dataesr/scientific-paragraphs-categorization huggingface dataset:
Previous history of exacerbations is the best predictor for future exacerbation  [6] , but since exacerbations are often not reported by patients [7e9], two innovative aspects were implemented in the study. Firstly, we used an electronic real-time transmission of EXACT diary data to enhance contact between patients and physicians and to improve the reporting of exacerbations. Secondly, by coordinating recruitment waves in the northern and southern hemispheres, different winter exacerbation peaks were captured across the globe, thus increasing the chance of catching winter respiratory viral infections. The primary aim of the study was to test the superiority of extrafine BDP/FOR 100/6 mg, 2 inhalations BID over extrafine FOR alone 12 mg, 1 inhalation BID in both the reduction of exacerbations and the improvement of pre-dose morning FEV 1 in COPD patients.
Patient characteristics at baseline Fig. 1  presents the flow diagram of the study. A total of 1693 patients were screened, of whom 1199 were randomised to BDP/FOR (N Z 602) or FOR (N Z 597) groups. The most common reasons for screen failure were failure to meet inclusion criteria and consent withdrawal (n Z 362 and n Z 108, respectively). The most common reason for early study discontinuation in both treatment groups was withdrawal of consent (4. 2% and 6. 0% in the BDP/FOR and FOR groups, respectively). A total of 7 randomised patients in the BDP/FOR group and 6 in the FOR group were not included in the ITT population due to the following reasons: violation of a major entry criterion (patients without COPD, n Z 5 in the BDP/FOR group and n Z 4 in the FOR group), lack of post-baseline efficacy data (n Z 1 in each group) and no intake of study drug (n Z 1 in each group, these patients were also not included in the analysis of safety).
Given the diversity of crop management options within a conventional, an integrated or an organic strategy, which might lead to contrasted performances, the generic value of experimental results ignoring this variability may be argued. We assessed the sustainability of 48 cropping systems located in regions of intensive arable farming and covering a wide range of pesticide use levels and cultivation techniques such as crop rotations, from monoculture to highly diversified crop rotations, soil tillage (e. g. inversion tillage, shallow tillage or direct drilling), fertilization (mineral or organic fertilizers), or weed management (e. g. only based on herbicide use, including mechanical weeding). More details about the cropping system sample are available in the online SI section (Dataset S1).
Cropping systems classification Details of cropping systems, including crop sequences, performances, and detailed crop management operations are made available in the Dataset S1 and S2. Cropping systems were considered as conventional, integrated or organic according to the following rules. Cropping systems complying with the organic farming specifications were treated as 'organic'. Other systems were considered as 'integrated', when they were based either on diversified crop rotations including unusual alternative crops for the production situation (i. e. not present in the local reference crop rotation), or when crop management included at least one nonchemical management approach that contributed to the control of pests, diseases or weeds. These included for instance biocontrol, mechanical weeding and false seed bed techniques.
Here we defined the registered dose as the lowest application dose which is recommended for a given crop. The TFI for a given crop season was then calculated as the sum of the TFI for each pesticide application performed during this crop season. Productivity was evaluated as the amount of energy harvested yearly. This approach allowed the comparison of different crop rotations that included crops with different yielding potentials and different energy content. For each crop, yields were transformed into the energy metric using their Lower Heating Value (LHV)  [48] , which corresponds to the amount of energy released per unit of mass by the combustion of the harvested biomass. Energy consumption was estimated from the conversion of inputs into energy according to the Dia'terre reference database  [48].
Dia'terre is an assessment tool developed by the Agency for the Environment and Energy Management (ADEME) in the framework of the French Plan for Energy Performance (PPE) to evaluate a carbon-energy balance at the farm scale. The reference database used to design this assessment tool provides energy values for indirect energy consumption associated with the production of farming inputs. For instance, the calculation of the energy cost associated with the production of nitrogen fertilizers integrates the energy necessary from raw material production (e. g. Haber-Bosch process) through materials processing, manufacture and distribution. We used the reference energy cost provided by the carbon calculator Dia'terre to compute the energy balances of the cropping systems. In this way, the inputs necessary for crop production were converted into energy, using the energy cost of fertilizers, pesticides, seeds, water spread for irrigation, fuel consumed by the equipment and the amount of steel necessary to manufacture this equipment, i.
e. energy cost of mechanization (see Dataset S3). The energy requirements for preparing farmyard manure are farm-specific and very difficult to quantify precisely. A simplification was consequently required: following previous studies based on energy balancing methods in crop production  [49] , the energy equivalent of farmyard manure was equated with that of the mineral fertilizers they substituted (using a substitution value related to the fertilizing efficiency of manure). Energy efficiency was computed from the ratio between productivity and energy consumption. For assessing the economic productivity, the gross product derived from the direct conversion of crops yields into economic values. The 'semi-net' margin was calculated as the gross product per hectare from which we subtracted the input costs (fertilizers, pesticides, seeds, fuel, water and mechanisation).
