dataesr/scientific-paragraphs-categorization huggingface dataset:
The model is learned from the data composed of the calculated Q ion of the small molecules with fewer constituent atoms and the electron numbers of the corresponding molecules in a training set by an SVM  [743]. Initial results are in broad agreement with experimental and semiclassical calculations, so they may be valid for higher energies, but whether they are robust enough for lower energies where the structural properties of the target are important and "resonances" are formed is an open question, and it is such low energy interactions that are most relevant in the myriad of low-temperature industrial plasmas. In conclusion, the need for the collection and compilation of fundamental data underpinning the operation of plasmas is widely recognized by the community, and there have been several attempts to address the challenge of providing such data to user communities with the creation of several international databases.
However, this work remains poorly supported and too often relies on the efforts of a few active individuals, which is not sustainable. A long-term strategy for the maintenance and review of databases is required and should be instilled in the training of the next generation of researchers. [Nigel J. Mason]  IX. CONCLUSION In this review article, the latest studies and their results in DDPS are summarized for various applications ranging from basic plasma physics to nuclear fusion, space and astronomical plasmas, and industrial plasmas. In addition, we presented a review of fundamental data science that serves as the basis for all analytical techniques used in different plasma applications and databases that serve as vital resources for the wide scientific community. It is seen that many common techniques and ideas are used for different applications.
ACKNOWLEDGMENT Zhehui Wang  wishes to thank  Christoph Räth  ( Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft-und Raumfahrt, Germany ),  Chengkun Huang  ( Los Alamos National Laboratory, Los Alamos, NM, USA ),  Ghanshyam Pilania  ( Los Alamos National Laboratory ), and  Platon Karpov  ( Department of Astronomy and Astrophysics, University of California at Santa Cruz, Santa Cruz, CA, USA ) for stimulating discussions. All examples described in this article are available and fully documented in the open-source AidaPy software at https://gitlab. com/aidaspace/aidapy. For Section VII-B, the authors would like to thank  Kaihan Ashtiani ,  Michal Danek ,  Paul Franzen ,  Sassan Roham , and other colleagues and mentors at  Lam Research Corporation  for valuable discussions over the years to help them better understand the impact that digital transformation will have on the semiconductor industry.
The work in Section VII-C was technically supported by the Executive Vice-President  Jinwoo Park ,  Jaehyung Lee ,  Jeonggen Yoo , and  Insso Cho  of  Samsung Display   Company Ltd. In Section VII-D, they would like to thank  Alok Ranjan  and  Hiromasa Mochiki  for their support and valuable feedback. For Section VII-E,  Jan Trieschmann  acknowledges valuable input from  Tobias Gergs , in particular, Fig. 35, helpful discussions with  Borislav Borislavov , and continuous support from  Thomas Mussenbrock. The work in Section VII-F was partly carried out within the framework of the  EUROfusion Consortium. Views and opinions expressed here do not necessarily reflect those of the European Union or the  European Commission. For Section VIII-B,  Izumi Murakami  acknowledges many atomic physicists supporting the AM database activities and users from the communities.
This work was supported in part by the  U. S. Department of Energy , the  Office of Science , and the  Office of Advanced Scientific Computing Research ,  SciDAC Program , through the  FASTMath Institute, Oak Ridge National Laboratory , under Contract  DE-AC05-00OR22725 ; in part by the  United States Department of Energy (DOE)/High Energy Physics (HEP)  under Grant  DE-SC0016804 ; in part by the  U. S. DOE  through the  Lawrence Livermore National Laboratory  under Contract  DE-AC52-07NA27344 ; in part by the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement  776262  (AIDA, www. aida-space. eu); in part by the  European Union  via the  Euratom Research and Training Programme  under Grant Agreement  101052200-EUROfusion ; in part by the  JSPS Core-to-Core Program  under Grant  JPJSCCA2019002 ; and in part by the  National Research Council of Science and Technology (NST)  grant by the  Korea Government (MSIT)  under Grant  CRC-20-01-NFRI.
