dataesr/scientific-paragraphs-categorization huggingface dataset:
The EBSD data sets were then processed using MTEX (version 5. 2. 8), a free Matlab toolbox for analyzing and modeling crystallography (http:// mtex-toolb ox. github. io;  Hielscher and Schaeben 2008; Bachmann et al. 2010). We used MTEX to identify grains and produce phase and orientation maps from the EBSD data, calculate pole figures of olivine and plagioclase preferred orientation, and calculate CPO strength and shape indexes. Grains were identified by choosing a 10° threshold, over which the misorientation between two adjacent pixels indexed for the same phase is assumed to be a grain boundary. Grains that have a surface smaller than 5 pixels could be erroneous measurements, and were removed from the data set. Twins in plagioclase were distinguished from grain boundaries by filtering out the boundaries with the relevant misorientation (180°) and rotation axes during the grain boundary identification procedure.
Major-element analysis Major-element compositions of olivine, plagioclase, clinopyroxene, orthopyroxene, and spinel were analyzed with a JEOL JXA-8800R electron microprobe analyzer (EPMA) at Kanazawa University (Japan). We used an accelerating voltage of 20 kV, a probe current of 20 nA, and a probe diameter of 3 µm, except for plagioclase, for which an accelerating voltage of 15 kV, a probe current of 12 nA, and a probe diameter of 3 µm were used. We used a peak counting time of 30 s, and a background counting time of 10 s except for Ni, for which a peak counting time of 45 s and a background counting time of 15 s were used. Natural and synthetic minerals (quartz, eskolaite, wollastonite, fayalite, jadeite, KTiPO 5 , corundum, manganosite, periclase and bunsenite) were used as standards. Raw data were corrected using the ZAF online correction program.
Fresh grains were selected for all the analyses. The laser energy density was set to 10 J/cm 2 per pulse at a frequency of 8 Hz. Data were collected in time resolved acquisition mode: signal integration times were set to 120 s for a gas background interval and 60 s for an ablation interval. Data was quantified with the GLITTER software  (Van Achterbergh et al. 2001 ) using the linear fit to ratio method. Sample concentrations were calibrated against the NIST SRM 612. Element concentrations of the NIST SRM 612 were selected from the preferred values of  Pearce et al. (1997). 43 Ca was used as internal standards for plagioclase and clinopyroxene, and 29 Si was used as internal standard for olivine. BIR-1G was measured during each analytical cycle and was compared with the preferred values reported by  Jochum et al.
10 ). These compositional features substantiate that the troctolites were crystallized from MORB melts. To constrain the crystallization process of the troctolites from the MORB melts, we conducted thermodynamic modeling using the rhyolite-MELTS software package  (Gualda et al. 2012)  as described in Appendix. The chemical evolution of primitive MORB melt after  Kinzler and Grove (1993)  was monitored at 0. 2 GPa (~ 6 km-deep oceanic crust, Frisch et al. 2011) during two possible crystallization processes assuming thermodynamic equilibrium: equilibrium crystallization (crystallized minerals remain in the system and   2013 ), those of troctolites from the Atlantis Bank are from  Dick et al. (2002)  (data name of OZA and MAE were used herein), and those of troctolites from the Atlantis Massif are from  Drouin et al.
2002). Chondrite values are from and  McDonough (1989). Abbreviations are the same as in Fig. 6  Fig. 10  Compositional variation of chondrite-normalized Ce/Dy and Sm/Dy of calculated melts. CN represents chondrite-normalized. The melt compositions were calculated using clinopyroxene/melt partition coefficients by  Kelemen et al. (2003). Basalt data from the EPR are referred from PetDB data base (http:// www. earth chem. org/ petdb). Chondrite values are from  Sun and McDonough (1989). Abbreviations are the same with Fig. 6  equilibrium with the plagioclase and orthopyroxene can be expected. The pressure was set at 0. 2 GPa corresponding to ~ 6 km-deep oceanic crust (e. g. ,  Frisch et al. 2011). The results show that the temperature ranges from 1182 to 1245 °C with the former thermometer (T REE ), and from 901 to 1059 °C the later thermometer (T BKN ) (Table  1 ).
