dataesr/scientific-paragraphs-categorization huggingface dataset:
The stabilization of these transcripts limits the decline in their mRNA concentration that would result from lower rates of transcription and thus could provide a selective advantage to the bacterium in case of future alternative carbon substrate availability. We found that the expression of numerous genes was under degradational control. Indeed, for 662 mRNAs, variation in concentrations was determined by changes in degradation in at least one pair of growth rates. Only two transcripts were under major degradational control in all three growth rate comparisons. As the growth rate increases, control of transcript concentration by degradation does not therefore constitute a simple and general response; degradational control appears to be a specific and flexible adaptive response. The number of mRNAs under mainly degradational control may, however, be underestimated by our analysis since many stable transcripts were excluded from the dataset due to the difficulty in accurately measuring the half-life of long-lived mRNAs.
htm). This resulted in a 3-D stack of 1500 virtual sections, each consisting of 1500 9 1500 isotropic voxels (where each voxel was 1. 7 lm 3 ) with a linear X-ray attenuation coefficient, displayed as a grey scale value calibrated between 0 and 255. The entire micro-CT dataset is given as Supporting Information Table  S1. The high resolution tissue scans were conducted at beamline ID19 of the European Synchrotron Radiation Facility (ESRF, Grenoble, France), that is using a parallel-beam geometry of a long (150 m) imaging beamline where the spatial coherence of the beam is particularly large (transverse coherence length in the order of 100 lm). Phase contrast imaging  (Cloetens et al. , 2006)  was used for edge detection at cell-cell interfaces. The X-ray beam generated from an 11 pole, variable-gap, high-magnetic field wiggle undulator with a 32 mm magnetic-field period was monochromatized to 19 keV using an artificial multilayer monochromator.
The sample-detector distance was set to 90 mm  (Weitkamp et al. , 2010). The image pixel size was 0. 75 lm. A total of 1200 projections with an exposure time of 0. 1 s was acquired for each sample during a continuous rotation over 180°. The tomographic reconstruction was performed with a filtered backprojection algorithm using the PyHST software, developed at ESRF  (Chilingaryan et al. , 2011) , after correction for sample motion using GNU Octave software (http://www. gnu. org/software/octave/). Volume renderings of all samples were obtained by 3-D image segmentation and isosurface representations in Avizo Fire (Visualization Sciences Group, Bordeaux, France). By applying the synchrotron radiation system we could detect objects such as pores somewhat smaller than 1 lm.
The linear equation system was solved by the conjugate gradient method available in Matlab (The Mathworks, Natick, MA, USA). The program was run on a 16 GB RAM node (Opteron 250; Xeon 5560 and Xeon 5650) of the high-performance computer at VSC K. U. Leuven (Flemish Super Computer Center, Leuven, Belgium). A numerical experiment was carried out in which O 2 partial pressure differences of 2 kPa were applied over the microscale geometry and the corresponding fluxes were calculated by means of the microscale model. The apparent diffusion coefficients of the tissues in the microscale sample were calculated from the fluxes and the gas partial pressure difference applied to the boundary of the sample. The diffusivity values of the different types of tissues were calculated. A macroscale reaction-diffusion model, developed previously  (Ho et al.
85 nmol oxygen mg FW -1 min -1 (as determined in prior tests using whole isolated rapeseed embryos using the method described by  Tschiersch et al. , 2011). To account for differences in the respiratory activity of the three embryo components, we used the flux values derived from a metabolic modelling approach  (Borisjuk et al. , 2013). Accordingly, the relative respiration rate for inner cotyledon, outer cotyledon and hypocotyl was set to 100%, 64% and 54%, respectively. The mitochondrial K m value was assumed to be equal to 0. 14 lM (1. 02 9 10 À2 kPa;  Millar et al. , 1994). The 3-D model of the rapeseed was developed and solved using the finite volume method in Matlab  (Ho et al. , 2011).
