dataesr/scientific-paragraphs-categorization huggingface dataset:
Within the framework of this study, we focused on the differences between carbon and water fluxes between grasslands under mowing and grazing managements. Flux data from the paired paddocks were used to parameterize and validate the CenW model. The specific objectives of the present study were to 1. test the ability of the CenW model to simulate water and CO2 flux dynamics of two temperate grassland ecosystems under mowing and grazing management, respectively; 2. evaluate the model's ability to capture the seasonal and interannual dynamics of CO2 and water fluxes in response to climate variability (five years) in interaction with two contrasting management practices (mowing and grazing); and 3. determine the effects of mowing and grazing on eddy covariance fluxes and on the CO2 budget of managed grasslands.
Meteorological Conditions at the Study Site Meteorological conditions for the two paddocks were acquired from a weather station coupled to a data logger (CR-10X, Campbell Scientific Inc. , Logan, UT) placed 1. 9 meters aboveground on the mown paddock  [17, 26, 61]. Briefly, the weather station provided 30 min averaged values of precipitation (SBS500, Campbell Scientific Inc. , Logan, UT), air temperature and relative humidity (HMP 45 AC, Vaisala), radiation components (CNR1, Kipp & Zonen), and wind speed (A100L2, Vector Instruments) and direction (W200P, Vector Instruments). Volumetric soil water content data were collected by time domain reflectometry (TDR) probes at 10, 20, 30, 60, 80, and 100 cm depths (CS616, Campbell Scientific Inc. , Logan, UT), and soil temperatures were measured at 5, 10, 20, 30, 60, 80, and 100 cm down the soil profile (PT100, Mesurex), but only for the mowed paddock.
Soil heat flux was measured at 5-cm-depth (HFP01, Hukseflux), and data were corrected for changes in heat storage in the soil layer above the flux plate  [62]. Over the study period (2006-2010), average air temperature and average annual precipitation were 11. 2 °C and 774 mm yr -1 , respectively. Half-hourly meteorological data, i. e. , air temperature, global radiation, humidity, and precipitation, were summed/averaged to daily values to be used as driving variables for the CenW model runs of both paddocks. The predominant wind direction was from the southwest and a secondary peak from the northeast (Figure  1b ). Throughout the five years of the study, daily maximum air temperature exceeded 25 °C for 10. 5% of the time, with a maximum of 35. 5 °C. Daily minimum air temperature was negative for 13.
Eddy Covariance (EC) Measurements and Processing We used paired eddy covariance systems for the 2006-2010 period because it provided EC data measurements for both mowed (P2) and grazed (P4) paddocks. The two EC systems recorded raw data at 20 Hz, and EddyPro® software (LI-COR Inc. ) was used for postprocessing and the calculation at 30-minute intervals for fluxes of CO2, momentum, and sensible and latent heat. Each EC unit included a fast response sonic anemometer (Solent R3-50; Gill Instrument, Lymington, UK) and an open-path CO2-H2O infrared gas analyzer (LI-7500; LI-Cor Inc. , Lincoln, NE, USA) placed at 1. 55 m above the ground. In this study, the micrometeorological sign convention is followed, with negative net CO2 fluxes (NEE) representing the transport from the atmosphere towards the surface (assimilation of CO2 through photosynthesis) and positive ones indicate that the system is a source of carbon (release of CO2 through respiration).
Based on previous work using these EC datasets  [17, 26] , flux measurements, and quality checks were done according to the CarboEurope-IP guidelines  [63]. The flux footprint distribution and random uncertainty were analyzed. High-frequency loss corrections  [18, 64]  were not considered in flux processing process  [26]. The Webb-Pearman-Leuning (WPL) correction  [65]  was applied except for the self-heating of the IRGA because of the sensor orientation  [66]. All years of flux measurements for the two EC towers were quality checked and filtered with a custom R program. The quality check led to the rejection of half-hourly flux observations based on nine criteria: 1. NEE values lower than -35 or higher than 25 μmol m -2 s -1 2. NEE values higher than 3. 5 μmol m -2 s -1 when PAR was above 400 μmol m -2 s -1 3.
