dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this paper, a CFD tool developed to study an offshore floating    wind turbine system in unsteady viscous flows has been described. The modelling tool is established via an open source framework OpenFOAM with our further developments on a numerical wave tank module and a static mooring line analysis module. With the aid of the sliding mesh technique dealing with multiple cell zones, a fully coupled FOWT system comprising a wind turbine, its floating platform and mooring system was successfully modelled. A series of simulations was firstly carried out to validate the results with published data and illustrated the accuracy of the tool to predict the above-mentioned three components individually. It was then further applied to the OC4 DeepCWind semi-submersible FOWT. By analysing in detail the motion responses of the floating platform, the vortex induced by the wind turbine and the forces on the blades, we demonstrated the existence of mutual interactions between the wind turbine and its supporting platform, the extent to which depends very much on the environmental conditions, i.
Acknowledgements Results were obtained using the  EPSRC  funded  ARCHIE-WeSt High Performance Computer  (www. archie-west. ac. uk). EPSRC grant No. EP/K000586/1. This work also used the  ARCHER UK National Supercomputing Service  (http://www. archer. ac. uk). The first author would like to acknowledge  Electricit e de France  for providing a scholarship for his PhD research. The authors are also grateful to the  UK Royal Society and China National Science Foundation Council (NSFC)  for partial support of this work. Royal Society  grant No. IE131059.
, 2004; Kuijken et al. , 2015; Rousseau et al. , 2015)  and tools for computational reproducibility  (Piccolo and Frampton, 2016). After pre-processing, images may be analyzed with tools that are developed for a specific purpose or currently available. The online resource http://www. plant-image-analysis. org curates currently available tools for morphological plant image analysis and enables user feedback and ratings  (Lobet et al. , 2013). Regardless of the analysis, adhering to minimum standards for data reporting is vitally important for reproducibility. We emphasize the adoption of the Minimum Information About a Plant Phenotyping Experiment (MIAPPE; http://www. miappe. org/;  Krajewski et al. , 2015)  standards.
Although more expensive and data-intensive, by imaging roots grown in soil, this technology promises to yield information more applicable to natural settings. One drawback of X-ray CT is the expense and the current inability to implement it in the field. In contrast, a straightforward, albeit destructive, method of field-based RSA phenotyping overcomes both of these obstacles. Termed 'shovelomics, ' this method examines only the upper region of the root system that can be removed from the soil without much damage  (Trachsel et al. , 2011). Roots are washed of soil, images taken in 2D, and root traits quantified with available software packages, such as the "Digital imaging of root traits" or DIRT package  (Bucksch et al. , 2014; Das et al. , 2015). DIRT uses an imaging pipeline to extract basic geometric root traits such as length, angle and diameter from the underlying graph representation, as well as novel descriptors of shape deformation.
SimRoot  (Lynch et al. , 1997)  is a structural-functional model that emerged from a large amount of empirically collected data. Additionally, SimRoot incorporates physiological models of nutrient and water uptake. Software such as SimRoot can model changes in RCA and make prediction on the effects of competition in root architecture. A recent developed software package, DynamicRoots, merges geometric and topological approaches to quantify the growth of a root system  (Symonova et al. , 2015). In this package 3D reconstructions of time-series RSA are first registered to the same coordinate system. These images are then converted to a series of graphs with nodes representing the voxels and edges representing the connections between neighboring voxels. Several calculations can now be derived from these graphs.
For example, the addition of new edges that persist likely indicates a new branch forming and the geodesic distance between a reference point and each voxel can be used to track tip growth. Using these types of information, DynamicRoots can decompose the RSA into individual branches and extract branchspecific geometries  (Symonova et al. , 2015). This software is the first aimed at analyzing growing root systems from timeseries data and has the potential to provide key insights into the local and global RSA impacts of changing environmental conditions.
