dataesr/scientific-paragraphs-categorization huggingface dataset:
al. , 2016). In relation to the CO2 impact of electricity power systems, numerous papers have made contributions in different directions. Ummel (2012)  calculates the CO2 impact of electricity production by plant worldwide giving birth to the CARMA database 5  , Marriot et al. (  2010 ) simulate CO2 scenarios using alternative energy mixes in the U. S. and Feng et al. (2009)  estimate the CO2 content of regional energy consumption in China. More recently, the attention has shifted to the air pollution avoided due to renewable installation and the evaluation of the subsidy costs with respect the social pollutant benefits Using data from the ERCOT market in Texas  Novan (2015)  introduces the analysis of the external benefits due to renewables, which consists on the avoided CO2 emissions related to each technology when the time of production and the whole generation mix are considered.
They use a bottom-up model to quantify the emissions embodied in the inter-provincial flows, and find a shift of environmental pollution away from economically well-off provinces to resource-rich, and less developed provinces. Although their study highlights regional flows, they do not consider losses as a parameter in their estimations, which is also presumably significant in terms of CO2 impact. Our approach is different because we study the country as a whole to focus on the understanding of the relation between losses and the system CO2 emissions. Our paper contributes to the evaluation of the energy and climate policy imposed on the power sector. In particular, we assess the CO2 impact that changes in the energy flows may have through losses. With this purpose, we empirically estimate the CO2 content of power generation as a function of the transmission and distribution losses using Spanish hourly data from 2011 to 2013.
We consider Spain because, among the five biggest economies of Europe, it had the highest share of energy generated by RES-E in 2013 (36. 39%) and its level of losses are in the average range for European countries 6. From 2004 to 2013, the five biggest economies in Europe increased their RES-E share of energy production from 9. 40 to 25. 59% in Germany, 3. 54 to 13. 85% in the UK, 13. 79 to 16. 87% in France, 16. 09 to 31. 30% in Italy, and 18. 98 to 36. 39% in Spain. Indeed, according to our calculations, energy losses in Spain represented the 8. 90% of the amount of energy injected in the grids (2012), which represented an annual cost of 1,160M€ 7  that is borne by all consumers. According to the World Bank Database 8  other European countries like Portugal and United Kingdom are in a close range with 10% and 8% losses, respectively, while the extreme high rank cases are Croatia and Lithuania with 18% and 19% of losses, respectively.
ACKNOWLEDGEMENTS We are grateful for the generous support of the  Chair of Energy Sustainability  ( Barcelona Institute of Economics, University of Barcelona ), and the  Generalitat de Catalunya SGR  project  2015-SGR-531. Financial support from the  Spanish Ministry of Economics  and  Competitiveness  project  ECO2015-69107-R (MINECO / FEDER ,  EU)  and  FUNSEAM (Energy and Environment Sustainability Foundation)  is gratefully acknowledged.
Given this scale of spending, more attention should be paid to social equity in the distribution of the benefits of these policies. This paper uses rich, comprehensive data for small statistical geographies in England and Wales to examine how the benefits of the feed-in tariff in England and Wales have been distributed across socioeconomic groups to date. The novel dataset links information about 564,074 small-scale PV installations to household socioeconomic and demographic information from the census of England and Wales, at what can be thought of as the 'neighbourhood' level. The combined dataset is used to measure the degree of inequality in the distribution of the benefit across socioeconomic groups and to test different explanations for this outcome that are relevant to feed-in tariff policy design.
Recent empirical studies of the distributional impact of clean energy subsidies have produced results consistent with these hypotheses. Borenstein and Davis (2015)  used tax return data from the United States to assess which households participated in tax credit programs for home weatherisation, solar panels, and hybrid and electric vehicles. They found that the bottom three quintiles of earners by income received about 10 percent of all tax credits, while the top quintile received about 60 percent. Rausch and Mowers (2014)  examine the distributional impact of clean energy standards on US households. They find that these policies are regressive because they place a larger burden on regions that depend on dirty fuels (coal), and a smaller burden on (richer) regions with abundant hydropower and wind resources.
