dataesr/scientific-paragraphs-categorization huggingface dataset:
Acute bacterial meningitis in the meningitis belt is most commonly caused by Neisseria meningitidis, Streptococcus pneumoniae and, since introduction of a conjugate vaccine, to a lower extent Haemophilus influenzae Type b  [18, 19]. Suspected and laboratory-confirmed cases correlate well usually  [20]  and suggest a relatively good performance of the surveillance system and appropriateness of the data for epidemiologic studies. Until 2010, and before the introduction of serogroup A meningococcal conjugate vaccine in December 2010, meningitis epidemics were predominantly caused by N. meningitidis across the belt. Pneumococcal meningitis contributes to meningitis hyperendemicity and mimics the seasonality of meningococcal meningitis across the meningitis belt  [21]. In this study, to estimate the unknown parameter values and to evaluate our models performances, we used data from routine surveillance of suspected acute bacterial meningitis cases recorded from 2004 through 2010 in health centres in Burkina Faso (a period preceding introduction of the MenAfrivac serogroup A meningococcal vaccine).
While data aggregated at the district level are available in routine surveillance reports, this database of original weekly health centre data had been compiled in a collaborative effort between the Direction de la Lutte contre la Maladie (DLM) of the Ministry of Health of Burkina Faso, EHESP French School of Public Health, and the Agence de Médecine Préventive (AMP), Paris, France. We selected four health districts (Houndé, Lena, Karangasso Vigué and Séguénéga) for the completeness of data, providing 126 health centre years. Seasonal hyperendemicity and localised epidemics are two distinct phenomena involving potentially different mechanisms  [16]. Therefore, we separated health centre years with localised epidemics from those with usual hyperendemic incidences, using the threshold definition of 75 weekly cases per 100 000 maintained during at least two consecutive weeks  [13].
Parameterisation We obtained parameters values including natural death rate, death rate from meningitis, recovery rate after bacterial meningitis and birth rate from the scientific literature (Table  1 ). Case fatality rates of 10-15% were reported during serogroup A epidemics meningitis in the meningitis belt  [1]. We inferred natural death rate as the inverse of life expectancy at birth (average life expectancy was 54 years in Burkina Faso)  [26] , and the average recovery rate as the inverse of duration of acute phase of meningitis (acute phase of bacterial meningitis would last a week on average)  [27]  (Table  1 ). Parameters that are not available in the literature were estimated using suspected bacterial meningitis cases report data from Burkina Faso; a country within the meningitis belt.
The data consist of weekly counts of new suspected cases of bacterial meningitis recorded at health centres of four districts of the country from 2004 to 2010 together with the population sizes covered by each health centre. The estimated parameters were: the average meningococcal transmission and invasion rates, the amplitudes of seasonal forcing of transmission and invasion rates, the rate at which asymptomatic carriers and ill individuals recover, the duration of temporary immunity and the timing of weekly incidence peak relative to January 1. Initial susceptibles and carriers population size at the start of calendar years were also estimated for each health centre year hyperendemic's curves, as they could not be inferred directly from the literature. We limited the space of potential parameters values to be tested to plausible values according to the published literature if possible (Table  1 ).
For example, we used the 95% confidence interval of the meningococcal case-carrier ratio estimate during the dry hyperendemic season in the meningitis belt  [7]  as plausible values range for the average bacterial invasion rate (a 0 ). We estimated all unknown parameters values using a maximum likelihood approach. For each model, parameters values were selected to maximise the Poisson likelihood of observed bacterial meningitis incident cases. We used the COBYLA algorithm, a derivative-free optimisation algorithm, implemented in the R package nloptr for parameters optimisation routine  [28]. We chose this algorithm as it is relatively fast, it allows good convergence of the coefficients estimated on our data and it supports optimisation constrains such as parameter range. Several initial values were tested, and best-fit parameters estimates were obtained after 40 000 iterations.
