dataesr/scientific-paragraphs-categorization huggingface dataset:
2016) , and thus transferred at some future point to a data repository, the situated and ever-changing nature of the information collected in knowledgebases make such a prospect somewhat difficult to entertain, especially in clinical domains characterized by the ongoing realignment of the normal and the pathological. Admittedly, the database/knowledgebase distinction is ideal-typical, given that COSMIC, for instance, is shifting from its initial exclusive focus on data acquisition to highlighting the value of its data (SF2). Oncologists consult COSMIC for research purposes but also to gather information about alterations detected in their patients, although they might do so via local resources that embed COSMIC. While there is an overlap, in terms of use, between COSMIC and the more specialized knowledgebases, the latter lie at one end of a wide spectrum of resources that range from large databases to smaller interpretative resources.
In the case of a database such as COSMIC that sits in the middle of this spectrum, the data journey metaphor may be used to describe how curators survey the literature, extract and refashion bits of information, assess their evidentiary strength, and decide whether and how to include them in the database. The addition of the PubMed reference number to those data in principle should allow users to travel back to the original source although, as already mentioned, this is not a straightforward task given the amount of curatorial work needed to locate specific statements. Knowledgebases, however, are less a data repository than a tool for (clinical) action, and the data journey metaphor misses this key aspect. Within knowledgebases bits of information are triangulated with other evidence, associated with levels of evidence and actionability, and embedded in carefully crafted statements that re-specify their meaning.
In the case of institutions such as MSK, the sheer number of sequenced patients  (Zehir et al. 2017; Eubank et al. 2016 ) makes such a solution impossible. Instead, a tumor profiling report associated with a clinical decision support tool, OncoKB, is sent electronically to the treating physician who can trust the provided clinical annotations because they are clinically vetted. "OncoKB", in this context, refers not merely to the knowledgebase, narrowly defined, but to the entire dispositif, that includes, for instance, the Clinical Genomics Annotation Committee staffed with leading clinicians. Knowledgebases, rather than a mere data repository, embed and perform interpretations that deploy a distinctive form of bio-clinical expertise. Conversely, in data-centric oncology human expertise can only be enacted via bio-clinical collectives properly equipped with tools and devices such as those provided by knowledgebases.
This apparently vicious circle becomes virtuous when those tools and devices are constituted and utilized at different places and different times by different collectives. Hence the temporal and relational nature of oncology databases and knowledgebases, which evolve in response to a number of other initiatives, for instance the introduction of new data-sharing projects sponsored by leading cancer centers. Last but not least, we should not forget the strictures that oncology, as a clinical domain, imposes upon knowledge production and knowledge flows, and which largely account for the difference between clinical-grade knowledgebases and the kind of databases deployed in other scientific domains. Jonah Campbell has an MA degree in Medical Sociology and is presently a Regular Research Assistant in the Department of Social Studies of Medicine.
Collection of Betacoronavirus Strains The NCBI genome assemblies page was searched, on January 26, for taxonid of genus Betacoronavirus, resulting in a list of 22 genomes, including four isolates of SARS-CoV-2. We downloaded in addition to this initial list two more assemblies one for a bat coronavirus (MG772933) and another for MERS (KF600630), see Table  1. For easy identification of genes and annotations from GenBank, we included unique locus_tag identifiers containing locus id and an index number. Metadata was collected to define user friendly strain names and to understand phylogenetic trees in the context of virus host, country, taxon id and data release date.
Creating a Pangenome for Betacoronavirus GenBank annotation and sequences were used as a starting point for this pangenomic analysis study. These GenBank annotations contain heterogeneous identifiers for genes so we included unique locus tags containing the strain accession number and gene index number for computational processing. In addition, we collected metadata for these coronaviruses for displaying on the interactive phylogenetic trees for analysis later on. Gene sequences were compared to each other for sequence similarity, followed by gene clustering. Phylogenetic analysis was carried out on the core gene clusters to produce a species tree and similarly for each gene cluster to produce gene trees. For this pangenome analysis we used panX  (Ding et al. , 2018)  that takes GenBank files as input and extracts gene coordinates, annotations from GenBank and sequences from these genomes on both nucleotide and protein levels.
