dataesr/scientific-paragraphs-categorization huggingface dataset:
for every BCD set. At this stage, the first measurements of each light curve are discarded if they correspond to deviant values for all or some of the external parameters (detector or pointing stabilisation). In average ∼10 min of data are rejected for each dataset (Fig. 3 ). Finally we perform a slipping median filtering for each light curve to discard outlier measurements due to, e. g. , cosmic hits. We also test a photometric reduction of the IRAC data with DECPHOT, without improving results. Still we use DECPHOT to assess the infrared variability of GJ 436. Indeed, aperture photometry performed on deconvolved images reconvolved by the best-fitting partial PSF model allows us to derive the aperture corrections required for deriving the observed flux of the star F *. For every dataset, we perform this procedure for a large range of apertures.
Then we average all the measurements and take the resulting value as the observed flux measurement for the dataset. The error on the mean is considered as its error bar. We apply the colour and inter-pixel corrections 8. We do not correct the intrapixel sensitivity as no complete correction map is available for the Warm Spitzer mission at the time of our analysis. The intrapixel behaviour of the InSb detectors for the Warm Spitzer mission substantially differs from the one of the cryogenic phase 9  , making the correction map for the cryogenic phase unsuitable for all our data. The observed stellar flux F * is given in mJy for each IRAC dataset in Table  1. Their temporal evolution is shown in Fig. 4. The standard deviations of the fluxes are 0. 77, 0. 78, and 0. 12 % at 3. 6, 4. 5, and 8 µm, respectively.
Global analysis Once the models selected and the errors rescaled, we perform a global MCMC analysis of our dataset to probe the posterior probability distributions of the jump and physical parameters. Two chains of 240,000 steps compose this analysis. We successfully check their good convergence and mixing with the statistical test of  Gelman & Rubin (1992) , all jump parameters showing indeed a value close to 1 at the 0. 01 level. The resulting median values and 68. 3% probability interval for the jump and physical parameters are given in Table  3. The fitted light curves are compared with the data in Figs. 1, 5, 6,  and 7. Note that the light curves are not binned prior to the analysis (except for the subarray images, see Sect. 2. 3). The fitted radial velocities are displayed in Fig. 8.
Flares-like structures in the light curves Previous studies of GJ 436 Spitzer datasets reported structures that could potentially be attributed to stellar flares. The first one was signalled just after an occultation at 3. 6 µm (2008 January 30, AOR : 24882688) by S10 and B11 and led to contradictory results. Their opinions diverged on how to take care of the post occultation spike they both identified. On our side, we notice that the amplitude of the structure highly depends on the chosen aperture radius, and disappears for aperture radii below 2. 1 pixels (Fig. 12 ). The insertion of Eq. 5 in the baseline model leads to a significantly decreased occultation depth (∼170 ppm instead of ∼535 ppm) and to a higher photometric accuracy (mean photometric error ∼ 5. 11 10 -4 instead of 10. 30 10 -4 , Fig.
098 day. The lack of a continuous monitoring between these two potential transits prevents us from identifying a single period. Still, our extensive dataset allows us to discard many fractions of 208. 98 days for which at least a third transit should have been present in the Spitzer data. Injecting fake transits at the corresponding phases, we check our ability to recover them in the Spitzer data with MISS MarPLE for each period and each expected transit. Fig. 16  shows the period ranges that we can keep from these tests. In this figure, the periods too close to GJ 436b's one are also discarded to allow the dynamical stability of the system (from ∼1. 8 to ∼3. 9 days,  Ballard et al. 2010b ). In the end 40 possible periods remain. One may assess their replicability with new ultra-precise time-series photometry based on the ephemeris 2455476.
Discussion on a putative GJ 436c S12 proposed two planetary candidates in the GJ 436's stellar system and named them temporarily UCF-1. 01 and UCF-1. 02 until their confirmation. They observed UCF-1. 01 transits during 2008 July 14, 2010 January 28, 2010 June 29, 2011 January 24 and July 30 datasets. UCF-1. 02 potential transits occurred during both of those 2010 datasets. In our analysis we pay particular attention to the behaviour of the measured external parameters. The following two examples may cast doubt on some observations of S12's candidate transits. A change of the background contribution (Fig. 17 ) indeed affects the 2010 January 28 dataset just at the end of the observation of UCF-1. 01 transit, while a larger x-FWHM alters the 2008 July 14 light curve during another UCF-1. 01 transit.
