dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We acknowledge the financial support from  European Union   FP7 -NMP EU-Mexico Program  under Grant agreement no. 263878 / by  CONACYT  no. 125141. The work has also benefited from financial support of the  National Science Centre (Poland)  within the framework of the project  2016/ 21/N/ST3/00455. Mr. Josue  at  LANE, Cinvestav  for the AFM facility. Venkatesan Rajalingam is also thankful for the scholarship jointly provided by  SEP  and CINVESTAV as well as the joint funding provided by  Cinvestav-Le Mans University.
Both the hot feed and the cold permeate loops were pumped by two centrifugal pumps in a counter-flow arrangement through the membrane module. The plant was equipped with control systems and data logging to enable continuous unattended operation. The temperature and pressure of the feed and permeate were monitored at their respective inlets and outlets using data logging. Electric conductivity (EC) sensors were used in the feed and permeate cycles to indicate the concentration of dissolved solids, which was translated to salt rejection. Cycle flow rates on both sides were adjusted using pressure regulators placed at their respective entrances to the membrane module. The typical flow was around 16 L•min -1 in the feed loop, and 14 L•min -1 in the permeate loop, controlled by the set module inlet-gauge pressure of 20-35 kPa on each side.
Results are shown in Figure  9  and Figure  S8  (profiles of the contrast laterally across the coating). The exposed rubber, without a coating, became noticeably darker in color. The uncoated substrate presented an optical contrast between the exposed and masked areas of around 35% (estimated using ImageJ analysis software) and, although the SFB and BCS coatings presented partial UV protection (ca. 10% contrast), the ACS coatings prevent fully the bleaching and fading of the substrate. This experiment demonstrates the potential of the ACS dispersion in UV-protective coatings applications. the exposed/masked areas of the substrate, which is used as a measure of UV bleaching.
Acknowledgements The authors acknowledge funding from the  European Union Seventh   Framework Programme BARRIER-PLUS project  ( FP7-SME-2012-2 , no. 304758 ). Technical support from  Violeta Doukova  is also gratefully acknowledged. We benefitted from useful discussions with  James Adams  and  Marian Florescu  ( University of Surrey ). The authors are indebted to the " Microstructure Technological Center" (CTµ), platform of the niversit Claude ernard, Lyon 1 (Villeurbanne, France ) for the access to the transmission electron microscope and the cryo-ultramicrotome.
e. , the sum of the protonconduction resistance in the membrane and the various electronic resistances, bulk and contact resistances) were determined using an AC perturbation of 1 kHz at three different current densities (25, 50 and 100 mA cm -2 ) and also using a current interrupt method. For each data point, the cell voltage was stabilized for 4 min where the current was measured. Multiple-path serpentine flow-fields (two and three parallel channels for the anode and cathode, respectively) machined into sealed graphite blocks were used for testing. The MEAs were conditioned by application of a constant current density of 500 mA cm -2 under H 2 /Air at 50 kPa gauge, 100% RH and 80 °C. The cell voltage was monitored until a stable value was observed. The conditioning step lasted 2 h unless specified otherwise.
The hydrogen crossover current was measured at 250-300 mV and corresponds to a current density of 2. 6 mA cm -2. Loading: 0. 20 mg Pt cm -2 Pt/C. Dotted lines: linear fits in semi-logarithmic plot of first 5, respectively 4 data points. which caused small deviations at Lab3 (large open red circle). This emphasizes once more the significance of obtaining good quality layers and following the strict protocol for evaluating catalyst performance. This, as well as the best choices for several operating parameters, are in good agreement with newer literature  [13, 22]. The activities obtained from RDE measurements were further compared to those obtained by FET and MEA testing. For this, one has to be aware of the differences between the methods. In RDE measurements, a correction for mass transport needs to be made to obtain the kinetic current at 0.
Acknowledgements The project leading to this application has received funding from the  Fuel Cells and Hydrogen 2 Joint Undertaking  under grant agreement No  700127. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme  and  Hydrogen Europe  and  N. ERGHY. TUB acknowledges  Annette Wittebrock  for her help in the laboratory. TUM acknowledges  H. El-Sayed  for helpful discussions.
