dataesr/scientific-paragraphs-categorization huggingface dataset:
Model parameters We discuss here the values of the model parameters used in the plots and how they were fixed. The cyan lines in Fig. 2C   Figure  3 (B  and D ) shows the result of numerical simulations, where the parameters were constrained from the data. The plots in Fig. 2  fix the values of p H and 〈t CþD′ 〉 for the three datasets in Fig. 3B , while Fig. 3D  corresponds to a wide range of growth conditions and required more general choices. Specifically, the numerical simulations use the following input parameters, directly fixed from the data: (i and ii) The average growth rate 〈a〉 and its variance s 2 a. In Fig. 3D , the average growth rates range from 0. 002 to 0. 04 min -1 to match the experimental values. The variance of the growth rate was chosen by keeping a constant coefficient of variation (CV) of 0.
(7)12)ent theoretical predictions and have a negative slope corresponding to Eq. 8 with p H = 0. 48, 0. 3, and 0. 75 for an adder between initiations (see below), respectively, for the Wallden et al. slow growth, Adiciptaningrum et al. , and Wallden et al. intermediate growth datasets(6,12). These values were fixed from Fig. 2Aand Eq. 8. The same values of p H are used in all other plots. In Fig. 2D, the orange lines are the best fit for the case where replication is limiting [corresponding to the model of(7)]. The average duration of the C + D period is set from the empirical averages to 160, 102, and 75 min for theWallden etal. slow growth, Adiciptaningrum et al. , and Wallden et al. intermediate growth datasets, respectively. Cyan curves follow Eq. 9 with p H fixed as above, and 〈t CþD′ 〉 was fixed from Eq.
Acknowledgments: We are very grateful to  N. Kleckner ,  C. Cadart ,  S. van Teeffelen ,  A. Amir ,  B. Sclavi ,  S. Tans , and  I. Iuliani  for their feedback. Funding: This work was supported by the  International Human Frontier Science Program Organization  (grant  HFSP RGY0070/2014 ). M. O. was supported by the " Departments of Excellence 2018-2022 " grant awarded by the  Italian Ministry of Education, University and Research (MIUR)  ( L. 232/2016 ). J. G. was supported by an  Omidyar Postdoctoral Fellowship  at the  Santa Fe Institute. G. M. was supported by grant nr. 31003A_169978  from the  Swiss National Science Foundation  to  Martin Ackermann.
Acknowledgments All members of the National Reference Center:  Marc Muraine  ( Department of Ophthalmology, Charles Nicolle Hospital, Rouen ),  Pascal Joly  and  Florence Tetart , ( Department of Dermatology, Charles Nicolle Hospital, Rouen ),  Olivier Dereure  ( Department of Dermatology, Saint-Eloi Hospital, Montpellier ),  Julie Waton  ( Department of Dermatology and Immunology, Brabois Hospital, Nancy ),  Brigitte Milpied  ( Department of Dermatology, Saint-André Hospital, Bordeaux ),  Anne-Marie Roguedas-Contios  ( Department of Dermatology, Morvan Hospital, Brest ),  Nadège Cordel  ( Department of Dermatology and Internal Medicine, Pointe à Pitre University Hospital, Guadeloupe ),  Claire Bernier  ( Department of Dermatology, Hotel Dieu Hospital, Nantes ),  Maria-Polina Konstantinou  ( Department of Dermatology, Larrey Hospital, Toulouse ), all in France.
The vector of spline coefficients is then used as a multivariate autoregressive process, as in  [15]. Thus, our approach is completely endogenous but with the ability of incorporating exogenous information (available at the time of the forecast) as covariates. This paper will be structured as follows. In Section 2, we describe the model we propose for forecasting electricity demand. We present the functional data, functional data representation in splines basis, the state space model that we propose and model estimation methods. Section 3 is proposed to show a model inference on a simulated dataset. We will talk about Kalman filtering and smoothing, functional principal component analysis. Section 4 will describe the experiments we make on real data with simple application of our procedure at the aggregation level of a single supplier.
Actual Prediction Procedure For each model variant, we build and fit the FSSM with the first 26 segments on the simulated signal. That is, each segment is projected on the B-spline basis, and these projections are used in an FPCA. We let the number p of principal components as a tune parameter of the whole procedure. Parameters are estimated and the states are filtered and smoothed as described in Section 2. The last state, together with the last segment coefficients are then used to predict the coefficients of the next segment of the signal, which is naturally not used in the estimation of the model. Using the reconstruction expression, the actual predicted segment is obtained that closes a prediction cycle. In order to provide more robust prediction measures, several prediction cycles are used where a sequential increment of the train dataset is done.
