dataesr/scientific-paragraphs-categorization huggingface dataset:
Modelling and Simulation of Ebola Outbreak with Vaccination In this section, we simulate the model by using parameters estimated on November 4, 2014, by Kaurov  [13] , who studied statistically the recent data of the World Health Organisation (WHO)  [18]. In his statistical study, Kaurov studied the outbreak by modeling it with Wolfram's Mathematica language. Here we use his SIR model for modelling and time-discrete equations for resolution of the set of equations through Matlab.
The Basic Model with Kaurov's Estimation of Parameters Let us recall the complete set of equations considered by  [13] :                dS(t) dt = -βS(t)I(t), dI(t) dt = βS(t)I(t) -µI(t), dR(t) dt = µI(t), (5) where, as before, β is the rate of infection and µ is the rate of recovery. The parameters obtained by Kaurov, knowing that 95% of population is susceptible and 5% of population is infected, are β = 0. 2 and µ = 0. 1  [13]. In agreement, the initial susceptible, infected, and recovered populations are given, respectively, by S(0) = 0. 95, I(0) = 0. 05, and R(0) = 0 (note that in contrast with Sections 2 and 3, the variables are now dimensionless). Our numerical simulations were done with the ODE solver of Matlab. The results are shown in Figure  4. To interpret the results, we describe the evolution of each group over time and the links between them.
Our goal is to reduce the infected individuals and the cost of vaccination. Precisely, our optimal control problem consists of minimizing the objective functional  subject to 0 ≤ u(t) ≤ 0. 9 for t ∈ [0, t f ], where the parameters A ≥ 0 and t f denote, respectively, the weight on cost and the duration of the vaccination program. We present the numerical solution to this optimal control problem, comparing the results with the numerical simulation results obtained in Section 4 via Matlab. As in Section 4, the rate of infection is given by β = 0. 2, the recovered rate is given by µ = 0. 1, and we use S(0) = 0. 95, I(0) = 0. 05, and R(0) = 0 for the initial number of susceptible, infected, and recovered populations, respectively (i. e. , at the beginning 95% of population is susceptible and 5% is already infected with Ebola).
Note that when u(t) ≡ 0 model  (7)  reduces to  (5) ; and when u(t) ≡ υ model  (7)  reduces to  (6). J(u) = t f 0 I(t) + A 2 u 2 (t)dt, (8) The numerical simulation of the system without control (u(t) ≡ 0) was done with Matlab in Section 4. 1. For our numerical solution of the optimal control problem, we have used the ACADO solver  [28] , which is based on a multiple shooting method, including automatic differentiation and based ultimately on the semidirect multiple shooting algorithm of Bock and Pitt  [29]. ACADO is a self-contained public domain software environment written in C++ for automatic control and dynamic optimization. Figures  8  and 9  show the significant difference in the number of infected individuals with and without control. In fact, in case of optimal control, the function I decreases greatly: the maximum value on the infected curve I under optimal control is 5.
Acknowledgments This research was initiated while Rachah was visiting the  Department of Mathematics of University of Aveiro, Portugal. The hospitality of the host institution and the financial support provided by the  Institut de Mathématiques de Toulouse, France , are here gratefully acknowledged. Torres was supported by funds through the  Portuguese Foundation for Science and Technology (FCT) , within  CIDMA  project  UID/MAT/04106/2013  and  OCHERA  project  PTDC/EEI-AUT/1450/2012 , cofinanced by  FEDER  under  POFC-QREN  with  COMPETE  reference  FCOMP-01-0124-FEDER-028894.
, 2014). Complementing the cell and tissue models, computational chemistry, quantitative structure-activity relationships (QSARs) and chemoinformatics tools, such as software made available through the QSAR toolbox (OECD, 2014a), provide the means to understand and predict key biochemical events such as protein binding and metabolic transformation. The attention within SEURAT-1 focuses on finding associations between the structural features of a chemical and its ability to trigger the key biomolecular events that initiate toxicological responses that may lead to adverse health outcomes  (Ellison et al. , 2011). Forming chemical categories based on combined structure-activity descriptors will ultimately facilitate more rapid and robust hazard profiling of chemicals and read-across between chemicals that have similar modes-of-action.