Funding: This research was funded by FP7 Marie Curie Pulmonary imaging network (PINET) and Ministerio de Economıa y Competitividad MAT2015-65184-C2-2-R; SAF2016-79593-P; SAF2017-84494-C2-1-R). This work was partially funded by Instituto de Salud Carlos III (DTS16/00059), CNIC (Centro Nacional de Investigaciones Cardiovasculares), and Comunidad de Madrid (B2017-BMD3731 and B2017-BMD3875). We thank Ligue contre le cancer, comité Charentes Maritimes which allows to free up some time to complete the redaction of this manuscript during a grant-not dedicated on this work-agreed to LIENSs, UMR CNRS 7266, La Rochelle.
Acknowledgements This research was supported by the  Fonds der Chemischen Industrie  (B. D. and J. S. ), the  French National Research Agency  ( Labex Program ,  ARCANE ,  ANR-11-LABX-0003-01 ) and the  CEA DRF   Impulsion 2017 Program  (Project " Light-Acc-Cat "). All calculations have been performed at the  Universitätsrechenzentrum of the Friedrich Schiller University Jena. We acknowledge the  COST Action   CM1202 ,  PERSPECT-H 2  O:  Supramolecular Photocatalytic Water Splitting.
Acknowledgements This work has been supported in part by  CNRS  ( UMR8640  and  UMR7141 ),  Ecole Normale Supérieure ,  French Ministry of Research, Faculté des Sciences et Ingénierie -Sorbonne Université , the " Fondation Pierre-Gilles de Gennes pour la Recherche "  FPGG0049 and the "Initiative d'Excellence" program  from the  French State  (Grant " DYNAMO ",  ANR-11-LABX-0011-01 ). G. L. and M. G. -C. are thankful for the  Institut Universitaire de France Fellowship Program. Dr Alison Tebo  is gratefully acknowledged for helpful comments.
Nonetheless, the investigation of nano-object properties requires the use of appropriated characterisation techniques to get comparable measurements when performed in similar conditions. Unfortunately, a given approach only probes specific properties as for example: interfacial charge transfer  17  , charge carrier lifetime  14, 18  , or photoluminescence  19. Yet, the only way to get a synthetic overviewconsists in comprehensively integrating the fully acquired dataset to enhance the overall understanding. To that extend, we propose an innovative approach combining the deposition of nano-structured films on the internal wall of glass capillaries with the electronic paramagnetic resonance (EPR) spectroscopy. The aim is to probe the photo-generation of reactive species through EPR within aqueous samples.
EPR measurements The coated capillaries were filled with 50 μL nondeoxygenated TEMPOL solution in water at 200 μMand sealed. A first spectrum was recorded to reach the initial EPR intensity (I0). For all measurements, the variations of the I0 values are less than 3%. Then, successive illumination steps of 30 s were applied. Between each illumination step, one spectrum was recorded (I = I(t)). The experimental procedure is detailed in Figure  S1c  (Online Supplementary material). All the intensities values, proportional to the concentration of TEMPOL concentration (I ∝ [T•]), were obtained after baseline correction and double integrations. EPR data were plotted as I/I0 vs. illumination time. Noteworthy, without the presence of paramagnetic probe, no EPR signal is detected.
Acknowledgments This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Université de Strasbourg  and the  Institut Universitaire de France (IUF). We gratefully acknowledge  Abdel Al Hamad  for his assistance in EPR experiments and  Marc Schmutz  ( ICS electronic microscopy platform ) for the scanning electron microscopy pertinent advices. We acknowledge the  French Ministry of Research and the Reseau NAtional de Rpe interDisciplinaire (RENARD, Fédération IRRPE CNRS  # 3443 ).
