dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  John Willis  and current and former members of his lab, including  David Lowry  and  Kevin Wright , for providing access to North American seed material collected over many years, and to the  Botanical Society of Britain and Ireland  for their continued support locating UK Mimulus populations. Arielle Cooley  kindly provided seed material from Chilean populations of M. luteus var. variegatus. We are very grateful to  Claudia Buser  and  John Bailey  for providing the New Zealand material, and  Nils Bunnefeld ,  Anna Maria Fosaa  and  Símun Arge  for their help while collecting Mimulus in the Faroe Islands. We thank  Oregon Genomics  ( University of Oregon ) for sequencing services, the  University of Stirling Controlled Environment Facility  for access to plant growth facilities, and  Sophie Webster  for help in the laboratory.
Acknowledgements. We acknowledge our laboratory colleagues  Carole Philippon  and  Luc Lienhardt  for sharing results of tests conducted at the  ATC MLab. We also gratefully thank  Dominique Baisnee  for the measurements of flask samples on the  GC-FID. We gratefully acknowledge the  GRTgaz company  for sharing data with us and helping to improve the manuscript, especially  Pascale Guillo-Lohan ,  Pascal Alas ,  Francis Bainier  and  Jean-Luc Fabre. Financial support. This research has been supported by the  European Union 's  Horizon 2020 research and innovation program  ( Marie Skłodowska-Curie  grant no. 722479 ). Review statement. This paper was edited by  Glenn Wolfe  and reviewed by three anonymous referees.
We describe and figure the skeletal anatomy of the head, labial, mandibular, hyoid, and branchial cartilages in both taxa as well as the muscles of the head and pharynx. We make new observations, particularly regarding the branchial musculature of Callorhinchus, revealing several previously unreported or previously ambiguous structures. Finally, we review what is known about the evolution of chondrichthyan cranial muscles from their fossil record and discuss the implications for muscle homology and evolution, broadly concluding that the holocephalan pharynx is likely derived from a more elasmobranch-like form. This dataset has great potential as a resource, particularly for researchers using these model species for zoological research, functional morphologists requiring models of musculature and skeletons, as well as for palaeontologists seeking comparative models for extinct taxa.
Recently, tomographic methods have allowed the advent of "digital dissections", where an organism's anatomy can be non-destructively visualized and communicated in the form of interactive datasets. These studies have run the gamut of mammals  (Cox and Faulkes, 2014; Sharp and Trusler, 2015) , archosaurs  (Klinkhamer et al. , 2017; Lautenschlager et al. , 2014) , lissamphibians  (Porro and Richards, 2017) , and actinopterygians  (Brocklehurst et al. , 2019). Although aspects of cartilaginous fish anatomy have been examined  (Camp et al. , 2017; Denton et al. , 2018; Tomita et al. , 2018) , three-dimensional information on the cranial musculoskeletal system is limited. We aim to address this with a digital dissection of the hard tissues and musculature of two representatives of the Chondrichthyes: Callorhinchus milii Bory de Saint-Vincent 1823, a holocephalan, and Scyliorhinus canicula Linnaeus 1758, an elasmobranch.
, 2008; de Beer, 1931; Hughes and Ballintijn, 1965; Oulion et al. , 2011; Reif, 1980). While accounts exist of its gross anatomy  (Allis, 1917; Edgeworth, 1935; Luther, 1909b; Nakaya, 1975; Ridewood, 1899; Soares and Carvalho, 2013) , detailed and fully illustrated accounts are surprisingly rare in comparison to the similarly common Squalus. Here we use a synchrotron tomographic dataset to provide accounts of anatomy of the cartilages and musculature of the head in Callorhinchus milii and Scyliorhinus canicula. We examine dissection-based reports of muscle anatomy in the light of our reconstructed models. By combining this information with what is known about fossil taxa, we assess scenarios of morphological evolution of chondrichthyan cranial muscles. More broadly, we aim to provide the research community with valuable three-dimensional data on the anatomy of these taxa.
