dataesr/scientific-paragraphs-categorization huggingface dataset:
We acknowledge the platforms of the Grenoble Instruct-ERIC center (ISBG;  UMS 3518 CNRS-CEA-UJF-EMBL ) within the  Grenoble Partnership for Structural Biology. Platform access was supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)  ( ANR-10-INBS-05-02 ) and the  Laboratoire d'Excellence Grenoble Alliance for Integrated Structural and Cell Biology (LabEx GRAL ), a project of the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). All experiments involving live NiV were carried out in the  INSERM Jean Merieux biosafety level 4 laboratory in Lyon, France.
The model also provides a close fit to the observed distributions of simple cell parameters such as size and spatial frequency  [Olshausen and Field, 1996a]. For the local delay code, the paramount experimental analysis issue is if the local phase delay is to be measured, the action potential needs a time reference. We chose the phase statistics from patch-clamp data  [Perrenoud et al. , 2016] , which consisted of membrane potential recordings from primary visual cortex of awake mice while observing images of moving gratings. Our software allowed us to extract the position of each action potential within its local gamma frequency profile. An overwhelming number of spikes occurred between the nearest preceding gamma minimum and the following maximum. We chose the minimum as a time reference and recorded the interval between the minimum and the position of the spike as an analog phase delay.
Results Our model used previously-obtained intracellular recordings from seven patchclamped pyramidal cells from a head-fixed awake mouse. These data were initially part of Perrenoud and colleagues' earlier study  [Perrenoud et al. , 2016]  and are freely available online on the Dryad repository. A session of the recording consists of a series of trials, each seven seconds long. Each trial starts with a two second-long stimulus-OFF baseline, followed by a 3-second presentation of a visual stimulus of a moving oriented sine grating, and finishing with a two secondlong stimulus-OFF period. The task of holding on to a cell for many seven second trials can be delicate; in our database of seven cells, the number of trials per cell ranged from 39 trials up to 80. Each trial consisted of a specific moving grating.
This assumption is one of ergodicity where sampling in time is representative of sampling in space. Next, we compared the distributions of phase delays with the distributions of sparse coding responses. The comparison assumes that the sparse coding model also uses parallel signal components. This decision is motivated by the theoretical constraint that the responses for each input image must be chosen probabilistically to conform to Bayesian principles  [Doya, 2007 , Jehee et al. , 2006a]. Thus we combined the model projections as if they were generated in parallel. The sparse coding samples are comparable with the experimental samples since both are ergo chosen probabilistically. Both distributions represent the coding of similar image information: oriented grating images responses in the experimental dataset, and image patches sample from natural outdoor images in the modeling dataset.
Thus both plots represented the frequency of responses, oriented so that each of the largest responses are located at their origins. Next, each normalized histogram was used to create a cumulative probability density function. The final step is to facilitate their comparison by comparing the two cumulative distributions. These two distributions were found to be very similar, as shown in Figure  1c. A straightforward test is Kolmogorov-Smirnov  [Massey Jr, 1951] , which tries to reject the distributions as being dissimilar. The parameter measured is the maximum difference between the two curves. We used Matlab's kstest2. The result is that the test produces a p-value of 0. 2606, implying that there is a 74% probability that the two distributions of data and model can be thought of as similar.
If this is not done, the relatively large spike amplitude will unduly influence the gamma filtering result. The method we chose, that  [Perrenoud et al. , 2016]  interpolates the membrane potential based on a smooth fit to the nearby potential either side of the spike. We chose ±2ms as defining the interval straddling the spike to be interpolated as giving highest rates of phasedetection across the database of cells. Figure  3  shows the result of computing the portion of the action potential to be removed in red. The software to detect the related delays addresses each spike in turn. Once the spike has been removed from its membrane potential vicinity, an area of a one-half second straddling the spike location is filtered with a Butterworth filter, whose parameters are shown in Table  1. The filter parameters are tuned to isolate the component of the signal in the low gamma range.