The full 3-D dataset is given as Movie S3. The analysis also detected a radial gradient across the hypocotyl with respect to both water and storage oils, with the highest water and lowest oil levels being present in the stele (Fig. 7a, b ). The levels of both the major storage protein cruciferin and starch were particularly low in the stelar cells (Fig. 7c, d ). An extended characterization of the metabolism in the hypocotyl was performed using laser-microdissection followed by metabolite analysis (Fig. 7e-j ). This showed that the most abundant metabolite was sucrose. The levels of glucose, hexose and total free amino acids were all about threefold higher in the cortex than in the stele. Glutamate (Glu) and glutamine (Gln) were the most abundant free amino acids throughout the hypocotyl, but in the stele there was a strong component of proline (Pro), lysine (Lys) and leucine (Leu).
Acknowledgements We thank  Peter M. Jakob  ( University of W€ urzburg, Germany ) for support in NMR,  Silke Schiebold  for support in laser microdissection and  Twan Rutten  for help in microscopy. Financial support of the  German Bundesministerium f€ ur Wirtschaft und Technologie (Zentrales Innovationsprogramm Mittelstand)  and the  Deutsche Forschungsgemeinschaft  ( BO1917/4-1 ) is acknowledged. Synchrotron imaging was possible with ESRF beamtime (experiment  EC587 ). The authors thank the  Research Council of KU Leuven  ( OT 08/023  and  OT/12/055 ), the  Flanders Fund for Scientific Research  (project  G. 0603. 08  and  G. 0645. 13 ) and the  Institute for the Promotion of Innovation by Science and Technology in Flanders  ( IWT scholarship   SB/0991469 ) for financial support. Quang Tri Ho  is postdoctoral fellow of the  Flanders Fund for Scientific Research (FWO Vlaanderen ).
Methods: Sierra Leone utilized a systematic approach, including situational assessment, selection of sentinel sites, preparation of implementation plan, adaptation of the standard operating procedures, supervision and training of staff, and monitoring of influenza surveillance activities. The methods used in Sierra Leone were adapted to its specific context, using the Integrated Disease Surveillance and Response (IDSR) strategy as a platform for establishing ISS. Results: The ISS system started functioning in August 2011 with subsequent capacity to contribute surveillance activity data to global influenza databases, FluID and FluNet, demonstrating a functional influenza surveillance system in Sierra Leone within the period of the WHO SISA project support. Several factors were necessary for successful implementation, including a systematic approach, national ownership, appropriate timing and external support.
Background Influenza remains an important global public health concern. Annual influenza epidemics are estimated to account for 3 to 5 million severe illnesses and 250,000 to 500,000 deaths worldwide  [1]. Though the burden of influenza is well understood in temperate regions, mainly in the United States and Europe  [2] [3] [4] [5] , data from tropical regions, including Africa, remains insufficiently understood  [6]. This was most apparent during the 2009 pandemic, which demonstrated the need for a strong worldwide influenza surveillance network. In 2011, the World Health Organization (WHO) funded the Strengthening Influenza Sentinel Surveillance in Africa (SISA) project in which eight countries were selected to receive support to implement or strengthen influenza sentinel surveillance (ISS) and improve epidemiological and virological data collection and reporting through the global databases FluID and FluNet  [7, 8].
Central national public health reference laboratory The molecular unit of the CPHRL served as the National Influenza Reference Unit (NIRU) and was responsible for developing protocols for influenza specimen chain of custody and transport, and for training of sentinel site laboratory personnel in specimen handling, bio-safety measures and cold chain maintenance. The CPHRL was also responsible for collecting samples from sentinel sites, logging information, packing and shipping samples to the Institut Pasteur in Dakar, Senegal, and entering laboratory data into the WHO FluNet database.
Developing guidelines and tools The Sierra Leone national influenza sentinel surveillance protocol and SOPs for influenza sample collection, sample handling, packaging, shipping and transport, and data handling and sharing were developed based on the generic WHO guidelines. Additional protocols were adopted for entering epidemiological and virological data into the global databases, FluID and FluNet, which are key tools in monitoring global influenza trends in realtime. Information from FluID and FluNet is available to health professionals and policy makers to assist in making informed decisions regarding influenza management. Using these databases, surveillance activity in Sierra Leone is incorporated into a global network for influenza epidemiological data collection.