7% and 40. 9% of NEE observations in the dataset for the mowed and grazed paddocks, respectively. Gaps in 30 minutes NEE were filled and NEE was partitioned between GPP and total ecosystem respiration rate (ER) using the online gap-filling and flux partitioning procedure described by  Reichstein et al. (2005)   [69] , hereafter referred to the Reichstein algorithm. This gap-filling method uses an improved, running-window look-up table that utilizes both the covariation of NEE with meteorological conditions and temporal autocorrelation of NEE  [70]. In the Reichstein algorithm, ER was modeled using the Lloyd and Taylor equation  [71]  fitted to air temperature. Following this approach, nighttime ER was first regressed against nighttime air temperature, and this relationship was then used to estimate ER for both nighttime and daytime.
Model simulations were optimized by selecting a set of parameter values that minimized the residual sums of squares across different EC measurements and ancillary observations. Measurements used for CenW parameterization were daily-and weekly-averaged estimates of evapotranspiration (ET) and net ecosystem exchange (NEE). We separated our five years of eddy covariance data into weekly sets, with one week of daily values used for parameter optimization and the other week for model validation. There are therefore two flux datasets for each paddock, one for model calibration, and one for model verification. CenW uses an automatic parameter optimization routine that worked by changing parameter values within specified boundaries to minimize the residual sums of squares. That was applied to both daily and weekly-averaged data within the data set selected for parameter optimization.
Initial parameter values to run CenW for managed grasslands were retrieved from a previous study where the model was run for a grazed dairy farm of New Zealand  [56]. Specific management practices from farm records were implemented in CenW and we used a spin up of the model to initialize soil carbon and nitrogen pools. Then, model simulations were optimized for these paddocks based on a selection of eddy covariance observations (NEE and ET) and ancillary data (amounts of vegetation mowed and grazed and soil water content) by the automatic parameter optimization procedure imbedded in the model that aim to maximize the agreement between model and observations. The overall goodness of fit was described by the Nash-Sutcliffe model efficiency (EF)  [78] : 𝐸𝐹 1 ∑ ∑ , (1) where yo represents the individual observations, ym is the corresponding modeled values, and  the mean of all observations.
CenW Performances to Simulate Carbon Dioxide and Water Fluxes of Mown and Grazed Grasslands Over the five years of the study period, a wide range of climatic conditions (Figure  2 ) were encountered as well as different management practices like different mowing, grazing, and fertilizer application frequencies (Appendix A) and different stocking rates for the various grazing events. Achieving good model/data agreement is challenging because the model needs to incorporate a wide variety of processes to simulate accurately such complex systems and to capture the variability of fluxes and vegetation dynamics affected by biotic and abiotic factors. The CenW model used only one fixed set of parameters for multiple years and after the calibration of the CenW model for the two grassland sites, daily modeled and observed carbon and water fluxes could be compared.
to limit the bias that would have resulted from the non or incomplete capture by EC of the large respiratory losses during measurement periods when grazing animals were present around the EC tower or when freshly cut or drying grass was present on the ground during mowing events  [56, 79]. For the NEE dataset, which was separated into parameterization and validation subsets, R 2 , and model efficiencies are slightly higher for the parameterization subset than for the validation one for the two grassland sites. Moreover, in general, model/data agreements (Figure  3  and Table  1 ) are better for GPP and ER than for NEE as it is easier to model large photosynthesis and respiration fluxes than the relatively small difference between the two (NEE). Model/data agreements are also better for mowing than for grazing, certainly because of the higher complexity of grazed systems compared to mowing.
Performances of the CenW Model to Simulate Gas Exchanges of Mowed and Grazed Pastures Mechanistic ecosystem models, like the CenW model, are useful tools to gain a better understanding of GHG emissions, yields, and carbon stock dynamics of managed grasslands as they can address, over long time periods, the complex interactions between climate, soil, vegetation and management practices  [80] [81] [82]. Modeling studies have shown that models could achieve high accuracy in simulating greenhouse gas (CO2 and H2O) uptakes and emissions, yields, and carbon source/sink activity of managed grasslands for a wide range of climate and management conditions  [17, 56, [83] [84] [85] [86]. By using observation and models in conjunction, it is possible to improve our knowledge of the systems under study, identify weaknesses in datasets and models and to correct them.