Model simulation and evaluation We implemented and simulated the models using R statistical computing software  [29] , and the lsoda function (deSolve package) for numerical integration of the ordinary differential equations with 1-day time step. We computed weekly incidence as: t+(1/52) t a t Cdt , (7) with a t C, the proportion of asymptomatic carriers who becomes ill at time t. We quantitatively assessed the models' performance accuracy using the coefficient of determination (R 2 ), the per cent bias (PB), and the ratio of the root-mean-squared-error (RMSE) to observation standard deviation (RSR) (Supplementary Material S1). These three statistics quantify errors in models' predictions. PB computes the average absolute bias in model predictions of observations. It gives an indication on whether the model results are consistently under-or overestimated compared with the observations  [30].
Acknowledgements. We acknowledge the staff of health centres in Burkina Faso who participated in the meningitis surveillance, the  Ministry of Health of Burkina Faso, The Ecole des Hautes Etudes en Santé Publique, Rennes, France  and the  Agence de Médecine Préventive, Ouagadougou ,  Burkina Faso  for their collaborative effort in combining and making the surveillance data available for use in this study. Financial support. This work was supported by the  Université Pierre et Marie Curie , Paris 6.
In this paper, PM flux linkage is estimated via a Gopinath style stator flux linkage observer and the estimated PM flux linkage is used for torque error compensation for SPMSMs under various temperature conditions in real time. In the following sections, a characteristic of the stator flux linkage observer is described and analyzed. The proposed torque error compensation algorithm is presented to improve torque development accuracy under various temperature conditions. Since flux weakening control of a SPMSM drive is not as typical as an IPMSM drives, the test SPMSM drive is controlled up to rated speed in this paper. The proposed real time torque error compensation algorithm is verified through MATLAB/Simulink simulation and experimental results under various temperature conditions in this paper.
Simulation Results The proposed torque error compensation control system is implemented and verified through MATLAB/Simulink simulation. In simulation, a 100 µs time step is applied and Rosenbrock is used as a solver. Also, a frequency response of a stator flux linkage observer with respect to parameter variations is evaluated in the simulation. A block diagram for the proposed torque error In this paper, PM flux linkage is estimated in real time using an estimated stator flux linkage from the stator flux linkage observer in Figure  1. The estimated stator flux linkage vector for a voltage model can be divided into the d and q axis. The d-axis stator flux linkage can be presented with respect to current and machine parameters as in Equation (  7 ): λr dv = λpm + Ld • i r d (7) In this paper, the considered operation speed range is up to rated speed of a SPMSM because flux weakening operation of SPMSMs is not as usual as in IPMSMs.
Simulation Results The proposed torque error compensation control system is implemented and verified through MATLAB/Simulink simulation. In simulation, a 100 µs time step is applied and Rosenbrock is used as a solver. Also, a frequency response of a stator flux linkage observer with respect to parameter Energies 2017, 10, 1365 5 of 15 variations is evaluated in the simulation. A block diagram for the proposed torque error compensation control system for SPMSM drives is shown in Figure  3. As seen in Figure  3 , the proposed torque error compensation algorithm and a stator flux linkage observer are integrated into a typical complex vector current control system. A q-axis current command, i r * q1 , is corrected using the estimated PM flux linkage from the stator flux linkage observer. The corrected q-axis current command, i r * q2 , is constrained by a limiter not to exceed the maximum current of a motor drive system.
Graph convergence on trees Recall that the eccentricity of a vertex v is defined as the greatest distance from v to any other vertex. A center of a graph is a vertex with minimal eccentricity. The radius 1,{1} 1,{1,2} 1,{1,3} 2,{2} 2,{1,2} 2,{2,3} 3,{3} 3,{1,3} 3,{1,2,3} 3,{2,3} 2,{1,2,3} 1,{1,2,3} Fig. 1  Graphic description of immediate snapshots. All immediate snapshot views by three processes starting on 1, 2, and 3, respectively Algorithm 2 T = (V , E) is an arbitrary tree. Code for robot p i. Function GraphConvergenceTree(v i , T) 1: for r i ← 1 to 2 log diam(T) do 2: s i ← ImmediateSnapshot(r i , v i ) 3: t i ← smallest tree of T containing all vertices in s i.
