Koutiala. In 2020, the Koutiala blood bank distributed 7,400 units of blood to the entire hospital.
Data collection and management According to routine procedures, each participant received a clinical examination and was administered a pre-donation questionnaire that had been modified for the study to include questions related to COVID-19. The interviews were conducted in Bambara. Each donor was informed about the TTI screening, including HIV, Hep B, Hep C, Syphilis, Malaria, and the additional COVID-19 serological test. Data, including questionnaire responses and laboratory results, were collected using electronic forms (Kobo collect) on password-protected mobile devices (Samsung pad). Data were regularly uploaded to a password-controlled Epicentre server and routine data checks were performed. Blood bank registers were used to capture data following standard laboratory procedures. The study forms were linked by a unique identification number assigned to each study participant.
Data analysis and statistical methods Data were analysed using R version 4. 1. 1 (The R Foundation for Statistical Computing, Vienna Austria). Descriptive analysis, including age and sex, was calculated as proportions. For the estimation of seroprevalence, a positive RDT result was defined as positive IgM, positive IgG or positive IgM and IgG. The main outcome of interest is the estimate of seroprevalence over the study period. An additional stratified analysis of seroprevalence by month of sample collection was performed. Two-sample Student's t-test and Pearson's chi-square statistics were used to compare continuous and categorical descriptive results, respectively. We explored the relationship between the RDT result, age, gender and the ECLIA signal value in univariate analysis considering the 20% threshold.
Acknowledgments First, we would like to thank the blood donors who agreed to participate in this study and the  Malian Ministry of Health  who facilitated the implementation of this study. We would particularly like to thank the health authorities of Koutiala and the workers of the blood bank for their major involvement in carrying out this work in the field. We are grateful to the CICM and PA et K laboratories in Bamako for having contributed to the performance of certain biological analyses in their facilities and by their staff. Finally, we thank the  MSF Ethics Review Board  (ERB) and the  National Ethics Committee for Health and Life Sciences (CNESS) of Mali  for their contribution to improving the quality of this work. Many thanks to  Rebecca M. Coulborn  who helped edit the manuscript.
Morpheus modeling framework We chose to implement the guidance-by-followers mechanism as a CPM in the modeling framework Morpheus  [32]. Morpheus is an extensible open-source software framework that supports userfriendly declarative modeling. It offers a graphical user interface (GUI) and the domain-specific modeling language MorpheusML to easily compose and extend multicellular models. MorpheusML provides a bio-mathematical language in which symbolic identifiers in mathematical expressions describe the dynamics of and coupling between the various model components. It represents the spatial and mechanical aspects of interacting cells in terms of the CPM formalism and follows the software design rule of the separation of model from implementation, enabling model sharing, versioning, and archiving in public model repositories.
A numerical simulation is then composed of parsing the MorpheusML model definition and automatic scheduling of predefined components in the simulator. Beyond its use in Morpheus, MorpheusML is a model definition standard that enables interoperability among multiple simulators, parameter estimation pipelines, and model repositories  [35]. Therefore, the FitMultiCell toolbox (see https://gitlab. com/fitmulticell/fit) supports the estimation of parameters for stochastic Morpheus models  [36, 37].
Finally, from a modeling perspective, this study highlights the role of individual-based approaches for understanding the collective phenomena at the population scale that emerges from cell-cell interactions. Here, we have specifically assessed the robustness of our developed IBM to variations in modeling assumptions and to cell-to-cell variations. From a data management perspective, we ensure the reproducibility of this computational study and the reusability and extensibility of the developed IBM by the strict separation of model definition from simulator code using the MorpheusML model description language [35] and a public model repository (https://morpheus. gitlab. io/model). developed, maintained, and extended the model simulator. AB, ND, and LB contributed to the conception and design of the study.
Funding ND acknowledges the support by the French ANR, Grant ANR-20-CE13-0016-03. LB acknowledges the support by the German BMBF through EMUNE (Grant 031L0293D) and the German DFG (Grant 391070520). The Article Processing Charges (APC) were funded by the joint publication fund of the TU Dresden, the Medical Faculty Carl Gustav Carus, and the SLUB Dresden, Germany.
Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluating the most suitable backbone extraction method may be challenging. This paper introduces netbone, a Python package designed for assessing the performance of backbone extraction techniques in weighted networks. Its comparison framework is the standout feature of netbone. Indeed, the tool incorporates state-of-the-art backbone extraction techniques. Furthermore, it provides a comprehensive suite of evaluation metrics allowing users to evaluate different backbones techniques. We illustrate the flexibility and effectiveness of netbone through the US air transportation network analysis.