For interactive visualization of pangenome clusters, alignments and phylogenetic trees we used panX visualization module obtain from github, https://github. com/ neherlab/pan-genome-visualization/. Other databases showing greater detail about many viruses, such as Virus Pathogen Resource (VIPR, https://www. viprbrc. org) and GSAID (https:// www. gisaid. org) are very useful but these resources do not provide gene clustering or pan-genome analysis.
Prediction of Function Using DeepGOPlus We used all genes from Betacoronavirus dataset in order to explore Gene Ontology predictions through our in-house DeepGOPlus  (Kulmanov and Hoehndorf, 2020)  tool that combines deep convolutional neural network (CNN) model with sequence similarity-based predictions to derive relevant functional classes from Gene Ontology alongside a confidence score. Results were included with a score 0. 1 and filtered for class specificity. Predicted Gene Ontology (GO) terms with a confidence score from DeepGOPlus were visualized using QuickGO (https://www. ebi. ac. uk/QuickGO/) for SARS-CoV-2 specific gene clusters. DeepGOPlus is available on github, https:// github. com/bio-ontology-research-group/DeepGOPlus.
Mutation Detection in SARS-CoV-2 Isolates We obtained high coverage isolate genomes for SARS-CoV-2 from http:/www. gisaid. org and filtered out any isolates which show any ambiguities such as stretches of Ns. Furthermore, we used Viral Annotation DefineR (VADR) from https://github. com/nawrockie/vadr to compare isolate sequences to reference SARS-CoV-2 model in order to further filter out any low quality isolates and to produce valid GenBank files. As a result, about 2,374 high quality isolates were obtained. GenBank files were further processed using panX  (Ding et al. , 2018)  for clustering and alignment of nucleic acid and amino acid sequence of genes.
Among the other two protein E gene clusters, one includes MERS (JX869059 and KC164505) and several coronaviruses from different animals, while the third cluster includes coronaviruses from two Rousettus bats. To validate the conservation and specificity of the SARS E cluster, we compared the SARS-CoV-2 E sequence with all known sequences in NCBI's non-redundant (NR) protein database. This comparison showed, consistent with our pan-genome analysis, that the subcluster of the protein E gene appearing in SARS-CoV-2, SARS and two bat coronaviruses, is defined by the same essential functional features, which are exactly conserved (100% identity) between bats and SARS-CoV-2, but differ slightly (95% identity with one deletion and three substitutions) from SARS (Figure  2B  and Supplementary Information).
Accessory Genes We found five accessory gene clusters (15% of the accessory genome of genus Betacoronavirus) common to SARS-like coronaviruses, including SARS-CoV-2, out of 33 total accessory gene clusters. Four of these gene clusters (ORF3a, ORF6, ORF7a, and ORF8, Figure  1A ), appear only in SARS-CoV-2, bat-SL-CoVZC45, and the SARS virus. A gene cluster annotated as ORF10 appeared to be unique to SARS-CoV-2 (Figure  1 ). However, a blast-based search against the DNA of all sequences in NCBI's NT database showed that the ORF10 sequence matches with 100% coverage in DNA of other SARS-like genomes, but there is no open-reading frame predicted for those genomes in the matching region (Figure  1A ). Hence, the apparent uniqueness of ORF10 might simply be an artifact of the GenBank annotation pipeline that did not predict an ORF for this gene in other matching genomes.
Annotation of Uncharacterized Genes Compared to the essential genes S, M, N, E, and ORF1ab, other genes such as ORF3a and ORF7a are poorly characterized, according to GenBank annotations (https://www. ncbi. nlm. nih. gov/genome/proteins/86693?genome_assembly_ id=760344). Our bioinformatic protein structural analysis confirmed that SARS-CoV-2 ORF3a (cluster 12), also known Location of the residues changed in SARS-CoV-2 E are labeled and shown in cyan. The protein structure was modeled based on the NMR model PDB id 5 × 29, and completed using our in-house modeling program (to be published). The position of the region C-terminal to K53 was adjusted compared to the NMR model (see Supplementary Figure  S6 ) to avoid its positioning entirely within the membrane, which appears unlikely given its amino acid composition (in particular R61, K63, and N64).
ACKNOWLEDGMENTS We are thankful to  KAUST Supercomputing Laboratory (KSL) ,  KAUST Information Technology (IT)  and  Muhammad Saif  from  CBRC ,  KAUST  for providing support in computing resources. We are grateful to all the authors and GSAID for providing access to SARS-CoV-2 isolates for research, contributing labs and authors are clearly shown in the metadata table at https:// trackncov. cbrc. kaust. edu. sa.
