dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, by modifying the constraints at the different spatial scales, the database of field characteristics (changes to field parameters; e. g. , change of slope due to remodelling), the nature and technical coefficients of the activities to be allocated to fields and the objective to optimize can be used to modify the cropping systems chosen by the simulated farmers at the field scale. These cropping system changes at the field scale reorganize farming systems at the farm scale and, in fine, the regional cropping system mosaic. Next, modification of the cropping system mosaics may modify the contribution of mosaics to sustainable development, which is assessed in the post-modelling component of the framework. Under optimized scenarios, the objective function in Equation 1 is replaced by the target indicator, e.
Adaptation of the MOSAICA model in Guadeloupe We explain our adaptation of the MOSAICA model that supports the proposed framework for Guadeloupe by briefly describing its principal elements for the simulation of multi-functional agricultural landscapes  (Chopin et al. , 2015b ). -The database on field characteristics obtained from the Agrigua association that gathers declarations of farmed land for subsidies, comprised 25,057 fields and includes biophysical and farm structure information represented by polygons covering 27,000 hectares (i. e. , 86% of the 31,300 hectares of all agricultural land in Guadeloupe). -We described 36 activities covering the eight main crops in Guadeloupe: sugar cane, banana, pasture, orchards, pineapple, plantain, crop-gardening and tubers, with different management strategies.
The impacts of these changes were assessed by running the model with the modifications of these activities, the geographical database and the equations defined at the different spatial scales. 1. The first exploratory scenario consisted of a combination of several changes, including increased market size at the regional scale (represented with regional thresholds in the model) for plantain, pineapple, and tubers at the regional scale, reduced variability of the gross margins of crops due to improved advice for local producers, an increase of 1000 in the workforce available at the regional scale and doubling of the overall availability of water for irrigation. These changes increased the generation of agricultural added value from local foodstuffs to 165 millions € per year, which is higher than the previously obtained optimized value of 104 millions.
A range of values to be tested for each driver needs to be defined. The modellers should then run the simulation of the different scenarios and present their results to decision-makers. Feedback from decision-makers should integrate the new drivers that emerged from group thinking (e. g. brainstorming) with the range of values to be tested. This loop between decision-makers and modellers could operate continuously as the model integrates new sustainability issues (e. g. , crop diseases) and indicators with a broader diversity of cropping systems or the addition of new fields within the field characteristics database. An analysis of the sensitivity of model outputs to model inputs or the drivers tested under the exploratory scenarios would also be important prerequisites for stakeholder discussions on the scenarios presented in this paper.
Apparatus and Procedure: Pupillometry. An EyeLink 1000 system (SR Research, Canada) monitored two-dimensional eye position and pupil diameter with an infrared camera mounted below a monitor screen (Barco Calibrator, 40 × 30 cm), which was only used for calibrating the eye tracker (13-point calibration routine). The eye-monitor (and eyecamera) distance was maintained to 57 cm by means of a chin rest. Eye-tracking data were acquired at 1000 Hz and streamed from the EyeLink to a Mac Pro 4. 1 through the EyeLink toolbox for Matlab  [50]. The setup is hosted in an experimental room illuminated only by the monitor screen. Pupil diameter values were output online by the EyeLink system (computed with internal algorithms) and we only used Matlab to receive and store them together with gaze position estimates.
2. 6. Analyses: Binocular Rivalry. The perceptual reports recorded through the computer keyboard were analyzed with custom Matlab scripts. During binocular rivalry, visual perception oscillates between the monocular images and periods of exclusive dominance of one of the two rivalrous stimuli are sometimes interleaved with periods in which the observer perceives a mixture of the two images, called mixed percepts. In order to quantify ocular dominance, for each subject and each experimental block, we computed the average duration of exclusive dominance of each stimulus, called mean phase duration, as well as the average duration of mixed percepts. The 180 sec blocks acquired after monocular deprivation were binned as follows: 0-8 min and 10-18 min. In order to obtain an index of the effect of deprivation, we computed the Deprivation Index (DI) described in  [4] , which summarizes the change in eye-dominance (defined as the ratio between the deprived and nondeprived eye mean phase durations) induced by monocular deprivation (see (  1 The deprivation indexes obtained for each of the two experimental blocks measured after eye-patch removal were compared against the value of 1 using a one-sample, twotailed -test.