The work of  Markus M. Becker  was supported in part by the  German Federal Ministry of Education and Research (BMBF)  under Grant  16FDM005  and Grant  16QK03A. The work of  M. Salman Asif  was supported in part by  U. S. Air Force Office of Scientific Research  Award  FA9550-21-1-0330. The work of  Sadruddin Benkadda  and  Satoshi Hamaguchi  was supported by the  CNRS International Research Project (IRP) FJ-IPL. The work of  Rick H. S. Budé  and  Jan van Dijk  was supported in part by the  PPS-Contribution Research and Innovation   of the Ministry of Economic Affairs and Climate Policy (The Netherlands)  and in part by  ASML. The work of  C. S. Chang  and  R. M. Churchill was supported by the  U. S. DOE Office of Fusion Energy Science  and the  Office of Advanced Computing Research  under Contract  DE-AC02-09CH11466  to  Princeton University  on behalf of the  Princeton Plasma Physics Laboratory , the  SciDAC Partnership Center for High-Performance Boundary Plasma Simulation , and the  Theory Department.
The work of  Satoshi Hamaguchi  was supported in part by the  Japan Society of Promotion of Science (JSPS)   Grants-in-Aid for Scientific Research  (S)  15H05736  and (A)  21H04453 , in part by the  JSPS Core-to-Core Program  under Grant  JPJSCCA2019002 , and in part by the  Osaka University International Joint Research Promotion Programs. The work of  Todd Munson  was supported in part by the  U. S. Department of Energy , the  Office of Science , and the  Office of Advanced Scientific Computing Research ,  SciDAC Program , through the  FASTMath Institute ,  Argonne National Laboratory , under Contract  DE-AC02-06CH11357. The work of  Habib N. Najm was supported by the  U. S. Department of Energy, the Office of Science , and the  Office of Advanced Scientific Computing Research, SciDAC Program.
The work of  Jan Trieschmann  was supported in part by the  Deutsche Forschungsgemeinschaft (DFG) ,  German Research Foundation , under Project  138690629-TRR 87  and Project  434434223-SFB 1461 ; and in part by  Tokyo Electron Technology Solutions Ltd. The work of  Zhehui Wang  was supported in part by the  U. S. Department of Energy  through the  Los Alamos National Laboratory. The work of  Kristian Wende  was supported by the  German Ministry of Education and Research (BMBF)  under Grant  03Z22DN12  and Grant  03Z22D511. The review of this article was arranged by Senior Editor  E. Schamiloglu.
Data limitations that arise from a change in the methodology used to compile the FAO's Food Balance Sheet dataset (FAO 2020) restrict the latest available data for use in our modeling framework to the starting year of 2010. The projections generated from 2010 to 2030 are used to inform a 20-year scenario outlook for a "what if" policy analysis that addresses the broader scale magnitude and direction of changes in the future instead of generating precise predictions. Econometric modeling frameworks, for example, could provide more precise predictions but those are more limited in their application to the analysis of alternative future scenarios. The integration of the models and scenarios was tested in a simulation of the effects of increased ASF demand and production in Tanzania. Given that the country has only a small share of the global market in agricultural and food commodities, the analytical framework adopted the assumption that demand, production, and trade of livestock in Tanzania, as well as policies guiding these outcomes, do not lead to significant impacts on the global market.
As such, the analytical framework accounted for impacts in Tanzania of changes occurring at the global level, but not for feedbacks to the global economy from the dynamics of ASF demand and livestock production within Tanzania. In this sense, the model interlinkages went in one direction only. Global scenarios and outputs from the IMPACT model provided input data and scenarios for simulation of land use changes in CLEANED-R, results of which were passed on to MESH to calculate ecosystem services (ESS) provision associated with the land use changes and land management options. Full descriptions of the assumptions, structures, and input and output data of IMPACT, CLEANED-R, and MESH have been published previously  (Robinson et al. 2015; Pfeifer et al. 2019; Johnson et al. 2019). Details relevant to this study are presented online in the Supplementary Information (SI) and summarized in the following sections.
Furthermore, the input parameters of CLEANED-R were defined to reflect animal numbers, productivity (e. g. , kilograms of milk produced per dairy cow), livestock feed ratio (e. g. , % planted fodder or agro-industrial by-product typically fed to livestock), and crop productivity (e. g. , maize production in metric tons per hectare per year, MT/ ha/year) associated with IMPACT's results. Year 2015 land cover data for Tanzania, from the database of the European Space Agency (ESA) Climate Change Initiative (ESA CCI Land Cover project), was used as the baseline  (ESA 2017). This was the most detailed land cover map available at the time of the analysis. The spatial allocation model assigns livestock impact associated to the land from which feed originates. It distinguishes between three feed categories: (1) feed and fodder from arable land (cereals, crop residue, planted fodder), (2) natural feed from grazing land and shrublands, and (3) agro-industrial by-products such as bran or oilseed cakes (concentrates).