Description of the Dataset Let us use some graphical summaries to comment on the features of these data. Naturally, we adopt the perspective of time series analysis to describe the demand series. Figure  4    First, we observe a clear, growing long-term trend that is connected to a higher variability of the signal at the end of the period. Second, an annual cycle is present with lower demand levels during the summer and higher during winter. Both the industrial production calendar and the weather impacts the demand, which explains this cyclical pattern. Figure  5  shows the profile of daily electricity consumption for a period of three weeks (from 31 October 2016 to 20 November 2016). This unveils new cycles presented in data that can be seen as two new patterns: the weekly one and the daily one.
Forecasting Results Forecasting is done in a rolling basis over one year in the data. Load data from 1 January 2012 to 30 October 2016 is used as a training dataset and, as a testing dataset, we choose a period from 31 October 2016 to 30 October 2017. Predictions are obtained at horizons one-segment ahead. This means that, actually, we are making predictions for the next 48 time steps (1 day) if we adopt a traditional time series point of view. Once the prediction is obtained, we compare it with the actual data and incorporate the observation into the training dataset. Thanks to the recursion in the SSM, only an update step is necessary here. To give a comparison point to our methodology, we propose using a simple but powerful benchmark based on persistence forecasting.
Salient Features on the Data Data is retrieved from RTE API (http://www. rte-france. com/fr/eco2mix/eco2mix-telechargement) where past effective demands but also forecast load demand obtained by RTE are available. The daily load demand is recorded with 96 points per day, which is equivalent to intervals of fifteen  (15)  minutes. We use a series of plots to highlight relevant features on the data that should be considered in the construction of a prediction model. The first view of the data is in Figure  8 , which shows the time plot of the electrical load from 2012 until the first two months of 2018 (vertical lines separates years). When compared to Figure  5 , the most important fact is the absence of a growing trend. Indeed, the gross yearly energy demand in France is quite stable during the period of study, which explains the constant trend.
Acknowledgements The authors thank  Lumiphore, Inc. for the gift of Lumi4 reagents. This work was partially funded by the  European Commission  ( H2020-FET-Open  project  PROSEQO ) and the  Agence National de la Recherche (ANR) France  ( ANR projects NEUTRINOS, NANOHYPE , and AMPLIFY). Y. T. W. acknowledges the  Taiwanese Ministry of Education and Université Paris-Sud  for her PhD fellowship. I. M. acknowledges  ONR ,  NRL , the  NRL-Nanosciences Institute , and a LUCI grant in support of the  VBFF program  through the  OSD. N. H. acknowledges the  Institut Universitaire de France (IUF)  for financial support.
coding sequences (CDSs), 81 tRNAs, and 30 rRNAs. The sequence was submitted to antiSMASH analysis of secondary metabolite biosynthesis gene clusters  (8). Among the 42 clusters identified, 18 were only putative, whereas 25 were assigned to functions. Eight were involved in NRP biosynthesis, whereas four were involved in hybrid polyketides/NRP biosynthesis. The other clusters were involved in the biosynthesis of 6 saccharides, 4 fatty acids, bacteriocin, lasso peptide, and resorcinol. Among the NRPS clusters, the gene cluster responsible for biosynthesis of polymyxin E was recovered and shown to be fully identical to the one we had previously described  (3). The other NRPS clusters shared low gene similarity percentages with those found in the anti-SMASH database, which could suggest their novelty.
I. INTRODUCTION An accurate description of the electromagnetic cascade is an essential prerequisite for the simulation of the response of a wide range of particle detectors, including in-core and excore instrumentation in nuclear reactors. For such applications, one possible simulation tool is provided by the TRIPOLI-4 R code  [1] , a Monte-Carlo particle-transport code developed at CEA-Saclay (France) that is employed in the domains of nuclear-reactor physics, criticality-safety, shielding/radiation protection and nuclear instrumentation. The latest TRIPOLI-4 version (v10) was released in December 2015 and will soon be available to OECD member countries through the NEA Data Bank. This extended abstract reports on some recent developments in TRIPOLI-4 for the simulation of nuclear instrumentation (Section II).