They evaluate the significance of nodes and edges using a hypothesis-testing framework. They remove nodes or links qualified as noise. The tool introduced by Coscia in  35  incorporates three statistical and three structural backbone-extracting methods for weighted networks. Its Python module uses pandas  126  to enhance the performance. The backbone extraction techniques operate on a DataFrame input. They can process directed and undirected networks. Neal presents backbone an R package to extract network backbones in  29. It implements seventeen backbone extraction methods. Six methods are primarily designed for bipartite projections, two for weighted networks, and ten for unweighted networks. It also provides the generic sparsify() function that allows the custom construction of many more backbone methods.
The methods operate on a R Matrix object, sparse Matrix object, a DataFrame object, or an igraph as input. They allow the processing of directed and undirected networks. Traditionally, users need to implement their code to compare different backbone extraction methods. We introduce netbone, a Python package specifically designed for extracting and comparing backbones from simple weighted networks. It offers an extensive collection of methods, including six statistical, thirteen structural, and one hybrid backbone extraction methods. It also provides filtering flexibility to tailor the backbone extraction process. Furthermore, it implements multiple ways to compare backbones. They include a boolean filter for extraction techniques that extract a single backbone. Threshold and fraction filters are dedicated to methods assigning scores to the nodes or links.
In the following sections, first, we briefly introduce the backbone extraction methods implemented in the netbone package. Then we present the package architecture and its modules, highlighting its numerous advantages. Next, we provide a simple toy example illustrating how to use netbone. Finally, we showcase the power of netbone's comparison framework through five experiments. The first experiment illustrates how the comparison framework can assist in evaluating the backbone extraction methods by comparing various topological properties. The second experiment highlights how the framework could aid users in determining the appropriate fraction or threshold for extracting backbones. The third experiment illustrates how users evaluate the distribution of property values of the extracted backbones.
The NetBone Package Netbone is a Python package freely available to the public on GitLab (https://gitlab. liris. cnrs. fr/coregraphie/ netbone). It provides a straightforward and easy-to-use framework for comparing and selecting the most appropriate method for a given case study. Figure  1  provides a diagram representing the various modules of the netbone package. We give a brief presentation of its architecture and present its various modules with their main features. • The boolean_filter() function is designed for methods extracting a single substructure of the network. Table  2  summarizes the filters associated with the various backbone extraction methods. • Measures Module:The measures module contains a set of evaluation measures allowing users to compute the topological properties of the extracted backbone.
NetBone in action: A toy example To illustrate the usage of netbone, we consider the high salience skeleton method with the Les Misérables network  128. We chose this extraction technique because it can be associated with the three filtering methods provided by netbone. To begin using the netbone package, one can install the latest release from either the PyPI repository (https://pypi. org/ project/netbone/) or directly from the project's GitLab repository: > pip install netbone > pip install git+https://gitlab. liris. cnrs. fr/coregraphie/netbone Once installed, the netbone package can be imported using:.
> import netbone as nb The netbone package can handle two types of inputs: a networkx graph or a DataFrame. In this example, we will load the Les Misérables network from networkx and apply the high_salience_skeleton() method. The resulting scores can be examined using the to_dataframe() function as shown below:   ---------|----------------|---------|------------------------|--------   ---------|----------------|---------|------------------------|--------|  The high salience skeleton method proposed by Grady exhibits a bimodal distribution of scores centered around 0 and 1. The default approach of this method is to keep only edges with scores greater than 0. 8. In netbone, it can be accomplished using the boolean_filter(). However, in that case, two nodes are missing from the extracted backbone in this particular example.
Conclusion In conclusion, netbone is a powerful, free, open-source Python package. It offers a variety of statistical and structural methods for extracting network backbones. Its filters can meet all use cases, and the comparison framework is a standout feature. It enables users to compare backbones with a wide range of evaluation measures. The Five experiments conducted in this paper illustrate the wide range of possible scenarios that can be analyzed using netbone. The first experiment showcases how the comparison framework can assist in evaluating the backbone extraction methods by comparing various topological properties. The second experiment highlights how the framework could aid users in determining the appropriate fraction or threshold for extracting backbones. The third experiment illustrates how users evaluate the distribution of property values of the extracted backbones.
Tumminello, Neal, and others  29, 36  have proposed backbone extraction techniques specifically designed for bipartite projections to address this limitation. A key objective is to extend the netbone package by incorporating these specialized approaches. Another crucial extension concerns temporal networks. Indeed, aggregating network snapshots into a static representation entails a substantial loss of information. It can lead to conventional classical methods of weighted backbone extraction neglecting significant aspects of the underlying network. Kobayashi and others  67  have introduced backbone extraction methods for temporal networks in response to this challenge. Consequently, our second goal is to enrich further the netbone package by incorporating these advanced techniques. Lastly, in a third major development direction, we aim to expand the scope of netbone to Multilayer networks.
