dataesr/scientific-paragraphs-categorization huggingface dataset:
Other genes identified by homology with the Sec protein database were found to contain a cysteine instead of an Sec at the same alignment position (supplementary table  S3 , Supplementary Material online). The possibility of the presence of unknown selenoproteins in the e-proteobacterial genomes was also considered. For each genome in our set, all possible ORFs longer than 30 amino acids were translated assuming TGA!U decoding and searched with tBLASTn. The results were parsed by calculating the occurrence of U:U and U:C matches with respect to U:X matches in sequence alignments. Using this procedure we rediscovered all the previously identified sequences and one additional candidate selenoprotein: A TGA-containing ORF of Campylobacter curvus in which the TGA codon corresponded to cysteines in homologous sequences of Arcobacter species (supplementary fig.
S4 , Supplementary Material online). Although the C. curvus protein has significant similarity with thiol peroxidases in dbTEU (best hit: YP_593855. 1, E = 2 Â 10 À8 ), database sequences contain Sec at a different position (supplementary fig. S5 , Supplementary Material online), thus explaining why this protein was not identified as a selenoprotein in our previous search. G R U R G Y A R Y G G Y A R R C R Y Y U A R Y R R G G U U C R A R U C C Y Y Y C R Acceptor D loop Anticodon T loop 5´G R A G U G G C G A G Y G G U Y G A A G C R C G G U C U U G A A A A C C G Y G A G G A R C C U C C G G G U U C G A A U C C C C U Y C G C C A 5´G G R G R G Y A G Y Y C U G G U G R R Y C Y R G R C U U C A A A Y C Y R U G R G Y R G U G C Y R Y Y G R G R R G U U C G A U U C Y Y Y Y R C Y Y U C U C 52.
SECIS Elements in e-Proteobacteria Genes Encoding Selenoproteins A comparative analysis of RNA secondary structures  (Hamada et al. 2009 ) of the region around the Sec codon indicated the presence of structured RNAs (supplementary fig. S6 , Supplementary Material online), and the bSECISearch program  (Zhang and Gladyshev 2005)  identified the presence of optimal SECIS elements in all sequences except in a putative SelW protein of Campylobacter fetus (see supplementary table  S2 , Supplementary Material online). Interestingly, an optimal SECIS element was also identified downstream the TGA codon of the C. curvus peroxidase, providing further evidence for its identification as a selenoprotein (supplementary fig. S4 , Supplementary Material online). From the output of the bSECISsearch program and the comparative analysis of secondary structures, the presence of a common 5/6-bp hairpin-known as the "apical loop"  (Chen et al.
Discussion Prompted by the presence of a putative selA gene in H. pylori and the lack of information on selenium and Sec metabolism in this bacterium, we have carried out a comparative analysis of the Sec-decoding trait and the selenoproteome in Helicobacteriaceae and related e-proteobacteria. Our analysis confirms that selenoproteins and components of the Secdecoding trait require special attention in genome annotation. Most of the selenoproteins and tRNA Sec genes identified in this work were misannotated or unannotated in sequence databases (see supplementary tables S1 and S2, Supplementary Material online). tRNA Sec genes were largely missed by searching with the standard tRNA model, but accurately identified by searching with the specific infernal PSec model. Nevertheless, the tRNA Sec structure in e-proteobacteria shows some differences with respect to the PSec model, as these tRNAs appear to form 7 bp instead of 8 in the aminoacyl stem.
The length of the aminoacyl stem in tRNA Sec is relevant to the positioning of the tRNA-bound amino acid at the SelA active site  (Itoh et al. 2013)  and has been shown to affect SelB binding in E. coli  (Baron and Bo ¨ck 1991). Known selenoproteins could be identified through comparisons with the specialized dbTEU database. However, the use of a covariance model of SECIS elements and a variation in the BLAST search procedure enabled the identification of an additional selenoprotein family (DUF466), representing the first example of a carboxyl-terminal Sec. As the local algorithm of BLAST does not extend the alignment to include terminal mismatches (e. g. , U:C), proteins ending with an Sec can escape identification by the standard search methods. A more exhaustive search in the available genomes could reveal the presence of other protein families possessing a terminal Sec.
