dataesr/scientific-paragraphs-categorization huggingface dataset:
2000). We used forward and reverse primer sequences for each marker as query sequences against the oil palm genome sequence as the target database  (Singh et al. 2013). We filtered the result hits of BLASTn with a minimum e-value threshold of 0. 5. The marker position was assigned easily when forward and reverse primers were found in the same genome location. In case of multiple hits on different chromosomes for the same primer pair, the marker position was assigned based on the hit with the greatest e-value. Inferred physical positions of markers are available in Table  S4. Based on the marker physical positions, the physical positions of 210 predicted R-genes in the oil palm genome  (Singh et al. 2013; Chan et al. 2017)  were projected on the Eg9PP genetic map in order to identify colocalizations with Ganoderma resistance QTL (See Table  S8 ).
IBD matrix computation: The variance component approach requires IBD kinship matrices as variance-covariance matrices for testing QTL effects at each genetic position. IBD analyses using SimWalk2 software  (Sobel et al. 2001)  were conducted to estimate the IBD matrices formed by empirical kinship coefficients for each pair of Eg9PP individuals and their parents. Simwalk2 implements a Bayesian framework using a Markov chain Monte Carlo (MCMC) algorithm to select the most likely genetic descent graph depicting inheritance patterns within pedigrees  (Sobel and Lange 1996). Empirical kinship coefficients were first calculated between the 604 fully genotyped Eg9PP individuals and their parents at every marker position and over a 3 cM interval grid, leading to 1006 evaluation points for QTL presence testing (available at https:// doi.
The theoretical approach was based on different works in shared frailty model framework  (Duchateau et al. 2002; Vu and Knuiman 2002; Claeskens et al. 2008 ) that assessed the limiting distribution of LRT. The resulting distribution is the asymptotic 50:50 mixture of the x 2 0 and the x 2 1 chi-squared distributions of 0 and 1 degree of freedom, respectively, that was used to calculate p-values associated to each QTL detected. Haplotype analysis: QTL mapping identified a strong QTL in the top of linkage group 1, and this region was analyzed in further detail. Based on the genotyping of all the 757 sampled individuals from the 14 Eg9PP families with seven SSR markers covering 20 Mb (see Table  S4 ), the kinship coefficients were calculated between the 757 Eg9PP individuals and used to perform QTL mapping with this extended dataset in order to determine precise QTL location in the candidate region.
Then, haplotype analysis was performed in the candidate region using SimWalk2 to identify the funder allele segregation in the Eg9PP individuals. Funder allele effects were then estimated including two fixed effects in model (3)-one for each heterotic group-in order to identify favorable alleles among the Eg9PP parents at the QTL peak closest marker. All analyses were performed with R software version 3. 2. 3 (R Development Core Team 2012) and the coxme package  (Therneau and Therneau 2015) , and R scripts, with example data files and documentation, are available at the github repository https://github. com/Denis-Marie/Eg9PP_Ganoderma. The genome scan results are available in Table  S6.
Data availability Eg9PP was uprooted in 2012, but Eg9PP families can be reproduced by crossing individuals derived from the self-fertilized progenies of Eg9PP parents. Requests for such genetic material should be addressed to PalmElit (www. palmelit. com). Raw genotype data are available in Table  S5  and IBD kinship matrices at https://doi. org/10. 6084/m9. figshare. 4780489. v1. Phenotyping of disease events are available in Table  S3. All R scripts, data file and documentation are available at https://github. com/DenisMarie/Eg9PP_Ganoderma.
45) but low significance levels (a = 0. 38 and 0. 19, respectively), with a risk BLUP distribution indicating the greatest QTL segregation in families from parents D3 and D5 for TD_3@0, and L1 and L2 for TD_14@13 (Figure  3B  and Table  1 ). Haplotype analysis of the major Ganoderma resistance QTL T1S_1@9, the major QTL for Ganoderma resistance, was investigated in further detail by genotyping all sampled Eg9PP individuals (n = 757) with the five SSR markers in the region (LG1, 0-44. 5 cM), by adding two additional SSR markers and performing IBD and haplotype analyses. IBD-QTL mapping on the augmented dataset confirmed the presence of a QTL for T1S on LG1, but the LRT peak position shifted to 0 cM on the mEgCIR1713 marker (Figure  4A ). While no QTL were found for TD in this region, a LRT peak was detected with the augmented dataset close to the mEgCIR3803 marker (Figure  4A ).