Summary and conclusions Microstructural and mineral chemical data on the troctolite suite recovered during IODP Expedition 345 suggest the following scenario for evolutional process of the lower crust at Hess Deep. Primitive melts ascending through the uppermost mantle reacted with adjoining wall harzburgites. Olivine-rich lithologies such as dunite and plagioclase-bearing dunite were left as reaction products along the melt path in the mantle. The mantle-derived melts constructed a framework of coarsegrained (~ 10 mm) olivine and plagioclase via in-situ fractional crystallization when they intruded into the lower crust level. Porous space along the grain boundaries were gradually closing as crystallization proceeded, and was occupied by evolved melt that eventually crystallized clinopyroxene.
Acknowledgements This research used samples and data provided by the  International Ocean Discovery Program. We wish to express our deep thanks to all members of the shipboard scientific party, marine technicians and specialists, and the captain and crew of  JOIDES Resolution  for their collaboration, kindness and friendship during IODP Expedition 345. We are grateful to  T. Morishita  for daily discussions,  C. Nevado  and  D. Delmas  for the production of high-quality thin sections,  F. Barou  for his assistance with EBSD analysis, and  O. Bruguier  and  A. Tamura  for their assistance with LA-ICP-MS analyses. This manuscript was improved by valuable comments from two anonymous reviewers.
Whole genome sequencing of bacteria and reconstruction of B vitamin biosynthesis pathways. DNA was isolated by phenol/ chloroform extraction from bacterial cells grown on LB agar at room temperature (Pacific Biosciences, https://www. pacb. com/ wp-content/uploads/2015/09/SharedProtocol). After quantification by Nanodrop (Eppendorf) and quality checking by gel electrophoresis, DNA was sequenced using the PacBio Sequel system at the UGA Georgia Genomics and Bioinformatics Core. De novo assembly of the raw PacBio data was performed independently using Flye V2. 8 for Serratia sp. UGAL515_B1 and Sphingobacterium sp. UGAL515_B2 or Canu v 2. 1. 1 for Acinetobacter sp. UGAL515_B3 and Delftia sp. UGAL515_B4 followed by quality assessment using QUAST v5. 02 and annotation using the PGAP pipeline  [81] [82] [83] [84].
RNAmmer and tRNAscan-SE was used for RNA annotation  85, 86. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed by annotating the genes in each bacterium we sequenced, E. coli K12, and A. aegypti with KO identifiers using the KEGG Automatic Annotation Server  87. KEGG Mapper was then used to match genes with the pathway in the KEGG database to infer biosynthetic capabilities  88. The basic local alignment search tool was used to assess whether a homolog for S. marcescens enhancin  63  was present in the genome of Serratia sp. UGAL515_B1. Axenic (AX) and conventional (CN) cultures. AX and CN adults were reared as larvae under identical conditions in 6 well culture plates (Corning) (Supplementary Fig. S1 ). Ten-14 day old eggs from the general culture were surface-sterilized as earlier described  16  with the exception that 0.
Acknowledgements We thank  Jena A. Johnson ,  Lilith South ,  Severen Brown , and  Jaron Brant Singco  for assistance with rearing of the insects used in the study. We also thank  Drs. Melinda Brindley  and  Courtney Murdock  for DENV-2 stocks, Vero cells, and suggestions for performing infectious feeds. This work was supported by funds from the  National Institutes of Health  under award numbers  R01AI106892  (M. R. S. and M. R. B. ) and  R01AI033108  (M. R. S. ). R. E. H. was also partially supported by  National Institutes of Health predoctoral  training grant  T32AI060546.
Data availability The genomes for Serratia sp. (strain UGAL515B_01), Spingobacterium sp. (strain UGAL515B_02), Acinetobacter sp. (strain UGAL515B_03), and Delftia sp. (strain UGAL515B_04) are deposited in the National Center for Biotechnology Information (NCBI) GenBank in the Sequencing Read Archive (SRA) under BioProject ID PRJNA889817. All other data supporting the findings of this study are available within the paper and its Supplementary Information. The uncropped and unedited immunoblots used for Fig. 2e  are provided in Supplementary Fig. S7.