We apply relative flux offsets in the above two plots for clarity. The upper dataset is the result of aperture photometry with a 2D elliptical Gaussian centring while the lower one is done with a double 1D Gaussian fit. The upper is corrected with a baseline involving the measured FWHM while the lower is not. Black dots are the binned data per interval of 5 min with 1-σ error bars. The supposed UCF-1. 01 (right) and UCF-1. 02 (left) transit events are shaded in green. more than 4-σ confidence. Our models correcting for instrumental systematics do not destroy transit signals. We wonder why our results diverge. We both opted for a similar aperture radius (S12 : 2. 25 px; our study : 2. 2 px) and for a BLISS mapping in the 2010 January 28 dataset, but we differ in the choice of the centring technique.
(2007)  (which differs from the rotational period identified by photometry by K11, P∼57 days) based on spec-Fig. 19. Same as Fig. 18  for the 2011 Jul 30 data (AOR: 42614016). The upper dataset is the result of aperture photometry with a 2D elliptical Gaussian centring and is corrected for all systematics minimising the BIC. The lower dataset is produced from a double 1D Gaussian fit of the computed x-FWHM and corrected in the same way. The shaded green area corresponds to UCF-1. 01 transit event. tral indices for a sub-sample of our HARPS spectra, and another power excess around 23 days, i. e. about half the 48-day period and thus possibly a harmonic. Nevertheless, they all have a power much below the power threshold for the 3-σ-confidence level (P=0. 187) and we therefore cannot draw any firm conclusion.
Atmospheric analysis In order to use our new measurements to draw inferences on the atmospheric properties of GJ 436b we employ a number of atmospheric modelling tools. We first derive planetary pressuretemperature P-T profiles using the 1D plane-parallel model atmosphere code described in  Fortney et al. (2005 Fortney et al. ( , 2008)). The opacity database is described in  Freedman et al. (2008)  and the equilibrium chemistry calculations in  Lodders & Fegley (2002). We use the base solar abundances of  Lodders (2003). We first generate thermal emission spectra for models representative of conditions on the dayside of the planet. Within the framework of the thermal emission calculations, we are constrained to pre-tabulated equilibrium chemistry abundances. In Fig. 22  we plot two models.
We next turn to the transmission spectrum of the planet to examine whether a large abundance of CO and CO 2 , and corresponding small mixing ratio of CH 4 , are also consistent with this dataset. For this purpose, we complete our Spitzer transit depths with those from EPOXI  (Ballard et al. 2010b, 0. 35-1. 0 µm), HST WFC3  (Knutson et al. 2014, 1. 14-1. 65 µm) , HST NICMOS  (Pont et al. 2009 , 1. 1-1. 9 µm), and from the ground in the H and K bands  (Alonso et al. 2008; Cáceres et al. 2009)  on Fig. 24. Here we model the planetary transit radius, which is inferred from the transit depth and von  Braun et al. (2012)  stellar radius (0. 455 R ), as a function of wavelength, using the transmission spectrum code described in  Fortney et al. (2003 Fortney et al. ( , 2010)). The transmission spectrum of the planet GJ 436b was previously modelled in some detail with this code by  Shabram et al.
(2011). An advantage of these calculations is that we are able to model arbitrary chemical mixing ratios, rather than equilibrium chemistry. We first compute the transmission spectrum of the 50×-solar model, shown on Fig. 24  in orange. We compare to the two bestfit chemical models of S10 in red and blue, which use low CH 4 and high CO and CO 2 mixing ratios. The models differ dramatically in the optical only because it is unclear if the neutral atomic alkalis Na and K are still present in the planetary atmosphere, or have condensed into clouds. The equilibrium chemistry model is strongly disfavoured by the Spitzer data, as it yields the wrong planetary radius ratio between the 3. 6 and 4. 5 µm bands. The blue and red curves reproduce the Spitzer data better. This is consistent with the dayside occultation data, where models with strongly enhanced CO and CO 2 and strongly depleted CH 4 are preferred.