DISCUSSION This study was based on data from monitoring natural field infection of an oil palm multi-parental population over 25 yr. This unique dataset provided an opportunity to map Ganoderma resistance loci in natural conditions according to an economic and efficient in silico approach, i. e. , based on data from breeding programs  (Parisseaux and Bernardo 2004). Four Ganoderma resistance loci were identified by combining different statistical methods, including survival analysis, infection spatial modeling, and a variance component approach for QTL mapping.
ACKNOWLEDGMENTS We thank  N. Marseillac  and  A. Berger  for partly genotyping the population and  N. Billotte  for providing former genotyping results and collected samples. We acknowledge  SOCFIN Indonesia (Indonesia)  for planning and carrying out the field trials with  CIRAD (France) , and authorizing use of the phenotypic data for this study. This research was partly funded by a grant from  PalmElit SAS.
The difference in area response of DGDG and PC lipids to the water content already points to distinct hydration mechanisms, as will   25  and DLPC  16  membranes. Error bars for the simulation data represent 1 s. d. of uncertainty and were estimated from the scatter of the points around the plateau value at high hydration. For DLPC, they are smaller than the size of the symbols. The experimental error can be estimated in the same way as r0. 02 nm 2. (b) Pressure-distance curves of DGDG  14  and PC lipid membranes  16, 28  as obtained in experiments (open symbols) and in the present simulations (filled symbols). Straight dashed lines in the semi-logarithmic plots indicate the best-matching exponential fits to the experimental data points for DGDG  14  and to the combined experimental data sets for egg PC  28  and DLPC  16.
py script  44  available from the Martini website (www. cgmartini. nl). The script can be used to prepare coarse-grained (CG) models of bilayers of varying composition by using lipid templates. After minimization and a short equilibration run at constant area, atomistic details were generated from the CG structures using the 'backward. py' script  45  , also available from the Martini website. Mapping files from the CG to the atomistic structures were written for the lipids relevant to these systems. The atomistic structures after backmapping were energy minimized and briefly equilibrated using stochastic dynamics with very short time step  46, 47. Computer simulations. All atomistic MD simulations are performed using the GROMACS simulation package  48. Simulation run files are available as Supplementary Software 1.
75 Å and Dl/l ¼ 0. 01. Data analysis is performed using the ILL in-house LAMP software (www. ill. eu/instruments-support/computing-for-science/cs-software/all-software/ lamp): The intensity collected on the 2D detector (Fig. 7a ) is reduced to 1D by vertical integration after solid angle and detector pixel efficiency correction. The data are corrected for background by subtraction of the empty chamber signal. The lamellar periodicity (D) is determined in a linear fit to three Bragg peak positions (Fig. 7b ) versus diffraction order h of the lamellar (h00) reflections, according to Bragg's law: q h ð Þ x ¼ 2ph/D, where q h ð Þ x is the magnitude of the scattering vector. Each peak is fitted with a Gaussian function on top of a constant background, yielding the following q x -values and errors (dq x ) for the peak position (Table  1 ).
Acknowledgements We thank  Peter Fratzl  for insightful comments and  Institut Laue-Langevin  for neutron beamtime. E. S. acknowledges financial support by the  Max Planck Society  and from an  Emmy-Noether grant  ( SCHN 1396/1 ) of the  German Research Foundation (DFG). R. R. N. acknowledges support of the  DFG  via  SFB 1112.
Implementation The network was implemented in Python 2. 7 and was partly compiled into C-code for efficiency using the Numba library  (The Numba Development Team, 2015). The source code is available online 1. Some numerical experiments were also conducted with smaller networks of Hodgkin-Huxley type neurons (cf. Stimberg et al. , 2009)  using the BRIAN simulator package  (Goodman and Brette, 2008)  (data not shown, example run see Supplementary Video). All data and parameter explorations were managed using the simulation toolkit pypet  (Meyer et al. , 2016).