Cow Respiration in Observed and Modeled CO2 Fluxes Discrepancies between modeled and observed ER and NEE fluxes, on the grazed paddock, could be caused by the uneven repartition of dairy cows on the field and the variability of the flux footprint: i. e. dairy cows were under-represented in the flux footprint, causing an under-estimation of animal respiratory losses of CO2 in EC data. Some large grazer respiration fluxes could have also been excluded through the filtering of NEE data to remove outliers (condition 1, 2 and 3 in section 2. 1. 2). Moreover, because the Reichstein gap-filling algorithm does not explicitly include respiration from grazing animals (e. g. , animals disturbance) to either fill gaps in NEE time series or in the partitioning of NEE into GPP and ER, substantial bias could be added to the dataset  [56, 79].
We advocate that a better way to process EC data on rotationally grazed pasture would be to exclude measurements taken during grazing periods and fill the gaps outside and inside these periods with data acquired when there was no cows in the paddocks  [97]. This would insure to get the flux from the pasture only  [79, 94] , and then calculate how much carbon is lost from the paddock due to grazers respiration based on stocking rates, grazing duration, and amount of ingested biomass. This imply that detailed information on cows movement and farming practices need to be recorded and used to process EC data from intensively manage grasslands, especially for systems with high stocking rates like rotational grazing. The same dataset (we reprocessed meteorological and EC data for this study) was previously used to derive the net carbon storage (NCS) of the mowed and grazed grasslands  [26].
Acknowledgments: We would like to thank the  National Research Infrastructure "Agro-écosystèmes, Cycles Biogéochimique et Biodiversité" (ACBB)  for their support in field experiment and the database. We are deeply indebted to  Christophe de Berranger ,  Marie-Laure Decau  and  Nicolas Mascher  for their substantial technical assistance. Last but not least we would like to thanks  Dr. Gregory Starr  ( Department of Biological Sciences, University of Alabama, USA ) for his technical support in refiltering the dataset.
Funding: The research leading to these results has received funding principally by the New Zealand Government to support the objectives of the "Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases under grants SOW12-GPLER-LCR-PM (Proposal ID 16949-15 LCR)", "The AnaEE France (ANR-11-INBS-0001)", "AllEnvi", and "CNRS-INSU".
Participants Thirty-two healthy Dutch participants were recruited for this study from the database of Wageningen Food & Biobased Research, social media and printed posters and received a monetary compensation for their participation. In order to be eligible for this study, participants filled out a questionnaire that included questions about drivers of food choice, frequency of meat and meatless consumption, and attitudes towards meat and meat replacers. A selected participant had to meet all of the following criteria: (1) Age between 18 and 65 years, (2) Dutch native language, (3) Healthy (self-reported), (4) BMI between 18. 5 and 27 kg/m 2 (self-reported), (5) Normal ability to taste and smell (selfreported), (6) Being a flexitarian and a light user of soy sauce (≥1x per month and < 1x per week), (7) Having given written informed consent, (8) Having access to a Windows laptop with Google Chrome and webcam and willingness to use these in the study.
64 ( ± 0. 64)), healthiness (4. 04 ( ± 0. 61)), and price (3. 72 ( ± 0. 68)) had the biggest influence on their decision to buy or consume a product (5-point scale). Environment (3. 48 ( ± 0. 87)) and convenience (2. 92 ( ± 1. 04)) had a smaller impact. Of the thirty-two recruited participants, only eighteen were included in the study results reported here. Twelve participants were excluded because their data were incomplete, i. e. too many data points were missing of the implicit and/or explicit tests on more than one of the ten test sessions. Most often their incomplete results were caused by technical problems with the internet and/or webcam. Additionally, two participants were excluded based on the poor quality of their video recordings (images were too dark).