Training, supporting and supervising staff The training workshop targeted all staff involved in surveillance, including surveillance officers at the national and district level, clinicians, laboratory technicians, monitoring and evaluation officers in charge of data at the selected health facilities, and staff at CPHRL. The topics covered in the training included an overview of influenza epidemiological patterns, clinical and virological diagnosis of influenza, and specimen collection, handling and transportation. Data management, including data collection, collation, analysis and flow, was also discussed. At the national level, additional information on weekly summary reporting and data analysis using EpiInfo software was discussed. On-site support at sentinel sites covered practical demonstration of nasal swab sample collection, data recording, compilation of weekly summaries, and transfer of samples.
Case recording and sample collection A sampling method was used for ILI cases based on the patient load at health facilities and the capacity of the laboratory to process samples. Accordingly, a nasal swab sample from every fourth ILI patient was collected. For SARI, a nasal swab was collected from all cases. Prior to sample collection, an explanation of the procedure was provided and verbal consent was obtained from the patient or guardian. Nasal swabs were collected according to the procedures described in the SOPs. The following data were recorded for each patient sampled: sentinel site identification number, date of symptom onset, date of specimen collection, date of hospitalization (for SARI cases), sex, age, temperature, pregnancy status, seasonal influenza vaccine status, antiviral use, and any comorbidities.
Results and discussion During the SISA project surveillance data collection period in Sierra Leone, from August to December 2011, 1,235 ILI cases (12. 9% of the total consultations) and 282 SARI cases (4. 6% of all hospitalizations) were identified in the four sentinel sites (Table  1 ). Nasal samples were collected from 268 ILI and 238 SARI cases with ages ranging from 1 month to 62 years. Laboratory results were available for 473 samples, of which 12. 7% tested positive for influenza virus ribonucleic acid (RNA). Among the positive samples, 55 (91. 7%) had subtype A(H3N2), 4 (6. 7%) had subtype A(H1N1)pdm09 and 1 (1. 7%) had a mixed infection of subtype A(H1N1) pdm09 and A(H3N2). No influenza B subtypes were detected. The majority (36%) of ILI and SARI cases were detected in children under the age of four.
Follow-up The SISA project provided in-country technical support from May to December 2011. Following this, Sierra Leone continued influenza surveillance successfully with previously existing mechanisms for implementation of IDSR. From January to December 2012, a total of 1,363 ILI and 281 additional SARI cases were reported, of which 264 ILI and 201 SARI cases were sampled. Despite fewer samples being collected and a delay in reporting in comparison to the initial project period, the surveillance activity continued to provide valuable data for national and global databases. By the end of December 2012, a total of 2,598 ILI and 570 SARI cases were reported. Figures  2  and 3  show the weekly number of ILI and SARI cases identified as well as the proportion of ILI and SARI cases among outpatient and inpatient visits.
Challenges and solutions The major challenges and constraints in the implementation of a successful ISS in Sierra Leone included rapid turnover of sentinel site staff; weak supply chain management leading to shortage of supplies, in particular sample collection kits; inadequate supply of fuel for transportation of samples from collection site to the CPHRL; and a lack of continuous electrical power supply in the health facilities to maintain cold chain integrity. Additional constraints included delays in uploading data to the global databases and prolonged turnaround time for laboratory results. The challenges highlighted the critical need for proper situation analysis, adequate preparation, prepositioning necessary materials and supplies, and prior anticipation of potential risks to ensure a smooth implementation process.
Despite the challenges and limitations, the ISS system in Sierra Leone produced valuable results that were incorporated into the global databases FluID and FluNet. Additionally, one year following cessation of active external technical support by SISA, the system continues to be operational and effective in monitoring the burden of influenza. National ownership was demonstrated to be a critical element in the success of the project and is critical for sustainability of the ISS system and other future surveillance activities. The ISS system, if sustained long-term, would provide valuable data to determine epidemiological and virological patterns and trends to assess the influenza disease burden that will ultimately guide national control strategies.