The author R. C. was partially supported by the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013 ) with  ERC  Grant Agreement nr. 615722   MOTMELSUM , by  KU Leuven   IF C14/17/083 , and thanks the  Labex CEMPI  ( ANR-11-LABX-0007-01 ). The author I. H. was partially supported by the research training group GRK 2240:  Algebro-Geometric Methods in Algebra, Arithmetic and Topology  and by the individual research grant No. 426488848 , both funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). The author S. R. was partially supported by  GeoMod AAPG2019  ( ANR-DFG ),  Geometric and Combinatorial Configurations in Model Theory. The author F. V. was partially supported by  KU Leuven   IF C14/17/083 , and partially by  F.
Building convergence maps To get the convergence map, we used the KS inversion: we projected the shear onto a 600 2  Cartesian grid (resolution of 1 arcmin per pixel), which we then smoothed with a Gaussian filter of width 0. 7 arcmin following  Giblin et al. (2018)  to reduce the impact of potential empty pixels. This was implemented using the Python package lenspack 1. We provide an example of our validation against the theoretical prediction in Appendix C.
Building galaxy maps Starting from the lens catalogue described in Sect. 3. 3, we built galaxy maps for each light cone by assigning the galaxies onto a 600 2 grid. We validated our maps by comparing our measured power spectra against the theoretical prediction obtained with the Python library pyccl 2. The shot noise of the galaxy maps was approximated as Gaussian noise with a variance equal to the mean of each galaxy map. As the map is intrinsically noisy (due to the presence of shot noise), while the theoretical prediction is obtained for a noiseless case, we computed the power spectrum of the noise map and then subtracted it from the power spectrum of the map. Since we did not have a model including non-linear bias, we started with a conservative analysis and included scales up to max = 780.
2021). We are aware that in our case the galaxy density and convergence maps can in reality be correlated, as shown in Fig. 1 , where the structures of the galaxy map are traced by the lensing convergence map. For this first application, we ignore these correlations that could actually bring additional information. For map level studies it has been shown that cross-probes can help in breaking degeneracies and in increasing the constraining power  (Kacprzak & Fluri 2022; Grewal et al. 2022). We leave for a future study the inclusion of their cross-correlation. The cosmological parameters are the ones that vary in the simulations, namely θ = [Ω m , σ 8 , h, w 0 ]. To model the summary statistics at arbitrary cosmologies, we employ an interpolation with Gaussian processes regression  (Rasmussen & Williams 2005) , using the scikit-learn 3  Python package.
Conclusions In this Letter we proposed using for the first time starlet-based non-Gaussian statistics on weak-lensing convergence maps and on galaxy maps to constrain cosmological parameters. We employed a mock source catalogue for weak lensing and a mock lens catalogue for galaxy clustering available from the cosmo- Fig. 4. Forecast contours at confidence levels of 68% and 95% using the starlet 1 -norm measured on convergence maps (blue), on galaxy density maps (green), and their combination as independent probes (red). SLICS simulations provided with a galaxy bias estimated from the LRG set in the fourth data release of the Kilo-Degree Survey. We performed a single probe and a combined non-Gaussian statistics analysis at the map level. We find that using the starletbased statistics, such as starlet peak counts and the starlet 1norm also in the context of galaxy clustering, can potentially help in improving the constraints, and that this gain can be also obtained when combining weak lensing and photometric galaxy clustering.
Acknowledgements. VA wishes to thank  François Lanusse , for useful discussions and help during the first phase of development of the project. VA wishes to thank  Tomasz Kacprzak  and  Alexandre Refregier  for useful discussions. VA acknowledges support by the  Centre National d'Etudes Spatiales  and the  project Initiative d'Excellence (IdEx) of Université de Paris  ( ANR-18-IDEX-0001 ) at the start and during the preparation of this work. VA acknowledges support by the grant  Cosmological Weak Lensing  and  Neutral Hydrogen ,  200021_192243  of  Prof. Alexandre Refregier. JHD  acknowledges support from an  STFC Ernest Ruther-ford Fellowship  (project reference  ST/S004858/1 ). The N-body simulations were enabled by the  Digital Research Alliance of Canada  (https: //alliancecan. ca/). L10, page 5 of 8.
Acknowledgments: The authors acknowledge the support of the  French Agence Nationale de la Recherche (ANR)  under reference  ANR-20-CE40-0025-01  ( T-REX  project) and of the  Energy oriented Centre of Excellence-II (EoCoE-II ), Grant Agreement 824158, funded within the Horizon2020 framework of the  European Union. Part of this work was also supported by the ExtremesLearning grant from  80   PRIME CNRS-INSU  and the  ANR  project  Melody  ( ANR-19-CE46-0011 ).