dataesr/scientific-paragraphs-categorization huggingface dataset:
Repertoire of SST cDNAs and/or genes in cartilaginous fish The entire repertoire of SST cDNAs and/or genes found from S. canicula, R. typus and C. milii databases is shown in Figures  S3-S8. S. canicula SST family appears as the richest with no less than five SST genes: the three genes previously reported in  Quan et al. (2013) , namely SSTa, SSTb and SSTc, plus two additional genes characterized in the present study, SSTd and SSTe. All these genes, with the exception of SSTb, were retrieved from R. typus and C. milii. Only a partial sequence of the SSTd gene could be detected from L. erinacea (already reported in  Liu et al. 2010).
Discussion The main aim of the present study was to determine the repertoire of the SST gene family in cartilaginous fish. For this purpose, the genome and/or transcriptome databases from three representative chondrichthyan species were examined, namely the small-spotted catshark S. canicula (unpublished, see  Gaillard et al. 2018  for more details), the whale shark, R. typus  (Read et al. 2017 ) and the elephant shark C. milii  (Venkatesh et al. 2014). In S. canicula, two new SST genes, named SSTd and STTe were found, in addition to the three SST genes, SSTa, SSTb and SSTc, previoulsly identified  (Quan et al. 2013). All these genes, with the exception of SSTb, were retrieved from R. typus and C. milii. It is noteworthy that the occurrence of partial sequences of the SSTc  (Tostivint et al.
Supplementary data Table  S1. Sequences of the oligonucleotides used for PCR amplifications. Table  S2. Primary structure of SST-related peptides from different vertebrate species. Peptides from chondrichthyan species are in red. Conserved residues are in bold. Table  S3. Pair-wise comparison of full-length SST protein sequences from S. canicula, R. typus, L. erinacea and C. milii. Values represent percentage of amino acid identity. Table  S4. Chromosomal locations of gene orthologs in the synteny analysis of the SST loci. All human gene names in the table are approved HGNC symbols and the genes from other species have been given the name of their human orthologs. Chromosomal locations of genes are expressed in bp. Blanks represent genes that have not been found in the current genome databases.
Figure2. Synteny of genes in the SST1-SST3 locus in R. typus and C. milii plus eight selected bony vertebrate species (human, chicken, anole lizard, western clawed frog, coelacanth, spotted gar, zebrafish and medaka). Genes are represented by block arrows. Genes with conserved synteny are coloured. For the teleost SST4 gene, three possible positions are proposed with respect to the gar SSTs (see the text for more details). Position of the genes (in megabases -Mb) is displayed below each box, according to the Ensembl database. Empty circles indicate the end of scaffolds. Letters inside the boxes refer to the specific and provisional nomenclature of S. canicula SSTs initially proposed inQuan (2013). The detailed chromosomal locations of genes displayed in this map are included in TableS4.
Acknowledgements This work has been supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Muséum National d'Histoire Naturelle (MNHN)  and the  Agence Nationale de la Recherche (ANR)  grant  NEMO  (n°  ANR-14-CE02-0020-01 HT ). The authors thank  Hélène Mayeur  ( Observatoire Océanologique de Banyuls, France ), and  Patrick Wincker ,  Jean-Marc Aury  and the  Genoscope staff (Evry, France)  for catshark genome sequencing and assembly. They also thank  Dr Nicolas Buisine  for helpful discussions. Finally, they are very grateful to  Mitchell Fleming  and  Stephan Couderq  ( Museum National d'Histoire Naturelle ) for the English corrections.
The authors proposed a branch-and-cut method which can solve instances to optimality with up to 12 sites (six demands), 1 transfer point and 2 vehicles. Rais et al. (2014)  formulated the same problem in a different way and a fourindex binary variable was used to track a demand. The service time was also not considered. The commercial solver GUROBI was used for solving instances with up to 14 sites (7 demands). Several limitations can be observed when using the MILP planners to solve the transfer-related problems: on one hand, the solving time increases exponentially while increasing the number of demands; on the other hand, finding the optimal solution for large instances (more than 7 demands) in a reasonable time is difficult using these MILP solvers. Since the solving time increases exponentially for large instances, an approached method is often chosen as an alternative.