Spatial Activity Patterns In order to determine the spatial scale of changing patterns we sampled 100 windows of 10 ms activity for each stimulus presentation and counted the spikes in these intervals for each excitatory cell. Accordingly, for each sample interval of activity of the 2D networks, we computed a spatial grid matrix of spike counts. The autocorrelation was calculated convolving the spike count matrix with itself using the fast Fourier method of the scipy package  (Jones et al. , 2001). Next, to compute the mean spatial autocorrelation of a network we normalized the autocorrelations and averaged across the sampled time windows and stimulus presentations. This procedure is visualized in Figure  2.
Fisher Information The quality of a population code was quantified using an estimate of Fisher information. The numerical information experiments were based on repeated presentations of oriented stimuli of -1 and 1 degree. After an initial phase of 1 s, 3 s of stimulation were used to estimate Fisher information. The number of stimulus presentations varied between 125 and 7,000 per oriented stimulus depending on the number of neurons we considered for readout. We chose at least as many stimulus presentations as the number of readout neurons that we sampled. This avoids sets of too few training data items of a number lower than the amount of Fisher estimation parameters (see below). Fisher information was estimated using an approach from  Seriès et al. (2004). In most experiments 60% of the simulation data were used to train a locally optimal linear estimator (LOLE) to predict the stimulus orientation from the spiking activity.
In the experiments based on 7,000 samples, however, we chose 80% of the stimulus presentations for training. The estimator has the form: ŝ = w T r + w 0 , ( 6 ) where w is a vector of weights, r = (r 1 ,. , r N ) T is a vector containing the spike response of the read out neuron sample in the fixed time window of 3 s, and w 0 a bias weight. w and w 0 were optimized to reduce decoding error based on the given training data. The parameters were fitted via stochastic gradient descent using the scikit-learn Python library  (Pedregosa et al. , 2011). Training was stopped in case generalization performance on the validation set-containing 20% (10% for 7,000 presentations) of the data-decreased for 500 consecutive epochs. After training was completed, mean and variance of the estimates were computed for both orientations (s 1 = -1.
To explore the responses of the model to different values of  NaCaratio, as resulted from (3) after insertion of experimental data, we set the values of  NaCa,t according to the following relation: 𝑓 NaCa,t = 𝐼 NaCa,t /𝐼 NaCa,s 𝐶 m,s /𝐶 m,t + 𝐼 NaCa,t /𝐼 NaCa,s. (4) The simulations were performed using the computational software MATLAB v. 7. 2 (MathWorks, Natick, MA, USA). To attain a dynamic steady state at each stimulation frequency (0. 1, 1, and 5 Hz), the model was paced for 600 s of equivalent cell lifetime by 1 ms pulses (the magnitude was set at twice the respective threshold value) under all specified conditions. The MATLAB code of the model is available at http://www. it. cas. cz/en/d3/l033/biophysics-cardiac-cells.
Selected articles and data A total of 91 articles was identified and screened. Details are presented in Fig. 1. Several articles were excluded for the following reasons: studies that were either on tolerability or recurrence of parasitaemia or did not report data on ACPR  [24, 25] , or based on the mechanism of resistance to ACT  [26] , or comprised only one ACT-arm as in Ref. [27] , which compared AL to azithromycin-chloroquine (AZCQ). Data comprising monotherapies and ACT were excluded. In addition, reference lists in 16 reviews were screened for recent studies  [12, 13, 22, 28, 29]. At the end of the screening process, 76 articles were included in the original analysis; K = 13 treatments were involved. The quinine arm was removed from a 3-arm study  [30] , and two studies published in French were included  [31, 32].