[6]  Several Al  18  F-NODA radiofluorinations have been reported in the literature  [7] [8] [9] [10]  and all optimizations studies have been performed by using the one variable (factor) at a time (OV(F)AT) approach. [11, 12]  Although easy to implement, this more conventional and scientific intuition-based approach does not detect potential interactions between factors and may lead to a false optimum response. Furthermore, an efficient process optimization technique is essential in the particular case of a radiosynthesis step that is highly dependent on compliance with radiation protection regulations. In this context, the design of experiments (DoE) optimization approach is particularly relevant, as it enables us to model a response (radiochemical conversion (RCC),  [13]  for example) in a multidimensional experimental space determined by the most important influencing factors and their range of variability, on the basis of a reduced number of tests judiciously selected using DoE software.
Regarding the influence of ethanol as a cosolvent, no effect was observed on our system by varying its proportion from 0 to 60% (Figure  S2 ). Furthermore, since the addition of ethanol can have an impact on the pH of the medium, we decided to perform this optimization in a 0. 1M AcONa/H buffered aqueous medium without any addition of organic cosolvent (Scheme 2). Once the factors and the study domain had been determined, the DoE software (Ellistat ® ) generated a series of 20 tests (Table  1 , Series A) to be carried out. This series was duplicated to increase the robustness of the model (Table  1 , Series B). Each couple of tests (Nb iA and iB, with i = 1 to 20) were performed on different days to take into account the variability in the quality of the fluorine-18 delivered. The experimental results (RCC obtained for each DoE test) are used to generate a mathematical model of the response surface design.
Coupling constants (J) are reported in Hertz (Hz). Mass spectroscopy analyses were performed using ElectroSpray Ionization in positive mode (ESI+). Low-resolution mass spectroscopy analyses (LRMS) were done on an analytic system composed of a LC2030 injection system (Shimadzu) coupled to a Micromass ZQ2000 detector (Waters®). Injections were operated by flow injection mode (FIA) with mobile phase being MeOH, MeCN, water or a mixture on these eluents at a flow rate of 0. 5 mL/min. Data were collected in the full scan mode at m/z 200-1500. Detection was done at a cone voltage (CV) giving the least amount of fragmentations (5 to 40 V); mobile phase and CV value are given for each compound. High-resolution mass spectroscopy analyses (HRMS) were performed by direct sample introduction on a LTQ Orbitrap ® XL High Definition mass spectrometer (Thermo Scientific®).
Data were collected in the full scan mode at m/z 150-1000. The source parameters were as follows: ion spray voltage 4000 V, capillary voltage -35 V, capillary temperature 275 °C, sheath gas flow 10 (a. u. ). Data were acquired in high-resolution mode (100,000 FWHM a m/z 500). Data acquisition was managed using Xcalibur™ software (version 2. 1. 0; Thermo Scientific ® ). Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analyses were performed on a Synapt ® G2 High Definition MS™ (Q-TOF) mass spectrometer (Waters ® ) combined with a HPLC system (Waters ® ). Chromatographic separation was performed on a Kinetex 5 µm EVO C18 100 x 4. 6 mm LC column (Phenomenex®, ref 00D-4633-E0) set at 20 °C. Compounds were eluted using isocratic mixture (A/B, 85/15, v/v) with water 10 mM ammonium acetate at pH 4.
5 as solvent A, and acetonitrile as solvent B. The flow rate was kept at 1 mL/min for 15 min. Data were collected in the full scan mode at m/z 50-1200 in positive ion mode (ESI+). The source parameters were as follows: capillary voltage 3500 V, cone voltage 30 V, source temperature 130 °C, desolvation temperature 350 °C, cone gas flow 50 L/h, and desolvation gas flow 900 L/h. Leucine enkephalin (2 ng/mL) was used as the external reference compound (Lock-Spray™) for mass correction. Data were acquired in the so-called resolution mode (20,000 FWHM a m/z 500) with a scan time of 0. 2 s. Data acquisition was managed using Waters MassLynx™ software (version 4. 1; Waters MS Technologies). HPLC and radio-HPLC were carried out on a Waters® system composed of a 600E pump and a PhotoDiode Array (PDA) detector with Max Plot mode on 220-350 nm range or on a ThermoFisher® Dionex Ultimate 3000 HPLC system equipped with LPG-3400SD pump, WPS-3000SL/TSL autosampler, TCC-3000SD column oven, DAD-3000 detector and coupled with a Eckert Ziegler® Mini-Scan TLC scanner and Flow-Count detector.
