dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: The authors would like to thank the patients and the staff of the hospitals of the French National Reference Center for Imported Malaria network. The authors thank Provepharm for providing the methylene blue  Proveblue ®. List  of the  French National Reference  Centre for Imported Malaria Study Group: The members of the  French National Reference  Centre for Imported Malaria Study Group are as follow:  V Augis  ( Groupe Hospitalier Pellegrin, Bordeaux ),  P Bastien  ( Centre Hospitalier Universitaire de Montpellier, Montpellier ),  A Berry  ( Centre Hospitalier Universitaire de Rangueil, Toulouse ),  P Brouqui  ( Institut Hospitalo-Universitaire Méditerranée Infection, Marseille) ,  P Chauvin  ( Centre Hospitalier Universitaire de Rangueil, Toulouse ),  M Cividin  ( Centre Hospitalier du Pays d'Aix, Aix en Provence ),  F Courtier  ( Centre Hospitalier de Valence ),  P Delaunay  ( Centre Hospitalier Universitaire de l'Archet, Nice ),  L Delhaes  ( Groupe Hospitalier Pellegrin, Bordeaux ),  M Drancourt  ( Institut Hospitalo-Universitaire Méditerranée Infection, Marseille ),  N Dubosc  ( Centre Hospitalier Robert Boulin, Libourne ),  T Gaillard  ( Hôpital d'Instruction des Armées Desgenettes, Lyon ),  A Genin  ( Centre Hospitalier du Pays d'Aix, Aix en Provence ),  E Garnotel  ( Hôpital d'Instruction des Armées Lav-.
Interaction fingerprints are vector representations that summarize the three-dimensional nature of interactions in molecular complexes, typically formed between a protein and a ligand. This kind of encoding has found many applications in drug-discovery projects, from structure-based virtual-screening to machine-learning. Here, we present ProLIF, a Python library designed to generate interaction fingerprints for molecular complexes extracted from molecular dynamics trajectories, experimental structures, and docking simulations. It can handle complexes formed of any combination of ligand, protein, DNA, or RNA molecules. The available interaction types can be fully reparametrized or extended by user-defined ones. Several tutorials that cover typical use-case scenarios are available, and the documentation is accompanied with code snippets showcasing the integration with other data-analysis libraries for a more seamless user-experience.
Da et al. [12]  developed an IFP that relies on the atomic environment of both the protein and ligand interacting atoms to set the positions of a bit in the fingerprint, rather than relying on protein residues and predefined interactions, which has the advantage of implicitly encoding every possible type of interaction. This protocol was later reimplemented in Python by Wójcikowski et al. [13] , but other more classical Python-based IFP implementations exist  [14] [15] [16] [17] [18] [19]. In this paper, we introduce a new Python library, ProLIF, that overcomes several limitations encountered by these programs, namely working exclusively with the output of specific docking programs, not being compatible with the analysis of MD trajectories, being restricted to a specific kind of complex (usually protein-ligand complexes), depending on residue or atom type naming conventions, or not being extensible or configurable regarding interactions (Table  1 ).
Here the selection of atoms is made using SMARTS queries (Table  3 ), which is more precise than relying on elements or atomic weights and is also more universal than relying on force-field-specific atom types. The library is designed so that users can easily modify existing interactions, as there is usually no consensus on the empirical thresholds (distance, angles) that should be used. For example, the hydrogen bond DH…A can be defined as a distance between H and A lower or equal to 3. 0 Å  [9]  or as a distance between D and A lower or equal to 3. 5  [4, 14, 20]  or 4. 1 Å  [15, 23] , and the angles constraints can also vary. ProLIF is also designed to let users define custom interactions. Each interaction is written as a Python class that implements a "detect" method which takes two RDKit molecules as input, typically a ligand and a protein residue, and outputs a Boolean (True if the interaction is present, else False) as well as the indices of atoms responsible for the interaction.