Fuel consumption and workload were estimated according to in-field cropping operations only, without considering fuel and time consumed for farm-to-field transports, or extra-workload dedicated to equipment maintenance or field observations. The size, fuel requirements and working output of the various equipment types were standardized for all cropping systems, and defined from a national database  [50] , consistent with the aim of evaluating management strategies, and of ignoring the potential effects of the equipment specifications (See Dataset S4 for the details of the equipment used for the calculations). Pesticide environmental impact was expressed as cumulated I-Pest  [24]. This indicator measures the risk associated with pesticide application for three compartments of the environment, namely the air, the surface water and the groundwater.
Computation of sustainability indicators at the cropping system level As a first step each indicator was calculated for each cropping operation our database (Dataset S2). These values of indicators were summed over the crop season year, and then averaged across years and across plots, each plot being considered as a replicate of a given cropping system. Each indicator was therefore calculated at the cropping system level, integrating (i) the different crops composing the crop sequence, (ii) the variability of crop production related with the inter-annual climatic variability, and (iii) the possible variation in plot properties. All sustainability indicators were expressed per hectare and per year. For distinguishing specifically the effects of the management strategy on cropping system sustainability from the effects of the production situation, each indicator computed for a given cropping system was then expressed as a ratio (or as a distance in the case of semi-net margin) between the system indicator and the local reference indicator.
Study setting Between 1985 and 1988, 10 308 British men and women (73% of those invited), aged 35-55 years and employed in London-based government departments, participated in the first phase of the Whitehall II study. 23 Two thirds of them were men. Clinical examinations in addition to postal questionnaires were part of every second phase, i. e. phase 1 in 1985-88, phase 3 in 1991-94, phase 5 in 1997-99, phase 7 in 2002-04 and phase 9 in 2007-09. The final dataset contained up to five repeated measurements per participant.
These results were reproduced in another cell type and with another strain of L. monocytogenes, L028, suggesting that LLO prevents H2AX phosphorylation (figure  S4  and 2A ). In addition, upon overexpression of LLO in a Dhly mutant background, the level of cH2AX was reduced to the same level as that observed in uninfected cells (figure  2A ). These data, along with the comet assay results, thus suggested that LLO dampens the DDR downstream of DNA breaks. We also measured the number of 53BP1 foci upon infection with a Dhly mutant. As shown in figure  2B  similarly to what is observed for cH2AX, infection with a mutant lacking LLO caused an over 2-fold increase in the number of 53BP1 foci compared to wild type bacteria (around 2000 cells were counted per condition). Together these data show that during infection LLO dampens the DNA damage response, which is normally activated upon DNA breaks.
Flow cytometry, immunofluorescence and immunoblot Bacterial infection was assessed by flow cytometry using the BD Cytofix/cytoperm kit (BD Biosciences). Briefly, cells were trypsinized and fixed for 20 minute with fixation and permeabilization solution. Cells were then harvested in PBS for analysis. For cell cycle distribution, cells were scrapped, resuspended in PBS and fixed in 70% ethanol for at least 1 h at 220uC. Cells were washed and re-suspended in PBS containing 15 mg/ml of propidium iodide and 100 mg/ml of RNase A. Flow cytometry acquisitions were performed on a FACScalibur flow cytometer (Becton Dickinson). Data were analysed using FlowJo software. For measuring infection, Listeria monocytogenes expressing GFP were used as described in  [31]. Infected cells were trypsinized and analyzed on a FACScalibure.