New dataset These instances are derived from  Li and Lim (2003). As indicated in  (Masson et al. , 2013) , the benefits of adding transfers into these instances were not significant due to their configurations. Therefore, only the coordinates from these instances were used, and the other information were re-constructed to create a new dataset (named Dataset-SPDPT). The format of new generated instances is described in table  1  and table 2. Each demand is associated with a pair of supplier and client. The profit of satisfying each demand is fixed at 20. The quantity is a random value less than 0. 9*100, where 100 is the capacity of vehicle. The loading and unloading times are fixed at 10. The coordinates of each site is randomly selected from the instances of  (Li and Lim, 2003). The time windows are generated in a way that each vehicle has enough time to collect and delivery one demand.
Impacts of profit and service time In the initial dataset, the profit and service time for each demand are fixed with identical values. To show the impact of variable profit and service time, two additional datasets were generated. For the first one (named Dataset-SPDPT-P), the profit was varied between 1 and 40 instead of fixed at 20. As to the second one (named Dataset-SPDPT-S), the service time was varied between 1 and 20 instead of fixed at 10. These two new datasets were tested and the results are compared with the initial one Dataset-SPDPT (Table  5 ). In the table, the first column gives the name of each instance. From column 2 to 4, the results on the initial dataset are presented. Column 2 gives the For the dataset with variable profit (Dataset-SPDPT-P), we could draw these conclusions:  (1)  In the case that all the demands can be satisfied (profit/n = 100/5), varying the profits will not influence the final delivery itinerary, which could be observed by comparing the values of the column distance.
(2) In the case that not all the demands can be satisfied (SPDPT14 and SPDPT16), varying the profits could influence the final decision since maximizing the profit is preferred to minimizing the distance in our lexicographic approach. (3) In terms of solving time, the Dataset-SPDPT-P consumes less (around 400s against 600s in average). This could be explained by the preference order obtained by varying the profit. The demands of high profit would be considered first, which could accelerate the searching process. For the dataset with variable service time (Dataset-SPDPT-S), we may conclude that a short or long service time could make the situation more or less flexible when choosing the next demand to satisfy. In other words, it could impact the visiting sequence of sites (the traveling distance) and the number of satisfied demands.
Running environment and parameters setting The programming language is Java, and our method is run on an Intel Core i7-4810MQ CPU, 2. 80 GHz processor with 16 GB of memory. Three parameters related to PSO are the w, c 1 and c 2. Chakraborty et al. (2011)  have given the convergence condition of MOPSO. The theorem is stated as follows: Theorem 1. If S is the initial swarm, then subject to restraints obtained in (34), in a multi-objective search space the MOPSO algorithm will cause the swarm mean to converge to the center of the Pareto optimal set Λ * ; that is, if µ t is the expected population mean after t iterations, then lim t→∞ = X * , where X * denotes the center of the Pareto-optimal set. w < 1, 0 < c 1 + c 2 2 < 2(1 + w) (34) The three parameters (w = 0. 8, c 1 = 1, c 2 = 1) used in this paper satisfy the convergence condition, which means that the swarm can converge to the center of the Pareto-optimal set Λ * when the number of iterations increases.
Datasets Tests are made on two datasets. The first one has been introduced in section 5. The second dataset is generated by modifying the instances of the SPDPTWPD  (Al Chami et al. , 2017a)  by adding one transfer point. This dataset of 23 instances was generated in a way that the distribution of sites (R: randomly, C: clustered and RC: half-random-half-clustered) and the number of sites (20, 50, 100) are considered. However, the complexity of the considered variant does not allow to find an optimal solution nor a feasible one in a reasonable time. Several previous studies on related problems have also shown the same difficulty  (Cortés et al. , 2010; Qu and Bard, 2012; Rais et al. , 2014). It should be noticed that our studied problem can be viewed as a generalization of the SPDPTWPD. In other words, the Pareto front for the latter one can be considered as a reference Pareto front for our problem.