, 2012; Yoon et al. , 2012). These models are adapted to the research work within SEURAT-1  (Péry et al. , 2013). In summary, SEURAT-1 has a work program that aims to demonstrate a proof-of-concept for the scientific and technological underpinning of the SEURAT strategy. The overall empha-tion in the cells versus transformation rates; the latter may cause metabolic activation) leading to: (i) repeated hits (of the parental compound or metabolites) on the same molecular target; (ii) overload of defense/repair mechanisms through accumulation of a chemical (parental compound or metabolites) at certain initiation sites; (iii) progressive change in the epigenome; (iv) effects on the immune system, such as proliferation of memory cells and progressive activation and transformation of, e. g. , hepatic stellate cells; and finally (v) induction of a sequence of adverse reactions involving different cell types (and organs).
Chemical selection The first sign of how the guiding principles of the research strategy outlined above have influenced the research cluster is reflected in the approach adopted for the selection of standard reference compounds to be used across the projects. The selection of standard reference compounds is a critical issue in any research program and a cross-cluster working group led by the servicing project ToxBank was established within SEURAT-1 for defining selection criteria according to the overarching research strategy and, ultimately, providing the research projects with a list of reference compounds. The selection was governed by the following basic considerations: (i) The standard reference compounds should be associated with well-known modesof-action; (ii) the standard reference compounds should be relevant for repeated dose toxicity; (iii) promiscuity, i.
g. , long shelflife, soluble in buffer, commercially available, etc. ). Not surprisingly, many of the reference chemicals are pharmaceuticals or such AOP descriptions. Note that SEURAT-1 is entirely focusing on the replacement of animal tests by the application of human cells and, thus, will not conduct in vivo experiments. Therefore, the development process relies heavily on a systematic review of the literature and publicly available toxicity study databases to extract the mechanistic knowledge applicable to the MoA in the postulation of the associated key events, including the molecular initiating event (Fig. 6A ). Examples of AOP descriptions based on this concept were published recently  (Vinken, 2013; Vinken et al. , 2013). The ongoing selection of modes-of-action (MoA) within SEURAT-1 is performed in the context of the development of The key events are the basis for hypothesis development and testing and should be experimentally quantifiable.
The obvious way of starting this endeavor is to select a few of the SEURAT-1 standard reference compounds (see Section 3. 1 and ToxBank, 2014) with known modes-of-action to evaluate the predictive toxicity systems (see Section 3. 2. 3) supported with additional methods developed within SEURAT-1. Further data-rich test chemicals can then be selected from existing databases or published surveys which classify chemicals according to target organs and suspected modes-of-action following repeated exposure to chemicals (e. g. ,  Vinken et al. , 2012).
, 2013). Although experimental investigation to elucidate mechanisms and modes-of-action is an important activity within SEURAT-1, there is widespread acknowledgement that mining publicly available databases that report both in vitro and in vivo toxicity studies (e. g. , the "Open TG-GATEs" database established by the Toxicogenomics Project in Japan, or the DrugMatrix database of the U. S. National Toxicology Program) can provide an invaluable source of MoA information. In particular, there is an enormous amount of toxicogenomics data that, with the appropriate analysis, could uncover hidden mechanisms and key events and provide supporting evidence for a MoA during its development and evaluation  (Kongsbak et al. , 2014). SEURAT-1 case studies use data mining approaches for the elucidation of pathways through integrated analysis of omics data as well as for the validation of biomarkers for toxicity, which were discovered within the case studies.
, 2012). Both the Tox21 and ToxCast programs aim to develop high-throughput decision support tools for prioritizing the thousands of chemicals that need toxicity testing. The experimental work is being accompanied by the development of models that can be used to more effectively predict how chemicals will affect biological responses. The different methods should be effectively combined as a toolbox of innovative chemical testing methods. Finally, the challenge of being able to provide the data generated from the innovative chemical testing methods to risk assessors for making decisions about protecting human health and environment is addressed (see above and  Thomas et al. , 2013). The common goal of Tox21, ToxCast and SEURAT-1 is to implement state-of-the art technologies emerging from recent scientific advances into safety assessment procedures.