2. 7. Analyses: Pupillometry. Eye-tracking data were analyzed with custom Matlab scripts. Pupillometry data consisted of 120 × 1000 time points (120 seconds at 1000 Hz). These included signal losses, eye-blinks, and other artifacts, which we eliminated before assessing the oscillatory behavior of the pupil. The majority of these artifacts were excluded based on pupil size being 0 (e. g. , during eye-blinks). However, this left time points with highly instable pupil size measures (e. g. , disturbances from eye-lashes) as well as short intervals, typically preceding and following a blink, where the pupils acquired very small or very large values. We cleaned these out by means of custom software that identifies and excludes the changes of pupil size that are too fast to be physiologically meaningful.
5625 Hz (exactly the same definition used in  [29] ). Horizontal and vertical gaze position data from time points where an artifact in pupil diameter was detected (see above) were excluded. Deviations from screen center were computed and the sign of horizontal gaze shifts was flipped for subjects where the right eye was recorded. In this way, a positive horizontal gaze shift implies a shift in the nasal direction and a negative shift implies a shift in the temporal direction. We took the average across time of horizontal and vertical gaze shifts as a measure of systematic gaze deviations and we estimated fixation instability by means of Bivariate Contour Ellipse analysis. This amounts to defining an ellipse around the ,  coordinates of gaze position samples. Its area is defined by BCEA = 2𝑘𝜎 𝐻 𝜎 𝑉 (1 -𝑅 2 ) 0.
Acknowledgments The research leading to these results was funded by the  European Projects ERA-NET Neuro-DREAM and ECSPLAIN (European Research Council  under the  Seventh Framework Programme ,  FPT/2007-2013 , Grant Agreement no. 338866 ) and the  Italian Ministry of University and Research  under the Projects " Futuro in Ricerca," Grant Agreement  no. RBFR1332DJ , and " PRIN 2015. "The authors are grateful to  Professors Morrone and Burr  for their continual advice and for providing laboratory instruments and spaces.
Pre-processing of Illumina reads and de novo transcriptome assembly Raw RNA-Seq reads were processed using Trimmomatic v0. 33 for trimming adapters as well as low-quality bases from ends of the reads  [29]. Poor quality reads with average Phred quality score < 20 and reads with length < 36 were also filtered out. The resulting set of good quality reads were then assembled with Trinity v2. 1. 1 software using default parameters  [30, 31]. The quality of the resulting assembly was assessed by various methods. First of all, RNA-Seq read representation of the assembly was checked using bowtie2  [32]. Ex90N50 transcript contig length (the contig N50 value based on the set of transcripts representing 90% of the expression data) was computed using contig_ExN50_statistic. pl script bundled with Trinity.
Then the representation of fulllength reconstructed protein-coding genes was studied. The assembled transcripts were compared with Swiss-Prot using BLAST and the hits were analyzed using a perl script blast_outfmt6_group_segments. tophit_coverage. pl, provided with the Trinity package. BUSCO (Benchmarking Universal Single-Copy Orthologs) was used to explore completeness of the transcriptome according to conserved ortholog content  [33]. Finally, TransRate was used to GyM-H and Ag-H buds sized similar to female buds were chosen for RNA seq study as described in our previous work  (Ghadge et al. , 2014). Scale bar = 1 cm compare the assembly to the publicly available Cucumis sativus protein-coding primary transcript sequences  [34].
Annotation of the de novo-assembled transcripts The de novo-assembled transcripts were compared with the viridiplantae sequences from nr and Swiss-Prot database using BLASTX with an e-value threshold of 1e-3  [35]. BLAST output generated from this comparison was loaded into BLAST2GO for mapping GO terms to the transcripts and annotation  [36]. Enzyme codes and KEGG pathway mapping were also carried out. ANNEX (Annotation Expander) was used to enhance the annotations. Finally, GO-Slim mapping was applied to get a broad overview of the ontology content. In addition to BLAST2GO facilitated annotation, Trinotate pipeline was used to carry out comprehensive functional annotation of the transcripts leveraging various annotation databases (eggNOG/GO/KEGG databases)  [31]. Trinotate pipeline also included identification of open reading frames, homology search against Swiss-Prot and TrEMBL.