2012 ). Assumed productivity (in MT/ha/year) of biomass is spatially explicit and was taken from the Global Agro-Ecological Zones (GAEZ) data portal  (Fischer et al. 2012). The agro-ecological potential productivity of grass for grassland and a weighted average on the feed ratio of actual productivity of cereal and planted fodder were used. The baseline land assigned to livestock and biomass productivity were initially set in the spatial allocation tool to reflect IMPACT's land use and production under the moderate growth scenario in 2010. This created a compatible and consistent land use map to link IMPACT with CLEANED-R and minimized datarelated discrepancies in how the different components of the integrated assessment framework are specified. CLEANED-R computes the livestock carrying capacity of an area by calculating the amount of grass, crop residue, and planted fodder that is grown for feed and fodder.
This area level feed and fodder supply can be compared to the total biomass that will be required (e. g. , to meet projected demand) from livestock production projected for that area. A negative biomass balance would suggest that feed demand surpasses the biomass-carrying capacity of the area. This is referred to as the biomass gap. The additional biomass that will be required to fill the gap, e. g. , compared to a base run, can be computed in CLEANED-R. This is a measure indicating how much additional biomass needs to be allocated to livestock production in the area. Furthermore, land use dynamics are driven in CLEANED-R by livestock feed and fodder demand and are computed in a land use change module to generate future land use maps. Parameters of the land use change module can thus be adjusted to assess the environmental impacts of different scenarios of feed demand, production, and management.
Data Availability Descriptions of the IMPACT, CLEANED-R, and MESH model versions and data used in this study are available in the Supplementary Information (https:// doi. org/ 10. 1007/ s10113-023-02038-x). The model assumptions underlying IMPACT are available open access in GitHub (https:// github. com/ IFPRI/ IMPACT) as are details of the CLEANED-R Model (https:// github. com/ ilri/ CLEAN ED-R). MESH is free to download (https:// natur alcap italp roject. stanf ord. edu/ softw are/ mesh) and comprehensive documentation on the InVEST models integrated into MESH and used in this analysis are available from the Natural Capital Project (https:// natur alcap italp roject. stanf ord. edu/ invest). The specific input and output data for the threemodel integrated framework in this study are posted to a special studies folder on GitHub (IMPACT/DriverAssumptions/Special_Studies at master • IFPRI/IMPACT • GitHub).
Results Opsin sequences. We retrieved 28 opsin gene sequences of 13 species from GenBank and reconstructed another 49 (representing 19 species) using SRA data deposited in NCBI (Table  S1 ). From 484,530 Illumina reads from our bait capture experiment, we further reconstructed 289 new opsin gene sequences representing 87 anuran species. In total we analyzed 366 sequences from 116 anuran species. Raw reads are available in the Sequence Read Archive (PRJNA856227); assembled sequences are available on DataDryad (DOI: TBD). All assembled sequences had an average read coverage of >10x, with 82% having coverage >50x (Table  S2 ). Analyses of selection. HyPhy site and branch-site selection tests identified a total of 15 sites across the four opsin genes as having experienced pervasive (FEL) or episodic (MEME) positive selection in diurnal (D) or diurnal and brightly colored (DC) frogs (Table  1 ).
2022 ). While color-based signaling occurs in several species of poison frogs (Dendrobatidae) and might exist in some harlequin toads (Atelopus) and pumpkin toadlets (Brachycephalus), very little is known in general about how anuran visual capabilities are related to intraspecific communication and signaling. Here, we present a taxonomically and ecologically comprehensive dataset of the four visual opsin genes (LWS, SWS1, SWS2, and RH1) for 116 anuran species spanning 20 families. Our analyses report 15 amino acid sites as under positive selection in diurnal species. We find no compelling evidence that conspicuous coloration is linked to the spectral tuning of visual pigments in our focal taxa. Moreover, our results provide evidence for the loss of the SWS2 gene in the ancestor of Dendrobatidae between 70-40 Mya, an event that coincides with the evolution of diurnal activity patterns in the clade.
