dataesr/scientific-paragraphs-categorization huggingface dataset:
• STA • • • • • PCA • Spectral analysis • • • • • • MaxtEnt (x/x-t) GLM • Raster generation • • •. with Poisson/Non Poisson • • •. with VIRTUAL RETINA • Abbreviations for software names (first and last initials) have been chosen for the presentation. Following software is discussed: CX, CHRONUX  (Ince et al. , 2010) ; FD, FIND  (Meier et al. , 2008) ; NT, NSTAT  (Goldberg et al. , 2009) ; SL, SIGTOOL  (Lidierth, 2009) ; NY, NEUROPY  (Spacek et al. , 2008) ; OY, OPENELECTROPHY  (Garcia and Fourcaud-Trocmé, 2009) ; ET, ELEPHANT; SR, STAR  (Pouzat and Chaffiol, 2009) ; NE, NEUROEXPLORER; SV, SPYKEVIEWER  (Pröpper and Obermayer, 2013) ; PS, PRANAS. Note that features selected in this table have essentially been chosen according to what PRANAS does. It is not an exhaustive list and information applies to the time of writing.
, 2006; Marre et al. , 2009; Tkačik et al. , 2010 Tkačik et al. , , 2013;; Ganmor et al. , 2011a,b). This method has been used to study pairwise correlations (see  Vasquez et al. , 2012; Nasser et al. , 2013b; Nasser and Cessac, 2014)  as well as more general spatio-temporal correlations  (Cessac et al. , 2009; Vasquez et al. , 2010; Cofré and Cessac, 2013; Nasser et al. , 2013b; Cofre and Cessac, 2014; Herzog et al. , 2014; Ravello et al. , 2015; Herzog et al. , in preparation). PRANAS provides a toolbox to compute a Gibbs distribution from the neuronal data i. e. , to estimate a probability distribution, reproducing as close as possible empirical space-and timestatistics of a raster, without adding additional unnecessary assumptions (maximizing entropy) 2. In the current version, the population analysis toolbox of PRANAS focuses on stationary distributions, i.
Simulation of Spike Trains from a Retina Simulator Another strength of PRANAS is to provide a way to generate spike trains that mimic retina's outputs. To do so, PRANAS has integrated and extended the VIRTUAL RETINA simulator  (Wohrer and Kornprobst, 2009)  formerly developed in our team  7. In a nutshell, VIRTUAL RETINA is a software to perform large-scale simulations of biologically-plausible retinas. Given a retina configuration which can be fully customized and an input video as visual stimulus, VIRTUAL RETINA simulates the spiking output for different cell types. VIRTUAL RETINA has been shown to reproduce a broad range of experimental data at single cell level, from salamander, cat and primate retinas, and has been used in several theoretical studies  (Masmoudi et al. , 2010; Masquelier, 2012; Mohemmed et al.
, 2012; Basalyga et al. , 2013; Doutsi et al. , 2015a,b; Vance et al. , 2015). The underlying model includes a non-separable spatio-temporal linear model of filtering in the Outer Plexiform Layer, a shunting feedback at the level of bipolar cells, and a spike generation process using a network generalization of the noisy leaky integrate-and-fire neurons to model Ganglion Cells (GCells). Note that VIRTUAL RETINA was designed to be used via a command line (there was no GUI available). By integrating VIRTUAL RETINA software into PRANAS platform, we allowed this simulation software to be used through a GUI. We also extended VIRTUAL RETINA with the aim to reproduce statistically coherent responses at a population level. Indeed, outputs of the initial VIRTUAL RETINA version were successfully compared to experimental data at single cell level but could not reproduce collective statistics at a population level.
This represents a major advantage to promote interdisciplinary neuroscience and we believe that PRANAS helps in this direction. However, we are certainly aware that this advantage could also be seen as a limitation of the software. Indeed, inability to script analyses could be a limitation that can make systematic analysis of large datasets tedious and error prone. It makes also difficult to link the functionalities of this toolbox to more extensive data analysis workflow. For thoses reasons, we also offer some functionalities that run on the command line. A non-exhaustive list of tools that can be used from the command line is SimulateRetina (simulate retinal response), Correlations (compute the average crosscorrelations of a given raster), SpikeTriggerAverage (compute the average spike triggered stimuli) 11.