Transcript quantification and differential expression analysis align_and_estimate_abundance. pl script from Trinity package was applied to align cleaned reads from each library to the de novo transcriptome using bowtie and to estimate the transcript abundance using RSEM  [37]. abundance_estimates_to_matrix. pl script was used to construct a matrix of counts and a matrix of normalized expression values. PtR script was used to generate correlation matrix and Principal Component Analysis (PCA) plot for comparing replicates across all the samples. Differential expression analysis was carried out with two biological replicates from the count matrix using run_DE_analysis. pl with edgeR as the method of choice  [38]. analyze_diff_expr. pl script was used to examine GO enrichment and to extract all transcripts that had pvalues at most 1e-3 and were at least 2^2 fold differentially expressed.
Validation of differentially expressed genes by qRT-PCR For expression analysis, qRT-PCR was carried out using aliquots of the same RNA samples that were used for RNA sequencing. Two micrograms (2 μg) of total RNA was used for complementary DNA (cDNA) synthesis by SuperScript IV reverse transcriptase (Invitrogen) using an oligo(dT) primer. CgACT2 gene was used as reference gene for normalization. qRT-PCR was performed on BIO-RAD CFX96 machine with gene-specific forward and reverse primers (Additional file 1: Table  S1 ). The reactions were carried out using Takara SYBR Premix Ex Taq II (Takara Bio Inc. ) and incubated at 95 °C for 3 min followed by 40 cycles of 95 °C for 15 s, 58 °C for 15 s and 72 °C for 15 s. PCR specificity was checked by melting curve analysis, and data were analysed using the 2 -ΔΔCT method  [39].
Results RNA sequencing, Trinity-based de novo transcriptome assembly and annotation using BLAST2GO and Trinotate A total of 306,575,536 paired-end reads (150 bp) were obtained after sequencing all the twelve libraries on the Illumina NextSeq 500 platform. Subsequently, 186,399,131 good quality paired-end reads were used for de novo assembly of Coccinia grandis flower bud transcriptome using Trinity software package with default parameters (Table  1 ). The resulting assembly consisted of 467,233 'Trinity Transcripts' clustering into 378,860 'Trinity Genes' with an N50 value of 881 bp (Table  2 , Additional file 2). The transcripts of 200-399 bp size were found to be most abundant in the length distribution of assembled transcripts (Fig. 2 ). However, a higher proportion of transcripts with length around 1000-2000 bp had a BLAST hit compared to the proportion of smaller transcripts (Fig.
2 ). Cleaned reads were mapped back to the transcriptome using bowtie2 with ~70% or more reads from each library aligning concordantly (Table  1 ). An Ex90N50 statistic calculated using 80,806 transcripts from the assembly (ignoring the rest of the transcripts with poor read coverage) was found to be 1784 bp (Additional file 3: Figure  S1 ). Altogether, 8916 unique BLAST hits in the Swiss-Prot database were represented by nearly full-length transcripts, having more than 70% alignment coverage, and 12,315 hits showed more than 50% alignment coverage (Additional file 4: Table  S2 ). BUSCO output [C:89. 8%(S:14. 5%,D:75. 3%),F:5. 0%,M:5. 2%,n:1440] showed that out of 1440 BUSCOs for Plants dataset, 1293 full length BUSCOs were detected in our de novo-assembled Coccinia grandis flower bud transcriptome indicating 89.
8% completeness. Finally using TransRate, we were able to detect C. grandis homologs for 84% (18,039) of proteincoding primary transcripts of C. sativus of which, 13,430 reference sequences had at least 95% of their bases covered by a CRB-BLAST hit (Additional file 5: Table  S3 ). Coccinia grandis flower bud transcripts were compared to plant protein sequences of the nr and Swiss-Prot databases resulting in 259,200 and 136,663 transcripts having at least one hit from the respective database. Species distribution analysis of the BLAST hits showed that majority of these hits were from Arabidopsis and rice for Swiss-Prot database whereas for nr database most top hits were from cucumber and melon (Fig. 3a, b ). The number of transcripts annotated with various GO terms of biological process, molecular function, and cellular component categories are provided in Fig.