The estimate in this paper is lower because it excludes all payments made to installations in Scotland and the Isle of Man; all payments made to non-PV installations  (wind, hydro, etc) ; and all payments to installations registered at non-domestic premises (community buildings, schools, commercial and industrial facilities). However, the census data do contain many proxies for income and wealth and these are used to construct an index of the socioeconomic status (SES) of each LSOA. The index is likely to be correlated with income and wealth in its own right. It is also common practice in the UK to assess the distributional impact of policy across social groups defined by characteristics other than income and wealth. 8 The SES index approach is consistent with that practice. The SES index is comprised of four component variables: (1) the percent of households with an approximate social grade AB (SOCIAL_AB), (2) the percent of households owning their home outright (without a mortgage) (OWN_OWN), (3) the percent of economically active people unemployed (WORK_UNEMP), and (4) the percent of households deprived in 4 dimensions (HH_DEP_4).
Data Table  1  gives descriptive statistics for the variables used in the distributional analysis, including the exploratory regressions. This includes the benefit measures derived from the FiT policy component of the dataset, the socioeconomic and demographic variables taken from the census, and the SES index. The unit of analysis for all variables is the LSOA. The dataset is entirely cross-sectional. As discussed above, the analysis in this paper only addresses the distribution of policy benefits. The authors did try to obtain detailed data on how the cost of the policy is being distributed across social groups but did not succeed. One reason for this lack of success is that responsibility for distributing the cost of the policy falls not with the government energy markets regulator, but with British electricity suppliers, who effectively administer the policy.
The action of BEs significantly influences the structure and property of these polysaccharides. Distinct types of BE have been identified in various organisms. BEs found in many bacteria display sequence similarities to those in animals, fungi, and plants  (10). These BEs are also distantly similar to ␣-amylase and other amylolytic enzymes, and they are collectively classified into the glycoside hydrolase 13 (GH13) family in the CAZy database (a sequence-based database of carbohydrate-active enzymes)  (11). As the number of GH13 family members became enormous, the family was subdivided into more than 40 subfamilies  (12). The eukaryotic and bacterial BEs are classified into the GH13_8 and GH13_9 subfamilies, respectively  (2, 12). In addition, a different type of BE belonging to GH57 has been identified exclusively in prokaryotes  (2, 13).
1C ). The relative position of the domains was stabilized by three hydrogen bonds and nine van der Waals contacts between domain N and CBM48 in addition to four hydrogen bonds and nine van der Waals contacts between domain N and domain A. Apart from this work, only one study has reported the crystal structure of BE (MtBE) determined with domain N  (20). In MtBE  (20) , domain N was located at one end of the elongated protein, making contact only with CBM48 via 10 hydrogen bonds and 26 van der Waals contacts (Fig. 1C ). Excluding domain N, the overall structure of cceBE1 was similar to those of previously reported BEs, including EcBE (PDB code 1M7X; root mean square deviation (RMSD) for 569   (22). When only domain N was considered, its structure was highly similar between cceBE1 and MtBE (Z score ϭ 10.
2, A  and B ). Among them, the C2 and C3 sites have not been described previously in any BE structure. In EcBE (PDB code 4LPC), oligosaccharide binding was found at SBSs corresponding to the 48_1, A1, A2, and C1 sites  (24). In OsBEI (PDB code 3VU2), sugar chains were bound at SBSs corresponding to the 48_1 and 48_2 sites  (23). The binding of sugars to an SBS equivalent to the 48_2 site was also observed in HsBE (PDB code 5CLW)  (22). The electron densities for sugar molecules bound to W610N-G7 were of good quality, and sugar molecules were unambiguously modeled into the structure. The electron density maps of the bound sugar molecules in the A1 and A2 sites are shown in Fig. 3 , B and C, respectively. The average B-factor for all bound oligosaccharide molecules in W610N-G7 was 47. 7 Å 2 , which was the smallest among all known BE structures, including EcBE in complex with G7 (PDB code 4LPC; 83.