BACKGROUND Registry data suggest that disease progression in pulmonary arterial hypertension (PAH) is indicative of poor prognosis. However, the prognostic relevance of PAH-related morbidity has not been formally evaluated in randomized controlled trials. OBJECTIVES The purpose of these analyses was to assess the impact of morbidity events on the risk of subsequent mortality using the landmark method and data from the SERAPHIN and GRIPHON studies. METHODS For each study, the risk of all-cause death up to the end of the study was assessed from the landmark time point (months 3, 6, and 12) according to whether a patient had experienced a primary endpoint morbidity event before the landmark. Each analysis was conducted using data from all patients who were available for survival follow-up at the landmark.
Patients who had a nonfatal primary endpoint event or who received double-blind treatment until the end of the study were eligible to receive the respective open-label investigational therapy or other commercially available PAH therapy. STATISTICAL ANALYSES. Landmark analysis is the established method for evaluating the effect of morbidity at a particular landmark time point on survival up to the end of the study  (13, 14). For both the SERAPHIN and GRIPHON trials, landmark survival analyses were performed up to the end of the study, with landmark time points set at 3, 6, and 12 months. Each analysis was conducted using data from patients who were available for survival follow-up at the landmark time point. Patients were not available for the analysis if they had died, had withdrawn consent, or had been lost to follow-up before the landmark.
Our analyses indicate that morbidity, as captured in the primary endpoint events of the SERAPHIN and GRIPHON studies, is prognostic for mortality. These findings are strengthened by consistent results in 2 independent studies and at 3 different landmark time points within each of the studies. The analyses of the GRIPHON data also highlight the relevance of hospitalization for worsening of PAH as a risk factor for subsequent mortality. Landmark analyses assess the association of an event before a landmark time point with events that occur subsequent to that time point, in this instance the association between morbidity and subsequent mortality. The nature of the analyses involves a compromise between the occurrence of the former and the latter events at each landmark time point.
The SERAPHIN trial (NCT00660179) enrolled 742 patients ($12 years old) with a diagnosis of idiopathic or heritable PAH, or PAH associated with connective tissue disease, repaired congenital systemic-topulmonary shunts, human immunodeficiency virus infection, drug use, or toxin exposure. Patients were randomized 1:1:1 to receive placebo, macitentan 3 mg, or macitentan 10 mg. Patients were permitted to take concomitant medications including an oral phosphodiesterase type-5 inhibitor (PDE-5i), or an oral or inhaled prostanoid, provided that the dosage was stable for $3 months before randomization. Patients who had received treatment with an ERA within 3 months before randomization were not eligible. The diagnosis of PAH had to be confirmed by right heart catheterization with a pulmonary vascular resistance $320 dyn $ s $ cm À5 , and patients were required to have a 6-min walk distance (6MWD) $50 m and to be in World Health Organization functional class (WHO FC) II-IV at baseline (11).
The GRIPHON trial (NCT01106014) enrolled 1,156 patients (18 to 75 years of age) with similar etiologies as were enrolled in the SERAPHIN trial. Patients were Ltd, Bayer, Bristol-Myers Squibb, Pfizer, and GlaxoSmithKline. Dr. Torbicki has served as a steering committee member for and received research grants and advisory board fees from Actelion Pharmaceuticals Ltd; has received consultancy fees from Actelion Pharmaceuticals Ltd, Arena Pharmaceuticals, Bayer, and Merck Sharp & Dohme; has received speaker fees from Actelion Pharmaceuticals Ltd, AOP, Bayer, and Merck Sharp & Dohme; and has received investigator honoraria from Actelion Pharmaceuticals Ltd, Bayer, Janssen, and United Therapeutics. Dr. Tapson has served as a steering committee member for Actelion Pharmaceuticals Ltd, Bayer, and United Therapeutics; has received consultancy fees from Actelion Pharmaceuticals Ltd, Arena Pharmaceuticals, Bayer, Daiichi-Sankyo, EKOS/BTG, Gilead Sciences, Janssen, Reata, and United Therapeutics; has received research grants from Arena Pharmaceuticals, Arena, Bayer, EKOS/BTG, and Riata; has received speaker fees from Bayer, Gilead Sciences, and Janssen.