Results and discussion By relying on the interoperability with popular open-source libraries (MDAnalysis and RDKit), it can support a wide range of molecular formats typically found in docking experiments and MD simulations. Because it directly relies on SMARTS patterns to define the chemical moieties that partake in interactions, it is also compatible with any kind of molecular complex, including complexes made of ligands, proteins, DNA or RNA molecules. Interoperability also allows for data analysis to be substantially easier: as mentioned in the Implementation section the IFP can be directly exported to a pandas DataFrame (one of Python's most popular data analysis library), and the documentation contains tutorials on how to visualize the interactions as graphs or how to display them on the 3D structure of the complex.
Analysis of an MD trajectory of a GPCR in complex with a ligand The code to run ProLIF on an MD trajectory can be as simple as follow:    Here, we showcase an analysis based on the fingerprint obtained from a 500 ns MD simulation of the 5-HT1B receptor (class A aminergic GPCR) in complex with ergotamine retrieved from the GPCRmd webserver (id 90)  [25]. In class A GPCRs, each position is annotated in superscript notation according to the Ballesteros-Weinstein numbering scheme  [26] , a generic residue numbering denoting both the helix and position relative to the most conserved residue labelled as number 50. (+)-ctd ≤ 4. 5 � -→ n , ------→ ctd • • • (+)� ∈ [0, 30] PiCation Aromatic Cation PiStacking Aromatic Aromatic ctd-ctd ≤ 6. 0 � -→ n , -→ n � ∈ [0, 90] min ≤ 3. 8 EdgeToFace Aromatic Aromatic ctd-ctd ≤ 6.
32 , I130 3. 33 , F330 6. 51 , V201 ECL2. 52 , F331 6. 52 , S212 5. 42 , W327 6. 48 , V200 ECL2. 51 , C133 3. 36 and F351 7. 35 which are all in contact with the ligand in at least 97% of frames. This is in agreement with the known interactions available from experimental structures as listed on the GPCRdb webpage  [27]  for the human 5-HT1B receptor, except for S212 5. 42 which isn't reported to make H-bond interactions with ligands. The difference is likely due to the fact that this analysis is based on an MD trajectory while GPCRdb gathers interactions from experimental structures. However, GPCRdb also lists mutational data for S212 5. 42 , and mutating this position to an alanine does not affect the binding affinity to ergotamine  [28]  which could coincide with the MD simulation since the ligand makes a hydrogen bond with the backbone and not the sidechain.
Mutating S212 5. 43 to a bulkier residue could potentially affect this interaction and decrease the binding affinity. Because ProLIF keeps track of the atom indices responsible for interactions, it is possible to display detailed 2D or 3D interaction plots. Examples of scripts to generate such plots are given in the documentation. An exception is made for the ligand interaction network diagram which has been directly included in the source code of ProLIF under the LigNetwork class. This LigNetwork diagram (Fig. 1 ) is interactive and allows repositioning the residues but also hiding specific residue types or interactions by clicking the legend. It can show the interaction diagram at a precise frame or aggregate the results and only display interactions that appear frequently, controlled by a frequency threshold.
Conclusions ProLIF is a new Python library that overcomes limitations encountered by other freely available IFP programs. One of the main differences is the support of MD trajectories, while still being compatible with other molecular structure files like docking and experimental structures. By design, it is also not restricted to a particular kind of molecular complex but supports any combination of ligand, protein, DNA, or RNA molecules, thanks to its absence of dependency to force-field specificities such as atom types or residue naming convention. It also has a user-friendly API, comes with several tutorials, and allows creating custom interactions or reconfiguring existing ones. Finally, it focuses on the integration with typical data-analysis packages and visualization tools for a seamless user experience within the Python ecosystem.
Funding This work was funded by the  French Ministry of Higher Education and Research  ( PhD Fellowship  to CB), by  GIRACT (Geneva, Switzerland)  (9th European PhD in  Flavor Research Bursaries  for first year students to CB) and the  Gen Foundation (Registered UK  Charity No. 1071026 ) (a charitable trust which principally provides grants to students/researchers in natural sciences, in particular food sciences/technology to CB).
Data sources All-cause mortality data were compiled from 203 of 204 countries and territories ranging from the years 2000 to 2019, for a total of 3097 location-years. Vital registration covered a total of 14 889 022 global under-5 deaths in this period (appendix p 119). A total of 8000 unique sources were used in estimating cause-specific mortality in GBD 2019. All input data sources for each component of analysis are available for download from the GBD 2019 Data Input Sources Tool.