Crystals of W610N in complex with G7 (W610N-G7) were obtained by soaking crystals in reservoir solution containing 300 mM G7 for 30 min at room temperature. Diffraction data were collected using beamlines BL-5A (for LF-WtBE1 and WtBE1-G6), AR-NW12A (for LF-W610N), and AR-NE3A (for W610N-G7), at the Photon Factory, High Energy Accelerator Research Organization (Tsukuba, Japan) ( ϭ 1. 00000 Å). Diffraction data were processed and scaled using the HKL2000 program  (41). The structure of LF-WtBE1 was determined by the molecular replacement method using the BALBES program  (42). The structures of WtBE1-G6, LF-W610N, and W610N-G7 were determined by the same method using MOLREP  (43) , with the structure model of LF-WtBE1 serving as the template. Manual model rebuilding and refinement were performed using Coot  (44)  and REFMAC5  (45).
The data collection and refinement statistics are shown in Table  1. The atomic coordinates and structure factors of LF-WtBE1, WtBE1-G6, LF-W610N, and W610N-G7 were deposited in the Protein Data Bank under the accession codes 5GQU, 5GQV, 5GQW, and 5GQX, respectively. The figures were drawn using PyMOL (Schro ¨dinger, LLC, New York).
reinhardtii (CrISA1) in complex with G7 (PDB code 4OKD) (32). The G7 molecule bound to the active site cleft of CrISA1 is superimposed on the surface model of W610N-G7. Domains in W610N-G7 are colored as described for Fig. 2B. The catalytically important residues (Asp 434 , Glu 487 , and Asp 555 ) are colored in magenta. The bound sugars in W610N-G7 and CrISA1 are shown as orange and white stick models, respectively. Black and gray indicate the reducing end of the bound sugars in W610N-G7 and CrISA1, respectively. The sugar-binding site (48_1) and subsites (Ϫ7 to Ϫ1) are labeled. C, superimposed structure of LF-WtBE1, WtBE1-G6, LF-W610N, and W610N-G7. Residues around subsites Ϫ7 to Ϫ5 of LF-WtBE1, WtBE1-G6, LF-W610N, and W610N-G7 are shown as yellow, magenta, blue, and green stick models, respectively, and labeled.
Acknowledgments-We thank  Drs. Sohei Ito ,  Takuya Ishida ,  Kiyohiko Igarashi , and  Zui Fujimoto  for help in conducting the study. We also thank the staff of the  Photon Factory  for the X-ray data collection. Pseudomonas isoamylase was generously provided by  Hayashibara Co. MARCH 31, 2017 •  VOLUME 292 • NUMBER 13 JOURNAL OF BIOLOGICAL CHEMISTRY 5473.
* This work was supported in part by  Shimadzu Science Foundation  Grant  120003  (to R. S. ) and  JSPS KAKENHI  Grants  15K18685  (to R. S. ) and  16K07467  (to E. S. ). The authors declare that they have no conflicts of interest with the contents of this article. The atomic coordinates and structure factors (codes  5GQU ,  5GQV ,  5GQW , and  5GQX ) have been deposited in the Protein Data Bank (.
Acknowledgements This work was partially funded by the  Spanish MINECO  ( CTM2014/56770-R ) and the  European Council Research  through a  Consolidator Grant  ( ERC-CoG-648161-PHOROSOL ). COA is grateful to all the PhD students ( Rocío Jiménez Carmona ,  Dr. Marta Andrade ) that have contributed to this work over the years. AGB thanks the  Spanish MINECO  for her PhD fellowship ( BES-2012-060410 ).
Acknowledgements Mich ele Heitz is grateful to  Le Fond Qu eb ecois de la Recherche sur la Nature et les Technologies (FQRNT)  for the grant for the research program in partnership contributing to the reduction of greenhouse gases. Marc Veillette  wants also to express his gratitude to the  National Sciences and Engineering Research Council of Canada (NSERC)  ( Alexander Graham Bell Canada Graduate Scholarship and Michael Smith Foreign Study Supplement ) for the Rhône.