, 2006; Hughes et al. , 2008; Bailey et al. , 2009a,b). Aboveand below-ground plant traits such as leaf area and shape, root formation, and above-to below-ground biomass ratios are morphological traits that allow ecologists to make predictions about plant ecological strategies and overall community response to environmental changes  (Suding et al. , 2008). While genetic variation has been shown to have consequences on community and ecosystem function (e. g. ,  Bangert et al. , 2006; Crutsinger et al. , 2006; Johnson et al. , 2006; Lankau and Strauss, 2007) , directly connecting genotypic diversity to phenotypic variation for ecologically important traits has been more difficult. The proliferation of open-source, global plant trait databases (e. g. , TRY plant trait database: https://www. try-db.
org/TryWeb/Home. php, Glopnet: http://bio. mq. edu. au/∼iwright/glopian. htm) as well as largescale plant phenotype databases (unPAK: http://arabidopsis. biology. cofc. edu/) allows plant ecologists and evolutionary biologists to quickly harness trait data to answer relevant ecological and evolutionary questions. Building these databases so that they incorporate trait data relevant across multiple scales and environments is essential to providing a holistic view of plant-environment and plant-plant interactions. Essential to the building of these databases is the rapid quantification of the plant morphological traits through the application of mathematical principles. The establishment of larger and standardized plant trait databases would provide ecologists with the necessary amount of data to gain insight into linkages between genotype and phenotype, and the potential impact on community and ecosystem processes.
ACKNOWLEDGMENTS We gratefully acknowledge the support of the  National Institute for Mathematical and Biological Syntheses (NIMBioS)  for coordinating the workshop on plant morphological modeling, sponsored by the  National Science Foundation  through  NSF  Award # DBI-1300426 , with additional support from The  University of Tennessee, Knoxville. EES gratefully acknowledges the support of her postdoctoral mentor,  Philip N. Benfey. LH thanks her postdoctoral mentor  Adrienne H. K. Roeder  for support.
As currently formulated however, these approaches have substantial limitations, in that the former may identify only large genomic regions in which several candidate genes could be equally implicated as master regulators of network expression, whilst the latter connects networks to TFs which in themselves usually lack chemical tractability as drug targets. Moreover, both methods have a requirement for sample-matched DNA variant and expression data which is often unavailable. Given the current constraints to realizing the promises of network-based drug discovery we aimed to develop a new framework for drug target discovery. Our developed method termed "causal reasoning analytical framework for target discovery" (CRAFT) combines gene regulatory information with a causal reasoning framework to predict cell surface receptors with a direction specified influence on disease module activity.
We specifically chose to develop a method connecting module expression to membrane receptors because more than half of all approved drugs target receptors  17  , thus maximizing the opportunity for drug repositioning and rapid experimental medicine proofs-of-principle. Although in this study we applied CRAFT to epilepsy, our method is equally applicable to any disease for which an underlying disease module can be identified. Epilepsy is a highly debilitating disease of the CNS, primarily characterized by recurrent unprovoked epileptic seizures, but often associated with additional brain features including cognitive and behavioral impairments, and a heightened risk of death  18. The causes of epilepsy can be broadly divided into cases that arise through no cause other than a genetic predisposition ("genetic epilepsy"), and epilepsy which develops secondary to an acquired brain injury such as following status epilepticus or head injury ("acquired epilepsy")  19.
To further prioritize the modules in terms of their functional relationship to epilepsy, we explored the correlation between each module's expression and seizure frequency. To this end we first quantified the frequency of behavioral seizures in each epileptic mouse by 14 days of continuous motion sensing 3D accelerometry synchronized with continuous video monitoring starting on day 28 post-SE (Methods). This revealed a diurnal variation of seizure occurrence in mice as well as clustering of seizures reflective of the classical patterns of human temporal lobe epilepsy (Figure S. 5)  30. Next, using hippocampal gene expression data from each mouse, we summarized each module's expression by its eigengene (i. e. , its first principal component, PC1) and calculated the correlation between a module's eigengene and seizure frequency (Methods).