CA practices have been widely adopted in some developed economies, particularly in the US where reduced tillage currently accounts for more than half of all acreage for corn, wheat, and soybeans (USDA, 2019). A variety of projects have promoted some or all aspects of CA in northern Ghana. 1  However, there appears to have been a general lack of evaluation of the extent to which these programs successfully increased adoption among farmers. As a result, we rely upon a household survey which we implemented immediately prior to the experiment to provide data on the extent of sensitization and adoption of CA practices in our population.
2009). 4  The experiment was structured such that the participant always had sufficient funds to choose either practice, irrespective of the outcome of prior rounds. 5  To ensure consistency all randomization was conducted in Stata prior to fieldwork and loaded into the software used for implementation. Enumerators were not able to change any randomized parameters since they were associated with a unique subject identifier. fixed throughout. The probability of each rainfall outcome was fixed and independent across rounds. The payments associated with each outcome could vary by round if participants were assigned to the incentive treatment (described below) and based on their adoption choices in the current and preceding rounds. The choice sheets are shown in Online Appendix B. For participants who chose MSD, if the choice was made continuously over multiple rounds the available payments associated with that technology would increase once and remain at that higher level so long as they continued to adopt.
Empirical strategy To evaluate the impacts of the respective treatments on adoption of MSD in the experiment, we estimate four primary specifications using ordinary least squares at the participant level, 14 There were cases where participants were members of the same household, so the total household survey sample is 1,117. For some cases, the field team was unable to match household data to individuals, as a result there are 25 experiment participants for whom we do not have a full set of controls for regression specifications. We retain these individuals and include indicator variables for the relevant missing data. 15  The main results do not vary by 2018 and 2019 FBOs. following our pre-analysis plan. 16  To address multiple hypothesis testing, we also control for the false discovery rate (FDR) by calculating sharpened q-values (Benjamini, Krieger and Yekutieli   2006; Anderson 2008).
In this way, users will not have to change their usual behavior to charge their vehicles, but rather the charging points will be strategically located where they are best suited to their needs. This methodology will be developed in this paper, which is structured as follows. First, the current status and potential of the electrification of land transport in the case study, Tenerife (Spain), will be analyzed. In addition, the main strategies proposed by the Government of the Canary Islands to achieve the goal of a complete electrification of the vehicle fleet will be examined. Subsequently, the principles on which the transport simulation software used for the development of this methodology, MATSim, is based will be detailed. The Supplementary Materials will also explain in more detail the procedure followed to carry out the simulation.
which the simulations will be carried out. It will be assumed, given the regulations, that the totality of this fleet corresponds to EVs. Once the fleet of electric vehicles had been estimated, the number of charging points required for this volume of vehicles was calculated. To do this, we used the information available from the Dirección General de Catastro del Ministerio de Hacienda  [19]  relating to data on buildings, plots, and infrastructure. On the other hand, data collected by the Instituto Geográfico Nacional (IGN)  [20]  and published in the Spanish Land Occupancy Information System (SIOSE)  [21] , related to service stations in Tenerife, as well as access to the entire road network of the island and parking areas in buildings and above ground, were also used. In the case of private buildings, whether residential, commercial, or workplace, the number of parking spaces available was estimated based on data obtained from the DGC and occupancy rates, which were determined by means of telephone surveys in the case of residential buildings and via web-based information in the case of commercial buildings.