Acknowledgements D. M. , J. A. and H. G. thanks financial support by the  Spanish Ministry of Economy and Competitiveness (Severo Ochoa   SEV2016-0683  and  CTQ2015-69563-CO2-1 ),  Generalitat Valenciana  ( Prometeo 2017-083 ). JA and D. M. also thank  UPV  for postdoctoral scholarship and  Spanish Ministry of Science for PhD Scholarship , respectively. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  GA694159 MONACAT ).
, and 2/numerical weather prediction data which contain the one day ahead forecast of the cumulative radiation each 3 h. As a result, a hybrid dataset is used as neural network input. In  [5] , the authors don't classify the input data, but rather they train four neural networks with four combinations of input features, with the aim of considering the influence of several meteorological parameters on the prediction results. They conclude that the best performance is obtained when using the following inputs: the year day, the mean daily solar radiation at the top of atmosphere, the maximum sunshine hours, the mean daily air temperature, the mean daily relative humidity and the wind speed. In some other studies, the neural network technique is combined with other nonlinear models. For example, in  [8] , the authors propose a diagonal recurrent wavelet neural network, in order to estimate the hourly solar radiation of the next day.
Based on the above decomposition of the solar radiation, the "clear sky" model and the cloud cover are used as inputs for ANN to predict the daily direct solar radiation on a horizontal surface. The main contribution of this research study is the development of a new prediction model which takes into account two important factors: the change of the sailboat localization and the changing character of the solar radiation during the year such as the daily sunshine hours and the daily solar peak. Considering the performance of the dynamic neural network in prediction issues and the advantages of the NARX models, the NARX-ANN is considered as prediction technique in this paper  [4, 23, 24]. All neural network simulations are performed in a Matlab environment. The rest of this paper is structured as follows: in Section 2, the technique used for the prediction is defined by describing the NARX network model.
Model Structure and Used Database As presented in Section 2, the prediction of a time series using ANNs requires two types of entries: the desired output of the ANN (named also the target) and a number of inputs which depends on the system to be modelled. Figure  3  shows the model structure defined in our study. In our research study, the target is the measured global solar radiation on a horizontal surface. It is acquired from the weather station of "Ecole Supérieure des Technologies Industrielles Avancées" (ESTIA)-Bidart France. The measurements of the global solar radiation are performed with a step of 5 min time interval. It should be noted that measurements must be performed for the direct solar radiation. The weather station of ESTIA doesn't measure the diffuse solar radiation, so the calculation of the real direct solar radiation is not possible (Global radiation = Direct radiation + diffuse radiation).
Results and Discussion Several tests are considered to select the database structure and the neural network configuration. Before starting simulations, the dataset is separated into three parts: training, testing and validation sets. The training phase uses the training set to compute the weights and bias of the neural network and the test set to test it. After finishing this phase the validation set is used to simulate the model and evaluate its performance. All simulations are performed using normalized dataset in order to adjust data and not saturate the neurons.
Choice of the Dataset Structure The first simulations of this research study are performed using a set of test and training containing several days of the year. Actually, the idea is to use a database which can represent all the year in order to obtain a sufficient generalization capability and facilitate the training process. It is assumed that the use of a dataset containing the 365 days of the year is not beneficial for several reasons. The use of more than a certain threshold of data increases the calculation time and mays cause an overtraining. In addition some extreme phenomena can never be fully predicted even if the dataset is extended. As a result, a chosen number of days is selected to perform the training and test phase, as analyzed later. As it can be seen in Figure  12 , the neural network cannot follow correctly the direct solar radiation curve, particularly at night when the solar radiation must be null.
The observed leaps in the predicted curve are explained by the fact that the set of test and training consists of data of non-successive days. Indeed, the use of these data generates two forms of inconsistency: the cloud cover inconsistency and the solar characteristics inconsistency. The cloud cover inconsistency is observed in the moment of the transition between non-successive days (at midnight) because the meteorological parameters are different. This transition engenders a linearity problem for the neural network training. The solar characteristics inconsistency is due to the temporal gap between data, which causes changing features between days such as the daily solar peak and the daily sunshine hours. This gap engenders a generalization problem for the neural network. On the premise of the daily training, a compromise must be found in order to obtain the adequate database size which allows to obtain good results without saturating the calculation process.