Assembly of opsin genes. The quality of reads was checked with FastQC (Andrews 2010), barcodes were excluded with Trimmomatic  (Bolger et al. 2014 ) and, because combining multiple assembly methods better characterize multi-gene families  (Holding et al. 2018) , the reads were assembled with default parameters for de novo with MEGAHIT  (Li et al. 2015) , Trinity  (Grabherr et al. 2011)  and SPAdes  (Bankevich et al. 2012). Final assemblies were reduced using CD-HIT set to >98% similarity. Reduced assemblies were annotated using a custom sequence library generated with the opsin sequences used to design the baits and the software suite provided by BLASTX. Reference sequences for BLASTX were derived from amphibian opsin genes available in GenBank: Xenopus laevis (Pipidae), Nanorana parkeri (Dicroglossidae) and other species with opsin sequences (e.
g. , Rana catesbeianus [Ranidae]). Any sequence matching one of the reference opsin genes with an e-value <10 -6 was pulled out for downstream analyses. Given that we de novo assembled genomic DNA that was captured using baits designed from mRNA, the sequences were often only partially assembled, and some shorter sequences were identified erroneously by BLASTX. We used the program BLAT v. 36x2  (Kent 2002)  with reference sequences from the Nanorana parkeri genome (LWS, XM_018560714. 1; SWS1, NW_017306744. 1; SWS2, NW_017307939. 1; RH1, NW_017306456. 1) to verify sequences from our dataset that were putatively identified as opsins using BLASTX as described above. A BLAT server was prepared using N. parkeri reference sequences with the "-trans" option to translate the database into protein for all six reading frames.
Then BLAT was run with options "-t=dnax" and "-q=dnax" to specify that the format of the database and query were both DNA sequences translated in six frames to protein. Sequences that matched N. parkeri references were pulled out from the BLAT results file generated using "-o=pslx". These query sequences were then aligned to the reference genome using MAFFT v47. 19  (Katoh and Standley 2013)  with the options "--auto" and "--adjustdirection". The smaller fragments whose sequences did not align to the exons were manually removed, and exons from the same individual of each species were merged. For most of these genes, some edges of exons and several short exons were still missing from the assemblies. To recover the sequences or fill these gaps, we implemented an in silico target capture using MITObim  (Hahn et al.
For each HyPhy analysis, we pruned the tree using the 'ape' R-package  (Paradis and Schliep 2019)  to contain the subset of species available for each opsin gene. In some cases, we replaced a tip in the tree with a closely related species that was used in our study (see Table  S1  for details). We then used the resulting tree as the backbone for two referring to the location of each amino acid according to the three dimensional structure of opsins, which encompasses seven transmembrane domains (TMD I-VII), three extracellular domains (ECD I-III), three intracellular domains (ICD I-III), and the amino-and carboxyl-termini (N and C)  (Palczewski et al. 2000 ; Table  1 , Fig. 1 ). Attempts to find SWS2 in Oophaga pumilio and Ranitomeya imitator. Because we did not recover SWS2 sequences from any dendrobatid species in our bait-capture dataset, we attempted to verify whether the SWS2 gene had been lost in this clade using three additional approaches.
Acknowledgments This research was funded by  NSF   DEB-1556967  to DCC; NSF GROW, NSF DDIG ( DEB-1404409 ),  National Geographic Society 2014 Young Explorer Grant , and  UC Berkeley start -up funding to RDT;  NIH 5P50GM068763 ,  NSF IOS-1822025  and  William F. Milton Fund  from  Harvard Medical School  to LAO; LAO is a  New York Stem Cell Foundation -Robertson Investigator. JCS was supported by  SJU  start-up funds and  NSF   DEB-2016372. MJN was supported by the  American Association of University Women  (AAUW). We thank  Roberto Ibañez  and  Brian Gratwicke  ( Amphibian Rescue  and  Conservation  Project),  Luis A. Coloma  ( Fundación Otonga  and  Centro Jambatu de Investigación y Conservación de Anfibios; Quito, Ecuador) ,  Adolfo Amézquita  ( Laboratory of Biodiversity and Cloud Forests Conservancy, Bioconservancy; Jardín, Colombia ),  César Aguilar  ( Universidad Nacional Mayor de San Marcos; Lima, Peru ), and  Travis LaDuc  ( Biodiversity Center, UT Austin ) for facilitating access to tissues and specimens.