This two dimensional numerical model based on the analytical development was developed using Matlab software. A geometric mesh was adopted by the program according to the geometry of the receiver flat, while the receiver thermophysical properties and geometric dimensions of the parabolic concentrator are introduced into a data file. The most important assumptions taken in this model are: the receiver flat is considered as an infinite solid, and their thermophysical properties (diffusivity, thermal conductivity, density) are independent of the temperature. As an application example, we take the mild steel receiver flat used in the experimental part and which is positioned in the center of the focal zone. Its dimensions are of 10 × 10 cm and a thickness of 0. 8 mm, giving a ratio e L of 0. 008 which verifies the condition of the infinite solid.
From these images, it is possible to obtain a daily trend of solar irradiance, what can be relevant in order to optimize the use of a storage system. Moreover, NWP models and satellite imagery can be combined with time series forecasting models, based on a historical climate database, with the aim of identifying cycles and/or trends in the solar resource  [3]. However, NWP models, even coupled with satellite imagery, are not suited to the real-time management of a solar power plant. Images can also be provided by ground-based cameras, named sky imagers. Such cameras allow solar radiation to be forecasted at high spatial (~100 m²) and temporal (< 30 min) resolutions. Indeed, sky imagers can provide in real-time a very short term forecast of the beam attenuation produced by clouds, allowing the power plant owner to make more accurate bids in the market with less risk of under-generation (leading to penalties) or over-generation (leading to losses due to curtailment).
Developing a clear sky model is therefore an essential step to forecast direct normal irradiance. Nowadays, clear sky models developed for satellite imagery  [2, 7]  or ground-based camera applications  [4] [5] [6]  are often inappropriate to real-time applications. Indeed, at the moment, there are no works dealing with the clear sky detection in real-time. As an example, in a previous work  [8] , we proposed a methodology based on a multi-resolution analysis (using the discrete wavelet transform) in order to detect clear sky days in our database. This approach is useful for data preprocessing and model development but not adapted to real-time applications like solar power plants management. Moreover, the existing models dealing with clear sky irradiance are relatively basic. For instance, an approach is proposed to obtain short-term forecasts of DNI at the ground level by authors assuming that DNI is constant (900 W.
Irradiance sensor A Rotating Shadowband Irradiometer (RSI) (Fig. 1. c) has been installed next to the sky imager in order to measure every minute, with a good precision, the solar irradiance components. Equipped with a silicon photodiode radiometer and a shadow band, this instrument allows Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI) and Diffuse Horizontal Irradiance (DHI) to be measured or deduced from measurements. The data are collected by the sky imager software and immediately included in the process analysis. In the present study, we only focus on DNI measurement because it is a key point in the clear sky model development and because in the CSP technology, the power production is only affected by the direct component.
Database The database used to develop and validate the clear sky model is composed of data collected by the experimental platform and data derived from the sun-earth equations given by the SG2 algorithm  [10]. Knowing the Julian date and the localization of the considered site (latitude, longitude, and altitude), this algorithm is able to give quickly and with high accuracy all information like air mass, azimuth/zenith angles, and extraterrestrial solar radiation. In order to avoid missing data, we considered measures from February the 5th to March the 21st, 2014.
Atmospheric turbidity Under clear sky conditions, the broadband DNI ( ) is given by (1): = (1) with the extraterrestrial solar irradiance ( = 1361. 2 W. m -2  [11] ), the sun-earth distance correction factor and the atmospheric transmittance resulting from both scattering and absorption of the sunlight. This atmospheric transmittance can be obtained using radiation transfer models  [12]  or a broadband turbidity coefficient like the wellknown Linke turbidity coefficient  [13]. Although radiation transfer models have proven to be able to forecast clear sky DNI with unsurpassed accuracy, they require Aerosol Optical Depth (AOD) data which are difficult to measure or rarely available with high accuracy. On the other hand, models based on a broadband turbidity coefficient give a lower accuracy than radiation transfer models but can be easily implemented because they only derive from broadband beam radiation measurement networks.