Temperature prediction of a battery plays a significant role in terms of energy efficiency and safety of electric vehicles, as well as several kinds of electric and electronic devices. In this regard, it is crucial to identify an adequate model to study the thermal behavior of a battery. This article reports a comparative study on thermal modeling approaches by using a LiCoO 2 26650 lithium-ion battery, and provides a methodology to characterize electrothermal phenomena. Three approaches have been implemented numerically-a thermal lumped model, a 3D computational fluid dynamics model, and an electrochemical model based on Newman, Tiedemann, Gu and Kim formulation. The last two methods were solved using ANSYS Fluent software. Simulations were validated with experimental measurements of the cell surface temperature at constant current discharge and under a highway driving cycle.
MSMD approach is implemented into Commercial software ANSYS Fluent through three sub-models: an equivalent circuit model (ECM)  [18] , a full electrochemical model based on Newman Pseudo-2D equations  [19] , and a Newman, Tiedemann, Gu and Kim (NTGK) model, which is a semi-empirical electrochemical model that requires discharging tests as input data  [20]. When using NTGK model, the differences between simulated and measured cell temperatures could be less than 1 °C  [21]. Furthermore, MSMD model is capable to simulate a battery pack with the cells connected electrically  [22, 23]  and thermal runaway  [24, 25]. Panchal et al. [19]  applied MSMD framework by solving a Newman model to determine the temperature and voltage of a 26650 LFP lithium-ion battery discharged at constant discharge rates, whereas G.
Ntgk Model The NTGK model based on dual potential MSMD framework computes the thermal and electric field of the battery as follows  [20] : ∂ρC p T ∂t -∇ • (k c ∇T) = σ pos ∇φ pos 2 + σ neg ∇φ neg 2 + q ech (14) ∇ • σ pos ∇φ pos = -j ∇ • σ neg ∇φ neg = j, ( 15 ) where σ is the electric conductivity, φ is the electric potential, and subscripts pos and neg refer to positive and negative electrode, respectively. -2 0 2 4 6 x 10 The volumetric current transfer rate j is formulated as  [37] : j = C N C re f Vol Y U -φ pos -φ neg , ( 16 ) where Vol is the volume of active zone, C re f is the capacity of the battery used to obtain the parameters of the functions U and Y (Table  3 ), which are determined by the Parameter Estimation Tool in Fluent by using discharging experiments. Based on the deep-of-discharge (DoD), these functions are computed as follow  [37] : U = 5 ∑ n=0 a n (DoD) n -C 2 T -T re f Y = 5 ∑ n=0 b n (DoD) n exp -C 1 1 T - 1 T re f , (17) where C 1 and C 2 are constants for a specific battery, and T re f is the reference temperature.
Experimental Set-Up An ICR 26650 battery (see specifications in Table  5 ) was fully charged following the constant-current constant-voltage (CC-CV) protocol using a battery charger (iCharger 208B). The first stage (constant-current) was at 2 A, then the charger turns to constant voltage. Subsequently, the battery was discharged using a BK Precision 8500 programmable DC load (Figure  5a ). Current rates applied were 2 A (0. 5C), 4 A (1C), 6 A (1. 5C), and also a variable current profile. This last profile was obtained by ADVISOR software selecting a highway HWFET-driving cycle as velocity input for an electric vehicle. The simulated current was scaled at a maximum of 5. 5 A, as shown in Figure  6 , and this cycle was repeatedly applied to the cell until the energy extracted was 4 Ah. Regeneration current was not considered.
All discharging tests were repeated five times to ensure repeatability measurements. Ambient temperature and cell temperature were recorded through a negative temperature coefficient (NTC) thermistor (ZX-Thermometer) connected to an Arduino processor board. This sensor operates between -20 °C and 85 °C with an uncertainty of ±0. 5 °C. It requires a direct-current supply voltage between +1. 8 V and +5. 0 V. A code was developed using MATLAB software to convert voltage input (from Arduino board) into temperature. Since ambient temperature fluctuations were less than 1 °C during all tests, this value was assumed to be constant. Heat flux from the cell was measured by a PHFS-01 FluxTeq heat flux sensor (Figure  5b ) connected to a FluxTeq DAQ (see Figure  5a ). Measurements of heat flux are plotted in Figure  7.