Several training are performed and the best results are briefly presented in Table  2. A test and training dataset of 10 days allows to obtain the minimal error. In this case, the MSE and the DMPE are 0. 00695 and 41. 19645 W/m 2 respectively. The global solar radiation of the weather station is measured with a time step of 5 min. Therefore the interpolated cloud cover and the calculated direct solar radiation have the same time step. However, as observed in the training with the previous dataset (beginning of 6. 2. 1), the output of the neural network is not meant to follow the rapid fluctuations of the measured solar radiation (Figure  12 ). Applying a filter on the input signal, it becomes more pertinent because its characteristic is close to the output signal. Thus it is interesting to consider the average of the measured data in the prediction process.
For these reasons, the idea is to use a set of test and training data containing a number of consecutive days and to repeat the training process one time per day to forecast solar radiation for one coming day. Using this approach, the used database becomes pertinent because it consists of a number of days just before the day concerned by the prediction. However, a daily training consumes calculation resources and can engender discontinuities if the radiation is not equal to zero at the moment of the training. Therefore, the choice of the training time is important. Since the solar radiation is null at night, and the training should be achieved every day, we have chosen to perform it at midnight, the transition moment between days. It must be noted that the daily training is useful for the solar radiation prediction in the sailboat.
• The localization influencing the direct solar radiation model variations, especially as in this study the localization data (longitude and latitude) are intercalated in a preliminary calculation (the SOLIS model) and not as inputs of the neural network. On the premise of the daily training, a compromise must be found in order to obtain the adequate database size which allows to obtain good results without saturating the calculation process. Several training are performed and the best results are briefly presented in Table  2. A test and training dataset of 10 days allows to obtain the minimal error. In this case, the MSE and the DMPE are 0. 00695 and 41. 19645 W/m 2 respectively. The global solar radiation of the weather station is measured with a time step of 5 min. Therefore the interpolated cloud cover and the calculated direct solar radiation have the same time step.
However, as observed in the training with the previous dataset (beginning of Section 6. 2. 1), the output of the neural network is not meant to follow the rapid fluctuations of the measured solar radiation (Figure  12 ). Applying a filter on the input signal, it becomes more pertinent because its characteristic is close to the output signal. Thus it is interesting to consider the average of the measured data in the prediction process. Therefore, several averages are performed and the best prediction results are stored in Table  3. There are moving averages of 10 and 30 min over intervals of one hour. It may be concluded that the two simulation results are similar. Therefore, in order to minimize errors and computing time, the chosen interval average is 30 min. The second important step in the determination of the solar radiation predictor is to find the adequate neural network structure.
Conclusions This paper proposes a NARX neural network model for the direct solar radiation prediction on a horizontal surface. The main finding of this research study is that the training phase of the neural network is performed periodically, taking into consideration several parameters, such as the cloud cover and solar characteristics and the sailboat mobility. Several simulations carried out with different evaluation criteria are performed and evaluated using MSE and DMPE errors. The best results (0. 00279 for MSE and 24. 0584 W/m 2 for DMPE) are obtained for: a dataset of 10 days with a moving average of 30 min over intervals of one hour, a NARX model consisting of 15 neurons on the input and the hidden layers, an input and hidden layer which contain the sigmoid function, an output layer which contain the tansig function, and a random initialization of weights.
Data source The most up-to-date genomes of the three brown algae (Ectoparpus siliculosus, Cladosiphon okamuranus and Saccharina japonica), the three diatoms (Phaeodactylum tricornutum, Thalassiosira pseudonana and Fragilariopsis cylindrus) and the Nannochloropsis gaditana are available from public databases. Briefly, genome sequences of E. siliculosus V2016 v e r s i o n w e r e d o w n l o a d e d f r o m t h e w e b s i t e http://bioinformatics. psb. ugent. be/orcae/overview/Ectsi (Cormier et al. 2016). Sequences of C. okamuranus were downloaded from http://marinegenomics. oist. jp/algae/ (Nishitsuji et al. 2016). S e q u e n c e s o f S.