Acknowledgements This study was endorsed by the  Ministry of Health and Sanitation of the Government of Sierra Leone. The authors would like to acknowledge the MOHS who facilitated the team's effort to collect and review the data. We especially would like to acknowledge the administrative and laboratory staff of the  Central Public Health Reference Laboratory in Lahka, Jenner Wright Children's Clinic, Ola During Children's Hospital, Lumley Government Hospital, and Blue Shield Hospital  for their participation in this process.
Breach tool All the computations have been made using Breach  [33, 48]. This MATLAB/C++ toolbox allows for efficient numerical simulation, for sensitivity computation, and for STL property and DLE evaluation. In particular, DLEs can efficiently be computed via forward sensitivity analysis  [52]. Breach is particularly oriented towards the analysis of parametric systems, in the sense that it offers efficient routines for global sensitivity analysis and parameter search, and that the graphical user interface facilitates the modification of parameters and initial conditions, and the exploration of parameter spaces.
g. ,  Sutton et al. , 1998; Flechard et al. , 1999; Trebs et al. , 2006; Massad et al. , 2010; Zhang et al. , 2010 , Wichink Kruit et al. , 2012; Bash et al. , 2013; Hamaoui-Laguel et al. , 2012) ; however, the application of these models remains difficult over the different canopies of African ecosystems owing to the lack of necessary input parameters. Nevertheless, the two-layer bidirectional model of  Zhang et al. (2010)  is applied in this study to estimate NH 3 surface-atmosphere exchange fluxes. In a recent paper,  Adon et al. (2010)  presented the longterm monitoring of ambient gaseous concentrations within the IDAF program. Our study presented an original database of 10 yr of measurements  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007)  of five important atmospheric gases (NO 2 , HNO 3 , NH 3 , O 3 , SO 2 ) obtained within the IDAF African network at seven sites in west and central Africa.
, 1989)  and explained in  Delon et al. (2010)  (Table  3 ). LAI is an important parameter for calculating canopy resistances. For the IDAF sites, LAI was obtained from MODIS (MODerate Resolution Imaging Spectroradiometer) satellite data for the period 2000-2007. The MODIS LAI is the ratio of one-sided green foliage area per unit horizontal ground area in broadleaf canopies, or the projected needleleaf area per unit ground area in conifer canopies, and is given in m 2 m -2  (Myneni, 1999). To that end, we used the 1 km MODIS LAI values that are processed over an 8-day period  (Yang et al. , 2006). The MODIS LAI product has already been validated with field measurements on many sites over the western African region  (Fensholt et al. , 2004; Samain et al. , 2008). The mean seasonal variations of the monthly LAI during the period 2000-2007 for IDAF sites are shown in Fig.
2  and the range values are displayed in Table  3. The MODIS LAI of IDAF specific sites are well comparable to the ones of the corresponding land covers of the ECOCLIMAP database  (Kaptue et al. , 2010). For our simulation of dry deposition velocity, we used the monthly LAI averaged over 8 yr  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007)  for savanna sites and a constant value of 5 m 2 m -2 for tropical forest sites. We note that  Zhang et al. (2003b)  and  Brook et al. (1999)  have used a constant LAI of 6 and 4. 5 m 2 m -2 , respectively, to simulate dry deposition velocity for tropical forests. Meteorological data required for the simulation are wind speed and temperature at a reference height in the surface layer, surface temperature, solar irradiance at the surface, precipitation, surface pressure, relative humidity and cloud cover fraction.
With the exception of cloud cover fraction, meteorological conditions are provided by the forcing developed in the frame of ALMIP (AMMA Land Surface Model Intercomparison Project) from satellite data, described in    Boone et al. (2009). The spatial resolution of this forcing is 0. 5 • × 0. 5 • with a 3 h temporal resolution. For the simulation of dry deposition velocity, we used a database of 6 yr from 2002 to 2007. Validations of surface temperature and moisture have been performed within the ALMIP project  (Boone et al. , 2009; De Rosnay et al. , 2009). However, the forcing in ALMIP is available at 10 m, whereas the concentrations of gases are measured at around 2 m for the savanna sites and 3 m for the forested ecosystems within IDAF network. As a first approximation, a logarithmic decrease of the wind forcing from 10 to 2 m, depending on the rugosity of the site, has been applied to calculate deposition velocities at 2 m for the savanna sites, as described in  Delon et al.
