dataesr/scientific-paragraphs-categorization huggingface dataset:
To identify cellular processes putatively targeted by these 2 miRNAs, target genes were predicted using TargetScan and Miranda (Supplemental Fig. S6 ). A total of 11 and 17 common genes were identified for miR-4785 and miR-6352, respectively. The expression profiles of some putative targets were also examined in medaka using the PhyloFish tissue repertoire database (http://phylofish. sigenae. org)  21 , with a special interest for genes expressed in ovary (Supplemental Fig. S6 ).
S6 ). Among the predicted targets of miR-4785 was fshr, a gene playing a major role in the ovary through its role in mediating Fsh (Follicle stimulating hormone) action on oogenesis in vertebrates. In zebrafish, disruption of fshr leads to a complete failure of follicle recruitment into vitellogenesis with all ovarian follicles arrested at the primary growth-previtellogenic transition  54. Similarly, we analyzed the putative target genes of miR-6352 and we predicted the smg8, ddx20 and ddx6 genes. These later are all expressed in the ovary, according to the PhyloFish database. Smg8 encodes for a RNA binding protein that controls the maternal mRNA degradation and zygotic genome activation in the early embryo  55, 56. This information is consistent with a possible role of miR-6352 during the early embryonic development.
RNA extraction. Tissues were homogenized in Tri reagent (Sigma-Aldrich, USA) and total RNA was extracted according to manufacturer's instructions. RNA quality was checked using an Agilent 2100 Bioanalyzer system. The presence of miRNAs in the samples was assessed using a small RNA chip (Agilent Technologies, USA) and RNA quantity was measured on a Nanodrop 1000 Spectrophotometer (Thermo Scientific, USA). Microarray design. We used a microarray platform displaying 3800 distinct miRNAs from different vertebrate species (fish, birds and mammals). The design was performed as previously described using e-array custom platform (Agilent) with miRBase version 19. 0 and subsequently deposited in Gene Expression Omnibus (GEO) database under the reference GPL21776  20. For each target miRNA in miRBase, 16 probes were synthesized that were divided into two to four groups and have limited variations in sequence and size (one or two nucleotide differences at the 3′ and 5′ extremities).
This led to robust miRNA expression analysis. All the available vertebrate miRNAs in miRBase version 19. 0 (http://www. mirbase. org) were used (Oryzias latipes, Danio rerio, Fugu rubripes, Tetraodon nigroviridis, Cyprinus corpio, Hippoglossus hipoglossus and Paralichthys olivaceus). The miR-NAs from rodents (Mus musculus and Rattus norvegicus), frogs (Xenopus laevis and Xenopus tropicalis), humans (Homo sapiens), and chickens (Gallus gallus) were subsequently added (Supplemental Fig. S5 ). Microarray hybridization, data processing and miRNA annotation. Samples were processed and hybridized on the miRNA 8 × 60 K microarray as previously described, using the miRNA Microarray System with miRNA Complete Labeling and Hyb Kit (Agilent v2. 2) according to the manufacturer's instructions with minor modifications  20.
Signal intensity was quantified using the Feature Extraction software (Agilent v10. 7. 3. 1). Features with low signal (lower than three times the background level) were excluded. The data were processed using the GeneSpring software (Agilent v. 13. 0) using GmedianSignal values and subsequently normalized using quantile normalization. MiRNAs for which at least 8 of the 16 probes displayed a significant overexpression in the ovary, with at least 2-fold overabundance in the ovary in comparison to other tissues, were selected for further analysis as previously described  20. Corresponding data were deposited in Gene Expression Omnibus (GEO) database under the reference GSE80823. Genome mapping, RNA folding and homologous primers design. To identify the corresponding medaka miRNAs, the overabundant miRNA sequences were aligned (ENSEMBL BLAST using short sequences setting) on the medaka genome sequence available in ENSEMBL (release 83).
For each miRNA, the 20 first candidates predicted by TargetScan were selected. Among them, genes predicted to be targeted by both algorithms (Targetscan and miRanda) were considered as putative targets. Reverse transcription. Total RNA (100 ng) was reverse transcribed in a final volume of 10 μ l. The reaction mixture included 1 μ l of 10x poly(A) polymerase buffer, 0. 1 mM of ATP, 1 μ M of RT-primer, 0. 1 mM of each deoxynucleotide (dATP, dCTP, dGTP and dTTP), 100 units of SuperScript III reverse transcriptase (Invitrogen, USA) and 1 unit of poly(A) polymerase (New England Biolabs, USA). The reaction was then incubated at 42 °C for 1 hour, followed by enzyme inactivation at 95 °C for 5 minutes. The sequence of the reverse transcription primer was 5′ -AAGCAGTGGTATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTVN-3′ (where V is A, C or G and N is A, C, G or T).