Since approximately 60% of existing drugs in clinical use target membrane receptors  17  , we decided to focus our search on finding membrane receptors exerting a regulatory influence over module activity. To this aim we developed and implemented a novel computational approach that combines 'causal reasoning' with gene regulatory information to ranks receptors based on the strength of their predicted effect on module expression (Methods). Briefly, using Clarivate Analytics MetaBase® (version 6. 15. 62452), we first extracted information relating to known interactions between membrane receptors and transcription factors (TFs) via linear canonical pathways, and then between TFs and their target genes. To provide context to this "interactome", only membrane receptors, TFs and target genes expressed in the mouse hippocampus were considered (resulting in a list of 1,624 expressed TFs and 307 expressed receptors).
7a-d ). In support of the validity of our causal reasoning results, we found that membrane receptors related to interleukin-1 (IL-1) type 1 receptor/Toll-like receptor (IL-1R/TLR) signaling had a predicted direction of effect on epilepsy that was in agreement with the previously reported experimental evidence for that receptor  34. Of the many membrane receptors predicted to influence the expression of module genes in a direction specified manner, M-CSF receptor (also known as colony stimulating factor 1 receptor encoded by the Csf1R gene in the mouse) was predicted to be a regulator of two of the seven prioritized candidate epilepsy modules (modules 18 and 22). For both these modules, CRAFT predicted that Csf1R should "activate" the subset of genes in the module that are over-expressed in epilepsy.
According to the CRAFT causal reasoning framework (Fig. 3 ), Csf1R dependent activation of genes in modules 18 and 22 that are over-expressed in epilepsy should result in an activation of the disease state. By the same token, small molecule blockade of Csf1R should act to reduce the expression of these genes and the resulting restoration of the module's expression toward health should be predictive of therapeutic benefit. Although there were several membrane receptors predicted to influence the expression of one or more candidate epilepsy module (Table  S. 9a), we chose to prioritize Csf1R for further analysis for the following reasons; (a) module 18 and 22 are enriched for functions related to inflammatory processes which have been previously linked to ictogenesis and epileptogenesis  35  , (b) Csf1R has not previously been linked to epilepsy (allowing for mechanistically new drug target discovery) and, (c) the availability of a tool compound, PLX3397, a known small molecule inhibitor of Csf1R  36  , by which we could experimentally test our causal reasoning predictions related to both module expression and impact on disease.
Neither of these two modules are enriched for microglial marker genes (Figure S. 2b), again consistent with an effect of PLX3397 on gene expression independent of gross microglial cell depletion. Reinspection of our causal reasoning results (Table  S 9a ), revealed that both module 17 and 27 are predicted by the CRAFT framework to be regulated by Csf1R, albeit in a direction unspecified manner. As neither module 17 or 27 are differentially co-expressed in epilepsy nor correlated with seizures, PLX3397-induced change in the expression of modules 17 and 27 are not expected to impact the epileptic phenotype. However, we cannot exclude that PLX3397-induced changes in expression of these modules might contribute to potentially undesirable "off-target" adverse effects of PLX3397. Taken together these analyses point to a restoration of the expression of module 18 in epilepsy toward health by PLX3397, mediated by Csf1R blockade.
The cell type specificity of these candidate modules and their functional processes were assessed using enrichment analyses, and the regulatory influence of cell membrane receptors over the selected modules was then inferred using gene-regulatory information in a causal reasoning framework (CRAFT). Of the many cell surface receptors predicted to influence the expression one or more candidate epilepsy module in a direction-specified manner we chose to validate Csf1R for a number of reasons including (a) an absence of prior information connecting Csf1R to epilepsy (allowing for proof-of-concept that CRAFT can predict mechanistically novel targets) and (b) the availability of a tool compound (PLX3397) by which to test our causal reasoning predictions related to both Csf1R's regulation of module 18 and, by extension, the relationship of module 18 to epilepsy.
e. , its interactome) and to combine this with direction-specified effects of membrane receptors on disease module activity offers considerable opportunities to accelerate the drug discovery process. Although we took advantage of experimentally validated interactions between TFs and target genes and between membrane receptors and TFs, meta-databases such as MetaBase® have limitations in terms of the accuracy and completeness of this information, which places restrictions on the scope and accuracy of our current predictions. For example, the direction of effect of an interaction is often not specified in a database, and the relationship between membrane receptors and TFs is currently determined using linear pathways where knowledge is still incomplete. However, as the completeness of our knowledge of these regulatory relationships improves, including more detailed knowledge of cell-type specific interactions between TFs and target genes, so the accuracy and scope of CRAFT is expected to improve.