Sparse coding volley model Four steps were performed to obtain the sparse coding response distributions. 1. The natural images were filtered appropriately for the lateral geniculate nucleus; 2. The receptive fields were learned from a database trained on natural images using the algorithm of Lee et al. [Lee et al. , 2007] ; 3. Fifty receptive fields were sampled probabilistically for each input image using our parallel algorithm; 4. Their distributions were scaled appropriately. Since the experimental patch-clamp data uses oriented moving grating images for visual input, a good choice of model is one that can account for the properties of simple-cell oriented receptive fields. As previously introduced, the best choice for a pyramidal cell's functionality is that of sparse coding, where a reduced set of cells chosen from a substantial surfeit can explain the parameter distribution of simple cells' receptive fields  [Olshausen and Field, 1996a].
Cytoplasmic incompatibility (CI) relates to the manipulation by the parasite Wolbachia of its host reproduction. Despite its widespread occurrence, the molecular basis of CI remains unclear and theoretical models have been proposed to understand the phenomenon. We consider in this paper the quantitative Lock-Key model which currently represents a good hypothesis that is consistent with the data available. CI is in this case modelled as the problem of covering the edges of a bipartite graph with the minimum number of chain subgraphs. This problem is already known to be NP-hard, and we provide an exponential algorithm with a non trivial complexity. It is frequent that depending on the dataset, there may be many optimal solutions which can be biologically quite different among them. To rely on a single optimal solution may therefore be problematic.
To this purpose, we address the problem of enumerating (listing) all minimal chain subgraph covers of a bipartite graph and show that it can be solved in quasipolynomial time. Interestingly, in order to solve the above problems, we considered also the problem of enumerating all the maximal chain subgraphs of a bipartite graph and improved on the current results in the literature for the latter. Finally, to demonstrate the usefulness of our methods we show an application on a real dataset.
Open Access Algorithms for Molecular Biology *Correspondence: blerina. sinaimeri@inria. fr 1 Inria Grenoble, 655, Avenue de l'Europe, 38334 Montbonnot, France Full list of author information is available at the end of the article of each of them present in the eggs and sperm (see for example  [6] ). This model currently represents the best hypothesis and is consistent with the data available (see e. g. [7, 8] ). In  [6, 9] , the cytoplasmic compatibility relationships that are observed in a given dataset are modelled as a bipartite graph with males and females in different partitions of the graph and edges representing an unsuccessful crossing. The aim is to find the minimum number of different Lock/Key molecules that explain the observed data. This is modelled as finding the minimum number of chain subgraphs (i.
To do so, we prove that this can be polynomially reduced to the enumeration of the minimal set covers of a hypergraph. To show the usefulness of our algorithms, we implemented Algorithm 1 and applied it to the Culex pipiens dataset  [6, 9]. We show that our method allows to identify solutions that are better than the ones presented in the literature, in the sense that they require less Lock and Key molecules to explain the data. The remainder of the paper is organised as follows. In "Preliminaries" section, we give some definitions and preliminary results that will be used throughout the paper. In "Modeling cytoplasmic incompatibility" section, we better explain the CI-model in terms of a graph problem. "Enumerating all maximal chain subgraphs" section provides a polynomial delay algorithm to enumerate all maximal chain subgraphs in a bipartite graph G and "Upper bounds on the number of maximal chain subgraphs" section presents an upper bound on their maximum number.
Modeling cytoplasmic incompatibility In  [6, 9] , the cytoplasmic compatibility relationships that are observed in a given dataset are represented as a binary n 1 × n 2 matrix C with the males in rows and females in columns. Entry C[i, j] = 0 represents a com- patible cross (offspring survival) between male i and female j; C[i, j] = 1 representing an incompatible cross. Under the quantitative Lock/Key model (see Section Quantitative Model in  [9] ) the unknown infections with Wolbachia strains are represented as an n 1 × k matrix L and an n 2 × k matrix K that describe the Lock and Key factors carried by the host males and females, respectively. Matrices L and K contain integer values and for each entry L[i, l] = q ( K [j, t] = q ), the value q indicates that the Lock molecule l is found in quantity q in male i (the Key molecule t is found in quantity q in female j).