Data was analyzed using the FlowJo software. To quantify positive cells for 53BP1 or cH2AX by immunofluorescence, infected cells were washed and fixed in 4% paraformaldehyde. After permeabilization, 53BP1 or cH2AX proteins were stained with an anti-53BP1 antibody (Novus biologicals) or anti-cH2AX (#9718, Cell Signaling), and a 546 nM goat anti-rabbit antibody (Invitrogen). References for the antibodies used in our studies are the following: NBS1 (Novus biologicals NB100-143), MDC1 (Novus biologicals NB100-395), RAD50 (Novus biologicals NB100-147). For Western blot analysis, cells were lysed with 26 Laemmli loading buffer (124 mM Tris-HCl [pH 6. 8], 4% SDS, 20% glycerol, 0. 02% bromophenol blue, 0. 03% dithiothreitol [DTT]), sonicated for 2 s, and then boiled for 5 min. After running on gels, proteins were transferred on a nitrocellulose membrane (GE Healthcare) that was then blocked in 10% milk.
Quantitative PCR mRNA was extracted from uninfected cells and cells infected with L. monocytogenes using the Qiagen RNeasy kit. cDNA was then synthesized using the Qiagen RT 2 first strand kit. Real-time PCR was performed using Qiagen RT 2 PCR arrays (DNA damage array PAHS-029Z) on a Biorad CFX384. Data was analysed using the Qiagen RT 2 Profiler PCR array data analysis. siRNA HeLa cells were reverse transfected with commercial siRNA for ATM (L-003201, Thermo scientific), CHK2 (L-003256, Thermo scientific), 53BP1 (s14313, Ambion), H2AX (s226270, Ambion). Briefly, 25 nM siRNA is mixed with 4 ml Lipofectamine RNAiMAX (Invitrogen) in a 400 ml volume. After 15 minutes of incubation 5. 5610 4 cells were added per well of a 6 well plate. Infections were done 48 h after siRNA treatment for an additional 24 h.
Work in the Cossart laboratory is funded by the  Pasteur Institute, Inserm  ( U604 ),  INRA  ( USC2020 ), the  Fondation Louis Jeantet , the  ERC  (Advanced Grant # 233348  MODELIST), the  Fondation Le Roch Les Mousquetaires , as well as the  ANRs (ERANET ''LISTRESS'', and EPILIS). This study has received funding from the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence ''Integrative Biology of Emerging Infectious Diseases' ' (grant nuANR-10-LABX-62-IBEID). LD is funded by the  European Research Council  under the  ERC  starting grant agreement #  310917 , the  Institute Pasteur , the  CNRS , the  Fondation pour la Recherche Medicale , as well as the  Ville de Paris. PC is an  international senior research scholar of the Howard Hughes Medical institute. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Finally, 2 mM MgCl 2 and 0. 2 mM EGTA were added to the reaction to transform Ca-G-actin into Mg-Gactin (magnesium-bound monomeric actin), and 25 mM KCl was added to the reaction to induce actin polymerization. Importantly, this low ionic strength allows myosin to form bipolar filaments. After the reaction was injected, the flow chamber was sealed with VALAP (a mixture of vaseline, lanolin and paraffin wax). Additional proteins such as a-actinin, mCherry-a-actinin, EGFP-Vh and EGFP-vinculin were also added. The short actin filaments used in several reactions were prepared by mixing prepolymerized actin with the barbed-end capping protein gelsolin at a 1:2,000 gelsolin/actin molar ratio. TIRF microscopy and data analysis. The assay described in the previous section was observed by TIRF microscopy.
TIRF images were acquired with an Olympus IX71 inverted microscope equipped with an oil 60 Â objective (1. 45 NA) (Olympus). EGFP-vinculin was excited with the 473 nm laser (Cobolt), and Alexa594-labelled actin and mCherry-a-actinin were excited with a 561 nm laser (Cobolt). The time-lapse videos were acquired with a Cascade II camera (Photometrics) controlled by Metamorph and subsequently analysed by the ImageJ software. Kymographs were generated in Image J by using the 'Kymo Tool Box' plugin. FRAP experiments. FRAP experiments were performed to measure the rate constant of fluorescence recovery of EGFP-Vh bound to talin. The experiments were performed as soon as the kinetics of EGFP-Vh binding to talin-coated discs reached the plateau (steady-state) in the presence of myosin and short actin filaments capped at their barbed ends.