Then, the robots proceed similarly as in GraphConvergenceTree: they move to the center of the subpath between their current vertices. Observe that Algorithm 3 G = (V , E) is a connected graph. Code for p i. Preprocessing: ∀(u, v) ∈ V 2 pre-define a shortest path from u to v Function GraphConvergenceTwoRobots(v i , G) 1: s i ← ImmediateSnapshot(1, v i ) 2: P i ← ⊥ 3: if |s i | = 2 then 4: P i ← precomputed path between vertices in s i 5: end if 6: for r i ← 2 to 2 log diam(G) do 7: the first round guarantees that the robots move along the precomputed between the initial vertices in all subsequent rounds. Moreover, the path process p i sets in P i in the first round is written in every round because in the case the other process p j goes faster than p i in the first round, it will be impossible to p j to know the initial vertex of p i , and hence set P j to the path P i (since a process does not read a shared memory of a previous round).
Funding The first author is supported by  UNAM-PAPIIT IA102417. The second author is supported by  UNAM-PAPIIT IN109917  and also received support from  ECOS-CONACYT  and LAISLA. The third author is supported by  CPSLab  project  H2020-ICT-644400. The first and second authors want to thank  INRIA  for its support in the context of the  INRIA-UNAM "Équipe Associée" LiDiCo (At the Limits of Distributed Computing ).
Motivation: RNA sequencing (RNA-Seq) is a widely used approach to obtain transcript sequences in non-model organisms, notably for performing comparative analyses. However, current bioinformatic pipelines do not take full advantage of pre-existing reference data in related species for improving RNA-Seq assembly, annotation and gene family reconstruction. Results: We built an automated pipeline named CAARS to combine novel data from RNA-Seq experiments with existing multi-species gene family alignments. RNA-Seq reads are assembled into transcripts by both de novo and assisted assemblies. Then, CAARS incorporates transcripts into gene families, builds gene alignments and trees and uses phylogenetic information to classify the genes as orthologs and paralogs of existing genes. We used CAARS to assemble and annotate RNA-Seq data in rodents and fishes using distantly related genomes as reference, a difficult case for this kind of analysis.
, 2015) , reconstructs a gene sequence by an iterative process where reads are collected by sequence similarity to a reference genome using BLAST  (Camacho et al. , 2009)  and then assembled. A different implementation was proposed in Kollector  (Kucuk et al. , 2017)  based on a k-mers approach. These methods show encouraging results, but have not been designed to be used on RNA-Seq data and for thousands of genes at a time. After assembly, transcripts should ideally be annotated with a gene name. Commonly, transcriptome annotation is based on sequence similarity between the transcripts and the transcriptome of already annotated species. This step is most often treated by Reciprocal Best Hits (RBHs)  (Rivera et al. , 1998) , typically using BLAST  (Camacho et al. , 2009) , which cannot handle species-specific duplications  (Altenhoff and Dessimoz, 2009; Tekaia, 2016).
This is an issue because many genes are duplicated. For instance, in the Ensembl database  (Herrero et al. , 2016; Yates et al. , 2016)  10% of all Human genes have no one-to-one orthology relationships with mouse genes. In principle, relying on gene phylogenies instead of RBH for annotation allows handling complex homology relationships  (Chen et al. , 2007; Kristensen et al. , 2011; Kuzniar et al. , 2008; Tekaia, 2016). We suggest to take such an approach: genes from annotated transcriptomes are clustered into homologous gene families either de novo  (Kristensen et al. , 2011)  or using existing families [EnsemblCompara  (Herrero et al. , 2016) , TreeFam  (Finn et al. , 2014) , Hogenom  (Penel et al. , 2009) , PhylomeDB  (Huerta-Cepas et al. , 2014) ]. Then, reconstructed transcripts are integrated into these gene families based on sequence similarity.
Alignments and trees are reconstructed for these enlarged gene families. Quality of the trees can be improved by using reconstruction methods that use the information provided by the species tree  (Boussau et al. , 2013; Ullah et al. , 2015). Finally, gene trees are reconciled with a species tree to annotate speciations, duplications and losses  (Kristensen et al. , 2011). Based on this scenario of gene family evolution, orthology and paralogy relationships are derived, and gene names are propagated from annotated sequences to novel transcripts  (Kristensen et al. , 2011). In this approach, accurate annotations are an outcome of accurate gene trees. Here, we present an automated tool, named CAARS, to assemble and annotate the whole transcriptome of non-model organisms from RNA-Seq data, using sequences from one or several species that can be closely or distantly related to guide transcript assembly and annotation.