Lipid class determination After the beta-carotene separation, lipid classes were quantified on the fraction B' without beta-carotene using thin layer chromatography (TLC) coupled with FID (flame photometric detection) in triplicate as described  [24]. A known mass of lipids was automatically spotted (5) µ(q) = μ 1 -q 0 q. (6) dq dt = ρ(s) -µ(q)q. (7) X * = ( μ -D)( μρ m s 0 -Dρ m s 0 -μq 0 k s D -μq 0 Ds 0 ) μq 0 ( μρ m -μq 0 D -Dρ m ). (SES A4100 Autospotter) onto silica coat rods (Chromarod-SIII), and then developed with two successive solvent systems (80 mL hexane/20 mL benzene/1 mL formic acid and 97 mL hexane/3 mL ether/1. 5 mL formic acid). Lipid class determination was performed with a Iatroscan (Iatroscan New MK 5 from Iatron and integrated using the software: Chromstar from SES Analysesysteme)  [25].
Parameters Units Value μ d -1 1. 99 q 0 µgN µgC -1 0. 053 ρ m µgN µgC -1 d -1 0. 15 k s µgN L -1 18 limitation was effective and in accordance with chemostat theory at both the cell (Fig. 3a ) and culture (Fig. 3b ) levels. Consequently, growth was balanced for the different conditions tested, confirming that lipid status was analyzed in stationary metabolic activities. Despite this effective agreement between data and model, the growth rate measured diverged from the Droop predictions (Fig. 3a ) for dilution rates lower than 0. 2 d -1. In these conditions, the C:N ratio was higher than expected (Fig. 3b ) suggesting that the steady state was not reached at the cellular level. Various authors have reported difficulties in observing the expected steady-state cell concentrations in continuous cultures maintained at low dilution rates  [15, 16, 22] , where supplementary limiting factors others than nitrogen can emerge (increased competition with bacteria, light and CO 2 limitation, accumulation of deleterious bio-products…, etc.
Measurement principles and instrumentation of the device In both cases studied (transient or stationary measurements), the heating wire is powered by an electric current of constant intensity I. The voltage U at the terminals of the wire is measured over time to determine changes in its electrical resistance R. In transient mode, a short sampling period and a short measurement duration are chosen. For stationary measurements, the measurement duration is extended until reaching a constant temperature of the wire. The processing of the experimental data differs for these two types of measurement.
High-resolution vacuum ultraviolet photoabsorption The high-resolution VUV photoabsorption studies of W(CO) 6 in the photon energy range of 3. 9-10. 8 eV (Figure  1 ) were performed at the AU-UV beam line of the ASTRID2 synchrotron facility, Aarhus University, Denmark. The experimental setup has been described previously  [31] , with recent modifications reported in detail by Palmer et al. [34]. Briefly, an absorption gas cell is fitted with a heated 1 Torr full-scale Baratron capacitance manometer (Setra model 774) and a photo-multiplier tube (PMT) detector to measure the transmitted light intensity. In order to ensure that the data is free of any saturation effects, tungsten hexacarbonyl absorption cross sections were measured at an appropriate pressure with typical attenuations of less than 40%.
The lamp (warmed up during 30 min out of the setup) was put inside the reactor, and 2 mL samples were periodically taken. During the dark period and all throughout the run, N 2 was bubbled into the reactor at a flow rate of 0. 3 L min -1 , to warrant anaerobic conditions. Each sample was first centrifuged and then filtered through a cellulose acetate membrane (Sartorius, 0. 22 m, 25 mm diameter) before analysis. Negligible changes on pH were observed at the end of all photocatalytic runs. At least duplicate runs were performed for each condition, and the results were averaged, with relative standard deviations smaller than 5%. The fitting of the experimental points was made with Origin 8. 0 software, with reduced 2 as the iteration-ending criterion.
Energy Researchers have also worked on providing decision-support software tools for nZEBs that try to choose the nZEB alternative with the most optimized energy  [17, 43]. Attia et al. [17]  proposed a so-called, ZEBO toolkit, which is a simulation-based design support tool that is intended to allow informed decision making for nZEBs during early design phases in Egypt. ZEBO allows users to test the energy performance of different building configurations by using EnergyPlus  [44]  as a simulation engine. The results of evaluating the different building design configurations are reported by means of energy performance graphs, which shall help the designer make informed decisions. Lin and Gerber  [43]  proposed the so-called EEPFD (Evolutionary Energy Performance Feedback for Design), which is a framework that is intended to generate design alternatives, perform an energy evaluation of these alternatives, optimize them, choose the ones that have better energy performance and, finally, provide a trade-off study of all of the generated results for design decision makers.