This coefficient ( ) is equal to atmospheric turbidity ( ) only if the measured DNI ( ) corresponds to the clear sky DNI ( ). So, in order to decide if ( ) corresponds or not to atmospheric turbidity, changes in atmospheric turbidity are considered via its maximum plausible change (C) and its measured change (D). These two variables are computed from the last time ( ) atmospheric turbidity is trustworthy (B). So, is the last time the coefficient related to atmospheric turbidity ( ) is used as atmospheric turbidity. The threshold has been computed on the basis of the transient atmospheric turbidity behaviour we highlighted in our database. Indeed, the bigger is, the more plausible the atmospheric turbidity increase is. However, according to its daily behaviour (atmospheric turbidity never increases all the day), a maximum change is defined.
Fig. 3  shows how has been computed according to. For now, a simple linear interpolation has been performed in order to determine. Therefore, is given by equation (  4 ), with and the slope and the intercept of the curve (between and ), respectively (5): = min (. + , ) (4) = ( ) ( ) and = and mean that atmospheric turbidity cannot increase more than for two consecutive DNI acquisitions ( = 20 seconds and = 0. 045). and mean that atmospheric turbidity cannot increase more than 1 through the day ( = 10 hours and = 1). These four parameters have been set taking into account the acquisition time step, accuracy in RSI measurements as well as changes in atmospheric turbidity one can observe in the database. As a result, they are proper to both the considered site and measurement devices used. is about the measured change in atmospheric turbidity.
It is computed as follows (6): = ( ) (6) Once these two thresholds are computed ( and ), a decision based on their comparison is taken (E) in order to determine if the computed coefficient ( ) is, or not, the "real" atmospheric turbidity ( ). This decision is taken according to the following conditions (7): > or < or > (7) where and are constants defined empirically (using our database) and derived from the minimum and maximum possible values of atmospheric turbidity. If  (7)  is true (i. e. is not plausible), the mean atmospheric turbidity value calculated over the last five minutes is assigned to ( ). However, the time of the day impacts the decision process. For example, during sunrise or sunset, is consistently used because atmospheric turbidity is not easy to compute in such periods. This approach provides more stable values.
On Fig. 4 , only five consecutive days are plotted. However, our study considers 45 days of the database, which is about 150 hours of clear sky data. In order to evaluate our clear sky model, we degraded a part of these 150 hours of data (c). Considering this noisy signal, we compared the clear sky values given by our model and measured clear sky data (without noise). Firstly, we generated a random sequence denoted (in which each element [0,1]). From we generated , the Pseudo Random Binary Sequence (PRBS), by using (  8 ), according to the ratio / dealing with the proportion of "1" in such a sequence: if > 0/1 then = 0 else = 1 (8) In our study, this ratio ranges between 0. 1 and 0. 9 (because it can be very different, depending on the season of the year). As a consequence, the initial signal has been degraded according to /.
Results and discussion Using our knowledge model and the noisy signal , one can obtain clear sky DNI values. To obtain more relevant and reliable results, we generated ten times the pseudo random binary sequence. The impact of / on both the MAE and NRMSE is highlighted by Table  2. Taking a look at this table, one can notice that the more the ratio / increases (i. e. the higher the degradation of the signal) the quicker the MAE and NRMSE increase. Indeed, considering a high degradation, clear sky data are less available to facilitate a good computation of atmospheric turbidity and, therefore, the clear sky DNI. With a ratio / set to 0. 1, the MAE and NRMSE are 16. 1 W. m -2 and 1. 01%, respectively. With a ratio / set to 0. 9, the MAE and NRMSE are 33. 4 W. m -2 and 2. 77%, respectively. Moreover, we clearly notice that the results are very dependent on the operating conditions considered for model evaluation ( / values).
According to the DNI measurements realized for this study, the mean ratio of clear sky data during a day is close to 32 %. This corresponds to a ratio / equal to 0. 68. So, the results obtained for the on-site measured ratio ( / = 0. 7) are satisfactory because of a MAE equal to 24. 6 W. m -2 and a NRMSE equal to 1. 9% (Table  2 ). Fig. 5  presents the results obtained using the proposed clear sky model for three consecutive days of March 2014. These three days have been selected in order to highlight how the model is able to work with different DNI behaviours. / has been set to 0. 7 in order to be representative of on-site measurements. The three selected days (March 1 to March 3) have different solar irradiance profiles. For each day, we can note sharp and frequent changes in DNI (i. e. cloudy periods of limited duration) and periods during which DNI is insignificant (overcast).