Experiments on first dataset HPSO is firstly tested on the new datasets SPDPT, SPDPT-P, and SPDPT-S. Two metrics are chosen to assess its performance: gap and convergence speed, and solving time (in seconds). Gap is related to solution quality and can be calculated as follows: Gap(A/B) = A -B B * 100% ( 35 ) Convergence speed is a value related to iterations. We suppose that the algorithm is converged if no better solution has been found during 100 successive iterations, and the related iteration number is recorded as the convergence speed of the algorithm. Let us note that the number of total iterations is also fixed to 1000, even though the algorithm is converged. The values presented in Table  6  are the averages on each dataset. The test is run 5 times, the best performance (Best), worst performance (Worst), and average performance (Average) along with the associated standard deviation (Std) are given.
The gap on three datasets varies from 0. 39% to 0. 52%. On dataset-SPDPT-P where the profit varies, HPSO gives the best and stable performance with a gap of 0. 39% and a standard deviation of 0. However, it takes more iterations to converge on this dataset (166. 70 in average). In fact, varying the profit could decrease the number of combinations of feasible solutions which correspond to the same amount of profits. As consequence, it is harder for HPSO to find a solution of a fixed profit, which could explain why it converges slower. From another aspect, once it finds a solution corresponding to a profit, it is easier to fall into the local optimum. It could explain why HPSO gives a stable performance on this dataset (std = 0). On dataset-SPDPT-S where the service time varies, HPSO gives a similar performance compared to Dataset-SPDPT in terms of gap and convergence speed.
Acknowledgments This work is supported by the  ANR (French National Research Agency)  in the framework of the project  TCDU (Collaborative Transportation in Urban Distribution ). This  project  ANR-14-CE22-0017  is labeled by the  Pôle Véhicule du Futur , and is jointly performed by four partners, the three french universities of technology ( UTT, UTBM, UTC)  and the society  Share And Move Solutions. This work is also financially supported by a program of the  China Scholarship Council  for PhD Scholarship No. 201504490057.
97 58. 20 SPDPT1-1 100/5 369. 90 2400. 00 73/5 369. 90 1101. 07 100/5 369. 90 1398. 31 SPDPT1-2 100/5 368. 05 2340. 00 129/5 368. 05 1822. 33 100/5 368. 05 1419. 11 SPDPT1-3 100/5 370. 21 1920. 00 142/5 370. 21 1912. 80 100/5 370. 21 1414. 64 SPDPT1-4 100/5 368. 19 1320. 00 102/5 368. 19 996. 69 100/5 370. 68 1623. 58 SPDPT1-5 100/5 378. 27 2760. 00 89/5 378. 27 2274. 01 100/5 378. 27 2082. 78 maximal profit obtained (profit) and the associated number of demands satisfied (n). Column 3 shows the traveling distance corresponding to the related profit. Column 4 presents the time (in seconds) consumed by Cplex. The text in bold means that the obtained solution has changed compared to Dataset-SPDPT.
First the sensor simulator was calibrated and characterized using synthetic datasets of known uncertainties defined from laboratory measurements and literature. Secondly, we used the sensor simulator and the characterized sensor models to simulate the acquisition of atmospheric and vegetation radiances from a synthetic dataset. Each of the sensor models predicted biases with propagated uncertainties that modified the simulated measurements as a function of different factors. Finally, the impact of each sensor model on SIF retrieval was analyzed. Results show that SIF retrieval can be significantly affected in situations where reflectance factors are barely modified. SIF errors were found to correlate with drivers of instrumental-induced biases which are as also drivers of plant physiology.
Data Simulation for Fluorescence Retrieval In order to test the effect of different uncertainties in the retrieval of SIF under controlled conditions, we produced a synthetic dataset of radiant fluxes: the Vegetation Dataset. Bottom of the atmosphere (BOA) down-welling irradiance spectra were generated for unvarying atmospheric conditions at different solar zenith angles (θ s ) between 0. 00 • and 83. 32 •. Direct (E ↓ dir ) and diffuse (E ↓ diff ) very high spectral resolution irradiances (FWHM = 0. 005 nm) were produced by the radiative transfer code MOMO  [57]  between 500 and 800 nm, and combined as a function of θ s (Equation (  1 )). E ↓ tot = E ↓ dir cos θ s + E ↓ diff , (1) where E ↓ tot is the total BOA down-welling irradiance. For most of the analyses in the manuscript, we used the irradiance spectra simulated for θ sun = 29.