Future directions Future work is required to measure and understand the direct (severe illness and death) and indirect (determinants of health) effects of COVID-19 on child mortality. First, this work will include collecting data on disruptions in basic childhood health services (eg, vaccines, integrated management of childhood illness, well-child visits), nutritional status (eg, food supply and distribution), perinatal health (eg, maternal and neonatal care), and socioeconomic indicators such as fertility, education, and household income. A second direction is to work towards an integrated framework for women's, maternal, and child health because of the inherent links between the health of mothers and their children. Third, integrating information from prevention and intervention trials into developing future health scenarios is a priority in order to provide information to motivated policy makers as to what their most effective options might be.
Aruna M Kamath  is funded by the  National Institutes of Health  ( T32GM086270 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  Bill & Melinda Gates Foundation  or the  National Institutes of Health. Lucas Guimarães Abreu acknowledges Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Código de Financiamento 001 ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  (404710/2018- 2 and  310797/2019-5 ),  Fundação de Amparo à Pesquisa de Minas Gerais (FAPEMIG) , and  Pró-Reitoria de Pesquisa (PRPq)  of  Universidade Federal de Minas Gerais. Olatunji O Adetokunboh  was supported by the  National Research Foundation , and  Department of Science and Innovation, South Africa. Syed Mohamed Aljunid would like to acknowledge the  Department of Health Policy and Management, Faculty of Public Health, Kuwait University, Kuwait and International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia, Malaysia  for the approval and support to participate in this research project.
Whole Genome Phylogenetic Analysis and Selection of PCR-HRM Markers The genomic sequences of 55 available C. psittaci strains used for comparison are listed in Supplementary Data 2. The whole genome SNP (wgSNP) pipeline of the BioNumerics software v7. 6. 1 (Applied Maths, Sint-Martens-Latem, Belgium) was used in order to detect SNPs on whole genome sequences and perform cluster analyses on the resulting wgSNP matrix. The input of the wgSNP module is raw data except for the reference. Each genome file were processed with the ART-MountRainier-2016-06-05 simulation tool that generates synthetic paired-end reads with coverage of 50  [20]. These reads were aligned and mapped against the reference sequence C. psittaci 6BC (CP002549. 1) using the BWA algorithm implemented in BioNumerics with minimum 90% of sequence identity.
Strain-specific SNPs were identified using the BioNumerics wgSNP module and then filtered using the following conditions: minimum 5× coverage to call a SNP, removal of positions with at least one ambiguous base, one unreliable base or non-informative SNP and minimum inter-SNP distance of 25 bp. A phylogenetic tree built using RAxML version 8. 2. 9 with the GTRGAMMA model and 1000 bootstrap replicates based on the filtered SNP matrix (4143 SNPs) from BioNumerics  [21]. These SNPs are distributed throughout the genome. For the eight lineages identified (groups I to VIII), an SNP was randomly selected and PCR-HRM primers were designed using Primer3Plus software  [22]. The post-real-time-PCR HRM analysis offers the possibility to detect sequence variation inside amplicons without the need for sequencing or sequence-specific probes.
With high precision, the melt profile of the PCR products is determined using double-stranded DNA binding dyes and accurate fluorescence data acquisition over small temperature increments. This method enables the discrimination of amplicons differing in a single SNP, according to their melting temperature (Tm). The positions of the selected SNPs in the C. psittaci 6BC genome and the primer sequences used in this study are listed in Tables  2  and 3 , respectively.
ompA and MLST Typing ompA sequencing using primers 3GPB and 5GPF was performed as previously described  [23]. The seven housekeeping genes of the MLST method  [9] , namely gatA, oppA, hflX, gidA, enoA, hemN and fumC, were amplified and sequenced using primers and conditions described on the Chlamydiales MLST website (http://pubmlst. org/chlamydiales/http: //mlst. ucc. ie/, accessed on 1 February 2020). Sequencing of both DNA strands was performed by Eurofins (Reichenwalde, Germany) and numbers for alleles and sequence types (STs) were assigned in accordance with the Chlamydiales MLST Database.