3c. Trinotate v3 pipeline was also used simultaneously for comprehensive functional annotation of the Coccinia grandis flower bud transcripts. Details regarding the Swiss-Prot/ TrEMBL BLAST hits, GO, KEGG and eggNOG mappings can be found in the Additional file 6: Table  S4. HMMER/ PFAM predicted protein domains, as well as information regarding signal peptides and transmembrane domains could also be found in the Additional file 6: Table  S4. Taken together, we have assembled a good quality transcriptome for early-and middle-staged flower buds of Coccinia grandis and comprehensively annotated the transcripts using wellestablished BLAST2GO and Trinotate pipelines.
Differential expression analysis reveals probable factors for pollen fertility and sex modification Following transcriptome assembly and annotation, differential expression analysis was carried out. First of all, RSEM was used for transcript abundance estimation. Following which, we checked for the correlation between the replicates for all the samples using PtR script. PCA analysis and correlation matrix showed a good correlation between the replicate sets for each of the six samples (Fig. 4 ). EdgeR was used to identify the differentially expressed transcripts for all the pairwise comparisons between the six samples (Table  3 ; Fig. 5 ; Additional file 7: Figure  S2 ). Differentially expressed transcripts at a minimum fold change of 2^2 with p-values at most 1e-3 were extracted and GO enrichment analysis was performed (Additional file 8: Table  S5 , Additional file 9: Table  S6 ).
Availability of data and materials The raw sequencing data were deposited in the NCBI Short Read Archive (SRA) database (http://www. ncbi. nlm. nih. gov/sra/) under the accession number SRP111347. De novo-assembled Coccinia grandis floral bud transcriptome is available at https://doi. org/10. 6084/m9. figshare. 5220874. v1. Annotation Report for the de novo-assembled C. grandis flower bud transcriptome generated using Trinotate is available at https://doi. org/10. 6084/m9. figshare. 5217652. v1.
As a result, many researchers have been developing numerical modelling tools for full-scale FOWT analysis. Among the most well-known tools developed is the software package named FAST (Fatigue, Aerodynamics, Structures, and Turbulence), developed by the National Renewable Energy Laboratory (NREL). As it is claimed and used by various researchers, FAST is able to perform fully coupled time-domain aero-hydro-servo-elastic simulations for FOWT systems  [3, 8, 20]. In principle, FAST uses an input of a hydrodynamic database computed by an external potential-flow solver (such as WAMIT) to predict hydrodynamic loading. As potential-based methods inherently cannot take viscous effects into consideration, a quadratic damping model from Morison's equation is normally adopted to include the drag force  [8, 48] , which requires an additional quadratic damping coefficient relying on experimental test data.
Tran and Kim  [48]  investigated the hydrodynamic responses of the DeepC-Wind semi-submersible platform using the commercial CFD software package STAR-CCMþ. Their modelling results showed generally good agreement with experimental test data. Using an inhouse CFD solver (naoe-FOAM-SJTU), Zhao and Wan  [53]  studied the effects of the presence of a wind turbine on a semi-submersible floating platform in waves. However, the influence of the wind turbine on the platform was simplified as an equivalent force without the inclusion of a fully resolved turbine model. Nonetheless, the effect of the wind turbine on the floating platform was apparently observed, especially for the platform pitch motion associated with a high wind speed. A full CFD analysis of wind turbine aerodynamics while constraining the motion of a floating platform was carried out by Tran et al.
[47]. Instead of modelling a floating platform with 6 Degreeof-Freedom (DoF) rigid body motions, Tran et al. [47]  imposed a prescribed sinusoidal pitching motion onto the platform. The unsteady aerodynamic performance of the wind turbine was investigated in response to the various platform motion amplitudes and frequencies using software package STAR-CCMþ. By comparing CFD results obtained from STAR-CCMþ with those from other tools, such as unsteady Blade Element Momentum (UBEM), FAST with BEM and Generalized Dynamic Wake (GDW), it was found that although good agreement was achieved for all cases at small oscillation amplitudes, large discrepancies occurred when the oscillation amplitude increased to 4. This was explained in their work via pointing out the limitations of the simplified methods used in modelling the dynamic interaction between wind turbine and wake, which was induced by the platform motion.