Individual Data We gathered individual data on 286 HIV-HCV coinfected individuals from 3 different European cohorts of patients diagnosed with AHC between 2002 and 2012. Cohort A comprised 211 patients enrolled into the observational European AHC cohort of the European AIDS Treatment Network. Patients were followed prospectively beginning in 2002. Retrospective enrollment of cases was possible provided that data on their follow-up were available as requested. Cohort B comprised 38 patients diagnosed in 3 centers in Paris, France, between 2003 and 2007, and followed up for 15 months. This cohort was described in detail by Schnuriger and colleagues  [10]. Cohort C was composed of patients followed in the HIV clinic of Saint-Antoine Hospital, Paris, who presented with AHC during the course of HIV management between 2010 and 2012.
Dating the Diagnosis of Hepatitis C Virus Infection In the model, the diagnosis of HCV was assumed to be based on the detection of an elevated ALT level during a routine follow-up visit of an HIV-positive individual. Our individual data allowed estimation of the duration between ALT elevationdefined as ALT level higher than the upper normal limit (40 IU/L)-and peak of ALT in 20 patients. Therefore, the date of ALT elevation (t ALT ) was retrospectively calculated by subtracting this duration to the date of ALT peak that coincides with seroconversion (see previous paragraph). In the model, 2 scenarios were explored regarding the frequency of HIV follow-up routine visits: 3 months (Scenario A) and 6 months (Scenario B). Accordingly, the date of diagnosis was randomly generated from a uniform distribution on the interval [t ALT , t ALT + 3 months] under Scenario A and [t ALT , t ALT + 6 months] under Scenario B.
Exploitation of the Data The characteristics of the cohorts A, B, and C are presented in Table  1. The mean maximal level of HCV-VL assessed in 48 patients of the cohorts A and B was estimated at 6. 37 log IU/mL (standard deviation [SD] = 0. 75). The duration between elevation and peak of ALT was available in 20 patients and its mean was estimated at 56 days (SD = 32 days). Our data allowed for estimation of the gradient of log(HCV-VL) in 13 patients. This quantity was found to range between 0. 05 and 0. 21 log(UI/mL)/day with a mean of 0. 11 log(UI/mL)/day (interquartile range [IQR], 0. 07-0. 18). The outputs of the Metropolis-Hastings simulation as well as the parameters used to generate the profiles of HCV natural history are presented in Supplementary Figures  S2  and S3.
In particular, we noted that SC occurred approximately 50 days earlier when considering the scenario of early seroconversion compared with the scenario of late seroconversion. The early seroconversion scenario could reflect the configuration of HCV monoinfected individuals in whom antibodies appear earlier  [28]. Thus, this highlights the fact that our results related to the possibility of very late SC are specific to HIV-HCV coinfection and that they may not directly lead to recommendations concerning the management of HCV monoinfection. One limitation of our study is that despite the large number of cases (286) in our combined dataset, not all data were used to calibrate the model. Indeed, the required information was not always available due to missing data and because patient details were not reported in the exact same way across the 3 cohorts.
Numerical Modeling The aim of this section is to introduce the numerical models used for the calculation of the temperature distribution inside the material when a laser is moved along its surface while depositing the molten metal. For this purpose, COMSOL multi-physics software is used to run an FEA on a 2D geometry (Figure  1 ). In this approach, the fluid flow is not studied, even though the phase change and its consequences on the material characteristics are implemented, the liquid is supposed to remain still in the melt pool. The approach is also limited to the workpiece, the interaction between the surrendering gas and the material is neglected. The simulation aims at understanding the temperature after solidification. After this bright moment, convection in the melt pool may be neglected.