OO1. 190004. 000. S. P. 2014. 000. 31235. Binaries can be freely downloaded. 11 These commands can be found in the directory pranas/bin. Help of each tool is given with the command line: toolName -h. 12 Parallelization concerns every loop in our C++ code where iterations can be computed independently. To do so, we used OpenMP so that PRANAS parallel processes can run on a personal computer equipped with multi-core processors. More precisely, four functions were parallelized: (i) the loading of rasters for all file formats, (ii) the statistical analysis functions to generate potentials, compute divergence and estimate patterns probabilities, (iii) the estimation of receptive fields and (iv) the retina simulator. By putting together functions related to retina simulation and analysis, PRANAS intends to be an original platform that will encourage joint modeling and experimental studies.
ACKNOWLEDGMENTS We warmly acknowledge  Michael J. Berry ,  Maria-Jose Escobar ,  Matthias Hennig ,  Ruben Herzog ,  Gerrit Hilgen ,  Evgenia Kartsaki ,  Gaia Lombardi ,  Olivier Marre ,  Thierry Mora ,  Adrian Palacios ,  Cesar Ravello ,  Horacio Rostro ,  Vivien Robinet ,  Selma Souihel ,  Evelyne Sernagor ,  Gasper Tkačik ,  Juan-Carlos Vasquez  for their help in designing PRANAS. The authors also thank the reviewers for their constructive criticisms and valuable comments which helped us improve the manuscript.
FUNDING This work was supported by  KEOPS ANR-CONICYT , the  EC   IP  project  FP7-ICT-2011-9  no. 600847  ( RENVISION ) and  FP7-IDEAS-ERC  no. 227747  (NERVI), the  French ministry of Research and University of Nice  (EDSTIC), INRIA ADT and by the  ECOS-Sud program  (grant  C13E06 ).
Two of these suggestions, namely crop damage from ozone pollution and extreme temperatures, are treated in the remaining chapters. The second part presents a study of ozone damages on historical crop yields. A newly developed ozone module is implemented in the global crop model LPJmL. The enhanced model is used to simulate global historical wheat and soybean yield losses from ozone pollution. Crop water status, temperature and CO 2 are considered as modulators of ozone damage, which constitutes an improvement over previous global assessments that were based on linear correlations between ozone and yield. The analysis indicates that ozone is a major problem for crop production, causing yield losses up to occasional 50%. The third part contains an analysis of the effects of high temperatures on yield losses in the US, a major crop producing country.
2. Following one suggestion derived from the meta-study, the third chapter analyzes the extent to which historical crop yields have suffered from ozone pollution. To answer this question, an ozone-damage module is implemented in the global vegetation model LPJmL (chapter three). The enhanced model is used to simulate historical global wheat and soybean yield losses from ozone pollution. The ozone module introduced here is a novel development integrating experimental findings, scaling them up to the global level and considering co-variates of ozone damage like water, temperature and CO 2. The latter is an advancement over previous assessments that only considered linear relations between ozone and yield. 3. High temperatures are known as a major cause for yield losses, as portrayed in the meta-study.
2. The study about ozone damages on crops in chapter three is based on results from the meta-study. Ozone was identified by myself as one possible avenue for improving crop models towards more realistic yield assessments. Thus I designed and implemented an ozone damage module for LPJmL. I performed the required literature search, designed the layout of the study and wrote the manuscript. Susanne Rolinski, Sibyll Schaphoff and Christoph M üller supported me in the whole process.
This ensures that interactions do not gain more weight just because they are frequently stated in the literature. With more details, the categories were subdivided. For each node and interaction it was annotated for which crop (wheat/maize/rice or all three) it is valid, thus creating crop-specific networks. Afterwards a systematic search for studies in the full ISI Web of Science database 3  was performed. A keyword list with 55 entries was created, using terms from the initial textbook-based network. General terms like 'yield variability' and more specific ones like 'temperature AND wheat AND yield' were included (full keyword list in SI table  S3 ). Only papers after 1990 were considered to limit the number of search results. This first search for the keywords in the 'Topic' fields yielded 460 765 studies in total, so the results were filtered to contain only 'Review' papers.