It was scanned in a 100% ethanol solution using a holotomographic approach at a voxel size of 7. 45 microns. Volumes for both specimens were reconstructed using the ESRF software PyHST. Segmentations of the 3D data were carried out using Mimics versions  [15] [16] [17] [18] [19] [20] [21]. Images of the resulting three-dimensional models were created using Blender v2. 80 (blender. org). Unfortunately, the resolution of the scans was not sufficient to observe innervation in most cases, so this is done throughout the text with reference to the literature.
Innervation: This is presumably innervated by the facial (VII) nerve, given its location, but no precise innervation can be established. Remarks: This structure has not been previously reported. Given that it has not been observed before, and its poor visibility in the dataset, we are cautious its definite existence pending its discovery in gross dissection. However, it is present on both sides of the dataset. Based on the apparent lack of fibres we presume this is a ligament rather than a muscle, but the latter is certainly not impossible. It may play a role in the operation of the operculum.
2018). However, resolution surrounding the spiracle in our dataset proved insufficient to locate this. The palatoquadrates (Fig. 8a, b ; palatoq. ) are about one third of the length of the neurocranium and joined at an anterior symphysis. They are low and flat, with a short, rounded ethmoid process on the medial face of the palatine process, via which the neurocranium is joined to the palatoquadrate by the ethmopalatine ligament (see below) (Fig. 8e ; eth. proc. ). Anteriorly to this process the dorsal edge is marked by a shallow groove. The inside edge carries a shallow sulcus for the teeth (Fig. 8c ; dent. sulc. ). Meckel's cartilages are about one and a half times as deep as the palatoquadrates (Fig. 8a , b; Meck. ), and are joined at an anterior symphysis. Dorsally it is grooved by a sulcus for the attachment of teeth (Fig.
Acknowledgments We thank the ID-19 beamline at the  European Synchrotron Radiation Facility  for assistance with scanning. Assistance in getting fresh Scyliorhinus canicula specimens from  Didier Casane  and  Véronique Borday-Birraux  from the  Laboratoire Evolution Génomes Comportement Ecologie -CNRS , and  Patrick Laurenti  ( Laboratoire Interdisciplinaire des Energies de Demain ;  Université Paris-Diderot)  is gratefully acknowledged. We thank  Florent Goussard  ( CR2P -MNHN ) for his inestimable help in 3D work. Some of this work began at the AMNH with the help of  John Maisey.
In this technical analysis, we studied the effects of complete electrification of space heating in the Texas residential sector on the energy consumption, peak power demand, and grid capacity utilization in the Electric Reliability Council of Texas (ERCOT) electricity grid. We utilized the National Renewable Energy Laboratory's (NREL) ResStock tool to develop geographically representative housing stock models and the physics-based EnergyPlus modeling software to create an aggregate building stock energy model that represents the residential sector in the ERCOT operating region. In this aggregate building energy model, we replace all natural gas and other fossil-fuel furnaces with reversible electric heat pumps of varying efficiencies that can provide heating in the winter and cooling in the summer.
We integrate spatially-resolved actual meteorological weather data with the building stock energy model to simulate a specific year (2016) of hourly-resolved energy usage in the ERCOT region. We find the annual electricity consumption, peak hourly power demand for each day, and load duration curves for each of 17 regions within ERCOT. From the base case, the absolute winter peak electrical power demand in the residential sector could increase by as much as 36%, or 12 GW. These results indicate that grid capacity would need to increase by 10 GW (a 25% increase for the residential sector) to accommodate a winter peaking residential sector. Though winter electricity consumption would increase for home heating, the annual amount of electricity consumption would stay roughly the same or decrease because the higher efficiency heat pumps provide more efficient cooling than the conventional air conditioners they also replace.