Outline of CAARS and implementation The general structure of CAARS is illustrated in Figure  1. As input CAARS requires data from three types of species: the species whose transcriptomes need to be assembled, which we call target species, the species with transcriptomes serving as guide for assembly and annotation, which we call guide species and the species with already assembled transcriptomes because they improve the resolution of   5 ) Transcripts are assembled again with an assisted and iterative method (Apytram). At the first iteration, genes from the guide species and target transcripts from the draft transcriptome corresponding to this family are used as bait sequences to fish reads in the reads database. Mate reads are used to enlarge this batch of reads. Reads are then de novo assembled, and a new iteration can begin with the reconstructed sequences as baits.
(6) Coding sequences from both assemblies are added to the existing gene family alignments. (7) A primary gene tree is obtained for the family. (8) Redundancy is removed by selecting the longest sequence or by merging sequences from the same species when appropriate. Then, sequences (from target species) with a low-scoring alignment to their sister sequences (from guide or helper species) can be discarded (not shown). A gene tree is re-computed to take into account potential changes. (9) The species tree and the gene family tree are used jointly to infer a reconciled gene tree placing gene losses and duplications along the gene family tree the gene trees in the pipeline, which we call helper species. More particularly, CAARS requires Multiple Sequence Alignments (MSAs) corresponding to gene families containing sequences from guide and helper species, a rooted species tree with all the species and RNA-Seq data for the target species.
Finally, we require to specify a set of guide species for each target species (Fig. 1  top): since target species may belong to various taxonomic groups, it may be useful to adapt guide species to each target species. CAARS is organized in two major parts. The first one sets up several pre-requisites for the second, which is the execution in parallel of a series of steps for each gene family. First, CAARS performs a de novo assembly using the commonly used program Trinity  (Grabherr et al. , 2011). The de novo assembled transcripts are dispatched to gene families using BLAST. In addition, RNA-Seq reads are formatted as BLAST databases, one per target species. Second, independently for each gene family, CAARS performs another assembly assisted by sequences from the guide species and by de novo assembled transcripts.
This latter assembly is performed by our in-house software Apytram (Supplementary Fig. S1 )  (Rey et al. , 2017) , a multi-species implementation of the TRAM algorithm  (Johnson et al. , 2013). Importantly Apytram is able to deal with several RNA-Seq samples simultaneously, which improves on the initial implementation  (Allen et al. , 2015). Coding regions of transcripts from both de novo and assisted assemblies are extracted using Transdecoder (v3. 0. 1) (http://transde coder. github. io) and then integrated into the MSAs. At this step, gene families typically include redundant transcripts, which can be alternative transcripts of the same gene at the same locus, or identical transcripts that have been assembled independently by the two methods. To remove this redundancy, the default option is to select the longest sequence (raw or aligned length), following  Yang and Smith (2014).
Alternatively, it is possible to merge transcripts from the same species that branch at the same position in the tree, by maximizing the information content in the alignments (Supplementary Fig. S3 ). Partial sequences may be filtered out based on their alignment to their sister sequence in the tree (see Section 3 or the detailed implementation on the CAARS website). Then, accurate gene trees are inferred using a phylogenetic pipeline that uses the information coming from both the alignments and the species tree  (Boussau et al. , 2013)  and identifies events of gene duplication and loss in a reconciliation step. Orthology and paralogy relationships are naturally deduced from the reconciled gene trees. Because CAARS grounds assembly and annotation on several guide species at the same time, and not on a single one, it is robust to species-specific gene duplications or losses in the guide transcriptomes.
The method and implementation are detailed in the Supplementary Material provided on the CAARS website. CAARS is written in the Python programing language for all intermediate steps and in the OCaml language for the main program orchestrating all computational steps. This program relies on bistro  (Veber, 2017) , an OCaml library that manages dependencies between steps, distributed computation and recovery upon error [also known as resume-on-failure ability  (Leipzig, 2016) ]. For ease of deployment, users do not need to install all external dependencies and may instead use the dedicated docker image available on DockerHub called carinerey/caars.