During this stage, unknown design parameters are deduced from valid and known properties of the design objects in the model. This stage is essential since it allows inferring missing data and at the same time avoids inconsistencies  [74] ; i. e. , it is possible to ask users to include all of the information for checking a given model, such as windows' surfaces, but this would cause erroneous data in the model. Furthermore, at early design stages, the information in the model is limited, which makes this stage necessary. The model importer reads the building model in IFC, the enriching rules and the labels and tries to derive new information and to extend the existing building model with further information. To summarize, enriching rules calculate implicit properties such as volume and size, derive new models and relationships such as spatial and graph models and add information from the label definition to the spaces with assigned labels.
For reporting, we have chosen the Open BIM Collaboration Format (BCF)  [77, 78] , proposed by BuildingSmart (http://buildingsmart. org/), which is an open file XML format that supports workflow communication in BIM processes. BCF allows reporting on the conflicting objects, addressing the conditions (rules) that are not fulfilled and adding a viewing camera at a given location describing the issue, which allows one to effectively communicate the detected issues to end users. Table  5  lists some of the rules extracted from the healthcare building code in France. For demonstration purposes, we have considered a list of similar rules, which have been then codified using the DSL proposed in the previous section. Please notice that we have only considered the rules that can be validated at the early design phase, with the limited level of details.
The needed power for heating and cooling of the spaces is not limited in the energy calculation tool; i. e. , the temperature requirements (set points) are fulfilled by the system immediately. This way, no delay, undershoot or overshoots of the desired temperature will occur. In practice, however, there will be an offset caused by the dynamics and power limitations of the system. This can result in an overestimation of the needed power related to the power required in the detailed design. Table  6  shows the simulation results for the two alternatives in our running example calculated by TECT. These values have been obtained based on the created designs from the EDC, the space assigned labels and using weather data from Paris, to perform the energy performance calculations.
The empirical studies depend on the country, and default values are used in this method. The second method allows using country-specific indices for both investment and operational costs to adjust the ballpark figures and make them suitable for usage in different countries' contexts. Such a method allows having more realistic data for the country where the project is being planned. The third LCC calculation method is more precise since it allows one to perform a calculation based on types or components instead of ballpark figures. The labels, described earlier, provide the foundation for such a system, where each label corresponds with both an investment and operational cost figure. However, such an approach requires counting on a large and complete dataset of cost figures, which cannot be easily acquired.
Dashboard It is essential to count on a user-friendly tool for displaying assessment results  [18, 79]. A dashboard is a possible solution for showing these results. Such a dashboard allows one, in a user-friendly interface, to view and manage the information and design alternatives created during the proposed workflow, as well as to choose the design that best fits  [85]. We propose to use a dashboard, which is part of the DST, to complete our workflow. The dashboard is the platform where the previous steps and the data that have been generated within those steps come together and are reflected upon. Aligned with the purpose of the software tools discussed in this paper, the following definition is applied: a dashboard is a visual representation of the most important information captured on one single screen, which enables a person to understand the required information at a glance in order to control the information or to achieve other goals.
The workflow starts with the building brief and rules, where user requirements and constraints are defined. Then, these requirements are used to create different design alternatives using an evolutionary algorithm, which tries to satisfy all of them. Code checking and design validation are then performed on the best alternatives, and only the ones that satisfy all of the rules are forwarded for the next steps. Then, energy and cost assessments are performed, and the obtained data are shown in the dashboard, which allows the designer to make informed decisions at the early design phases and to start the next design phases from an optimal design. There are some limitations in our proposed workflow that need to be investigated in future work, namely: (i) adapting the workflow for different kinds of buildings, and not only healthcare buildings; (ii) the user comfort indicators are not being evaluated in this workflow, and this could be a step to be added just after the cost assessment and after analyzing the user comfort indicators; and finally; (iii) because of the freedom IFC offers, the open standard digital representation of a building that is used in this proposed workflow, all stakeholders in the workflow need to be aware of which information resides where in this building model.