Conclusion We performed an independent and global analysis of all available Spitzer data for GJ 436 combined with our new HARPS RV measurements. In this analysis, we optimised the data reduction procedure for each Spitzer instrument with the adaptation of partial deconvolution photometry or aperture photometry, and we paid a particular attention to the modelling of the systematic effects. We recommend the use of the FWHM of the PSF in both directions as parameters to model the instrumental systematics. The insertion of the HARPS RVs complements well the photometric data. Our results are globally consistent with previous studies (e. g. , S10; B11; K11; S12) but some discrepancies disclose another facet of GJ 436b. In particular we obtained constant values of the transit depth with time, a flat transmission spectrum and a significantly lower 3.
Acknowledgements. This work is based in part on made with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. Support for this work was provided by  NASA  through an award issued by  JPL/Caltech. M. Gillon  is  FNRS Research Associate. N. Astudillo  acknowledges support from" Becas de Doctorado en el Extranjero, Becas Chile " (grant  72120460 ). V. Neves  and  N. C. Santos  acknowledge the support by the  European Research Council/European Community  under the  FP7 through Starting Grant  agreement number  239953. NCS was also supported by  FCT  through the  Investigador FCT  contract  reference IF/00169/2012  and  POPH/FSE (EC)  by  FEDER funding through the program " Programa Operacional de Factores de Competitividade -COMPETE ".
Follow-Up and Assessment of Outcome The Université de la Méditerranée and the Direction and the Ethical Committee of the Timone University Hospital (CPPRB 1) approved our study. Follow-up information was obtained in two ways: direct clinical evaluation or telephone interview by the first author (C. T. ) who was not involved in the selection of the cases for treatment. We evaluated the initial pain cessation, the onset of the sensory disturbance and the recurrence. We analyzed the data regarding the latency intervals to achieve the features, making sure to date every event, the use of medication and the need for surgical procedures, so as to accurately assess all the information available. Pain was scored using 3 different scales: Barrow Neurological Institute scale (BNI; class I: no trigeminal pain, no medication; II: occasional pain, not requiring medication; IIIa: no pain, continued medication; IIIb: controlled with medication; IV: some pain, not adequately controlled with medication; V: severe pain, no pain relief), Burchiel (class I: pain free, no medication; II, pain free on medication; IIIa: pain improved, no medication; IIIb: pain improved, on medication; IV: pain not improved) and Regis (class I: no trigeminal pain, no medication; II: no pain, with medication; III: pain frequency reduction >90%; IV: pain frequency reduction 50-90%; V: no pain reduction; VI: pain worsening)  [14, 16, 17].
Statistical Analysis Data were recorded using Microsoft Excel 2000. All statistical analysis was performed using the R software, version 2. 12. 0 (R Foundation for Statistical Computing, Vienna, Austria). The survival R package was used for survival analysis. First, a descriptive analysis of recorded data was realized among the MS-related TN population. For the evaluation of outcomes such as initial pain cessation, hypoesthesia and recurrence, time to event was estimated by using the Kaplan-Meier method. A bivariate analysis was then performed to identify predictive factors among the collected variables. For qualitative variables, Kaplan-Meier curves were used to graphically represent survival among the different groups, and they were compared using the univariate log-rank test. For all variables, the effects were estimated and tested by fitting univariate Cox proportional hazards regression models.
Patients who enrolled after Protocol Amendment 2 were treated with a target maintenance dose of 2403 mg/day. Physical examination and clinical laboratory tests were performed at prespecified intervals in each study. A directed history, including a review of adverse events, concomitant medications, hospitalizations and compliance, was conducted at each study visit in all four studies. Written informed consent was required from all patients, and all study protocols were approved by the institutional review board or ethics committee at each centre. All trials in this report are registered at http:// www. clinicaltrials. gov (NCT00287729, NCT00287716 and NCT00662038).
Statistical analysis Data from the ongoing open-label studies are based on the interim data cut-off date of 29 April 2011. Demographic and baseline characteristics were summarized using descriptive statistics for continuous variables, and number and percentage counts of patients for categorical variables. The total duration of study drug exposure, total person exposure years (PEY) and mean daily dose of study treatment were summarized for the integrated pirfenidone population. The total duration of exposure is defined as the time between the first dose and the last dose of study drug. TEAE are defined as adverse events with an onset or worsening between the first and 28 days after the last dose of study drug. TEAE rates were computed for the integrated pirfenidone population as well as the CAPACITY studies.