An example that illustrates the connection between incompatibility matrix, bipartite graph and chain graph is depicted in Fig. 2. Given the incompatibility matrix in Fig. 2a , the corresponding bipartite graph is constructed in Fig. 2b. Recall that an edge corresponds to a cross incompatibility. We apply the procedure described in the proof of Lemma 1 and thus we can define B 3 = {M 1 }, B 2 = ∅, B 1 = {M 2 } , F 1 ∈ N (B 1 ) and F 2 , F 3 ∈ N (B 3 ) \ ∩ 2 l=1 N (B l ). We can explain the dataset with only one pair of Lock-Key molecule. The assignment of the quantities of the lock and key molecules to the males and females is done as described before and is depicted in Fig. 2d. It is not difficult to check that the cross between male i and female j is successful if and only if the female has enough of the key molecules for the lock molecules of the male.
A case study In this section we show an application of our methods to a real dataset. We implemented Algorithm 1 and ran it on the graph G representing the CI of Wolbachia in Culex pipiens  [6] , stored by means of the incidence matrix in Fig. 3. The code is available at https ://githu b. com/sinai meri/Chain Enume ratio n. On this dataset, the algorithm to list all the maximal chains took only 1 second on a single core of a 6-core MacBook Pro 2. 2 GHz i7. The maximal chain subgraphs of G came out to be 16, and by doing a simple exhaustive search algorithm, we found a chain cover of G constituted by 4 chain subgraphs (represented in Fig. 4  in colours on the matrix storing G, where rows and columns have been conveniently permuted in order to highlight the triangular shapes of chain subgraphs, that are called quantitative shapes in  [6] ).
4  The Culex pipiens dataset in  [6]. A minimum chain cover for the graph G, constituted by four chain subgraphs Fig. 5  The Lock and Key matrices for the Culex pipiens dataset in  [6]. The Lock matrix and the Key matrix resulting from the solution of the quantitative model. In each cell the relative amount of lock and key molecules is indicated. Notice that a value 0 indicate that no molecule is inferred from the analysis. Each color symbolizes a single Lock-Key pair value 0 indicates that no lock or key molecule is inferred from the analysis. Each colour symbolizes a single Lock-Key The values of the quantities are indicative. Indeed, Lemma 1 provides a assignment explains incompatibilities but what important are not the values themselves but the relative order among them. Thus, for instance we can safely substitute the value 17 in the table by 5 and the relative order will not change.
Acknowledgements We started this paper together with Mattia Gastaldello; unfortunately he prematurely passed away in 2018 and we like to publish this work in his memory. T. Calamoneri was supported in part by the  Italian Ministry of Education, University, and Research (MIUR)  under  PRIN 2012C4E3KT  national research project " AMANDA -Algorithmics for MAssive and Networked DAta " and in part by  Sapienza University of Rome  project " Graph Algorithms for Phylogenetics: A Promising Approach ". M. Gastaldello was supported by the  Università Italo-Francese  project " Algorithms and Models for the solution of difficult problems in biology". A. Mary  was supported by the  ANR  project  GraphEN "Enumération dans les graphes et les hypergraphes: algorithmes et complexité ",  ANR-15-CE40-0009.
Abstract. A carbonate chemistry balance module was implemented into a biogeochemical model of the planktonic food web. The model, named Eco3M-CarbOx, includes 22 state variables that are dispatched into 5 compartments: phytoplankton, heterotrophic bacteria, detrital particulate organic matter, labile dissolved organic, and inorganic matter. This model is applied to and evaluated in the Bay of Marseille (BoM, France), which is a coastal zone impacted by the urbanized and industrialized Aix-Marseille Metropolis, and subject to significant increases in anthropogenic emissions of CO 2. The model was evaluated over the year 2017, for which in situ data of the carbonate system are available in the study site. The biogeochemical state variables of the model only change with time, to represent the time evolution of a sea surface water cell in response to the implemented realistic forcing conditions.
, 2009). The present study is dedicated to the implementation of a carbonate system module into a preexisting biogeochemical model of the planktonic food web. This new model, named Eco3M-CarbOx (v1. 1), is then evaluated in a highly dynamic coastal site, i. e. , the Bay of Marseille (BoM) in the northwestern Mediterranean Sea. This evaluation is performed on the seasonal dynamics of biogeochemical and carbonate modeled variables against that of the corresponding in situ data available over the year 2017. This study is extended by a fine analysis of the variability of the marine carbonate system (stocks, fluxes) in relation to physical (e. g. , wind events, river intrusions, temperature increases, changes in the atmospheric pCO 2 levels) and biogeochemical processes (gross primary production (GPP) and respiration, R) in the study site.