Test foods This study included samples of four commercially available foods: (1) stir fried chicken breast seasoned with salt ("plain chicken"), (2) stir fried chicken breast seasoned with soy sauce ("soy chicken"), (3) stir fried tofu seasoned with soy sauce ("soy tofu"), and (4) stir fried vegetarian chicken breast (Dutch brand: 'De Vegetarische Slager') seasoned with soy sauce ("vegi chicken"). As a warm-up sample a piece of minced meat seasoned with salt was used (Dutch brand: Unox), which was a rather different product but with comparable sensory properties particularly concerning its salty taste and meat like origin. The warmup sample was presented as first sample during each session and corresponding data were not used for further analysis. Participants took two bites (approximately 6 g per bite) per food and evaluated per session a total of 60 g of food, scoring the sensory attributes and liking.
All samples were purchased from supermarkets in the Netherlands. The Dutch Food Safety Authority (Nederlandse Voedsel-en Warenautoriteit) ensures that food products sold in the Netherlands are safe for consumption. Samples were prepared in the morning of each test day and stored in the refrigerator at 4 °C according to the hygiene regulations set by the Wageningen University. Samples were served at room temperature in transparent plastic cups labelled with random three-digit codes. Samples were randomized with the software TimeSens® (ChemoSens, Dijon, France) according to a William's Latin Square experimental design, and the attribute order was kept the same across all participants throughout sensory evaluation.
During each session instructions were provided on the computer monitor via internet and the TimeSens® (ChemoSens, Dijon, France) software. The same software was used to present the test items, collect the responses, and to video record the participant's mimics during sample evaluation. Prior to each evaluation, participants used a video feedback of themselves to verify that the camera position was accurate. Participants were instructed to consume two bites. The first bite was used to evaluate the liking of the food after the food was chewed and swallowed. The second bite was used to evaluate the sensory food properties using a set of 10 sensory attributes. In between the tasting of two samples, participants rinsed their mouth with crackers and water. Each bite was processed at the participant's own speed.
Facial expressions, heart rate and consumption duration Video segments of the consumption of each bite were stored together with the participant's code, the product code, and time and date information. The length of the video segments were used as indicator for consumption duration. Facial expression data were automatically analysed per time frame of 0. 04 sec by FaceReader 7. 0 (Noldus Information Technology, Wageningen, The Netherlands) in three steps. The face is detected in the first step using the Viola-Jones algorithm  (Viola & Jones, 2001). Next, the face is accurately modelled using an algorithmic approach (Den Uyl &  Van Kuilenburg, 2008). Based on the Active Appearance method described by  Cootes, Edwards, and Taylor (2001)  the model is trained with a database of annotated images that describes over 500 key points in the face and the facial texture of the face.
Finally, the actual classification of the facial expressions is based on an artificial neural network trained with 10,000 manually annotated images. The face classification provides the output of seven basic expressions (happy, sad, angry, surprised, scared, disgusted, and contempt), one neutral state on the basis of the Facial Action Coding System developed by  Ekman and Friesen (1978) , three "affective attitudes" (interest, boredom and confusion), and arousal and valence dimensions based on combinations of facial expressions. FaceReader scores for each emotional expression range from 0 (emotion is not detected) to 1 (maximal detection) and is based on intensity judgments of human experts. FaceReader allows for the simultaneous presence of multiple emotions. FaceReader was validated by others using the Radboud Faces Database, a standardized test with images of expressions associated with basic emotions.
Data analysis Positions on liking and attribute line scales were converted into scores ranging from zero to ten. Video segments used for analysis of facial expressions and heart rates can be as short as 15 s or as long as 55 s depending on the participant and test food. To verify systematic changes in facial expressions during consumption (e. g. , initial expressions of interest may be replaced by expressions of boredom during consumption) differences in durations needed to be normalized first. For normalization, each segment was divided in three tertiles reflecting the start, middle and end of consumption. Per tertile, maximum intensities of expressions and average heart rates were calculated to verify systematic changes during consumption. Statistical analyses were performed with mixed model ANOVAs (using IBM SPSS statistics, version 22) with participant as random factor, and with test food (4), bite (2), test location (2), and replicate per location (5) as fixed factors.