To model vector boson fusion at next-toleading order, we include all interfering topologies at O(α 4 ) and propose a method for introducing generator-level cuts within the MC@NLO formalism. Our results are obtained using a Monte Carlo tool chain linking the FeynRules, NloCT and MadGraph5 aMC@NLO programs and have necessitated the development of new, publicly available, Universal FeynRules Output libraries that encode the interactions between the Type II Seesaw scalars and Standard Model particles. Libraries are compatible with both the normal and inverted ordering of Majorana neutrino masses.
1) [72]  and Fey-nArts (version 3. 9)  [73]  to renormalize the bare Lagrangian introduced in the previous section with respect to O(α s ) QCD interactions. This allows one to extract UV counterterms and so-called R 2 Feynman rules that are needed to numerically evaluate one-loop integrals in four dimensions. Together with a description of tree-level interactions, these counterterms are packaged into a UFO library that can be used by the MG5aMC event generator for LO and NLO calculations in QCD, as well as by Herwig++  [74]  and Sherpa  [75]  at LO. Our model files are publicly available, both for the NH and IH neutrino mass hierarchy, from the FeynRules database feynrules. irmp. ucl. ac. be/wiki/TypeIISeesaw.
ACKNOWLEDGMENTS The authors are grateful to  Olivier Skywalker Mattelaer  for enlightsabering discussions on VBF MC generation. RR thanks  Elisabetta Barberio  and  Federico Scutti  for discussions that motivated this work;  Joshua Isaacson  and  Carl Schmidt  for helpful discussions regarding CT PDF uncertainties;  Co Co Tamarit  for useful suggestions; and the  Jožef Stefan Institute  for its generous hospitality. The authors also thank the organizers of the Chennai MadGraph School where some of this work was completed. RR is supported under the UCLouvain fund " MOVE-IN Louvain ," and the  F. R. S. -FNRS "Excellence of Science" EOS be. hProject No. 30820817. The authors acknowledge the contribution of the VBSCan COST Action CA16108. MN was supported by the  Slovenian Research Agency  under the research core funding No.
P1-0035  and in part by the research grant  J1-8137. MN acknowledges the support of the COST actions CA15108 -" Connecting insights in fundamental physics ",  CA16201 -"Unraveling new physics  at the LHC through the precision frontier". MN is grateful to the  Mainz Institute for Theoretical Physics (MITP)  of the  DFG Cluster of Excellence PRISMA+  (Project  ID   33083149 ), for its hospitality and its partial support during which some of this work was done. Computational resources have been provided by the  Consortium des Équipements de Calcul Intensif (C ÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS)  under Grant No. 2. 5020. 11and by the  Walloon Region. This work received funding from the  EU 's  Horizon 2020 research and innovation programme  as part of the  Marie Sklodowska-Curie Innovative Training Network MCnetITN3  (grant No.
(see Table  1  and discussion in the next section). The full archives of POLDER-1, POLDER-2 and PARASOL were processed with GRASP, and the resulting datasets are available for the public at the official GRASP algorithm website (http://www. grasp-open. com, last access: 28 March 2020) and the AERIS/ICARE Data and Services Center (http://www. icare. univ-lille. fr, last access: 16 October 2018). This paper presents and discusses new publicly available aerosol products generated by the recently developed GRASP algorithm  (Dubovik et al. , 2011 (Dubovik et al. , , 2014) )  applied to PARASOL observations, which represent the longest-todate satellite MAP record  (Tanré et al. , 2011; Dubovik et al. , 2019). Hereinafter we perform quantitative analysis of PARASOL/GRASP aerosol products (the longest POLDER dataset) through validation with AERONET reference data, as well as by comparisons with the operational products and the widely used MODIS DT, DB and MAIAC aerosol products.
, 2016)  and so on. For POLDER, GRASP utilizes radiance and polarization observations from all available spectral channels, including minor gaseous absorption for some of them, i. e. , for total radiance five channels for POLDER-1 and 2 and six for PARA-SOL and for polarized radiances (three spectral channels for all instruments). The retrieval uses a unique global set of constraints (no location-specific assumptions) and a single initial guess globally. GRASP performs radiative transfer computations fully accounting for multiple interactions of the scattered solar light in the atmosphere online without using a traditional LUT. Since these RT computations are complex and time consuming, significant effort has been put into optimization and acceleration of the code for operational processing of voluminous datasets.