To gain further knowledge on somatic and meiotic HR in plants, we investigated the role of RAD51B in somatic and meiotic recombina-tion. The results show that RAD51B is indispensable for DNA damage repair and HR in somatic cells and that this role is independent of the SRS2 anti-recombination helicase. Moreover, we show that if some RAD51 functions are clearly dependent on RAD51B, others, like its role in development of Physcomitrella, are RAD51B independent. Finally, our data show for the first time an essential role for RAD51B during meiosis.
Gene identification and isolation Genomic DNA and total RNA was isolated from Physcomitrella as previously described. For verification of gene models, RNA was extracted as described in  (44). Physcomitrella genomic sequences encoding the RAD51B and SRS2 genes were identified by Basic Local Alignment Search Tool (http://www. phytozome. net/Physcomitrella er. php). The available gene models were used for the design of polymerase chain reaction (PCR) primers to amplify cognate genomic sequences, which were cloned in the TOPO-TA (Life Technologies, USA) or pBluescript (Stratagene, USA) plasmids. PCR primers used are listed in Supplementary Table  S1. In order to obtain a correct gene model for each sequence, full-length cDNA sequences were amplified from Physcomitrella polyribosome-derived RNA by rt-PCR  (44).
Targeted gene knockin In order to replace the PpRAD51B gene from ATG to stop by the AtRAD51B cds, we produced the pPpAtRAD51B-KanR plasmid. First, we amplified a PCR fragment covering the AtRAD51B cDNA region (21) using Arabidopsis cDNA as the starting template (coordinate 47-1037 on Genbank: AB194809) and cloned it into a pUC18 plasmid to obtain pAtRAD51B. To generate the hybrid Physcomitrella-Arabidopsis RAD51B expression cassette pPpAtRAD51B, a 575 bp 5 targeting fragment (partial promoter/5 UTR of PpRAD51B, coordinates 11047187-11046613 on chromosome 25) and a 560 bp 3 targeting fragment (partial 3 UTR of PpRAD51B, coordinate 11042647-11042088 on chromosome 25) were amplified from Physcomitrella genomic DNA and cloned in pPpA-tRAD51B upstream of the ATG and downstream of the stop codon of the AtRAD51B cDNA, respectively.
Does this mechanism affect untargeted integration of a non-homologous vector? In S. cerevisiae, illegitimate recombination of non-homologous DNA fragments occurs at low frequencies  (64) , and the absence of RAD51 leads to a further decrease in the number of Illegitimate Recombination (IR) events compared to WT  (63). By contrast, in A. nidulans, S. pombe and Physcomitrella  (41, (65) (66)  the absence of RAD51 increases IR rates. Our data show that in Physcomitrella, deletion of RAD51B, like deletion of RAD51, leads to an increase in IR. This implies that whereas in S. cerevisiae the HR machinery is necessary for the integration of exogenous DNA with or without homology, in moss there is clearly competition for DNA integration between HR and IR (probably NHEJ). Thus, the idea that the outcome of transgenesis reflects the balance between HR-and IR-mediated DNA repair is strongly supported in moss.
FUNDING Agence National de la Recherche  [ ANR-09-GENM-402  and  ANR-11-BTBR-0001-GENIUS  to A. E. and F. N. ]. UK Biotechnology and Biological Research Council  [ BB/I006710/1  to A. C. C. and Y. K. ]. Funding for open access charge:  INRA ,  Institut Jean-Pierre Bourgin   UMR1318 ,  Saclay Plant Sciences, Versailles, France.
The food supply was therefore based on the statistical data on the main local yield for the potential supply, on-farm surveys on the crop yields for the current supply, on-farm surveys of farm crops for the urban market (mainly through alternative and short food supply networks) for the actual supply. In the case of livestock, the calculation is more complex because meat or milk production processes do not only depend on farm management, but also on the local organisation of the supply-chain, e. g. presence of slaughterhouses. Figure  3  shows the overall method to assess the food production capacity. Finally, the food production capacities evaluated on the basis of potential, current and actual supplies were compared in order to assess whether there were any gaps for each meat type and any differences between meat types.