A data-driven framework based on data from a broad range of high-throughput in vitro assays from the Tox21 program (see Section 5 below), which invokes successive tiers of testing with margins-of-exposure as the primary metric, was proposed recently  (Thomas et al. , 2013). This framework provides a risk-based approach to the evaluation of chemical safety, drawing broadly from previous experience and incorporating technological advances to increase efficiency. Even though emerging from a high-throughput screening approach, it is fully complementary with the abovedescribed conceptual framework of the SEURAT-1 approach. The discussion about these concepts and frameworks highlights the fact that SEURAT-1 is operating in a very dynamic field of research, and a number of related research projects in different parts of the world are active in parallel, advancing the field rapidly.
On track? The roadmap and SEURAT-1 Developing new strategies for toxicity testing inevitably requires alignment of different areas of science such as chemistry, systems biology, computer sciences and toxicology. The SEURAT-1 research initiative assembles the available resources in a mode-of-action driven case study approach. This was in- Joint task force: toxicity database to gather all current data on a wide variety of compounds tiered testing systems and decision trees (ItS) Understand signalling pathways: (i) As long as not all pathways of toxicity are known, complex systems addressing more apical endpoints should be used too. (ii) Use modern tools (e. g. , RNAi) for pathway elucidation. Considerations for the development and validation of in vitro systems: (i) (Functional) limitations of in vitro systems (ii) In the near future, focus on 3D-systems and co-cultures.
SEURAT-1 approaches Generation of a curated database for cosmetics (COSMOS); Collection of SeURAt-1 data in a data warehouse (toxBank) establishment of integrated testing strategies for toxicity prediction based on selected modes-of-action; Development of safety assessment strategies based on integrated data from non-animal methods (i) SeURAt-1 will establish high-throughput assays for wellknown pathways of toxicity (SCR&tox) as well as complex bioreactors with diverse compositions of cell-types (allowing cell-cell interactions; HeMiBio). (ii) A battery of omics technologies are being used for pathway identification in DETECTIVE and NOTOX. (i) targeted development of test systems (according to the selected modes-of-action), full characterisation of biological models used in the test systems (ii) emphasis on organotypic cell cultures in NOtOx and HeMiBio (iii) Development of test systems according to pre-defined prediction goals.
endpoints: Functional readouts and "-omics" (SCR&tox, HeMiBio, DeteCtIVe, NOtOx) (iv) Activities within COSMOS and in the cross-cluster biokinetics working group (v) Selection of reference compounds at the beginning of the project by a cross-cluster working group led by toxBank. Information on mechanisms of toxicity and potency collected and now publicly available as a wiki (http://wiki. toxbank. net/ wiki/) Activities in the cross-cluster working group on integrated data analysis led by ToxBank. Definition of standards for quality control / quality assurance for data compilation in the toxBank data warehouse (annotation of respective measures as part of the data upload procedure). "paradigm. " Providing such a blueprint is challenging, but implementing a new strategy into regulation with the intention of changing the traditional safety assessment paradigm is probably even more challenging.
Cervical spinal cord injury (SCI) causes loss or impairment of control of respiratory muscles. Life-sustaining ventilation can be provided by mechanical ventilators (which have numerous side effects) or open-loop electrical stimulation respiratory pacing systems  [1]. The use of adaptive control strategies in respiratory pacing systems can simplify initial setup procedures and allow the system to adjust stimulation values to account for changes due to muscle fatigue and/or respiratory demand. We have implemented a neural network based adaptive controller  [2]  with a biomechanical model of human ventilator dynamics and diaphragm stimulation in Simulink/SimMechanics/ Matlab. The adaptive controller uses sensor information to automatically determine a stimulation pattern that will produce a prespecified desired lung volume trajectory.