Analytical Model The analytical solution is developed and implemented in Matlab software. The geometry simulated is in two dimensions and is supposed to be semi-infinite. The model solely considers the heat transfer inside the material, without any phase change and without interaction with the surrounding gas. Both convection and conduction inside the material are considered in this section. Equation (  7 ) is the general convection-diffusion equation for a heat source moving at the constant velocity V in a laminar heat flow  [16]. It is valid for an isotropic homogeneous material. h e is the enthalpy: ∂(ρu) ∂t + ∂(ρh e V) ∂x = ∇ • (λ∇T). (7) The steady state approximation leads to the elimination of the first term of Equation (  7 ). Furthermore, it can be simplified given that du = dh e = c p dT: ρ c p V ∂T ∂x = ∇ • (λ(T)∇T).
From this starting point, the equation may be solved for an arbitrarily shaped source by integrating solution (9) over the laser spot domain ([x s , x e ], [y s , y e ]) as follows  [28] : T(x, y) = T 0 + 1 2πλ • y e y s x e x s q(u) e -V √ (x-u) 2 +y 2 -(x-u) 2D (x -u) 2 + y 2 du dv. (10) In this study, the penetration of the laser inside the workpiece is neglected (y s = y e = 0). In order to solve Equation  (10) , the material properties (ρ, c p , λ,) are assumed constant with respect to the temperature. The material is isotropic and homogeneous. As a consequence of modeling a semi infinite body, the bottom and side boundaries are not added to the model. Furthermore, the equation is solved without heat heat loss on the surface by radiation or convection. Equation  (10)  is solved in Matlab under the previous assumptions with a heat flux of Gaussian shape as proposed in  [29, 30] : q(x) = APα a πr 2 e -B( x r ) 2 , ( 11 ) where A and α a are the laser absorption coefficient and the workpiece absorptance (A • α a = 0.
Material and Experiment The proposed models in this work are implemented and compared to experimental data performed on the DMD process using Ti6Al4V. The DMD machine used is an OPTOMEC LENS 850 R (Optomec Inc. , Albuquerque, NM, USA). The dimensions of the samples are 2 mm in width, 70 mm in depth and 80 mm in length. The chemical composition of the alloys powder can be found in Table  2. The temperature on the workpiece surface is measured using a thermocouple of type K (chromel/alumel), in contrast to the thermal camera, thermocouples are not sensitive to the variation of the emissivity of the material induced by the phase change. In order to control the experimental setup, the authors used a thermal-camera and a high speed camera to provide comparison bases for the temperature and the melt-pool size.
Acknowledgments: The authors would like to acknowledge the support from the research centre 'SFI Manufacturing' and the  Material Knowledge for Robust Additive Manufacturing  ' MKRAM ' project (Grant No. 248243 ), which is sponsored by the  Research Council of Norway  and industrial partners. The authors from BTU would like to acknowledge funding from the  EUROPEAN UNION  under the  European Regional Development Fund  and the  Federal State of Brandenburg. The authors are grateful for this financial support.
8 mCi) and various dye concentrations. After culturing for 42 h at 37 C under the same atmosphere conditions, parasite growth was determined based on the amount of [ 3 H]-hypoxanthine incorporated using a multidetector scintillation counter (PerkinElmer 1450 Microbeta). Six concentrations (100 mMe1 nM) were tested to calculate IC 50 of active compounds using the ICEstimator 1. 2 online software (www. antimalarial-icestimator. net)  (Le Nagard et al. , 2010; Kaddouri et al. , 2006). Each concentration was tested in triplicate for each compound. We define the biological activities as follows: very good activity, IC 50 < 0. 2 mM; good activity, 0. 2 < IC 50 < 1 mM; moderate activity, 1 < IC 50 < 10 mM; weak activity, 10 < IC 50 < 20 mM; inactive, IC 50 > 20 mM.
Acknowledgments This work was supported by  European commission  ( REGPOT-CT-2011-285837-STRonGer ), by  Institut Pasteur  ( ACIP A10-2013 ) and by  Investissement d'Avenir of the ANR  ( CEBA :  ANR-10-LABX-25-01 ). We thank  MR4  for providing us Plasmodium falciparum 7G8 (MRA-926 contributed by  Thomas Wellems)  and  3D7 (MRA-102  contributed by  Daniel J. Carucci ) malaria parasites.
Materials and Methods An anatomically realistic computer model of a male human liver, with its blood vessels (down to 0. 4 mm in diameter), bones, and surrounding tissues, was employed in this study. The 3-dimensional (3-D) anatomical geometry was developed by the Korea Institute of Science and Technology Information. 18 The geometry was decimated and prepared for simulation using the Graphical Interface for Medical Image Analysis and Simulation software platform developed by the Center for Computational Imaging and Simulation Technology in Biomedicine at the Universitat Pompeu Fabra. 19 The main geometrical features of the modeled liver and its vasculature are indicated in Table  1. Electroporation across pairs of needle probes was simulated. The 2 probes of the pair were parallel and aligned so that they would form the opposite sides of an imaginary rectangle.