[27]  on an FOWT with a superimposed three Degrees-of-Freedom (3DoF) platform motion, (i. e. surge, heave and pitch) concluded that the thrust and torque of the wind turbine and thus the power were largely influenced by the motion of the platform. To well reflect the real situation, a successful analysis of an FOWT, via either CFD simulations or experiment, should consider the complex fluid flow of combined wind and waves. Recently, some researchers have studied the coupled response of a floating offshore wind turbine system under both wind and wave conditions. Ren et al. [36]  carried out a CFD analysis of a 5 MW floating wind turbine system supported by a TLP under coupled wave-wind conditions using the commercial software FLUENT with their User Defined Function (UDF). The numerical results were validated against experimental data.
It was pointed out that though hydrodynamic forces played a dominant role in the dynamic surge response of a floating system, the aerodynamic forces contributed to the average/mean surge response of the system. Unfortunately, only the surge motion was considered in their study, which obviously simplifies the problem. Quallen et al. [35]  performed a fullsystem, two-phase CFD simulation with an OC3 spar-type FOWT model considering both the wind and wave excitation forces. By comparing CFD results with those from FAST simulations, the predicted mean surge motion with CFD modelling was 25% less than the results from FAST, likely due to a constant drag coefficient adopted in FAST. More recently, Tran and Kim  [50]  modelled a fully coupled aero-hydrodynamic OC4 semi-submersible FOWT using a dynamic fluid body interaction method coupled with an overset moving grid technique embedded in the commercial CFD software STAR-CCMþ.
A comparison between the CFD results with FAST data showed overall good agreement. Both codes adopted the quasistatic method for modelling the mooring lines. However, in terms of the maximum wind turbine power, as large as four-fold discrepancy was revealed between the power predicted from the CFD calculations and that from FAST, along with a 32. 2% difference in the predicted average mooring tension, indicating the importance of accurate full-system FOWT simulations. In this paper, we will present a numerical modelling tool based on the open source CFD framework OpenFOAM  [34]  for fully coupled dynamic analysis of floating offshore wind turbine systems under combined wind-wave excitation. Unlike the previous study by Ren et al. [36]  where only the surge degree of freedom was considered, three degrees of freedom responses of the floating structure, which are surge, heave and pitch, are taken into account here while the other three modes of motion (sway, roll and yaw) are ignored.
[50] , a license has to be obtained. On the other hand, for some in-house codes like the CFDShip-Iowa  [35] , the FoamedOver library  [6]  and the naoe-FOAM-SJTU solver implemented in OpenFOAM  [44] , either the commercial overset grid assembly software SUGGAR or an improved version Suggarþþ, which is intended to generate the domain connectivity information (DCI), also requires the costly licence keys. In this work, a built-in sliding mesh technique in OpenFOAM termed AMI (Arbitrary Mesh Interface) is adopted to cope with the relative motion problem in FOWT applications instead of developing a complex overset grid approach. The AMI method is designed for rotating machinery problems, which allows simulations across disconnected but adjacent mesh domains either stationary or moving relative to one another  [32].
During the simulations, the aerodynamic thrust and torque of the FOWT are recorded and results over the last four periods are used for further data analysis. Both thrust and torque are translated to the local wind turbine coordinate system so that they are consistent with the definition in Eqn. (15)  and can be compared with the data from the fixed turbine simulation in Section 5. 1. A comparison of the aerodynamic thrust and torque is made for the floating wind turbine simulation and the fixed turbine simulation in Case #1 and is illustrated in Fig. 24 (a) , where the data from fixed turbine is extracted from the case with a medium mesh density. It is easily seen from Fig. 24  (a) that the instantaneous thrust and torque are time dependent due to the motion of the floating platform, while the time-averaged thrust and torque are quite similar to those from the fixed turbine simulations.