Dataset The modeled variables of the carbonate system (DIC, TA, pH, and pCO 2 ) and chlorophyll a are hereafter compared to observations collected at the SOLEMIO station (Figs. 2c  and 3 ), which is a component of the French national monitoring network (Service d'Observation en Milieu Littoral -SOMLIT, http://somlit. epoc. u-bordeaux1. fr/fr/, last access: January 2020). Major biogeochemical parameters have been recorded since 1994. Carbonate chemistry variables (pH, pCO 2 , DIC, and TA), sampled every 2 weeks, have been available since 2016.
Design of numerical experiments In the present work, the Eco3M-CarbOx model was run for the whole year of 2017. This year was chosen because in situ data of carbonate systems (DIC, TA, pH, and pCO 2 ) are available for the whole year at the SOLEMIO station (Fig. 2c ). The biogeochemical variables were initialized using in situ data from winter conditions (Table  B1 , Appendix B). The model was forced by time series of sea surface temperature and salinity, wind (at 10 m), light, and atmospheric CO 2 concentrations. The sea temperature time series is from in situ hourly data recorded at the Planier station (Fig. 2c ). For salinity, hourly in situ data from the SOLEMIO station and from the CARRY buoy were used (Fig. 2c ). Wind and light hourly time series were extracted from the WRF meteorological model at the SOLEMIO sta- tion  (Yohia, 2017).
Conclusions A marine carbonate chemistry module was implemented in the Eco3M-CarbOx biogeochemical model and evaluated against in situ data available in the Bay of Marseille (northwestern Mediterranean Sea) over the year 2017. The model correctly simulates the value ranges and seasonal dynamics of most of the variables of the carbonate system except for the total alkalinity. Several numerical experiments were also conducted to test the sensitivity of the carbon balance to physical processes (temperature and salinity), biogeochemical processes (GPP and respiration processes), and external forcing (wind, river intrusion, and atmospheric CO 2 ). This set of numerical experiments shows that the Eco3M-CarbOx model provides expected responses in the alteration of the marine carbonate balance regarding each of the considered perturbation.
Acknowledgements. We thank the  National Service d'Observation en MILieu Littoral (SOMLIT)  for its permission to use SOLEMIO data. We wish to thank the crew members of the RV Antedon II, operated by the  DT-INSU , for making these samplings possible. We wish to acknowledge the team of the  SAM platform (Service Atmosphère Mer) of the MIO institute  for their help with the field work. For the collection and analyses of the seawater sample, we thank  Michel Lafont  and  Véronique Lagadec  of the  PACEM (Plateforme Analytique de Chimie des Environnenments Marins ) platform of the MIO institute and also the SNAPO-CO2 at LOCEAN, Paris. The SNAPO-CO2 service at LOCEAN is supported by  CNRS-INSU  and  OSU Ecce-Terra. We acknowledge the staff of the " Cluster de calcul intensif HPC " platform of the  OSU Institut PYTHEAS (Aix-Marseille Université, INSU-CNRS)  for providing the computing facilities.
We gratefully acknowledge  Julien Lecubin  from the  Service Informatique de OSU Institut PYTHEAS (SIP)  for their technical assistance. Moreover, we thank  Camille Mazoyer  and  Claire Seceh  for their contribution to the Eco3M-CarbOx model development. Financial support. This study is part of the  AMC  project ( Aix-Marseille Carbon Pilot Study ,  2016-2019 ), funded and performed in the framework of the  Labex OT-MED  ( ANR-11-LABEX-0061 , part of the " Investissement d'Avenir" program  through the  A* MIDEX  project  ANR-11-IDEX-0001-02 ), which is funded by the  French National Research Agency (ANR). The project leading to this publication has received funding from the  European FEDER Fund  under project  1166-39417. Review statement. This paper was edited by  Sandra Arndt  and reviewed by  Guy Munhoven  and  Sebastiaan van de Velde.