Differences in priorities reflect the potential for supplementing one approach with the other. Many of the process improvement options for crop models are targeting currently less well represented physiological interactions. These general suggestions have to be adjusted for each particular model. There is no guarantee that a crop model eventually becomes better in modeling yield (variability) with a finer resolution of processes or by adding new ones. More processes require more parameters, which could entail model or calibration errors. The necessary experimental data are not easy to find, but one possible starting point is the AgTrials database 4. Some processes may not yet be implemented for their high complexity paired with unclear benefits for the model. Examples are the crop responses to pests and diseases (high specificity of crop-pathogen-environment interaction; Luck et al 2011) or to an elevated ozone concentration (lack of global databases, unclear effect on aggregate level).
The approach could be extended by a semi-automatic text mining, extracting the most relevant information from literature databases. Text mining has successfully been applied in medical bioinformatics  (Zhu et al 2013 , Fluck and Hofmann-Apitius 2014 , Fleuren and Alkema 2015). Our network-based review could serve as a first step towards this. We have shown its methodical validity as a structured, consistent and scalable dictionary of literature knowledge. The approach is easily applicable to many other fields of research.
Article place) such that of the 100 simulations with the lowest RMSE values the parameter set with the lowest penalty factors was chosen. This is justified by lower observed damages in reality than in experiments  (Morgan et al. , 2003). The LAI max management parameter was adapted for each experiment before calibration such that the control yield level was correctly simulated. The   S1 ). The experimental conditions described in these studies, including in particular O 3 and CO 2 concentrations, water provision and temperature, were provided as input for LPJmL. Output variables for comparison were extracted from the manuscripts, involving the use of digitizing software (engauge  2 ). An out-of-sample calibration was performed to evaluate the reliability of the calibration process. Each experiment was omitted from calibration in turn and the best parameters identified for the reduced experiment set.
Ozone data The ideal historical ozone data set for this exercise would contain daily surface O 3 concentrations, as mass or volume mixing ratio, over several years on the whole globe on a 0. 5° spatial resolution. But such a data set does not exist. Therefore we use an ensemble of daily global surface ozone concentrations, derived from chemical transport models participating in the ACCMIP model inter-comparison  (Lamarque et al. , 2013). Four models are included in the ensemble: GEOSCCM, GFDL-AM3, MIROC-CHEM and UM-CAM provided hourly data around the year 2000. Hourly data were aggregated to daily data by extracting, separately for each model, the daily mean and maximum concentration between 8 am and 4 pm and constructing a weighted average of these two values: a 'low' average 2 http://markummitchell.
github. io/engauge-digitizer/; accessed on March 08, 2017 assigns double weight to the mean, while a 'high' average assigns double weight to the maximum concentration. The daily aggregates were then downscaled from model resolution to 0. 5° spatial resolution with a double-conservative remapping that conserves fluxes and spatial gradients. From these downscaled daily values a 'high' and a 'low' ensemble were created by taking the median of the four models in the respective low or high setting. The usage of model ensembles is motivated by a better agreement of ensemble than single-model values with observed data  (Fiore et al. , 2009). The data flow for ozone input preparation is shown in SI Figure  S2. A comparison of both 'low' and 'high' ensemble to observed values can be found in SI Figure  S3.
Discussion We have implemented a novel ozone damage module into the widely used global crop model LPJmL. Parameters were calibrated to experiments and with these we have estimated global historical yield losses. Losses range from virtually 0% up to occasional 50% and agree with previous yield loss estimates in several cases. This study is the first to consider water stress, temperature, management intensity and CO 2 concentration as co-variates of ozone effects at the global level. To account for distinct sensitivities to O 3 , Western and Asian wheat were simulated separately.