Meat demand Despite the growing interest in food-related issues worldwide, little is known about the extent of local food consumption. In fact, although large-scale and national data is often available and up-to-date, such as the data from the Italian annual surveys on human food human consumption, it only offers an average estimation of the national population consumption  (ISMEA, 2013). For the purpose of our case study, only little data about meat consumption was available and was mainly based on surveys with a limited geographical scope and a specific focus (e. g. Fastelli, 2011). This meant we had to approximate the local demand, considering it equal to the regional meat demand  (Timmons et al. , 2008). Because no regional information was available, the regional meat demand was calculated on the basis of the national meat demand using the following equation: Cr = (Cn¥Vr)¥Vn -1 (1) where Cr is the regional (i.
Meat supply For the potential meat supply, we mainly relied on the most recent agricultural census data (ISTAT, 2011b). However, for our case study, local data were only available on animal breeds in local farms, whereas no information was available on the number or type of animal slaughtered. Complete data was only available for Tuscany. Therefore, we estimated the potential meat supply for the urban region of Pisa on the basis of the following equation: Cmp = (Cap¥Cmt)¥Cat -1 (2) where Cmp was the meat slaughtered in the urban region of Pisa, Cap the meat breed in the urban region of Pisa, Cmt the meat slaughtered in Tuscany (which may also include imported meat from outside the region), and Cat the meat breed in Tuscany. Cap, Cmt and Cat data was derived from the national agricultural census.
In order to define the kilos of meat produced in the studied area, Cmp was multiplied by the average live weight per livestock type in order to obtain the total live weight, which was then multiplied by the slaughtering yield. All data is summarized in Table  2. For the current meat supply, we used data recorded in 14 farms through on-farm surveys (2012-2013). The farm sample was representative of the livestock farms of the area, covering 80% of the cattle and sheep livestock units according to the most recent agricultural census (ISTAT, 2011b). Dairy cattle farms were also included as contributors to meat supply, despite meat was not their primary production. During the surveys we defined the origin of the meat produced by the farm (cattle or sheep), the breeds (local or non local) along with the type of sold animals (i.
Discussion and conclusions In this paper we compared different estimates of meat production capacity of periurban farming systems. We believe that our method would also be suitable for other meat productions (e. g. pork or poultry) along with other food productions, such as vegetables, bread, or milk. Results showed that food capacities based on potential, current and actual food supplies give different outcomes. These outcomes need to be analysed in order to understand the capacity of local farming to fulfill the urban food demand. In fact, this method is data-driven and depends on the availability of statistical and on-farm datasets. Statistical datasets used for potential capacity and food consumption assessments are rarely annual and cannot be either updated or compared. Also their coverage is usually national or regional, while a more locally-based dataset about food production would help the analysis (Van den  Bergh and Verbruggen, 1999).
Furthermore in our model the demand and potential supply need to be estimated using different sources of data, thus limiting their comparability. For example, data on the meat slaughtered could also include meat imported, and not from animals bred in the region. To overcome this issue, further investigation could be done at slaughterhouses as was done by  Fastelli (2011) , livestock producer associations or hygiene services to estimate the number of locally-produced slaughtered meat. However, besides these limits, the use of these datasets enables us to compare different urban regions, as well as different methods, such as those developed in food system studies  (Timmons et al. , 2008; Colasanti and Hamm, 2010). The use of on-farm datasets used for current and actual food supply assessment improved the knowledge on the farm management, local livestock yields and urban market destination.
, 2007). Therefore, future analysis should be focused on the importance of local supply chains in farmland protection in periurban areas. This means understanding the agri-food paradigm, which is more related to the interconnections between products, actors, various supply chains  (Sonnino and Marsden, 2006). In addition, our results show that only a few farms (14% of the sample) are exclusively part of a short food supply chain, and there is a tendency towards the coexistence of conventional and short or alternative food chains that also involve a new and more complex understanding of the relationships between conventional food chains and alternative food chains  (Renting et al. , 2003; Sonnino and Marsden, 2006). Generally speaking, we believe that gaps between potential, current and actual food supplies do not completely depend on the different databases.