Images were acquired every 10 min using AxioVision 4 (Zeiss). All aspects of image acquisition were fully automated and controlled, including temperature, focus, stage position and time-lapse imaging. Images were acquired for 4120 h in standard experiments and up to 240 h in the experiment using cells with elongated telomeres. Image analysis and single-cell lineage tracking. A custom software written in Matlab, phyloCell 2. 1, was used to segment and track cells and to assign motherdaughter links  16. Time-lapse images were exported as high-resolution TIF files and analysed directly with the graphical user interface of phyloCell. To follow cell-cycle progression and determine the mother-daughter relationship, cells expressed a fluorescent Cdc10-mCherry fusion protein that localized to the septin ring from bud formation to cytokinesis.
This fluorescent marker was also used to determine the cell-cycle stage based on the morphology and size of the bud. For details about the routines and algorithms implemented in phyloCell, see ref. 16. In contrast to ref. 16, the cell at the tip of the cavity would frequently be replaced by its daughter cell, which was intended in our approach. To efficiently track such lineages, in which we frequently switched focus from a given cell to its daughter cell, the time-lapse images were retrospectively analysed starting from the last image. This avoided tracking of lineages in which the cells were ejected from the microcavity. Computational and statistical analyses. All computational and statistical analyses were performed in Matlab. The functions developed in this study are available on request.
For cluster analysis of telomerase-negative lineages, two independent criteria were used: the number of long cell cycles (greater than the mean þ 3 s. d. of wild-type cell cycle, which may be different from strain to strain) per lineage and the number of transitions per lineage. A transition was defined as two consecutive cycles with a difference in their duration greater than a threshold S. The number of transitions per lineage was calculated for S ¼ 60, 70, 80, 90 and 100 min and averaged for each lineage. For each of these two criteria, we created a mixture model with univariate Gaussian distributions and performed a cluster analysis of the mixture, which defined clusters as data points belonging most probably to the same distribution. The gmdistribution class and related methods of the statistics toolbox of Matlab were used for this analysis.
Typically, A(k) rapidly drops to 0 after several cell cycles, and the timescale t of this decrease indicates how fast the information of a cellcycle duration is lost. Simulation of competitive culture. To compare cell growth in the microfluidic chip and conventional bulk culture, we numerically simulated a virtual culture in which the analysed lineages would be in competition. From the single-lineage data (Fig. 2c ), we extracted the probability of death and the cell-cycle duration landscape (Supplementary Fig. 3b ) and used them as inputs to simulate competitive growth (that is, the total number of cells in the virtual culture as a function of time). The simulation of a telomerase-negative culture was performed with a custom automaton developed in Matlab. All parameters were directly derived from the independent lineage data (Supplementary Fig.
This simulation is performed over 60 time points, corresponding to 10 h. The total number of cells is recorded. Then, 1,000 cells are randomly picked from this virtual culture and are run in a second round of simulation (dilution). After 13 rounds, all data are collected and converted into a plot, as shown in Supplementary Fig. 3a. For estimation of the fraction of type A and type B cells in a bulk culture (Fig. 3b ), two cell-cycle duration matrices M A and M B were derived from the two sets of lineages. The automaton was slightly modified to accommodate the simultaneous use of both matrices. The automaton starts with 1,000 cells, as for the standard simulation, but a tag is added to each cell to indicate its type. The initial ratio was 60:40% of type A:type B cells (Fig. 3b ). When a cell divides, its type is inherited and the next cell-cycle duration is extracted from either M A or M B.
Acknowledgements We thank  David Shore  and  Victoria Lundblad  for yeast strains and plasmids;  Miguel Godinho Ferreira  and  Claus Azzalin  for discussions; and  Stephane Lemaire ,  Gae ¨l Yvert ,  Tom Cech ,  Jim Haber  and  Joachim Lingner  for critical reading of the manuscript. We also thank all members of the  UMR8226  for technical support. This work was supported by the  European Research Council  ( ERC-2010-StG 260906-D-END to M. T. T. ) , the  Mairie de Paris  ( Programme Emergences  to M. T. T. ),  ITMO Cancer  and the ' Initiative d'Excellence' programme  from the  French State  (Grant ' DYNAMO ,'  ANR-11-LABX-0011-01 ).