In addition, predicted yields from the regression model based on the AgMERRA data are in close agreement with the observed yields in terms of mean growing season temperatures (Supplementary Fig. 32 ). Future: all future model results (statistical and process-based) are forced by bias-corrected 55  climate projections from the HadGEM2 climate model under the RCP8. 5 scenario at 0. 5°spatial resolution. We applied only one climate model, instead of an ensemble, since we study relative temperature responses rather than absolute yield levels. Yield data. Historical observed US county yields from 1980 to 2010 (to 2008 for wheat) were downloaded from the USDA Quick Stats tool 23. Historical yield simulations were calculated under the default and harmnoN harmonization scenarios (differing in fertilizer input, growing season definition and irrigation choices, cf.
Yield forecasting The model was applied to forecast yield anomalies during the growing season up to two months before harvest. We clipped the last one or two months, respectively, from the MIRCA2000-defined growing season and calculated all weather variables based on this reduced season. Afterwards the model was trained on the reduced weather data set, relating yield anomalies to weather anomalies observed up to one or two months before harvest. The one-out-of-sample performance of this reduced model is then a measure for its forecasting capacity.
The performance of our semi-empirical model, when run with reported yield data, was equal or superior to several previously applied statistical approaches  (Iizumi et al. , 2013a , Lobell & Field, 2007 , Ray et al. , 2015 , Urban et al. , 2012). We analyzed GGYD yields as an alternative to reported yields in areas where such data are currently not available. But the model-based nature of the data set could introduce a bias to our results. The robust performance of the semi-empirical model in the US, Germany, Russia, Burkina Faso, Tanzania and Brazil allows its usage for identifying cases where GGYD yields presumably suffer from a construction bias. We speculate that an existing weather influence on crops could be blurred by GGYD construction steps and is therefore less detectable with our (or any weather-driven) model.
This visual exploration can support the implementation of selected processes into crop models. Second, a detailed list of amendment suggestions for crop models is derived from the interaction network. Third, the study introduces a new approach for reviewing experiment-based knowledge on plant processes at different scales and levels of complexity. This method is scalable, can be generalized to different topics and has potential for automatization, which can fasten the tedious process of knowledge mining and structuring. There is no direct connection of this study to food security, but the manifold interactions extracted from the literature reflect the diversity of influences on agricultural performance and have been useful in delineating the research questions of the other chapters. In the third chapter, a newly implemented ozone-damage module in the global vegetation model LPJmL has been used to simulate historical wheat and soybean crop losses from O 3 pollution.
The varying quality of the utilized yield data, due to their derivation from remote sensing, makes estimations more difficult. In the study it is shown that a high quality of the yield data base, used for developing and validating the model, is an indispensable component of assessing environmental influences on agricultural production. Furthermore, the substantial share of weather influence on crop production shows risks for global food production if weather patterns change under global warming, in particular if there is an increase of heat days. Third, the capacity of the model for near-term forecasts of yields within the growing season is measured. The results of this exercise illustrate its application potential, with robust results that allow for a deepening of this approach. Such near-term forecasts can help to mitigate or avoid food security crises by supporting the preparation of adequate counter measures.
Open questions for future food security remain Three paths for further studies of climate change effects on food security are accentuated, based on findings in the previous chapters. First, the effect of ozone on crop yields in the future is of relevance for availability and stability of food supplies. To this end, existing uncertainties of the module introduced in chapter three should be reduced and its spectrum of crops be extended. Then LPJmL with the O 3 module can be used for such an assessment. A combined assessment of impacts on agricultural production and human health from ozone would be of interest, too. Second, the effect of extreme heat above approximately 36°C -where there are only few observations available currently -needs to be studied in experiments and then implemented in crop models.
Extremes 5-6 67-77 Clarke C J, George R J, Bell R W and Hatton T J 2002 Dryland salinity in south-western Australia: its origins, remedies, and future research directions Aust. J. Soil Res. 40 93-113 Coumou D and Rahmstorf S 2012 A decade of weather extremes Nat. Clim. Change 2 1-6 Craufurd P Q and Wheeler T R 2009 Climate change and the flowering time of annual crops J. Exp. Bot. 60 2529-39 de Bossoreille de Ribou S, Douam F, Hamant O, Frohlich M W and Negrutiu I 2013 Plant science and agricultural productivity: why are we hitting the yield ceiling? Plant Sci. 210 159-76 Deryng D, Conway D, Ramankutty N, Price J and Warren R 2014 Global crop yield response to extreme heat stress under multiple climate change futures Environ. Res. Lett. 9 034011 Donat M G et al 2013 Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset J.