Patient populations included children and adults: 82% were children less than 15 years old among whom more than 80% were children under 5 years. Some published articles involved both children and adults or adults only. Trial characteristics are described in the Additional file 1. Most of the studies presented the outcome on day 28 only, while few followed the patient until day 42 or even day 63. Hence, only a few studies had longitudinal data on days 28, 42 and 63. Accordingly, the obtained information was pooled on the primary end-point day 28 using the observed number of ACPR. The percentage of ACPR was estimated using the number of enrolled population or the number followed as far as the intention-to-treat (ITT) analysis or a per protocol (PP) approach is concerned, i. e. when the ITT results were not reported, the percentage of ACPR obtained in a PP approach was used to estimate the number of patients with positive outcome.
Statistical data analysis First, pairwise random effect meta-analyses were conducted. The method was implemented with the metaanalysis package of the software R (rmeta) downloadable at https://cran. r-project. org/. Treatments involved were the five WHO-recommended ACT medicines: ASAQ, ASSP, AL, DHAP and ASMQ. Direct estimates were extracted from head-to-head trials using the odds ratios and 95% confidence interval (CI). The heterogeneity of variance for each pairwise comparison was estimated using the I 2 statistic. Secondly, data were analysed by Bayesian method. Prior information was defined for all unknown parameters d bk and τ 2. The following prior distributions were considered: µ i ∼ N (0, 0. 0001); d bk ∼ N (0, 0. 0001). The inverse of the study variance was given a uniform distribution, i.
Third, an adequate number of trials was not available for a robust inference for the recently tested new combination therapies. Despite this shortcoming, the findings described in this work reflect the reality in the field. Moreover, regarding the change in efficacy, inadequate dataset was available on day 42, day 35 and day 63 to perform separate analyses. Fourth, drug efficacy was limited to the outcome on day 28, as recommended by the WHO. According to WWARN  [12] , there is variability in efficacy related to drug formulation. As this variability was not known in the past, it was assumed in the present study that drug efficacy does not vary with formulation. The analyses included studies on both children and adults, and the majority of the included patients were children less than 5 years of age.
Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multiobjective flux variability analysis (MO-FVA).
Computational procedures For each guild, a metabolic model was built in MATLAB and an ecosystem stoichiometric matrix S σ was constructed, as described above. MO-FBA was carried out using BENSOLVE  [52]. In order to analyze nitrogen and carbon fluxes through MO-FBA results, a MO-FVA was performed using GUROBI  [42]  through Python interface over a mesh of 5 151 equally distributed points in the Pareto surface at 90% fraction of optimum. Then, we subdivided the Pareto surface into 225 similar regions; for each of these regions, we calculated their maximum (as well as their minimum) as the average of MO-FVA maxima of mesh points contained (this procedure was repeated for the minima). Thermodynamics calculations were performed over the same mesh as the MO-FVA using a truncated Newton conjugate algorithm  [59]  contained in scipy optimization module.
Heatmaps and surface illustrations were generated using matplotlib  [60]  with ad-hoc scripts. From methods discussed in Biggs et al. [21]  and Perez-Garcia et al. [22] , OptCom  [25]  was chosen for comparison, as this method resembles the approach applied to the present work. We applied OptCom and Descriptive OptCom to the hot spring mat model, as follows: first, 11 points were calculated using OptCom, as described by  [25] , each with a different upper boundary value for SYN biomass; these values ranged from 1. 0 to 0. 0 with a step of 0. 1 (i. e. 1. 0, 0. 9, 0. 8,. , 0. 0). Second, Descriptive OptCom was applied three times using SYN to FAP ratios of 1. 5, 2. 5, and 3. 5, respectively. All programs were written in GAMS language and solved using BARON  [61]  through the NEOS Server  [62] [63] [64].
Acknowledgments MB is supported by  CNRS  and  Region Pays de la Loire  funding ( GRIOTE  project). This study was supported by  ANR  ( IMPEKAB ,  ANR-15-CE02-001-03 ). We would like to thank  Editage  (www. editage. com)for English language editing. The authors would like also to thank the anonymous reviewer for his valuable input and comments regarding the manuscript.