The entire geometry was meshed using the COMSOL Multiphysics regular refinement method user-controlled tetrahedral mesh with a minimum element size of 0. 1 mm. The total number of elements was above 2 200 000. The electric field distribution was computed using the "electric currents (ec)" application mode of the AC/DC module of COMSOL. As in previous studies,  15, [21] [22] [23] [24]  nonlinear behavior was assumed for the hepatic tissue in order to model the increase in tissue electrical conductivity during pulse delivery due to electroporation. This behavior was modeled with the following sigmoid equation describing the dependency of tissue conductivity (s) of electric field magnitude (j Ẽj): s ¼ s 0 þ ðs f À s 0 Þ 1 1 þ e À j Ẽ jÀT W " # ; ð1Þ where s 0 is the conductivity of the tissue when no field is applied (ie, initial conductivity), s f is the maximum conductivity the tissue can reach due to electroporation (ie, final conductivity), and T and W are 2 parameters that describe the morphology of the sigmoid (coarsely, inflection magnitude and slope, respectively).
The set of values used for the hepatic tissue model was: s 0 ¼ 0. 2 S/m, s f ¼ 0. 5 S/m, T ¼ 950 V/cm, and W ¼ 200 V/cm. These values would correspond to hepatic tumors and have been inferred from experimental data by Prakash et al. 25  The values for healthy (normal) liver tissue would be s 0 ¼ 0. 05 S/m, s f ¼ 0. 3 S/m, T ¼ 950 V/cm, and W ¼ 200 V/cm, as also inferred from data by Prakash et al. 25  This set of values was also assayed in this study and the results obtained are reported as supplemental material. We deemed it more realistic to model tumor properties rather than healthy liver properties because IRE treatments mostly occur on tumor tissues (ie, the electrodes are inserted into the tumors). The electrical conductivity of the other tissues was modeled as field independent. The conductivities of the vessels (modeled as blood containers), bones, and soft tissues filling the anatomy (modeled as a combination of muscle, fat, and body fluids) were 0.
For one of five districts in the Nord region, no data on respiratory infections could be retrieved for any year (Yako). Overall, 13 districts provided thus data (Fig. 1 ). Due to logistical constraints, 2004-2005 data were collected for three districts (Houndé, Orodara, Séguenéga) only, and 2013-2014 data for five districts (Boulsa, Dédougou, Houndé, Orodara, Séguenega), only. These districts are situated across the four regions. Ninety-five health centres in the Nord region were excluded as population size was unknown. Another 13 district years were excluded due to missing data on respiratory infections. The data used in this project had previously been collected by health authorities for the purpose of routine surveillance and disease control. The data consists of aggregated case counts without any individual-level information.
As standard clinical procedures were not changed by routine surveillance and individual data were not recorded, no informed consent had been collected from patients. The present project of data analysis received authorisation from the Department of Disease Control (Direction de la lutte contre la maladie, DLM) at the Ministry of Health, Burkina Faso. To validate the resulting database, we aggregated the observations into district weeks and compared them to the number of weekly cases per district routinely reported by the Ministry of Health to the World Health Organization. Based on over 4,000 district weeks, 62% of the health centre weeks showed a mismatch of ≤2 cases and 93% of ≤5 cases (median error: 0).
Results Among the 512 health centres included, which corresponded to 67,584 health centre months theoretically targeted, meningitis or population size data were not be retrieved for 33,477 health centre months (Fig. 1 ). Reasons included partial collection for most ancient and most recent years, missing population size (in particular for private clinics or regional reference hospitals) and missing meningitis data (in particular for health centre months prior to the creation of a new health centre). Meningitis incidence data were thus available from 427 health centres (34,107 health centre months), during which 15,420 suspected meningitis cases were reported. The typical incidence peak at the end of March was found in all districts, but declined over the observed time period (Fig. 2 , and supplementary material S1 and S2a).