In-Silico MLST Typing Assemblies of the 55 C. psittaci strains were downloaded from NCBI and a script (https://github. com/tseemann/mlst, accessed on 1 February 2020) was used to scan contig files against the PubMLST chlamydiales scheme. The resulting table was used to build a tree in BioNumerics using the parameter "categorical values" to calculate the similarity matrix and UPGMA to reconstruct the tree.
The strain 99DC05 isolated from a horse in Germany  [25]  clustered in group V_Mat116 and Ful127, a strain recently isolated from a fulmar (Procellariidae)  [26] , clustered in group VIII_WC. The MLST sequences of these two strains are both close to the MLST ST24, corresponding to the reference strain 6BC (group I_psittacine), with only two mutations in the hflX gene for Ful127 and one mutation in the enoA gene for 99DC05. In this case, the SNP-based typing generates a different topology than MLST analysis (Supplementary Data 4). Our study was conducted on a large set of SNPs (4143 SNPs) distributed through the genome of C. psittaci strains whereas only seven housekeeping genes were analyzed for MLST. It is likely that WGS analysis will help, in the near future, to refine the classification of different genotypes.
This result was confirmed by analysis of the ompA (genotype C) and MLST (ST28) results. Interestingly, analysis of human samples recently collected in France (20-1105-A041) and in Sweden (19-5617_I078_G2662, 19-5617_I080_K12016) revealed the group V_Mat116 genotype, a genotype poorly described so far and also isolated from a horse in Germany (strain 99DC05). While the original Mat116 strain was isolated from an unspecified psittacosis outbreak in Japan (Genbank CP002744), the French and Swedish human cases are likely to have wild birds as the probable origin, since no contact with poultry or psittacine birds has been identified. The PCR-HRM typing tool presented in this study should be considered as a scheme under development, which should be enriched with sequencing data as genomes are contributed and which may then require an update of the SNP and corresponding primer panels.
PCRs reactions were run with 20 ng genomic DNA and GOTAQ-polymerase (PROMEGA, Charbonnieres, France) in 7 µL final volume. Forward primer was attached to M13 bacteriophage tail (CAC GAC GTT GTA AAA CGA C). Two µL of PCR products diluted 1:50 were then mixed with formamide supplemented with GENSCAN-500 LIZ Size Standard (APPLIED BIOSYSTEMS, Foster City, CA, USA). The genotyping was performed on the BIOGENOUEST platform at INRAE Le Rheu. Electrophoresis and allele detection were carried out on an ABI3130xl Genetic Analyser (APPLIED BIOSYSTEMS) with 36 cm capillaries. The fluorescence dye used was 6-FAM. Output was analyzed with GeneMapper 4. 0 Software (APPLIED BIO-SYSTEMS, https ://www. therm ofish er. com/order /catal og/produ ct/44409 15#/44409 15). All marker data were verified manually by visual inspection to eliminate any errors introduced by the automatic allele assignation.
3 µM pyrosequencing primers in annealing buffer (QIAGEN, Germany). Pyrosequencing primers (Supplementary Table  S8 ) were designed using MethPrimer 2. 0 (THE LI LAB, http://www. uroge ne. org/methp rimer /). After annealing at 85 °C during 2 min, the plate was loaded into the PyroMark Q96 pyrosequencer (QIAGEN, Germany) with the Pyro ID cartridge containing appropriate amounts of enzyme, substrate and dNTPs. Pyrosequencing with PyroMark Q96 ID 2. 5 software (QIAGEN, https ://www. qiage n. com/us/servi ce-and-suppo rt/learn ing-hub/techn ologi es-and-resea rch-topic s/pyros equen cingresou rce-cente r/techn ology -overv iew/pyrom ark-q96-id/) included a bisulfite conversion control on cytosines not included in a CpG site. All analysed samples had bisulfite conversion rate above 98%.
Acknowledgements The authors thank the staff from  INRAE U3E (Rennes)  for providing goldfish breeders. F. Borel ,  A. Patinote ,  J-M. Aubry ,  C. Duret  and P-L. Sudan took great care of the goldfish at the  INRAE LPGP experimental facility (Rennes). The members of the INRAE LPGP histology service,  A. Branthonne  and  B. Porcon  are also acknowledged for training and technical support. L. Marandel  and  N. Chênais  contributed skillfully to the highly demanding monitoring of the clone fate during development, and  N. Chênais  set up the histological methods used in this work. A special acknowledgement goes to  Gilles Boutet , from the  Biogenouest platform , for his support in microsatellite analyses. This work was funded by the  French CRB   Anim  project,  ANR-11-INBS-0003.