We propose then to analyze flows of influence in a citation networks as an ascending flow. From this point on, we can take a finer look at the diffusion of knowledge through the lens of a multiplex network. In this network, each citation of a specific work constitutes one layer of interaction. Within our framework, we design three measures of multiplex flows in DAGs, namely the aggregated, sum and selective flow, to better understand how citations are influenced. We conduct our experiments with the arXiv HEP-Th dataset, and find insights through the visualization of these multiplex networks.
These indicators are used for the evaluation of scientists, however they can be subject to controversy  (Pendlebury 2009)  and are designed to reflect only the productivity of a scientist rather than measuring the overall production of knowledge. One reason explaining these indicators' popularity is their simplicity in terms of computation, as opposed to previous network analysis that was seen as too complex to deploy. However, progress in modern graph databases has grown, easing the analysis of dynamic networks  (Cattuto et al. 2013). Inspired by the seminal work from  Strahler (1957)  and from  Hirsh (2005) , we propose to bring a fresh look at the production of knowledge based on the analysis of flows in Directed Acyclic Graphs (DAGs). This view is not limited to the production of indicators but allows a more in-depth analysis of the process and diffusion of knowledge.
In "Preliminaries" section, we introduce the Strahler numbers and the h-index in a generalized flow framework, and how those two notions belong to one greater notion of flow. We introduce our ascending flow in "Ascending flow in citation networks" section -modeled on the notion of flow of knowledge. We then discuss the parameters of this ascending flow and put it in relation with classical measures, while proposing a dynamic algorithm that allows for quick update. Finally, in "Experimental results" section we run experiments on a publicly available dataset, the arXiv HEP-Th  (Gehrke et al. 2003). We then extend our ascending flow to the multiplex networks context from "The citation network as a multiplex network" section, which fits in our generalized flow framework. After defining our multiplex network in "Preliminaries" section, we propose three multiplex flow measures in "Multiplex extensions of the ascending flow" section, namely the aggregated, sum, and selective flows, with implementation in "Dynamic implementation" section.
Analysis of multiplex DAGs has also recently been proposed by  Speidel et al. (2015)  using the same arXiv HEP-Th dataset that we use, with the difference that layers are defined by year of publication. Beyond community detection,  Pujari and Kanawati (2015)  investigated the multiplex citation networks to predict the creation of link between authors: from the citation network they infer a multiplex network of authors and predict links, based on community structure. Our context differs in many ways from those works. The object we are studying, although from the same data, is very different. We keep the citation network as a support for modeling multiplex relationships, with the basic assumption of having a DAG structure. The network we construct presents a very large number of layers (one per co-citation) which is often an issue in the methods presented above.
Searching for a needle in the haystack We observed all publications with h-index = 6 (1054 publications in the dataset). With this sample of fixed h-index nodes, the ascending flow does not correlate well with other classical measures: ρ = 0. 136 with the 2-degree which was the most correlated value over the network with the ascending flow and the h-index, ρ = 0. 096 with the number of citations, ρ = 0. 228 with the total descending nodes (∞-degree). However, if we plot the distribution of the flow coefficient (Fig. 6 ) we can notice one outlier node that cannot be noticed in any other distribution: this node happens to be the oldest publication (ID9201019 2 ) among all publications with h-index=6. We also tested Fig. 4  An example of the ascending flow metric in an excerpt of 22 nodes (60 edges) of our dataset, rooted by a publication by Lorenzo Cornalba.
The size of nodes corresponds to their ascending flow in this subgraph. The color of nodes and edges (from blue to red) is actually their ascending flow in the real global dataset -we can see that Hong Liu's publication has probably been a seed for more knowledge than of its ancestor Lorenzo Cornalba if the node was an outlier in all other measures, including date and DAG depth (which could be the longest if the node has deep descending nodes), but the work is not an outlier in any of those measures. If we look in details at why this node received so much flow, we can "follow down" the path of maximum flow passed by its citing publications: we recursively take the edge that shares the maximum value until there is no other citing node. The path is only 22 edges long and the influence received by the nodes constantly increases up to 7 nodes before decreasing (Table  2  presents the statistics of the 8 first nodes of the path).