Finally, the study carried out by the ITC also includes an additional analysis of the current service stations as a complement to the network, as these will provide a network of fast charging points that will support the network in the event of possible contingencies. Specifically, a total of 205 stations were counted in Tenerife, which would provide the system with 4315 fast charging points, considering that the surface area of the stations for this use accounts for approximately 40% of the total. This figure is considered sufficient to ensure that the support provided to the network is of sufficient quality to deal with the contingencies mentioned above. In the case of private buildings, whether residential, commercial, or workplace, the number of parking spaces available was estimated based on data obtained from the DGC and occupancy rates, which were determined by means of telephone surveys in the case of residential buildings and via web-based information in the case of commercial buildings.
Software Preparation After the analysis of the study explained in the previous section, we proceeded to the correct configuration of the simulation software, MATSim (Multi-Agent Transport Simulation), and the subsequent launching of its simulation to obtain the expected results. This section will describe the fundamentals of this software, as well as the process necessary for its preparation before proceeding to perform the relevant simulations. This procedure will be further detailed in the Supplementary Materials part. Energies 2023, 16, 1178 8 of 24.
MATSim Fundamentals MATSim is an open-source multi-agent simulation framework implemented in Java language. It is based on the principle of coevolution, i. e. , each agent (vehicle in our case) repeatedly optimizes their daily activity schedule while competing for the same time slots with all other agents in the transportation infrastructure  [22]. Each MATSim run contains a configurable number of iterations consisting of the following stages summarized in Figure  7. correct configuration of the simulation software, MATSim (Multi-Agent Transport Simulation), and the subsequent launching of its simulation to obtain the expected results. This section will describe the fundamentals of this software, as well as the process necessary for its preparation before proceeding to perform the relevant simulations.
MATSim Fundamentals MATSim is an open-source multi-agent simulation framework implemented in Java language. It is based on the principle of coevolution, i. e. , each agent (vehicle in our case) repeatedly optimizes their daily activity schedule while competing for the same time slots with all other agents in the transportation infrastructure  [22]. Each MATSim run contains a configurable number of iterations consisting of the following stages summarized in Figure  7. On the one hand, the initial demand comes from the population's daily activity chains. The people modelled are called agents, who have daily plans, where in turn each plan is composed of a daily activity chain and a score associated with that activity, known as an econometric utility function. On the other hand, there is the mobility simulation itself, known as Mobsim.
This stage consists mainly in that, in each iteration, each agent selects a plan from its memory according to the scores assigned to each plan, which are calculated in each mobsim run. Finally, the replanning module produces the modifications of the plans, with the objective of finding the optimal option. For this, MATSim considers four characteristic parameters of the simulation: the departure time, the planned route, the way in which it will be carried out, and the destination of the activity. The described iterative process is repeated completely until the average score of the population stabilizes  [23]. Another important note is that since MATSim is designed for large-scale scenarios, the software adopts the queuing approach, i. e. , an agent enters a network link or road segment from an intersection and is added to the queue.
• Config: It contains all the possible MATSim configuration options. In this section, you will specify among other parameters the "controller", the number of iterations, the type of parameters, and the configuration of the parameters of the utility function, which will assign scores to the different plans, which will be used probabilistically for the choice of each plan by the software. On the one hand, the initial demand comes from the population's daily activity chains. The people modelled are called agents, who have daily plans, where in turn each plan is composed of a daily activity chain and a score associated with that activity, known as an econometric utility function. On the other hand, there is the mobility simulation itself, known as Mobsim. This stage consists mainly in that, in each iteration, each agent selects a plan from its memory according to the scores assigned to each plan, which are calculated in each mobsim run.
Finally, the replanning module produces the modifications of the plans, with the objective of finding the optimal option. For this, MATSim considers four characteristic parameters of the simulation: the departure time, the planned route, the way in which it will be carried out, and the destination of the activity. The described iterative process is repeated completely until the average score of the population stabilizes  [23]. Another important note is that since MATSim is designed for large-scale scenarios, the software adopts the queuing approach, i. e. , an agent enters a network link or road segment from an intersection and is added to the queue. Such an agent will remain there until the time to traverse the link has elapsed, it is at the head of the queue, and the next link allows it to enter.