Background 1. 2. 1. Literature review A variety of energy simulation software packages are available to quantify a specific building's energy usage and performance. These programs often calculate energy usage through dynamic modeling of conduction, convection, and radiation within the structure  [16]. Two commonly used dynamic modeling software programs are TRNSYS and EnergyPlus  [17, 18]. EnergyPlus, developed by the U. S. Department of Energy, allows users to modify household attributes and collect energy consumption data of specific end uses within the structure. Unlike computationally-lean static models, EnergyPlus requires a moderate amount of computing power. It can take on the order of a few minutes to process hourly usage information over one year for a 100 square meter home using a modern personal computer.
Some studies project the large-scale grid impacts of heating electrification via heat pump adoption. Love et al. [39]  uses a relatively large heat pump trial dataset to project future electricity use from electrification. The heat pump trial data was adjusted for diverse occupancy behavior and scaled up to as large as a 20% heat pump adoption rate in the UK. Love et al. 's  [39]  utilization of heat pump trial data to project future demand contributes a novel estimate of the UK electric grid's future peak demand and ramp rate. Eggiman et al. [40]  studies the DR potential of a high uptake of heat pumps in the UK through a bottomup modular demand simulation model dependent on scenario data and socio-technical drivers such as technology improvements, population changes, and occupancy behavior changes.
Rather, they use existing datasets and/or future technical projections to model future electrification demand scenarios. Love et al. [39]  and Eggiman et al. [40]  model the demand from heat pump adoption in a location that sees cool winters, rather than a climate with colder winters that could push heat pump performance to its limits. Boßmann & Staffell  [41]  model heat pump uptake in the colder climate of Germany, but the adoption rate in the study is less than 10%. While the contributions of the three studies are valuable for the future of electric grid operations and planning for mass residential electrification, the lack of physics-based modeling and lack of variation in extreme cold weather and heat pump penetration rate is seen as a limitation.
Methodology flowchart for quantifying energy usage after full electrification of space heating in the ERCOT residential sector. To quantify residential electric load, the building simulation software, EnergyPlus, simulates thousands of households that are representative of the building stock within the ERCOT residential sector. NREL's ResStock analysis tool is used to generate the group of households from a statistically representative housing parameter space sample. The building simulation data is processed into electricity consumption and peak demand data for the residential sector.
Research question Using physics-based modeling and existing building stock datasets, this paper answers the question of how the energy usage of a large, diverse residential sector would change if all space heating were electrified. Electrification of the residential sector changes energy use patterns and affects both the electric grid's annual energy consumption and seasonal peak demand. The remainder of this paper is structured as follows: Section 2 gives the methodology to quantify energy usage of an electrified residential sector, Section 3 presents the results of this study, focusing on peak demand, energy consumption, and emissions impacts and Section 4 formulates conclusions from the study.
Aggregate building energy model EnergyPlus is a dynamic building energy modeling software that simulates a building's thermal envelope and models its energy usage according to thermal equations  [17]. This software simulates the energy usage for every household in this study. The software requires many discrete inputs to describe a household (e. g. , 3D coordinates of all surfaces, insulation type, HVAC parameters, glazing information). This high number of inputs has been listed as a limitation on its usage as an modeling software  [33]. This paper's methodology uses the ResStock analysis tool to overcome this limitation. The ResStock analysis tool automatically generates a group of households from a statistically representative housing parameter space sample  [44, 45]. The parameter space uses housing stock data from more than 11 different sources to determine probability distributions for each residential housing parameter as a function of location  [42].
To simulate a historical weather environment, our study utilizes actual meteorological year (AMY) weather data from 2016 across 17 locations in Texas. The year 2016 was chosen because it involved a relatively hot summer corresponding to an ERCOT peak demand of 71 GW and a particularly cold stretch of weather in February. ResStock uses the U. S. Census American Community Survey and other geospatial data sources to estimate how many households correspond with each weather file location  [45]. The individual simulations are typically executed on a high performance computer (HPC) or cloud computing system-this study used NREL's Eagle HPC  [47]. The raw output data from each simulation are then processed and analyzed. The analysis process shown in Fig. 1  was repeated four times to include different heat pump technologies that reflect increasing cooling efficiency, using seasonal energy efficiency ratio (SEER), and heating efficiency, using heating seasonal performance factor (HSPF), as shown in Table  1.