Code availability. Eco3M is freely available under the CeCILL license agreement (a French equivalent to the L-GPL license; http://cecill. info/licences/Licence_CeCILL_V1. 1-US. html (last access: 10 February 2020). The Eco3M-CarbOx model is written in Fortran-90/95 and the plotting code is written in MATLAB. The exact version of the model used to produce the results presented in this paper is archived on Zenodo (Lajaunie-Salla et al. , 2020; https://doi. org/10. 5281/zenodo. 3757677)). A short user manual is given in Appendix C of this study.
8 Until the recent approval of cannabidiol and stiripentol by the US Food and Drug Administration, stiripentol in combination with valproate and clobazam remained the only approved treatment for DS outside of the United States. Despite treatment with stiripentol-inclusive antiepileptic drug (AED) regimens, 38% of patients were reported to present with weekly seizures (>3 per month). 8, 9 Given the risks of sudden unexpected death in epilepsy, cognitive deficits, and other neurological consequences putatively caused at least in part by poorly controlled seizures, there remains an urgent unmet need for treating DS. [9] [10] [11]  Results from a recent phase 3, placebo-controlled randomized clinical study (Study 1 [NCT02682927; NCT02826863])  12  demonstrated that fenfluramine provided a significantly greater reduction in monthly convulsive seizure frequency (MCSF) vs placebo.
Trial Design, Ethics, and Oversight In this double-blind, parallel-group, placebo-controlled, phase 3 randomized clinical study, patients with DS with seizures that were poorly controlled with current AED regimens, which had to include stiripentol plus clobazam or valproic acid, were enrolled at approximately 28 sites in Canada, France, Germany, the Netherlands, Spain, the United Kingdom, and the United States (NCT02926898; eFigure 1 in Supplement 1; trial protocol in Supplement 2). The study complied with current International Council for Harmonisation Good Clinical Practice guidelines, as described in International Council for Harmonisation Topic E6 Guidelines. 14 The protocol was approved by applicable regulatory authorities and an independent ethics committee or institutional review board at each participating institution.
Trial Procedures This study evaluated the safety and efficacy of twice-daily fenfluramine (administered as a fenfluramine hydrochloride oral solution containing 2. 2 mg/mL of fenfluramine) added to a stiripentol-inclusive AED regimen (plus valproate or clobazam, at a minimum) in children and young adults with DS (eFigure 1 in Supplement 1). In a drug-interaction study (Cohort 1 [NCT02926898]), fenfluramine was administered with clobazam and valproate and with and without stiripentol. Data from this study and a phase 1 study in healthy adults  15  were used in an early version of a physiologically based pharmacokinetic model to help select fenfluramine dose for this study (Study 1504, Cohort 2 [NCT02926898]; 0. 4 mg/kg/d; maximum dosage, 17 mg/d).
Background and Objective In context of the End TB goal of zero tuberculosis (TB)-affected households encountering catastrophic costs due to TB by 2020, the estimation of national prevalence of catastrophic costs due to TB is a priority to inform programme design. We explore approaches to estimate the national prevalence of catastrophic costs due to TB from existing datasets as an alternative to nationally representative surveys. Methods We obtained, standardized and merged three patient-level datasets from existing studies on patient-incurred costs due to TB in South Africa. A deterministic cohort model was developed with the aim of estimating the national prevalence of catastrophic costs, using national data on the prevalence of TB and likelihood of loss to follow-up by income quintile and HIV status.
Parameterizing the Cohort Model: Population Characteristics We created an individual-level deterministic cohort model that simulated progression through the TB care cascade in order to estimate the prevalence of catastrophic costs in South Africa (Fig. 1 ). The model contained a hypothetical cohort of 1000 South Africans with drug-susceptible (DS) TB, with population characteristics mirroring those of the national population of people with DS-TB. Individuals in the cohort were first distributed across national income quintiles 1-4 using data on the national income distribution and distribution of TB across income quintiles  [13] [14] [15]. We then sampled employment status by income quintile, and household size reflecting national distributions of each  [16]. Individual income was estimated by dividing household income by household size; individual income took a value of zero if unemployed or otherwise not income-earning.