Dataset, common inputs We used paired-end RNA-Seq libraries from adult mouse kidneys (2 Â 51 bp, about 12. 5-15. 3 million reads per library, SRR636916, SRR636917, SRR636918) and from adult stickleback kidneys (2 Â 100 bp, about 16. 5 million reads per library, SRR528539, SRR528540).
CAARS additional inputs and assembly In addition to the reads for the target species and an annotated transcriptome for the guide species, CAARS also requires MSAs corresponding to gene families containing sequences from guide and helper species. We downloaded the Ensembl Compara dataset from the Ensembl database [release 91,  Herrero et al. (2016) ;  Yates et al. (2016) ]. This dataset contains MSAs for 22 340 gene families with sequences from 97 chordates, including 71 mammals. From this set of species, we extracted a subset of 17 species of which 13 representative mammals, 1 bird, 1 reptile and 2 fishes. We obtained their phylogeny from the Ensembl Github repository  (Herrero et al. , 2016; Yates et al. , 2016)  (Supplementary Fig. S2 ). We did not want to favor CAARS during the tests, and we voluntarily removed the rodents belonging to the mouse sub-order and the fishes belonging to the stickleback order.
To be usable in CAARS, MSAs must contain at least one sequence from the guide species (Human), and at least 3 species in total (for the reconciliation step). A total of 8622 MSAs satisfy both criteria. We launched CAARS on a Linux server (16 threads, 64 G RAM) with a running installation of Docker and an imported CAARS image from DockerHub (carinerey/caars). CAARS tutorial contains the material (dataset and scripts) to replicate the analysis presented here as a demo and can be found on the wiki page of the Github repository. A smaller data set is also provided for a quick test.
Additional inputs for the standard pipeline and assembly On the same hardware, for each target species, we first assembled RNA-Seq reads into transcripts with Trinity  (Grabherr et al. , 2011)  using default parameters. Then, we used Cap3  (Huang and Madan, 1999)  (default parameters) to assemble overlapping Trinity contigs. We removed redundancy using CD-HIT-EST  (Fu et al. , 2012) , which clusters nucleotide sequences that meet a sequence identity threshold (99%) and finds one representative sequence per cluster (-c 0. 99 -n 11 -d 0). We then used Transdecoder (v3. 0. 1, -retain_ long_orfs 150) to extract the coding region of each transcript. Finally, we retained only transcripts associated by RBH with a guide species transcript using BLAST  (Camacho et al. , 2009)  with an evalue of 1e À 6 and using blastn as task option.
Reference transcriptome To assess the accuracy of the assemblies and annotations made by CAARS or the standard pipeline, we compared them for each target species with their corresponding known transcriptome. We extracted mouse and stickleback sequences from the Ensembl Compara dataset (v91), as reference transcriptomes  (Herrero et al. , 2016; Yates et al. , 2016). They are composed of 22 388 coding DNA sequences (CDSs) for the mouse and 20 072 for the stickleback distributed in 10 350 gene families. We removed strictly identical sequences using CD-HIT-EST  (Fu et al. , 2012) , keeping respectively 22 060 and 20 020 sequences. Of note, the mouse and the stickleback sequences are distributed in more families (10 350) than were used as input for CAARS (8622) because CAARS needs Human homologous sequences as bait sequences.
Identification of partial and alternative transcripts Assembled transcripts may be incomplete compared to the reference transcriptome, because they represent shorter alternative transcripts, or because the coverage for this transcript in the kidney expression data is low. To identify these partial and alternative transcripts, we aligned each transcript of each assembly to the reference transcriptome [BLAST  (Camacho et al. , 2009)  with evalue ¼ 1e À 10, and otherwise default parameters], and retrieved the Best Hit sequence (BH). By using BH instead of RBH, we allow that several sequences in a given assembly match a single transcript of the reference transcriptome. This ensures all sequences of an assembly with a possible hit have an associated reference sequence.