Higher SEER or HSPF ratings reflects a more efficient heat pump-one that uses less energy as to cool or heat the same cooling or heating load. Note that all existing electric heating efficiency levels (primarily electric resistance heating) were left as is. As a simplification, fuel-fired heating systems serving multiple dwelling units in multifamily buildings were assumed to be replaced with individual heat pump units serving individual dwelling units. The raw output data from EnergyPlus contains energy usage for each of the 38,000+ housing units from each of the 8784 h represented in the 2016 historical weather files. This hourly energy usage data The hourly energy usage and daily peak demand datasets can be found at public data repository associated with this study  [46].
Daily peak demand data is shown for the current state of the ERCOT residential sector ''base'' (yellow thick line) and an electrified ERCOT residential sector with standard-efficiency ASHPs ''standard'' (pink long-dashed line), high efficiency ASHPs ''high'' (blue dotted line), and ultra-high efficiency MSHPs ''ultra-high'' (dark gray thin line). During the majority of December, the electrification scenarios' daily peak demand is higher than the base scenario. because of lower load  [58]. Grid investments are often driven by summer heat derating limits of transmission lines and power plantssomething that might not be as big a factor for a winter peaking system. Additionally, we find that the change in magnitude of peak hour between the current residential sector and an electrified sector varies by location.
System-scale effects Figs. 5  and 6  show the daily peak demand for the base scenario (the current mix of residential heating types) and each respective electrification scenario. For example, Fig. 6 's graph with the triangle and square datapoints reflects the high-efficiency scenario which involves replacing all residential fossil-fuel powered heating units with a variable speed SEER 22, HSPF 10 electrically-driven air-source heat pump. Each data point on Fig. 6  is the peak hour of averaged power demand for each day of the year in 2016 for each scenario. Fig. 5  shows the same data but only for the peak summer month and peak winter month. Both figures show the highest amount of demand from the residential sector that the electric grid will be required to meet for each day out of the year.
Fig. 5 's focus on August and December allows for a closer view of the summer and winter peak demand values and seasonal trends. The base scenario's data show the typical demand trends from the ERCOT residential sector: (1) a system peak in late July or early August driven by high air-conditioning demand, (2) lower demand during late fall and early spring, and (3) local maxima during the winter when demand spikes because of cold weather. The electrification scenario shows how electrification changes the base case's trends: (1) the system peak occurs during the winter in December at 45 GW (25% higher than the base summer peak) for the standard efficiency scenario and (2) peak demands during the summer months are 6% to 22% lower, because the modeled heat pumps provide more efficient cooling than the air conditioners they replace.
The reduction in the summer peak load could help mitigate increases in median summer outdoor temperature due to climate change. The scenarios show that heating electrification in the residential sector is likely to push the ERCOT residential sector from a summerpeaking sector to a winter-peaking sector. Additionally, the scenarios indicate that lower summer peaks allow households with newly Fig. 6. Simulated residential sector daily peak demand (maximum hourly demand on each of the 366 days) using 2016 weather data for the current state of the ERCOT residential sector ''base'' (diamond) and an electrified ERCOT residential sector with standard-efficiency ASHPs ''standard'' (circle), high efficiency ASHPs ''high'' (triangle), and ultra-high efficiency MSHPs ''ultra-high'' (square). The electrification scenarios (circle, triangle, and square) show a residential sector that peaks in the winter.
This usage of non-baseload emission rates is necessary because a widespread building stock change would be a driving force on the daily residential peak when non-baseload plants are active. The displaced emissions from replacing fossil fuel heating furnaces are derived from EPA stationary point and air sources data  [65]. To estimate how these changes in emissions might change under future grid scenarios, Table  3  also includes annual CO 2 emissions reductions using annual average long-run marginal emission rates for three 2050 scenarios from the NREL Cambium dataset, which provides data from capacity expansion and generator dispatch modeling for 2020 Standard Scenarios  [66]. The long-run marginal emission rates consider socio-technical drivers like population growth and increased residential demand.