Corrections for within-canopy concentration data for forests The inferential method requires atmospheric concentrations and turbulence intensity above the canopy to predict rates of dry deposition over the forest. In the IDAF network, gas concentrations are measured at about 3 m in forests. Moreover, there are very few published within-canopy vertical gas concentration profiles (HNO 3 , NH 3 ) in the literature for forest  (Flechard et al. , 2011). Thus, we have carried out a pilot experiment by measuring simultaneously gas concentrations at 10 and 3 m (or 2 m) from the ground in the forested ecosystem of Zoetele (and in the wet savanna of Lamto and dry savanna of Banizoumbou) over the period of September 2010 to December 2011. For the forested ecosystem, NO 2 , HNO 3 and SO 2 monthly concentrations measured simultaneously at 10 and 3 m show no significant trend and are on the same order.
No specific trend in the variability is observed, as already concluded in the study of  Delon et al. (2012). The interannual variability is largely attributable to the variability of gaseous concentrations due to the potential variation of the intensity of atmospheric sources and the variability of meteorological data. To give the relative contribution of dry to total deposition fluxes, we calculated total nitrogen wet deposition fluxes from the IDAF database (http://idaf. sedoo. fr) for the same study period  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007)  for each site  (Adon, 2011). The wet deposition flux has been calculated as the product of the ammonium and nitrate concentrations in rain (annual volume-weighted mean VWM) by the annual rainfall (Galy-  Lacaux and Modi, 1998; Galy-Lacaux et al.
Acknowledgements. This work is part of the  IDAF (IGAC/DEBITS/AFRICA ) project, and was funded by the  INSU/CNRS "Institut National des Sciences de l'Univers/Centre National de Recherche Scientifique" in France  and the  API "African Monsoon Multidisciplinary Analyses " (AMMA in 2005). Edited by:  A. B. Guenther The publication of this article is financed by  CNRS-INSU.
The Hot Ion Analyser (HIA), part of the Cluster Ion Spectrometry experiment, has the objective to measure the three-dimensional velocity distributions of ions. Due to a variety of factors (exposure to radiation, detector fatigue and aging, changes in the operating parameters etc. ), the particles detection efficiency changes over time, prompting for continuous in-flight calibration. This is achieved by comparing the HIA data with the data provided by the WHISPER experiment on magnetosheath intervals, for the high sensitivity section of the instrument, or solar wind intervals, for the low sensitivity section. The paper presents in detail the in-flight calibration methodology, reports on the work carried out for calibrating HIA and discusses plans to extend this activity in order to ensure the instrument highest data accuracy.
Conclusions Figures Back Close Full -The detection of low-energy ions may be affected by the spacecraft charging to a positive floating potential that repels these ions. -On some occasions, instrument artifacts (wrong time-tagging, sudden density drops, high-voltage discharges, wrong discriminator levels etc) may occur. These events are listed on the CIS Data Caveats list, available on the instrument webpage. Since the beginning of the mission, the HIA sensors were operational only on Cluster 1 (C1) and C3. Since November 2009, after almost 10 yr of very good performance, the CIS experiment onboard C3 is not operational any more. Also, after June 2011 the HIA operations on C1 are restricted to magnetospheric modes only, with the instrument switched into a safe stand-by mode when in the solar wind.
e. by analyzing, both actively and passively, the electric signals in the neighboring plasma. In active mode, WHISPER measures the total electron density, while in passive mode it provides a survey of natural emissions from about 2 to 80 kHz, that covers the electron plasma resonance frequency. There are a number of assumptions involved in the absolute calibration procedure, there is a symmetric anode-dependent efficiency evolution with time, and therefore the relative anode-dependent efficiency coefficients Cheff(θ), determined in the pre-flight tests, have not changed. the coefficients A, B, T 0,1,2 , describing the efficiency energy dependence do not change as well and assume the values determined in the pre-flight tests. the WHISPER data are well calibrated and free of errors (at least in the statistical sense).