At present, the major challenge was to establish proof-ofconcept that disease networks combined with causal reasoning offer a valid framework to discovering mechanistically novel membrane receptors as drug targets, and this is what the framework described here makes possible. Although our causal reasoning framework was implemented using regulatory interactions from Clarivate Analytics MetaBase®, several other databases provide similar sources of information that can be adapted to the CRAFT framework. For example, biological pathway databases such as the Reactome pathway Knowledgebase  42  and Pathway Commons  43  provide well characterized linear signalling pathways which can be used to connect membrane receptors to transcription factors, whilst transcription factor target databases such as TRRUST  44  provide information relating TFs to target genes.
Although it could be argued that depletion of brain microglia might act to cause an "apparent" reduction in expression of genes in module 18 which are over-expressed in epilepsy, two other microglial modules were not significantly enriched for genes down-regulated by PLX3397 suggesting a more focused effect on module 18 expression according to our causal reasoning predictions. Additionally, we observed discordant changes in expression of microglial marker genes such as Aif1 (which is a component of module 18 and predicted to be down-regulated by Csf1R blockade) and Sall1, which is not predicted to be a target of Csf1R and was not down-regulated by Csf1R blockade. In conclusion, our approach (CRAFT) provides a gene regulatory and causal reasoning framework to identify membrane receptors as novel drug targets.
Coexpression modules meeting these criteria were considered candidate gene networks for epilepsy, and subjected to gene-regulatory analysis in a causal reasoning framework (CRAFT, see Text) to identify membrane receptors predicted to influence the expression of the diseaserelated network in a direction specified manner. Pilocarpine naive mice were treated daily for 7 days with vehicle or PLX3397 at 3mg/kg/day or 30mg/kg/day. At the end of the treatment exposure, hippocampal RNA was extracted and the expression of marker genes in module 18 (predicted to be under the control of Csf1R) and marker genes in modules 12 and 22 (predicted not to be under the control of Csf1R) were assayed by reverse transcriptase qPCR. Consistent with the causal reasoning predictions, module 18 marker genes, but not marker genes from module 12 or 22, were significantly Figure 2b.
5 A new satellite-derived product (DSLFnew) was developed by combining satellite observations and AROME forecasts. An evaluation against in situ measurements showed lower errors for DSSF than AROME and SAFRAN in terms of solar irradiances. For longwave irradiances, contrasted results falling in the range of uncertainty of sensors did not enable us to select the best product. Spatial comparisons of the different datasets over the Alpine and Pyrenean domains highlighted a better 10 representation of the spatial variability of solar fluxes by DSSF and AROME than SAFRAN. We also showed that the altitudinal gradient of longwave irradiance is too strong for DSLFnew and too weak for SAFRAN. These datasets were then used as radiative forcing together with AROME near-surface forecasts to drive distributed snowpack simulations by the model Crocus in the French Alps and the Pyrenees.
Irradiance datasets Several irradiance datasets were used in this study: forecasts from the NWP model AROME, reanalyses from the SAFRAN analysis system, LSA SAF irradiance products derived from remotely-sensed observations and a hybrid LW↓ irradiance product based on a combination of LSA SAF algorithms with AROME forecasts. An in situ observation dataset was built up for validation in mountains. 90 2. 2. 1 NWP system: AROME AROME (Application of Research to Operations at MEsoscale) is the meso-scale NWP system of Météo-France  (Seity et al. , 2011) , operating over France since December 2008 at 2. 5 km grid spacing (1. 3 km since 2015;  Brousseau et al. , 2016). It is a spectral and non-hydrostatic model. The physics and data assimilation schemes are detailed in  Seity et al. (2011). In particular, AROME 95 uses the radiation parametrizations from the European Centre for Medium-Range Weather Forecasts (ECMWF), with the SW scheme from  Fouquart and Bonnel (1980)  and the LW scheme from  Mlawer et al.