The three archives (Optimized, HP and Models) are released publicly and can be found at the AERIS/ICARE Data and Services Center (http://www. icare. univ-lille. fr, last access: 16 October 2018) and on the GRASP-OPEN website (https://www. grasp-open. com/products/, last access: 28 March 2020) in slightly different formats. AERIS/ICARE is the official distributor of POLDER Level-1 and 2 data and allows the user to dive into the data using a web tool, which plots the results online. AERIS/ICARE provides detailed visualization of the data, while the GRASP-OPEN site is faster in releasing new products but with no visualization. The original PARASOL/GRASP retrievals are stored at Level-1, Level-2 and Level-3 products and are publicly available in the form of daily, monthly, seasonal, yearly and climatological datasets.
These data records are much shorter than PARASOL and therefore not included in the following analysis. However, based on limited comparisons (not presented here), the quality of the POLDER-1 and 2/GRASP retrievals is expected to be similar to that of PARASOL/GRASP retrievals. Also, recently a new "GRASP/Component" approach has been developed  (Li et al. , 2019 (Li et al. , , 2020a, b), b). This approach retrieved the sizeresolved fractions of aerosol components representing the different composition species, like black carbon, brown carbon, fine-and coarse-mode non-absorbing soluble and insoluble, coarse-mode absorbing, and aerosol water. The retrieved fractions drive the aerosol spectral index of refraction in modeling atmospheric radiances. This provides a fourth retrieval archive; however, the results have not yet been fully analyzed and are not released in a user-friendly format, so the GRASP/Component dataset will not be considered in this study.
MODIS Deep Blue The Deep Blue (DB) algorithm retrieves over both bright (except snow) and vegetated land surfaces. It is able to retrieve over brighter surfaces than DT because it makes use of the much darker surface reflectance in the deep blue (412 nm) channel  (Hsu et al. , 2004 (Hsu et al. , , 2006 (Hsu et al. , , 2013)). Depending on the processing path, determined by observed reflectance and vegetation indices, the algorithm will invoke empirical spectral relationships of surface reflectance similar to DT (vegetation), rely on a pre-calculated database of surface reflectance (arid/deserts) or apply a hybrid method (urban surfaces). The MODIS DB aerosol products have also gone through several version updates  (Hsu et al. , 2013; Sayer et al. , 2015). Within the MODIS official products, the DB algorithm is applied for only land aerosol retrieval.
AERONET dataset The Aerosol Robotic Network (AERONET) is a global distributed network of well-calibrated sun-sky photometers  (Holben et al. , 1998). By measuring direct sun radiance, AERONET provides high temporal (every 3 or 15 min in daytime depending on the operation mode of the instruments) multi-wavelength AOD products with high reliable accuracy (∼ ±0. 01 to ±0. 02)  (Eck et al. , 1999). Strict protocols for the calibration and maintenance assure homogeneity among all its instruments. Due to its high data quality, the AERONET AOD products are widely used as "ground truth" to evaluate satellite remote sensing aerosol products  (Bréon et al. , 2011; Chu et al. , 2002; Kahn et al. , 2005; Liu et al. , 2004; Remer et al. , 2005 Remer et al. , , 2002;; Sayer et al. , 2013). In addition to direct sun observations, AERONET radiometers conduct routine measurements of the sky-scanning diffuse radiation.
, 2007b; Lyapustin et al. , 2018)  and feed the climate models used to characterize aerosol climate effects  (Kinne et al. , 2003; Sato et al. , 2003). In this study, the up-to-date AERONET Version 3 Level 2. 0 dataset (http://www. aeronet. gsfc. nasa. gov, last access: 3 September 2019)  (Giles et al. , 2019)  with standard cloud screening and quality control was used  (Smirnov et al. , 2000). We make use of all AERONET sites with data during the POLDER/PARASOL archive  (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013). The AERONET direct-sun AOD, Ångström exponent, fine-and coarse-mode AOD from the spectral deconvolution algorithm (SDA)  (O'Neill et al. , 2003) , AAOD, and SSA product are chosen as references for satellite products comparison and evaluation.