TmNrdD possesses the 10-stranded α-β barrel fold with a "finger loop" wound through its core, a fold common to RNR and other glycyl radical enzymes  [28]. The root mean square deviation (rmsd) to the previously determined crystal structure of the bacteriophage T4 enzyme [T4NrdD, PDB ID 1HK8] [29] is 2. 34 Å for 426 equivalent Cα atoms out of 505 in T4NrdD and 606 in tmNrdD (Fig  1C ). This large structural deviation is consistent with the very limited sequence identity of ~18% between the two proteins. A full comparison of all known structures of RNR large subunits is given in S3 Table. The tmNrdD structure was also very recently solved by another group  [23]  (PDB ID 4U3E). The crystal form these workers obtained was very similar, but due to subtly different crystal packing they were able to resolve a further 17 residues at the C-terminus of chain A and 4 in chain B.
maritima from log-and midexponential phase, and their abundance is above the average for all expressed proteins [see Table  S9  in  [44] ]. Secondly, studies of gene expression levels in T. maritima during hypothermic coculture with Methanocaldococcus jannaschii  [45]  or after chloramphenicol challenge  [46]  reveal significant drops in the levels of tmNrdD and tmNrdG expression compared to the levels in pure untreated mid-log cultures. Thirdly, tmNrdD, tmNrdG and the class II RNR tmNrdJ are all predicted by the RegPrecise database  [47]  to fall under the control of the transcriptional regulator NrdR (http://regprecise. lbl. gov/RegPrecise/regulon. jsp?regulon_id=7297) in eight Thermotogae. Finally, the 3'-terminus of nrdG in the T. maritima genome overlaps with the 5'-terminus of nrdD by 35 base pairs.
Such overlapping genes are commonly taken as strong indication of co-regulation  [44]  Taken together, this evidence indicates that the anaerobic RNR system is enzymatically active and highly relevant for the viability of Thermotogales. What is the active conformation of tmNrdD? Wei and coworkers recently demonstrated that thioredoxin can be used as reductant by the class III RNR from N. bacilliformis, nbNrdD  [23] , in contrast to formate for other previously characterized NrdDs. NbNrdD belongs to group NrdDh3 in our phylogenetic analysis. The attribution of reduction activity to a protein is consistent with our finding for T. maritima that the essential component is found in the retentate when cell extracts are filtered using a 10 kDa cutoff. The N. bacilliformis study also included a crystal structure of tmNrdD, albeit without substrate or allosteric effector (PDB ID 4U3E).
S2 Fig). The identity of the metal ion has been confirmed by anomalous diffraction at the Zn and Fe K edges (S1 Table and S2 Fig). The Zn 2+ ion is coordinated by the motif CxxxHx 6 CxxC [CxxCx 14 CxxC in the T4 and E. coli enzymes]. This motif is representative of 87 of the 1502 NrdD sequences in the RNR database RNRdb [31] (RNRdb2 beta, http://rnrdb. pfitmap. org; only NCBI sequences from organisms with fully sequenced genomes counted). The Zn-binding domain is more compact than in T4NrdD due to the shorter loop joining the Zn ligands. It is also oriented differently with respect to the barrel surface (S2 Fig).
Acknowledgments We thank  Andrew Frock ,  Robert Kelly ,  Gustav Berggren  and  Yannick Combet-Blanc  for valuable discussions, anonymous reviewers of a previous version for valuable comments,  Mohamed Atta  and  Florent Colin  for assistance with molecular biology and biochemical data analysis,  Annika Rogstam  at  Lund Protein Production Facility  for cloning and expression of MBP-tmNrdG,  Maria Håkansson  for help with crystallization screening and staff at  ESRF  and MAX II beamlines for assistance with data collection.
( S2 Fig). The identity of the metal ion has been confirmed by anomalous diffraction at the Zn and Fe K edges (S1 Table and S2 Fig). The Zn 2+ ion is coordinated by the motif CxxxHx 6 CxxC [CxxCx 14 CxxC in the T4 and E. coli enzymes]. This motif is representative of 87 of the 1502 NrdD sequences in the RNR database RNRdb [31] (RNRdb2 beta, http://rnrdb. pfitmap. org; only NCBI sequences from organisms with fully sequenced genomes counted). The Zn-binding domain is more compact than in T4NrdD due to the shorter loop joining the Zn ligands. It is also oriented differently with respect to the barrel surface (S2 Fig).