(1997). In this study, we built a continuous atmospheric forcing dataset using hourly AROME forecasts issued from the 0 UTC analysis time, from + 6 h to + 29 h, extracted on a regular latitude/longitude 100 grid with a 0. 025 • resolution over the period and domains of study (Sect. 2. 1, Fig. 1 ), similarly to  Quéno et al. (2016)  and  Vionnet et al. (2016). Besides incoming shortwave and longwave radiations, 2 m temperature and humidity, as well as 10 m wind speed and precipitation (amount of rainfall and snowfall) are part of the AROME forcing.
In situ irradiance observations To assess the distributed irradiance datasets, ground measurements of SW↓ and LW↓ were extracted 180 from Météo-France station network and additional Automatic Weather Stations (AWS). Stations with altitude higher than 1000 m were selected. Since the elevation is one of the most significant factor of surface radiation spatial variability  (Oliphant et al. , 2003) , stations were not used for evaluation if the difference between the station elevation and the elevation of the four closest AROME and LSA SAF grid points was higher than 300 m. The resulting observation database, represented in 185 Fig. 1 , includes 14 mountain SW↓ stations (8 in the French Alps, 1 in Jura and 5 in the Pyrenees), 4 mountain LW↓ stations (3 in the French Alps and 1 in the Pyrenees).
An additional station located in plains at Carpentras (Fig. 1 ) has been included in the database since it is the reference station for SW↓ and LW↓ measurements in France. These stations and their characteristics are listed in Table  1. Radiation measurements are scarce in mountainous terrain and their quality is often lower than 190 plain measurements, due to the difficulty to maintain these stations and the possible occurrence of frost or snow on the sensors in winter  (Lapo et al. , 2015a). The pyranometers from Météo-France network (Kipp&Zonen CM5, CM6B and CM11) meet the good quality standards of the World Meteorological Organization  (WMO, 2014) , hence an uncertainty of hourly total SW↓ irradiance from ±5% to ±8%. Due to their location in altitude implying difficulties of maintenance, it seems more re-   2012 ) reported a total uncertainty on the order of ±10% (including site-dependent uncertainties).
215 Snowpack simulations driven by different irradiance datasets were performed with the detailed snow cover model Crocus  (Brun et al. , 1992; Vionnet et al. , 2012)  coupled with the ISBA land surface model within the SURFEX simulation platform  (Masson et al. , 2013) , to fully simulate the interactions between snowpack and soil. SURFEX/ISBA-Crocus (called Crocus hereafter) simulates the evolution of the snowpack physical properties along its stratigraphy, under given atmospheric forc-220 ing data (temperature and specific humidity at a given height above the surface, wind speed at a given height above the surface, SW↓ and LW↓ irradiance, solid and liquid precipitation). The simulations were carried out over the French Alps and Pyrenees domains (Fig. 1 ), on the AROME regular latitude/longitude grid at 0.
In situ snowpack observations 240 To assess the quality of Crocus simulations, an observational dataset of SD measurements was constituted in the French Alps and the Pyrenees, within SAFRAN massifs. Only stations with less than 150 m elevation difference to the model topography were selected. This dataset contains a total of 172 stations (89 in the French Alps and 83 in the Pyrenees) with daily manual measurements at ski resorts (at 6 UTC) and daily automatic measurements by ultra-sonic sensors at high altitude sensors, 245 as described in details in  Vionnet et al. (2016)  for the French Alps and in  Quéno et al. (2016)  for the French and Spanish Pyrenees. 3 Evaluation of radiation products over the Alps and the Pyrenees 3. 1 Comparisons with in situ measurements SW↓ and LW↓ irradiances from LSA SAF products, AROME forecasts and SAFRAN reanalyses 250 were evaluated using in situ measurements.