CONCLUSIONS Morbidity events, as defined in the SERAPHIN and GRIPHON studies, were prognostic for mortality in patients with PAH. These results highlight the risk associated with disease progression in PAH and emphasize that treatment regimens and management strategies that prevent deterioration are essential. In addition, our findings support the robustness and the clinical relevance of the SERAPHIN and GRIPHON primary endpoints. ACKNOWLEDGMENTS Medical writing assistance was provided by James Glasper (nspm ltd, Meggen, Switzerland) and was funded by Actelion Pharmaceuticals Ltd (Allschwil, Switzerland). ADDRESS FOR CORRESPONDENCE: Dr. Vallerie V. McLaughlin, Department of Internal Medicine, University of Michigan Health System, 1500 East Medical Center Drive, SPC 5853, Ann Arbor, Michigan.
ACKNOWLEDGEMENTS B. H. S. , G. M. and O. H. were funded by the  Research Council of Norway , through the grant  NAPEX (229778 ). Supercomputer facilities were generously provided by  NOTUR. D. S. thanks the  NASA   High-End Computing Program  through the  NASA Center for Climate Simulation at Goddard Space Flight Center  for computational resources. M. K. and A. V. are supported by the  Natural Environment Research Council  under grant number  NE/K500872/1. Simulations with HadGEM3-GA4 were performed using the MONSooN system, a collaborative facility supplied under the  Joint Weather and Climate Research Programme , which is a strategic partnership between the Met Office and the  Natural Environment Research Council. T. T. was supported by the supercomputer system of the  National Institute for Environmental Studies, Japan , the  Environment Research and Technology Development Fund  ( S-12-3 ) of the  Ministry of the Environment, Japan  and  JSPS KAKENHI  Grant Number  JP15H01728  and  JP15K12190.
We felt that the whole process should be rigorously simple, with several key steps (go/no-go decisions) from the basic setup of the assay (and its validation with reference compounds) to the automation and test on a small scale of 10,000 compounds and finally both full-deck campaigns. Figure  2  shows a series of typical examples of data obtained with this set of methods. In general, we screened about 10,000 compounds using the final conditions (set up previously) in two independent campaigns with the same compounds and different operators, if possible. The analysis of those data is what will permit moving forward to the full campaigns of 250,000 compounds, tested twice on the target. Without going too much into practical details, we found this methodology to be the most appropriate. Even if 2 or 3 out of 30 HTS campaigns turned out to be mute (no compound out of the HTS campaigns), the rest delivered several classes of compounds that could be pushed to the program downstream.
The oceanographic mooring deployed over the eastern Beaufort Sea continental slope in October 2003 recorded current velocities through depths of 28-108 m until September 2005. Data analysis revealed that these highly energetic currents have two different modes of depth-dependent behaviour. The downwelling-favourable wind associated with cyclones passing north of the Beaufort Sea continental slope toward the Canadian Archipelago generates depth-intensified shelfbreak currents with along-slope northeastward flow. A surface Ekman on-shore transport and associated increase of the sea surface heights over the shelf produce a cross-slope pressure gradient that drives an along-slope northeastward barotropic flow, in the same direction as the wind. In contrast, the upwelling-favourable wind associated with deep Aleutian Low cyclones over the Alaskan Peninsula and/or Aleutian Island Arc leads to surface-intensified currents with along-slope westward flow.
Data and methods We used velocity data from oceanographic mooring CA13 deployed over the upper Canadian Beaufort Sea continental slope at 300-m depth from 9 October 2003 to 4 September 2005 at 71°21. 356'N, 228°38. 176'E (Figure  1a ). The mooring description can be found in  Dmitrenko et al. (2016). For this study, we used only velocity records from the 300 kHz upward-looking Workhorse Sentinel acoustic Doppler current profilers (ADCPs) by Teledyne RD Instruments at 119-m depth. The velocity data was obtained at 8-m depth intervals, with a 1-h ensemble time interval and 30 pings per ensemble. The first bin was located at 10 m above the transducer. The RDI ADCP precision and resolution are ± 0. 5% and ± 0. 1 cm s -1 , respectively. The standard deviation for an ensemble average of 30 pings for the 8-m depth cell size is reported by RDI to be 1.