80 • (angles were taken as available in a look-up-table generated in the FLUSS project). Irradiances at different θ sun were only used for the study of the diffuser directional response (see Section 2. 3. 6). A typical vegetation reflectance factor (R) and top of the canopy SIF radiance in the observation direction (F ↑ ) spectra were generated using the model SCOPE  [58]  version 1. 7. For simplicity we assumed a Lambertian vegetation canopy, limiting the analysis of directional effects to those induced by the sensor. R and F ↑ smooth spectra were interpolated using cubic splines to MOMO spectral bands in order to compute at-nadir BOA up-welling apparent radiance (L ↑ app )-which combines both reflected and emitted radiances-as in Equation (  2 ): L ↑ app = E ↓ tot π •R + F ↑ , (2) From these datasets we simulated spectra observed by a typical field spectroradiometer dedicated to the retrieval of SIF in both major telluric oxygen absorption bands: O 2 -B (~687.
A set of radiometric calibration coefficients of a FloX (JB Hyperspectral, Dusseldorf, Germany) spectroradiometric system was used for this simulator. We also generated a model predicting sensor random noise in digital numbers (Appendix A) as a function of t int and of the ratio (N λ -N 0λ )/t int ; a variable proportional to L λ (Equation (3)). "Measured" radiances were generated applying spectral convolution [59] to L ↑ app and E ↓ tot , assuming Gaussian SRF  [60]. Figure  1  shows the original and the convolved radiances, as well as the F ↑ used to generate these datasets. spectroradiometer featured 1024 channels between 650 and 810 nm, with a spectral sampling interval of ~ 0. 1564 nm and FWHM = 0. 32 nm. Constant values were chosen for simplicity, but these actually vary along the detector.
A set of radiometric calibration coefficients of a FloX (JB Hyperspectral, Dusseldorf, Germany) spectroradiometric system was used for this simulator. We also generated a model predicting sensor random noise in digital numbers (Appendix A) as a function of tint and of the ratio  -  ⁄ ; a variable proportional to Lλ (Equation (3). "Measured" radiances were generated applying spectral convolution [59] to L ↑ app and E ↓ tot, assuming Gaussian SRF  [60]. Figure  1  shows the original and the convolved radiances, as well as the F ↑ used to generate these datasets. From this dataset and the datasets generated in the following analyses (Section 2. 3), we retrieved F ↑ in both O2-A and O2-B bands using Spectral Fitting Methods from the FieldSpectroscopyDP R package. The package is available on the GitHub platform at https://github.
com/tommasojulitta under GNU v3. 0  [61]. These results were compared with simulated F ↑ values to assess the impact of the different uncertainties and instrumental biases on SIF retrieval in both oxygen bands. Notice that this work does not aim to test the performance of different retrieval algorithms and uses only one of them. Further analyses on the performance of From this dataset and the datasets generated in the following analyses (Section 2. 3), we retrieved F ↑ in both O 2 -A and O 2 -B bands using Spectral Fitting Methods from the FieldSpectroscopyDP R package. The package is available on the GitHub platform at https://github. com/tommasojulitta under GNU v3. 0  [61]. These results were compared with simulated F ↑ values to assess the impact of the different uncertainties and instrumental biases on SIF retrieval in both oxygen bands.
Characterization of Sensor Models and Instrumental Uncertainties Figure  2  summarizes the dataflow of the different datasets and models generated as well as the uncertainty propagation. Uncertainty propagation methods are described in Section 2. 4. In order to analyze the impact of different instrumental error sources on SIF retrieval, we first simulated the characterization and calibration of the sensor simulator as follows (Step 1 in Figure  2 ): 1) We reproduced the exposure of the sensor simulator to a given radiance source of known properties (and uncertainties), as well as the operation of different simulated sensor models ideally reproducing certain instrument responses to environmental and instrumental factors. Notice that the simulated sensor models biased the radiance measurements but did not add any random noise in this case since they are supposed to replicate the actual behavior of an instrument.