FRAP experiments were first performed on a spinning disk confocal microscope setup (Nikon TIE) using a 60 Â Plan APO objective (1. 49 NA) and a FRAP module equipped with a 491 nm laser (iLas 2 , Roper Scientific SARL, Evry, France). Circular regions (10 mm in diameter) were drawn around the discs and bleached by 20 iterative pulses of 20 ms each using the 491 nm laser at 50% power. FRAP was monitored on a single plane for 1,500 s (1 frame per 10 s) using the 491 nm laser at 9. 5% power. We bleached 70±5% of the fluorescence and observed a 72±4% recovery. The fluorescence of nonbleached discs did not change during the recovery of the bleached discs. The rate constant of recovery was slow (k ¼ 0. 0029±0. 0005 s À 1 , mean±s. d. , n ¼ 6, data collected in two independent experiments, Supplementary Fig.
Recovery was monitored for 1,500 s (1 frame per 10 s, 20 ms exposure time). We bleached 70 ± 9% of the fluorescence and observed an 80±9% recovery. The rate constant of recovery measured on the TIRF microscope (k ¼ 0. 0033 ± 0. 0006 s À 1 , mean ± s. d. , n ¼ 28, data collected in three independent experiments, Fig. 4d , Supplementary Fig. 3c, d , Supplementary Movie 5) was similar to the rate constant measured on the spinning disk microscope mentioned earlier (k ¼ 0. 0029 ± 0. 0005 s À 1 , mean ± s. d. , n ¼ 6, data collected in two independent experiments, Supplementary Fig. 3a, b ). In preliminary attempts, we determined that the maximum rate of recovery was observed at 0. 5 mM EGFP-Vh. On the basis of this observation, we chose to perform the FRAP experiments at 1 mM EGFP-Vh. In these conditions the fast reassociation is not a limiting parameter.
Acknowledgements C. L. C. was supported by the  Agence Nationale pour la Recherche  ( ANR-09-JCJC-0111  ADERACTIN). We thank the members of the  'Cytokeleton Dynamics and Motility ' team for helpful discussions. FRAP experiments on the spinning disk confocal microscope were performed at the  Imagif Cell Biology Unit of the Gif campus.
This article proposes a fast and open-source implementation of the well-known Non-Local Means (NLM) denoising algorithm, in its original pixelwise formulation. The fast implementation is based on the computation of patch distances using sums of lines that are invariant under a patch shift. The optimal parameters of NLM (in the average peak signal to noise ratio -PSNR -sense) are computed from an image database, thereby leading to a parameter-free NLM implementation. Comparison is performed with the parameter-free blockwise NLM implementation already proposed in IPOL journal by Buades, Coll and Morel. As expected the blockwise implementation offers better PSNR, at least when the noise standard deviation is large enough, but there is no significant difference in quality when performing visual inspection.
Despite the number of NLM codes that can be found in the Internet, it seems that there are only two implementations that fulfill these conditions: the nlmeans module included in MegaWave2  [11]  and written by Lionel Moisan and the IPOL article  [4]  from Buades, Coll and Morel. The first code implements the pixelwise NLM algorithm given in  [1]  (Section 5. 1) for gray-level images only and using the Euclidean norm instead of the uniform one to define neighbor patches. The parameters' default values correspond to those in  [1] , but they turn out to be non optimal for most images. Finally, this code does not implement any algorithmic trick or parallelization of computing, making it a slow running program. While the IPOL article  [4]  describes both the pixelwise NLM (NLM-P) (  [1]  Section 5.
(11) In such a case, NLM-weights write w NLM-P (x, y) = 1 n(x) e -V (x)-V (y) 2 2 h 2. ( 12 ) We denote by NLM-P the pixelwise NLM denoising scheme with the Euclidean norm instead of the Gaussian Euclidean one. When mentioned in the literature, the reason for using NLM-P rather than NLM-Pa is that the increase in quality provided by the Gaussian kernel is low, while it requires the additional parameter a to be tuned. However, it seems that there is no experimental study using image databases to quantify the effect of the Gaussian kernel. It is a goal of this article to examine to what extend the previous assertion is valid. To unify the presentation of NLM-P and NLM-Pa, we may denote by K the kernel used to weight the norm between patches (K = K Ga for NLM-Pa, K = 1/d 2 for NLM-P) and the corresponding norm will be written.