For each participant with RIS, the first 2 available age-and sex-matched healthy controls were selected from a larger dataset (i. e. , 1:2 ratio). Standard protocol approvals, registrations, and patient consents. The protocol was approved by the Committee on Human Research at UCSF, and informed consent was obtained from all participants. MRI acquisition. All study subjects were scanned on a single 3T GE Signa scanner using a standardized protocol. The protocol included an inversion recovery spoiled gradient echo T1-weighted isotropic volumetric sequence (3D-IRSPGR, 1 3 1 3 1 mm 3 , 180 slices, echo time/repetition time/inversion time 5 2/7/400 ms, flip angle 5 15°, 256 3 256 3 180 matrix, 240 3 240 3 180 mm 3 field of view, number of excitations 5 1). MRI postprocessing. The 3D-IRSPGR served as the input image into the postprocessing pipeline.
Statistical analyses. Volumes (i. e. , total gray matter, total white matter, deep gray nuclei) were extracted directly from FreeSurfer's automated segmentation and normalized for head size using the total intracranial volume (also taken from FreeSurfer). These values (unitless) were compared between patients with RIS and controls using t tests. Among patients with RIS, thalamic volumes were correlated with log-transformed white matter lesion volumes using Pearson correlation. Statistical analyses were performed using SAS (v9. 2). All hypothesis tests in this portion of the analysis were performed at a nominal a 5 0. 05 significance level without adjustment for multiple comparisons. Adjustment for multiple comparisons is generally performed when several independent hypothesis tests are conducted on one population.
To our knowledge, there are no other published studies examining cortical thickness in patients with RIS. We report our finding of a ;3 cm area of thinning in the right superior and inferior parietal gyri but regard it as a preliminary finding without a specific rationale for why these particular gyri are affected in our dataset. In a group of patients with CIS, this region was not significantly different from controls using different FreeSurfer methods and input images. 5 Further studies examining whole brain and regional gray matter volumes and cortical thickness in early demyelinating disease will be of interest in order to replicate these findings. Our study has certain limitations, including a small sample size and a single-center design. In addition, cortical lesions are of great interest in the field currently but are generally not visible on conventional MRIs, including the IRSPGRs we used; as such, this work is not able to address the impact of cortical lesions on cortical thinning.
The design of the calorimeters have to take the following guidelines into account • Optimisation of the number of calorimeter cells. • Choice of the absorber material and the infra-structural components such as cooling, power supplies, readout cables and the very front end electronics. For the electromagnetic calorimeter at lepton colliders these criteria has lead to the choice of tungsten with a radiation length of X 0 =3. 5 mm, a Molière of R M =9 mm and an interaction length of λ I =96 mm. In the years 2005 to 2011 the CALICE collaboration has performed beam campaigns at DESY, CERN and FNAL in order to demonstrate the principle of highly granular calorimeters and to confront the concept of particle flow with real data. The first results of the data analysis have been published in three articles  [8, 9, 10, 11].
Data Acquisition System The DAQ is divided in four parts. The detector interface (DIF) is reading data directly from the chips and format their data stream in packets. These packets are aggregated by two levels of aggregation cards. The data concentrator card (DCC) aggregates data from the DIF to use the full capacity of the protocol (similar to FastEthernet) on HDMI cable. The Giga concentrator card (GDCC) converts this streams to Giga-Ethernet encoding. The acquisition clock and the detector-level commands are sent through a specific card named the Clock and Control Card (CCC) which convert the analogue signal into numeric words. This commands words are fanedout by both GDCC and DCC to the DIF. A software suite, named Calicoes, handles the data acquisition and control-command of the whole system.
Acknowledgments The research leading to these results has received funding from the  European Commission  under the  FP7 Research Infrastructures project AIDA , grant agreement no. 262025 , the  French ANR program  (Project:  CALIIMAX-HEP ,  2010 BLANC 0429 01 ), the  French IN2P3 Quarks and Leptons program  and the  Japanese JSPS program. The authors would like to thank the DESY test beam facility team for their support and kind hospitality. The author would like to thank the  CALOR2014  team for the organisation of the conference and the opportunity to present the results.