Ideally, one would know the marginal emissions for the generation mix at different hours of the year, for years into the future; however, changes in load of this magnitude would require further modeling of capacity expansion or supply dispatch, which is beyond the scope of this study. Using average emissions data is sufficient to estimate coarse directional impacts. Because the annual electricity consumption is not increased in any of the electrification scenarios, emissions decrease for each electrification scenario. This emissions reduction occurs because fossil-fuel furnace emissions are getting replaced while no increase in electricity usage occurs. True emissions reductions of an electrified residential sector will depend on a future emissions mix that likely falls within the bounds of the 2050 scenarios' long-run marginal emission rates, a percent reduction between approximately 6 and 9 percent for the standard efficiency scenario.
Acknowledgments Research reported in this work was supported by the  Alfred P. Sloan Foundation, USA , the  Electricity Reliability Council of Texas (ERCOT) , and the  National Science Foundation, USA  under award number  1638258. These funding sources had no involvement in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. We would like to especially thank  Joseph Robertson ,  David Roberts , and  Achilles Karagiozis of the National Renewable Energy Laboratory (NREL) This work was authored in part by the  National Renewable Energy Laboratory , operated by  Alliance for Sustainable Energy, LLC , for the  U. S. Department of Energy (DOE)  under Contract No. DE-AC36-08GO28308. Funding provided by  U.
The resultant dataset displays the total electricity, natural gas, and propane consumption of every hour of 2016 for the ERCOT residential sector. The maximum hour of electricity consumption (kWh) for each day is divided by the change in time (one hour) to create an absolute peak hourly demand value (kW) for the day. These values for maximum hourly demand on each day are referred to in this paper as daily peak demand values. These 366 daily peak demand values are used to create a daily peak demand curve for ERCOT. Consumption and peak demand data from the base simulation are compared to the different heat pump technology scenario data to analyze annual consumption, net emissions, and capacity utilization.
Wild-caught sharks Wild neonates (visible umbilical scar, <4 weeks of age;  Chin et al. , 2015;  total length, 526-600 mm; n = 6; 4 male and 2 female) were caught via either hook and line (n = 2) or gillnet (n = 4). Wild adults (1020-1512 mm; n = 6; 3 male and 3 female) were hand caught by hook and line using fresh tuna as bait. For both age classes and capture techniques, the duration of capture was less than 2 min. Immediately after capture, a 1 and 3 ml blood sample was taken from the neonates and adults, respectively, in a similar manner as described for the laboratory-based experiment, except an 18-gauge needle was used to sample adults. Morphometric data were then collected for each shark over 5 min, and then a second blood sample of equivalent volume was taken from each individual to assess stress parameters over this timeframe.
Glucose and 3-HB concentrations were determined by subtracting the first recorded value (immediately following addition of respective enzymes) from the final recorded value (when the enzyme reaction was complete) and interpolating values from the assay specific standard curve. The time variation in adding enzyme to an assay across the 96-well plate was less than 30 s. All assays were analysed at room temperature. Absorbance was measured with a plate reader (Power wave XS2, Biotek) using Gen5 software (Biotek, Winooski, VT, USA).
Linear mixed effects models ('lmer' function in 'lme4' package)  (Bates et al. , 2015)    to determine the significance of each treatment over time, as compared to baseline values. Shark identification was added to the model as a random effect, thus accounting for repeat sampling of each shark and non-independence  (Zeger and Karim, 1991). Corticosterone values were non-parametric in all datasets except within the 27 • C treatment. Therefore, corticosterone values were subject to Wilcox tests instead of Student's t-tests. The corticosterone values within the 27 • C treatment were subject to the same linear mixed effects model with Tukey's HSD post hoc test previously mentioned. Correlation plots between corticosteroids and metabolites using samples collected from the laboratory-based sharks only were analysed using Pearson product-moment correlations.