A test facility is designed and is under construction to experimentally verify the effect of thermal energy storage systems in overall performance of a coupled Building Integrated PhotoVoltaic / Thermal (BIPV/T) and Air Source Heat Pump (ASHP). This study shows how the loads for the test facility were adjusted by a regular size single family residential building. Moreover, the article explains different unique options of storing thermal energy in the test facility using the thermal mass of the building itself. Numerical models of Insulated Concrete Form (ICF) wall and Ventilated Concrete Slab (VCS) were developed using SolidWorks software' Flow Simulation module and ANSYS Fluent software.
Considering the geometry and material of the test facility, a detailed model of the building was developed in TRNSYS software. The model provides the hourly heating and cooling demand of the building for a whole year. Maximum calculated thermal demand of the test facility based on the simulated model is 3. 77 kW. Figure  1  illustrates the heating/cooling demand of the test facility for every hour in 365 days using Toronto weather library data. Since the size of the test facility is smaller than a single family house, the maximum thermal demand of the building is also relatively lower than a residential house. The objective of this research is to test and validate the feasibility of mechanical systems such as heat pumps and thermal energy storages under real life conditions which fits a regular size building.
A portion of the stored thermal energy also will be transferred to the room as well. However, in this study the goal is to store the maximum thermal energy and use it to preheat the heat pump source air rather than releasing it to the room. The floor is also equipped with infloor/radiant heating system. This system will use hot water for space heating and is not a storage system. A simplified 3D model of the ventilated concrete slab was simulated in ANSYS Fluent program to study the heat transfer phenomena between air and concrete. This model helps designers to fully In order to find the optimal heat transfer rate inside the channels variety of air velocities were numerically tested. Also the percentage of missed/rejected thermal energy for each case was calculated. A set of sample air outlet temperatures for few velocities is shown in Table  1.
Insulated Concrete Form (ICF) Wall Flow Simulation module of Solidworks program was used to investigate the heat transfer process of the embedded hot water pipes inside the ICF wall. A simplified three dimensional model of the ICF wall with a unit length (1 m) of PVC pipe was created. All pipes are parallel to each other and located in a fixed distance of 20 inches apart. Insulations (EPS boards) were assumed to be perfect. Various water temperatures for inlet was numerically tested to address the associated changes in the heat transfer rate and temperature distribution in concrete. A sample set of simulation results for three inlet temperatures is presented in Table  2. Also rate of heat transfer for different configurations were calculated. The heat transfer rate in Table  2  is the rate of transferred thermal energy between the hot water inside the embedded pipe and concrete.
Acknowledgments This work was supported by a grant from  Agence Nationale de la Recherche  ( ANR-12-BIME-0005 , DiaDomOil), a bioenergy grant from  CEA Life Science Division (EliciTAG) , the  IRTELIS program  (PhD grand of LJD), the  OCEANOMICS program  from the  French Ministry of Research , and the  Institut Carnot LISA (Lipides pour la santé  et l'industrie). The authors wish to thank  Melissa Conte  for copyediting.
A fast and high-resolution UPLC-MS E analysis was used to identify phytoplankton pigments in an ethanol extract of Porphyridium purpureum (Pp) devoid of phycobiliproteins. In a first step, 22 standard pigments were analyzed by UPLC-MS E to build a database including retention time and accurate masses of parent and fragment ions. Using this database, seven pigments or derivatives previously reported in Pp were unequivocally identified: β,β-carotene, chlorophyll a, zeaxanthin, chlorophyllide a, pheophorbide a, pheophytin a, and cryptoxanthin. Minor amounts of Divinyl chlorophyll a, a chemotaxonomic pigment marker for prochlorophytes, were also unequivocally identified using the database. Additional analysis of ionization and fragmentation patterns indicated the presence of ions that could correspond to hydroxylated derivatives of chlorophyll a and pheophytin a, produced during the ethanolic extraction, as well as previously described galactosyldiacylglycerols, the thylakoid coenzyme plastoquinone, and gracilamide B, a molecule previously reported in the red seaweed Gracillaria asiatica.