Acknowledgements We acknowledge the work of the Burkina Faso health centre nurses who reported cases in parallel to clinical care over one decade. We thank regional and district level health officers and statisticians for their collaboration in the data compilation. Compilation of the health centre level data was supported by the  World Health Organization. This work was conducted with support from the  TELEDM  project located at  Centre de Recherches de Climatologie (CRC) / BIOGEOSCIENCES  ( University of Burgundy ) and funded by  Centre national d' études spatiales (CNES). It was conducted in the frame of a Masters internship of the  ENS, École normale Supérieure, Paris (MW).
This paper deals with the design of a continuousdiscrete time high gain observer (CDHGO) for sensorless control of an induction machine (IM). Only two weakly sampled stator current measurements are used to achieve a real-time estimation of the rotor flux, the mechanical speed and the load torque. The feasibility of implementing our algorithm on the FPGA target is discussed in term of the best word format choice for internal variables and in term of making up for problems attached with complex bloc diagram VHDL conversion. Before an eventual implementation on the Virtex FPGA board, a validation of the proposed observer is performed through the ModelSim software where we show that the waveforms of estimates bring closer the true ones.
Such the feature can considerably reduce the execution time of the algorithm computing. Among the solutions for fast prototyping high performance control algorithms, Simulink HDL (Hardware Description Language) Coder is a suitable software to generate HDL codes (VHDL or Verilog) from fixed point Simulink models  [10]. By exploiting its model blocks, users have to build the Simulink model with provided library to generate HDL codes which facilitate the implementation of complex control systems that require dedicated multipliers and computing units. It is well-known that the digital implementation of nonlinear observers when using continuous-time measurements requires a fast sampling processes and large memory which may increase the cost and reduce the reliability. In the contrast, using a relatively slow sampling process involves a degradation in the stability and the convergence properties of the state estimation error.
V. IMPLEMENTATION METHODOLOGY AND VALIDATION The evaluation of the proposed observer in such AC drive application is achieved using an experimental setup shown in Fig. 3. It consists of an induction machine supplied by a voltage source inverter (VSI) and it is associated with a load and an incremental encoder. The system works without a speed sensor, while only the inverter input voltage and output currents are measured. The VHDL code is generated with HDL coder from Matlab-Simulink. The specification of the word format for the observer design is directly tuning with the fixed point toolbox. It is worth noticing that some inversion module of the observer algorithm are replaced by lookup table method which can alleviate the computational time  [16]. The switching signals of the inverter are provided by the FPGA Virtex board.
In the second step, we switched to the discrete solver. So, a particular attention has been taking in the choice of each couple (W, F ) 2 for all internal variables. Then, a VHDL file is generated using the HDL Coder Toolbox. Finally, in order to validate our observer design we have exploited the ModelSim software. At this stage, we have taking int account the size of the allowed resources that is indicated by the ISE software and this though and optimisation of the couple (W, F ). The characteristics of the involved IM are given as follows: P = 1. 5kw, p = 2, L s =. 13H, M =. 083H, L r =. 069H, R s = 3. 9Ω, R r = 3. 9Ω, J = 0.
VI. CONCLUSION The FPGA implementation of a CDHGO with less knowledge as possible of output measurements is studied in this paper. Precisely, for an induction motor application, we have focus at the estimation of the rotor flux, the mechanical speed and the load torque by using only the two weakly sampled stator currents. After a comparison with the continuous version of the HGO algorithm when the sample frequency is imperfect, the sampled output observer design shows the best reconstruction 2 W:Word length, F: Fractional part of the original output signal as well as more realistic estimation of the missing states. Driving profit from the advanced Matlab Simulink tools such as HDL Coder and Fixed point toolbox, the implementation of the nonlinear observer is achieved and validation results are obtained through ModelSim soft-ware.
The data used in this study comprise of all Fermi -LAT event and spacecraft data taken during the first 8 years of the Fermi -LAT science mission, from 2008 August 4 to 2016 August 8, equating to a Mission Elapsed Time (MET) period of 239557417 [s] to 492328870 [s]. All 0. 1 < E γ < 300 GeV source events, across all point spread function (psf) classes, within a 15 • radius of interest (RoI) centred on the Cen A core position were considered. In accordance with criteria for pass8 data analysis, a zenith cut of 90 • was applied, and good time intervals selected by removing data that did not satisfy the criteria 'DATA QUAL> 0 && LAT CONFIG== 1'. A summary of the criteria used in the analysis is given in Table  1. The model employed during our likelihood analyses consisted of point-like γ-ray sources, spatially extended γ-ray sources and diffuse γ-ray emission.