Data accessibility statement Raw Illumina sequencing data are deposited in the  Sequence Read Archive (BioProject PRJNA856227). del Ecuador, Centro Jambatu de Investigación y Conservación de Anfibios (Quito, Ecuador), Federal do Paraná, (Paraná, Brazil), or Museo de Historia Natural ANDES  at the  Universidad de los Andes. All scripts and other data are available in the supporting material. This publication is based in part on work by  DCC  while serving at the  National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or United States government.
Third, was the loss of SWS2 consequential in that it affected other aspects of dendrobatid vision? Other diurnal hyloid clades in our dataset have not lost SWS2: all Atelopus and Brachycephalus frogs maintain this gene. Whether these clades have the green rod cell type (which expresses SWS2) remains unknown. Nevertheless, dendrobatids are known to differ in at least two visual properties from close relatives. First, the lenses of some dendrobatid species (Oophaga pumilio, Epipedobates tricolor, Dendrobates auratus, D. leucomelas, Allobates femoralis and Adelphobates castaneoticus) transmit less short-wavelength light (lens 𝝀t50 413-425 nm; except for D. leucomelas lens 𝝀t50 326 nm) than those of bufonids (Rhinella marina, R. icterica, R. ornata, Bufo bufo, Rhaebo guttatus, Sclerophis maculata, and (Donner and Yovanovich 2020;Yovanovich et al.
Qualitative Part: Participant Interviews Due to the different conditions of the simulation studies -with or without a virtual reality device -the usersʹ perceptions collected after the two computer-based simulation studies were analyzed separately. This allowed us to examine the impact of various forms of Visual Blood information presentation -with a virtual reality headset and without. After the simulations, participants were asked to respond to two open-ended questions: ʹWhat do you LIKE about Visual Blood?ʹ and ʹWhat do you DISLIKE about Visual Blood?ʹ Participants had unlimited time to answer these questions. Following data collection, we initiated our qualitative analysis by translating statements from German and Spanish into English using the online translator deepl. com (DeepL GmbH, Cologne, Germany).
Statistical Analysis Qualitative data analysis, including coding and data organization, was conducted using Microsoft Word and Excel (Microsoft Corp. , Redmond, WA, USA). The results of the qualitative data analysis are presented using descriptive statistics, as the number of statements per theme with their respective percentages. We provide interrater reliability as a percentage. The online survey results are presented using descriptive statistics: numbers, medians, and interquartile ranges. Furthermore, we performed a one-sample Wilcoxon signed ranked test (SPSS Inc. , Chicago, IL, USA) for each question to determine whether the median value deviates significantly from a neutral point on the Likert scale. Statistical significance was indicated as P < 0. 05.
Then, we used HSF on , subjects from the HCP development, young adults, and aging datasets to study the e ect of age and sex on hippocampal subfields volumes. Firstly, we showed HSF to be closer to manual segmentation than other currently used tools (p <. ), regarding the Dice Coe cient, Hausdor Distance, and Volumetric Similarity. Then, we showed di erential maturation and aging across subfields, with the dentate gyrus being the most a ected by age. We also found faster growth and decay in men than in women for most hippocampal subfields. Thus, while we introduced a new, fast and robust endto-end segmentation tool, our neuroanatomical results concerning the lifespan trajectories of the hippocampal subfields reconcile previous conflicting results.
Unfortunately, most deep learning solutions are currently provided as a proof-of-concept, with either no public implementation, no pre-trained models, or are trained on small and specific datasets limiting generalizability. The current literature lacks an end-to-end deep learning segmentation protocol trained on a heterogeneous database to ensure segmentation quality across (i) contrast, (ii) magnetic field intensity, (iii) age range, or (iv) health condition. Even though segmentation protocols still need to be uniformized, there is a disparity of available segmentation tools for the hippocampal subfields. The current understanding of the effect of age and sex on volumetric changes in hippocampal subfields across the lifespan is based on manual or (semi-)automatic segmentation studies. Uematsu et al.