2) For each calibration or instrument response characterized, the combination of the three abovementioned elements led to the generation of a set of radiances (the characterization dataset) which were similar to the actual laboratory datasets used for instrument calibration and characterization. 3) Each characterization dataset was used to estimate the parameters of a sensor model (the fitted sensor model) together with their corresponding uncertainties. Fitted sensor models were able to predict sensor responses with propagated uncertainties. In the second phase (Step 2 in Figure  2 ) we simulated the proximal observation of atmospheric down-welling and vegetation up-welling radiances from which we retrieved SIF. In order to analyze the impact of different instrumental error sources on SIF retrieval, we first simulated the characterization and calibration of the sensor simulator as follows (Step 1 in Figure  2 ): (1) We reproduced the exposure of the sensor simulator to a given radiance source of known properties (and uncertainties), as well as the operation of different simulated sensor models ideally reproducing certain instrument responses to environmental and instrumental factors.
Notice that the simulated sensor models biased the radiance measurements but did not add any random noise in this case since they are supposed to replicate the actual behavior of an instrument. (2) For each calibration or instrument response characterized, the combination of the three abovementioned elements led to the generation of a set of radiances (the characterization dataset) which were similar to the actual laboratory datasets used for instrument calibration and characterization. (3) Each characterization dataset was used to estimate the parameters of a sensor model (the fitted sensor model) together with their corresponding uncertainties. Fitted sensor models were able to predict sensor responses with propagated uncertainties. In the second phase (Step 2 in Figure  2 ) we simulated the proximal observation of atmospheric down-welling and vegetation up-welling radiances from which we retrieved SIF.
(1) First, we combined the fitted sensor models and the sensor simulator to reproduce the "measurement" of the vegetation dataset. In this case, the biases and uncertainties of each fit model were applied individually, and with no noise contribution from the sensor simulator itself. This approach allowed us to isolate the effect of each instrumental response under and its characterization uncertainties. Measurements were simulated at different levels of the drivers (e. g. , ambient temperature, etc. ) of the fitted sensor model under study. (2)  The combination of these datasets and models produced set of atmospheric and vegetation radiances (the retrieval dataset) with noises and biases included, from which we retrieved SIF. (3) Retrieved and simulated SIF were compared, and errors analyzed as a function of the instrument response drivers.
Simulated and fitted sensor models allowed us to study different instrumental responses that can be separated in two groups:  (1)  those that modify the spectral characteristics of the sensor and; (2) those that affect the input-output relationship, therefore related to radiometry. As described above, characterization datasets are essentially synthetic. They do not attempt to accurately replicate any specific behavior of an actual sensor; rather, they represent realistic models similar to those that could be characterized for individual spectroradiometers. This approach allowed us to account for the lack of suitable characterization data, in some cases, and to control the uncertainty propagation. Characterization datasets were produced from a calibrated radiance source of known uncertainty that was "observed" by the sensor simulator.
This source was closely modelled based on the high-quality, traceable RAdiance STAndard, (RASTA)  [62] , or to a Mercury-Argon lamp used for spectral calibration  [60]. Sensor simulator noises and lamp uncertainties were propagated to each characterization dataset. In each case, a different sensor model operated, and in some cases, the uncertainty of other variables involved (e. g. , temperature, illumination angle), was also propagated. We fitted different models commonly used in the characterization of field spectroradiometers to those datasets and computed uncertainties and covariance of the estimated fitted sensor model parameters. In order to facilitate uncertainty propagation, these fitted sensor models were the assumed to be the measurand. Thus, we did not evaluate the capability of the different model formulations to mimic the simulated sensor models, nor did we analyze the consequences of lack of fit between them.
Spectral Calibration Uncertainties Uncertainties related to the spectral characterization of center wavelength (λ c ) and FWHM were estimated from the work of Mihai et al. [60] , who characterized two spectroradiometers using both line emission lamps and a double monochromator. Since the former light sources are more commonly available, we estimated the uncertainties from the fit of a Gaussian function on 30 repeated observations of a Mercury-Argon (Hg-Ar) emission lamp, as described in  [60]. We selected a line close to 750. 387 nm to quantify uncertainties in λ c and FWHM. In addition, the impact of the spectral calibration uncertainties was compared with the effect of spectral shifts (∆λ). Together with the λ c and FWHM uncertainties we applied shifts ranging between -1 nm and +1 nm to the down-welling radiance (L ↓ tot = E ↓ tot /π) respect to L ↑ app and to L ↑ app respect to L ↓ tot in the Vegetation Dataset.