Experiments on Databases: Parameters Estimation and Experimental Results Having fast implementations, the NLM usability is now determined by the ability to set its 3 (NLM-P) or 4 (NLM-Pa) parameters in order to obtain an effective denoising. Methods that propose to automatically adjust NLM parameters according to local or global characteristics of the image will not be considered here, insofar as they lead to algorithms that differ from the original NLM. The reader interested in such NLM extensions may consult  [15, 9]  and references therein. To estimate NLM parameters, the optimal values are calculated from a set of noise-free natural images on which is added on each plane a Gaussian noise with mean 0 and given standard deviation, so that input images may be deemed to satisfy the noisy image model (  1 ) with a known σ.
Parameters Estimation Using Two Image Databases To build the image database, the choice was to take the 21 color images proposed in the on-line demo of the IPOL article  [4]. These images are assumed to be almost free of noise, as they have been obtained by taking a good quality photograph in broad daylight and they have been post-processed with a low-pass filter followed by a sub-sampling of factor 8. These color images contain 3 color planes in the RGB color model, each channel being coded using 8 bits. In the following, the corresponding database will be called Color IPOL. Since the patch distances (  11 ) and (  9 ) are computed using all color components, the estimation of the oracle |u(x) -u(y)| will be more reliable as the number of channels N c increases. Therefore, parameter's values (especially the patch side length d) should depend on the number of channels.
To assess this effect, another database made of gray-level images (one 8-bits channel) is used. Such images are simply the grayed version of Color IPOL ones, using the Rec. 601 Luma weights  3. The resulting database will be called Gray-level IPOL. From any of these two databases, the procedure used to get optimal parameters is as follows. The standard deviation is quantified between 1 and 100 with a step of one. Gaussian noise with mean 0 and standard deviation σ ∈ [  [1, 100] ] is added to each image in the database, using the Mersenne Twister pseudo-random number generator provided by IPOL development tools  4. In order to limit the impact of the noise realization, 10 noisy images per noise-free image are generated. This results in 210 different noisy images per database that are used as input of the optimization process.
The function f σ to be maximized is the arithmetic mean of the Peak Signal to Noise Ratio (PSNR) between each noisy image and its noise-free version and the optimization problem may be written, in the case of the NLM-Pa denoising scheme and for B the image database, f NLM-Pa,B σ (d s , D s , h , a ) = max ds,Ds,h,a 1 10|B| u∈B 10 i=1 PSNR(u, ũNLM-Pa (u, i, σ, d s , D s , h, a)) ,  (26)  where ũNLM-Pa (u, i, σ, d s , D s , h, a) is the denoised image computed from the noisy input u + i ( i between a noise realization of standard deviation σ), using NLM-Pa algorithm with parameters d s , D s , h, a. The same writing is obtained for f NLM-P,B σ , without the last parameter a. Since the complexity of f σ does not allow the use of conventional optimization algorithms, the only safe method is to discretize the parameters and try as many combinations as possible.
Fortunately, parameters d s and D s are integers and they can reasonably be limited to small bounds. As mentioned in the literature (see e. g. [1, 4] ), the parameter h that controls the decay of the weights should be proportional to the value of σ and in practice, a value of h slightly greater than σ seems to be the best. The literature provides little guidance on the choice of the parameter a. In  [11]  (nlmeans module), the default value a = d s /2 is proposed. Based on these considerations, Table  2  gives the quantized values and their bounds that have been used for the parameter exploration. Exploring all of these parameters would require 329120 calls to the NLM-Pa code for a given input image and standard deviation, so the parameters estimation for the Color or the Gray-level IPOL database would need more than 6.
Experimental Results and Comparison With Blockwise NLM (NLM-B) Color    Table  4 : Optimal parameters for NLM-P on Color and Gray-level IPOL databases. Color IPOL database σ d s D s h ]0, 3] 1 2 1. 5σ ]3, 8] 1 3 1. 4σ ]8, 9] 1 4 1. 3σ D s h ]0, 7] 1 3 1. 5σ ]7, 9] 1 4 1. 4σ ]9, 19] 1 5 1. 3σ 5. 5. 2), also called patchwise NLM in the IPOL article  [4]. The code used for NLM-B is the one published in  [4]  with the given parameters (see Gray and Color tables). The article does not mention how these parameters have been set. In NLM-B, the denoising process is performed by blocks using a vectorial NLM. From these restored overlapping blocks, the final estimate is computed by an aggregation formula. To know what to expect about comparison between pixelwise and blockwise NLM, let us quote  [4] : "The main difference of both versions is the gain on PSNR by the patchwise implementation, due to the larger noise reduction of the final aggregation process.