In doing this, we follow the principle of seeking the simplest solution capable of describing the observed post-seismic displacement field. Moreover we apply a blind-source-separation algorithm to characterize the temporal evolution and spatial features of the post-seismic deformation signal across the 2016-2017 epicentral area, separating tectonic sources from non tectonic ones. In fact, geodetic data are also affected by non-tectonic deformation sources, and to properly interpret the observations, it is crucial to disentangle the various contributions. The remaining of the paper is organized as follows: in Section 2, we describe the GPS data set used and the results of the independent component analysis (ICA) applied to the displacement time-series; in Section 3, we interpret the non tectonic signals as due to hydrological sources detected by the ICA; in Section 4, we analyze the post-seismic deformation, modeling it by inverting the GPS time series for afterslip and discussing a possible viscoelastic contribution to the measured geodetic displacements; in Section 5, we discuss the findings of this study and in Section 6, conclusions are drawn.
Blind Source Separation Given a certain number M of displacement time series relative to sensors distributed at different locations in space, we can organize the data into a spatio-temporal matrix X. In particular, we organize it in such a way that each row is a different time series, and each column represents the record at a given time or epoch. The data at our disposal has a daily sampling in time and spans the time range 2012-2019. The total number of stations used in the analysis is 85. Since we have three-dimensional records (east, north, and vertical), the matrix X has size M × T = 255 × 2525. A well-established approach to deal with the separation of the sources contributing to the observed deformation consists in the application of multivariate statistical techniques that attempts to maximize the independence of the sources generating the observations.
Each row of S contains a different source. The ICA attempts to find these sources imposing them to be as statistically independent as possible while still fitting the data. Given the way we have built X, we are performing the decomposition in so-called T-mode, that is, we attempt to find sources that are independent in the time domain. We underline here that one of the strengths of these techniques consists in the fact that they are data driven and do not impose any specific prescribed functional form to the underlying sources that we want to investigate. In fact, ICA techniques belong to the so-called unsupervised learning approaches to pattern recognition. Unfortunately, the independence condition is not straightforward to impose and approximations are introduced in order to generate a suitable cost function to minimize or maximize.
We thus investigate a possible contribution of the viscoelastic lower crust and upper mantle to the displacement field. We use the open-source software RELAX 1. 0. 7  (Barbot & Fialko, 2010). For simplicity, we consider here as an initial stress perturbation the one generated by the coseismic slip distribution of the major event of the sequence (i. e. , the Norcia Mw 6. 5 earthquake) as described in  Cheloni, Falcucci, and Gori (2019). We model the viscoelastic medium through Maxwell rheologies even though we are aware of the potential importance of power-law rheologies in controlling viscoelastic relaxation (e. g. ,  Freed & Burgmann, 2004). Due to the length of our post-seismic time series (∼2 years) and the relaxation rates in the order of a few millimeters per year characteristics of moderate earthquakes  (Riva et al.
Taking η lc < 10 18 Pa s, we observe a non-monotonic temporal evolution of the displacement at some GPS sites (Text S7) which differs from IC1. Moreover, due to the short relaxation time (in the order of 0. 1 year), we predict a cm-scale post seismic displacement, which is not justified by our data. Hence, we fix the viscosity of the lower crust η lc = 5 × 10 18 Pa s, and the viscosity of the mantle η m = 10 21 Pa s. The mantle is located too deep and its viscosity is too high for the effects of its relaxation to be observable on our data set  (Riva et al. , 2007). However, the same data set suggests to bound the horizontal length scale of the displacement pattern by limiting to the lower crust only the depth extent of the low viscosity region. As already observed by  Freed et al. (2006)  and by  Hetland and Zhang (2014) , vertical polarities can help to constrain the parameters of the model, namely the thickness and the viscosity of the viscoelastic layer: a thicker low viscosity layer produces a broader subsidence area that is not confirmed by our data.