Identi fication of P450s The P450 genes within stramenopiles were identified as described below. First, the P450 domain PF00067 was downloaded from the Pfam website http://pfam. xfam. org/ (Finn et al. 2016). HMMER3 http://hmmer. org/ (Eddy 2011) software with the default parameters was used to search for PF00067 domain in the proteomes of each species including the three brown algae, three diatoms and N. gaditana. Then, we used acquired proteins as query sequences for tBlastn and Blastp searches  (Lobo 2012)  against the genome sequences and predicted proteome sequences, respectively. Redundant sequences were discarded from the data set to obtain unique P450 genes. All of the obtained P450s were further manually examined using the NCBI online BLAST tool https://blast. ncbi. nlm. nih. gov/Blast.
Sequence structure analysis For each lineage (i. e. oomycetes, brown algae and diatoms), sequence structural features were explored to reveal a lineage-specific conservation pattern. Multiple protein sequence alignments were obtained using the hmmalign program in HMMER 3. 1 package, with CYP protein sequences and PF00067. hmm as input files. Sequences logos of conserved motifs w e r e g e n e r a t e d u s i n g t h e o n l i n e W e b l o g o p r o g r a m http://weblogo. threeplusone. com/create. cgi (Crooks et al. 2004 ) based on the sequence alignment results. Intron and exon information of the CYPs was obtained from their GFF3 files and was used for the graphic display using the Gene Structure Display Server of Peking University http://gsds. cbi. pku. edu. cn (Hu et al. 2014). Subcellular protein localization was predicted using ASAFind http://rocaplab.
ac. uk/Tools/msa/clustalo/  (Sievers et al. 2011). Codon alignments were generated using the protein sequence alignments as a guide  (Suyama et al. 2006). The corresponding gene tree of each family was generated using MEGA 7. 0. Then we used one ratio model (M = 0) of Codeml program in the PAML package  (Yang 2007)  to calculate ω within each CYP family. Besides, site model, free ratio model, branch specific model and branch-site model were used to detect positively selected genes. To elucidate the evolutionary process of CYP families, we performed family history reconstruction using the COUNT software with default parameters  (Csűös 2010). This software implements phylogenetic birth-and-death model, in which family size evolution on an edge is governed by a linear birth-and-death model. Ancestral reconstruction can be carried out by computing posterior probabilities for the family sizes at inner nodes.
Expression pro file analysis Previous published microarray data from E. siliculosus were used to explore the expression levels of P450s in response to abiotic stresses, including hyposaline stress, hypersaline stress, oxidative stress, and Cu stress  (Dittami et al. 2009; Ritter et al. 2014 ). The expression level was determined by averaging the expression values (previously quantile normalized by Roche NimbleGen, Madison, WI, USA) of four replicates for each experimental condition. Furthermore, expression levels of S. japonica P450s were examined in different life cycle stages including sporophytes, male gametophytes, and female gametophytes. Their transcriptomes were sequenced by our group using Illumina 2500, with three biological replicates in each life stage. The SRA accession numbers in the NCBI database are SRR5860560, SRR5860561, SRR5860562, SRR5860563, SRR5860564, SRR5860565, SRR5860566, SRR5860567, and SRR5860568.
Gene expression levels were estimated by FPKM (Fragments Per Kilobase of transcript per Million fragments mapped). Differential expression analysis among life stages was performed using the DESeq  (Love et al. 2014 ) R package using a model based on the negative binominal distribution. The resulting P values were adjusted using the Benjamini and Hochberg's approach for controlling the false discovery rate  (Benjamini and Hochberg 1995). Genes with an adjusted P value < 0. 05 found by DESeq were assigned as differentially expressed. Transcriptome-wide microarray expression data of diatoms were acquired from the pan-transcriptomic analysis  (Ashworth et al. 2016 ) http://networks. systemsbiology. net/diatom-portal/. The integrated data used in this analysis included: silica, iron, and nitrogen limitation, low temperature and elevated pH  (Mock et al.