The model domain covers the Arctic and the Northern Hemisphere Atlantic with two open boundaries, one close to Bering Strait in the Pacific Ocean and the other one at 20°S across the Atlantic Ocean. Open boundary conditions (temperature, salinity and horizontal ocean velocities) are derived from the monthly Global Ocean Reanalysis and Simulations produced by (Smith et al. , 2014)) et al. , 2017). The simulation was integrated from 1 January 2002 to 31 December 2016, driven with atmospheric forcing data of high temporal (hourly) and spatial resolution (33 km) provided by the Canadian Meteorological Centre Global Deterministic Prediction System ReForecasts dataset(Smith et al. , 2014).
MALDI-TOF MS analysis MALDI-TOF MS produces a mass spectrum that represents the masses of each ionized peptide in a sample (more specifically, the mass/charge ratio). When applied to bacteria, mass spectra may be obtained from either colonies in pure culture on agar or in complex samples such as blood. Consequently, MALDI-TOF MS is particularly suitable for the identification of pathogenic microorganisms in the medical field. It allows the identification of a strain by matching its mass spectrum with a database of Main SPectra from numerous well-characterized bacteria. The IHU-FMI institute possesses eight MALDI-TOF mass spectrometers (MicroFlex LT/SH; Bruker Daltonics, Bremen, Germany). The procedure is simpler and faster than conventional phenotypic and molecular methods (Fig. 1 ). The cost for analysing a single sample is quite low.
45, including V0. 05 for the cost of consumables and labor. Because the identification by MALDI-TOF MS is simple, fast, reliable and cheap, the IHU-FMI institute has developed protocols and implemented databases for numerous applications (Table  1 ). In microbiology, MALDI-TOF MS is routinely used to identify bacteria and yeasts  [2] [3] [4] [5] [6] [7]. Proofs of concepts have been validated for the Archaea  [8]  as well as giant viruses  [9]. In addition, some bacterial resistances to antibiotics can be surveyed by following the disappearance of the antibiotic peaks degraded by the bacteria  [10]. Furthermore, because arthropods are vectors of several tropical diseases, we investigated whether MALDI-TOF MS could be used in entomology  [11]. We demonstrated that it is possible to identify at the species level various arthropods  [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]  based on the legs at the adult stage for ticks and mosquitoes, or other body parts for fleas and Culicoides, but also at the larval stage  [25, 26].
This approach has been further developed for the dual identification of arthropods and vector-borne pathogens such as Plasmodium  [27] , filariae  [28] , Borrelia  [29]  and Rickettsia  [30] , as well as for the identification of mosquitoes' blood meal origin  [31, 32]. This  illustrates that MALDI-TOF MS is well suited for vector-borne disease survey. Some developments have been made for cell or tissue identification  [33] [34] [35] [36] [37] , including tumours  [38]. Applications in the field of traceability are also available for fast detection of microorganism contamination in platelet concentrates  [39, 40]  and animal food origin  [41]. The performance of the identification depends on the sample (protein extraction, molar ratio between sample and matrix), which should be the most reproducible possible, and the quality of the reference spectrum (databases should be exhaustive).
Our laboratory notably made huge efforts to cultivate as many bacterial members from the human gut as possible using the culturomics strategy, which enabled the discovery of more than 197 new species  [42]. For each of these new species, a reference mass spectrum was added to the in-house bacterial database. The laboratory also helped install a MALDI-TOF MS system (Vitek MS, bioMérieux, Marcy l'Etoile, France) in the Principal Hospital in Dakar in 2012. This was the first MALDI-TOF MS implemented in Africa. This device is currently routinely used in the clinical microbiology laboratory  [7, 43, 44]. It has allowed the discovery and description of new bacterial species  [45] [46] [47] [48]  as well as the creation of a database for the identification of Culicoides biting midges  [24]. This illustrates that MALDI-TOF MS is a robust, reliable and appropriate instrument for Africa.
Non-linear multidimensional scaling (NL-MDS) methods are widely used to give an insight on structures of a dataset. Such a technic displays a "map" of data points onto a 2 dimensional space. The reader is expected to have natural understanding of proximity relationships between items. In our experience, MDS are especially helpful as a support for the collaboration between data analysts and specialists of other fields. Indeed, it often allows understanding main issues, major features, how to deal with data and so on. However, we observed that the classical/rectangular display of map causes confusion for non-specialists and long explanation is often required before reaching the fruitful step of the collaboration. The meaning -the absence of meaning, actually-of axes can be subject for many questions and skepticism from many naive persons.