Biol. 24 2845-9 Rosenzweig C et al 2014 Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison Proc. Natl Acad. Sci. 111 3268-73 Rötter R P 2014 Robust uncertainty Nat. Clim. Change 4 251-2 Rötter R P, Carter T R, Olesen J E and Porter J R 2011 Crop-climate models need an overhaul Nat. Clim. Change 1 175-7 Ruane A C, Goldberg R and Chryssanthacopoulos J 2015 Climate forcing datasets for agricultural modeling: merged products for gap-filling and historical climate series estimation Agric. Forest Meteorol. 200 233-48 Sakurai G, Iizumi T, Nishimori M and Yokozawa M 2014 How much has the increase in atmospheric CO 2 directly affected past soybean production? Sci. Rep. 4 1-5 Sánchez B, Rasmussen A and Porter J R 2014 Temperatures and the growth and development of maize and rice: a review Glob.
Acknowledgments The authors want to thank  Frank Wechsung ,  Christopher Reyer ,  Christoph Gornott ,  Katharina Waha  (for helpful discussions) and  Deepak Ray  (for data provision). BS acknowledges financial support from the  German National Academic Foundation , SR from the  MACSUR  project ( 031A103B ) funded by the  German Federal Ministry of Education and Research (BMBF) , and CM from the  MACMIT  project ( 01LN1317A ) funded by  BMBF.
Acknowledgements We thank  F. Wechsung ,  S. Szopa ,  D. Hauglustaine ,  J. Lathiere ,  Y. Balkanski  and  P. Ciais  for helpful discussions and  T. Bendixen  for data provision. BS acknowledges funding from the  German National Academic Foundation. SR and CM acknowledge financial support from the  MACMIT  project ( 01LN1317A ) funded through the  German Federal Ministry of Education and Research (BMBF). Author contributions: BS initiated, designed and performed the study and wrote the manuscript with contributions from all co-authors.
Acknowledgements We thank  Frank Wechsung  from  PIK Potsdam  for insightful discussions. B. S. acknowledges support from the  German National Academic Foundation. The work was supported within the framework of the  Leibniz Competition  ( SAW-2013-PIK-5 ), from  EU   FP7  project  HELIX  (grant no. FP7-603864-2 ),  EU   FP7  Project  LUC4C  (grant no. 603542 ) and by the  German Federal Ministry of Education and Research (BMBF , grant no. 01LS1201A1 ).
ACKNOWLEDGEMENTS BS acknowledges funding from the  German National Academic Foundation. The research was supported by projects  Trans-SEC , funded by  BMBF  and co-financed by  BMZ , and EXTRA, funded by  BMBF. We thank  T. Iizumi  for data provision and four anonymous reviewers that helped to improve the manuscript. Author contributions: FW initiated, BS and FW designed the study with support by CG. BS performed the study and wrote the manuscript with contributions by FW and CG.
, 2008) , which are not distinguished in our reproductive season and could lead to meaningless coefficients in the default model. Second, the growing season dates in MIRCA2000 could be wrong, leading to an improvement when omitting a too long part. Third, the vegetative and reproductive season split could be misplaced. These reasons will have to be investigated in further studies. Again, the importance of high-quality input yield data for model training is highlighted: only then reliable within-season forecasts are possible, as evidenced by the lower performance with GGYD yields. The forecasting scheme could be modified in two directions. Both require near-term monthly weather forecasts published, for example, by the NOAA (NOAA Climate Forecast, 2017). First, the full growing season can be used for training.
Acknowledgements The first three authors wish to thank  CNES , the  French Space Agency, Herakles , the European solid rocket manufacturer, and  ONERA , the  French Aerospace Laboratory. This work is based on the doctoral work of the first author, M. B. , under sponsorship from both  CNES  and  ONERA. The last author, J. M. , was sponsored partly by  Auburn University , through the  Hugh and Loeda Francis Chair of Excellence , and partly by the  National Science Foundation.