Motion detection represents one of the critical tasks of the visual system and has motivated a large body of research. However, it remains unclear precisely why the response of retinal ganglion cells (RGCs) to simple artificial stimuli does not predict their response to complex, naturalistic stimuli. To explore this topic, we use Motion Clouds (MC), which are synthetic textures that preserve properties of natural images and are merely parameterized, in particular by modulating the spatiotemporal spectrum complexity of the stimulus by adjusting the frequency bandwidths. By stimulating the retina of the diurnal rodent, Octodon degus with MC we show that the RGCs respond to increasingly complex stimuli by narrowing their adjustment curves in response to movement. At the level of the population, complex stimuli produce a sparser code while preserving movement information; therefore, the stimuli are encoded more efficiently.
In turn, stimulation with naturalistic images generates sparser code  15, 16  and eye tracking responses become more precise  17. In the retina, the most basic form of motion detection is the response to moving textures in which Y-like Retinal Ganglion Cells (RGCs) increase their firing rate when sub-regions of their Receptive Field (RF) alternate between light and dark luminosity levels through time, regardless of the direction of motion  18, 19. It has been demonstrated that these mechanisms arises from the RF properties of ON and OFF Bipolar Cells (BCs) that connect to the RGCs  20  and transmit their activity in a non-linear fashion. This non-linear integration of the activity of BCs allows that the alternating activation of cells of both polarities do not cancel each other upon reaching RGCs  21.
In support of this, we showed that population sparseness increases for the naturalistic stimuli, so in this respect, the retina is performing better under these types of stimulation, in the sense that a sparse code is more efficient from an information transmission point of view. To see if these changes in tuning curves affect the coding of the motion signal, we implemented the stimulus reconstruction and speed decoding method. We chose speed as the relevant parameter for motion estimation, but we expect that with the proper adjustment, any property of the stimulus could be recovered, provided one can obtain a sufficiently good reconstruction  53. Another simplification that we took advantage of, is that our experimental design is based on the independent presentation of a single speed for each stimulus set (with slight variations around an average value for the MCs); this means that for the duration of each segment of stimulation, the whole population of RGCs is "seeing" the same speed.
In that respect our main results and conclusions, at photopic conditions, are extracted from the response of M-pigment cones. Stimuli were generated using the code available at https://github. com/NeuralEnsemble/MotionClouds. Timing of the images was controlled using a custom-built software based on Psychtoolbox for MATLAB. Spike sorting analysis was performed using Spyking-Circus  59. Data was analyzed via redistributable Jupyter notebooks using SciPy and running Python 3 kernels. All data fitting procedures were performed with LMFIT. Figures were generated with Matplotlib. Maximal speed projection is given by the time needed for a point to cross half of the projected image in one frame time, i. e. , 200 px/frame. As the projector works at 60 frames, it gives us an estimation of 12000 px/s.
Acknowledgements Fondecyt  1150638  and  1140403 ,  CONICYT-Basal  Project  FB0008 ,  Millennium Institute   ICM-P09-022-F. L. U. P. was supported by  ANR  projects " BalaV1 " ( ANR-13-BSV4-0014-02 ) and " TRAJECTORY " ( ANR-15-CE37-0011 ). C. R. R. was supported by a  CONICYT Ph. D. scholarship. We thank  Dr. John Ewer  for proofreading the manuscript.
Samples drawn before (0 min) and after (60 min) auxin addition were analysed by calibrated ChIP-sequencing (Scc1-PK6). Cohesin ChIP profiles along chromosomes four is shown. Also see Figure  1 -figure supplement 1H and I. (G and H) Data form (E and F respectively) are plotted to show the ratio of average cohesin levels 60 kb on either side of all 16 centromeres before and 60 min after auxin addition (I and J) The Wapl-independent activity that releases cohesin from DNA is active only in G1 (I) and not in mitotic cells (J). DOI: https://doi. org/10. 7554/eLife. 44736. 004 Elbatsh et al. , 2016). If smc1D1164E really eliminated ATP hydrolysis, then Scc2 cannot prevent release by inducing ATP hydrolysis. We therefore purified wild type (WT), walker B mutants in both Smc1 and Smc3 ATPase (1EQ 3EQ), smc1D1164E mutant cohesin tetramers and compared their ATPase activity stimulated by Scc2 in the presence and absence of DNA (Figure  4 -figure supplement 1C).