dataesr/scientific-paragraphs-categorization huggingface dataset:
46, GenBank AF141168. 1; Identities = 512/551 (93 %), 10 gaps (1 %)) and Mollisina un cinata (voucher KUS-F52307, GenBank JN033404. 1; Identities = 481/518 (93 %), 11 gaps (2 %)). Closest hits using the LSU Typus. caNada, New Brunswick, Charlotte Co. , 1. 5 km SW of Little Lepreau, 45. 135614° -66. 492269°, on buds of Abies balsamea (Pinaceae), 4 May 2021, D. W. Malloch, HPC 3633 (holotype CBS H-24961, culture ex-type CPC 41918 = CBS 149042, ITS, LSU, rpb1 and SSU sequences GenBank ON603784. 1, sequence are Phialea strobilina (strain CBS 643. 85, GenBank EF596821. 1; Identities = 799/810 (99 %), no gaps), Rhytisma acerinum (voucher J. Platt (DUKE), GenBank AF356696. 1; Identities = 775/787 (98 %), two gaps (0 %)) and Calycellina ON603804. 1, ON605645. 1 and ON603974. 1, MycoBank MB 844264).
fagina (voucher SBRH925, GenBank OM218631. 1; Identities = 795/810 (98 %), (voucher J. Platt (DUKE), GenBank AF356695. 1; Identities = 865 /878 (99 %), no gaps), Dicephalo spora huang shanica (voucher MFLU 18-1828, GenBank MK585051. 1; Identities = 864/878 (98 %), no gaps) and Dice phalospora rufocornea (voucher MFLU 18-1827, GenBank MK585050. 1; Identities = 863/878 (98 %), no gaps). Colour illustrations. Abies balsamea in Charlotte Co. , New Brunswick. Conidiophores and conidiogenous cells on SNA giving rise to septate conidia. Supplementary material Scale bars = 10 µm. FP1397 Phylogenetic tree. Pedro W. Crous & Johannes Z. Groenewald, Westerdijk Fungal Biodiversity Institute, P. O. Box 85167, 3508 AD Utrecht, The Netherlands; e-mail: p. crous@wi. knaw. nl & e. groenewald@wi. knaw. nl David W.
Typus. sPaiN, Granada, Huétor de Santillán, Arroyo Palacios, under Quercus ilex, Q. faginea, and scattered Pinus halepensis and P. pinaster on calcareous soil, 1300 m a. s. l. , 24 Nov. 2018, F. Armada & M. -J. Díaz de Haro (holotype in Herbarium of Granada: JA-CUSSTA 9619; isotype in herb. pers. F. Armada: FA 4700, ITS and LSU sequences GenBank OM964838 and ON032996, MycoBank MB 844316). Additional material examined. See Supplementary material page.
Typus. fraNce, Hautes-Pyrénées, Gavarnie ski station, 1850 m a. s. l. , in a grazed subalpine nutrient poor basophilous grassland with other Entoloma species, 10 Sept. 2003, G. Corriol, GC03091006 (holotype in BBF; ITS-LSU sequence GenBank ON006551, MycoBank MB 843348). Additional materials examined. deNmark, Biowide 089 Eskebjerg Vester- lyng, 23 Sept. 2015, T. Laessøe, DMS-718511, ITS sequence GenBank ON006557; Biowide 079 Melby Hede, 18 Sept. 2020, T. Laessøe, DMS-716711, ITS sequence GenBank ON006556. -germaNy, Lückendorf, Parkwiese, 19 Sept. 2019, A. Karich, IHI-19Ent01, ITS sequence GenBank ON006552; 2 Sept. 2021, A. Karich, IHI-20Ent06, ITS sequence GenBank ON006554; Herrnhut, Gottesacker, 23 Sept. 2020, A. Karich, IHI-20Ent05, ITS sequence GenBank ON006553. -NetherlaNds, Utrecht Province, Soesterberg, former military airport, 30 Sept.
2019, M. E. Noordeloos, J. van Dongen & JP. Keizer, L-0607879, ITS sequence GenBank ON006558. -Norway, Telemark, Pors- grunn, Heistad, Lundebukta, 11 Oct. 2013, A. Molia & T. Laessøe, O-F-21950, ITS sequence GenBank ON006555.
Typus. swedeN, Härjedalen, Tännäs, Andersborgsvägen, subalpine area on sandy soil, associated with Salix lapponum (Salicaceae), 15 Aug. 2006, E. Larsson, EL85-06 (holotype GB-0207621, isotype AH, ITS-LSU sequence GenBank FN550892, MycoBank MB 843360). materials examined. sPaiN, Catalonia, Girona, Vall de Núria, Ras de l´Ortigar, 2250 m a. s. l. , alpine meadows with Dryas octopetala and Salix retusa on calcareous soil, 23 July 1999, J. Vila 990723-8, AH26730 (ITS sequence GenBank OM891092). -swedeN, Lule lappmark, Jokkmokk, Padjelanta, Oarjep Slampet-Jåhkkå, in mosaic rich alpine vegetation on calcareous ground associated with Salix reticulata and Dryas octopetala, 18 Aug. 2016, E. Larsson, EL244A-16, GB-0207622 (ITS-LSU sequence GenBank OM891091). Additional.
Typus. thailaNd, Chiang Mai, Kanlayaniwattana district, on soil under Pinus merkusii trees (Pinaceae), 7 Nov. 2019, S. Sommai (holotype BBH 49227; ITS, LSU and rpb2 sequences GenBank, MT940809, MT940819 and MT965685, MycoBank MB 841178). material examined. thailaNd, Chiang Mai, Kanlayaniwattana district, on soil under P. merkusii trees, 7 Nov. 2019, S. Sommai, BBH 49229; ITS, LSU and rpb2 sequences GenBank MT940810, MT940820 and MT965686. Additional.
Typus. australia, New South Wales, Mallanganee, Sandilands plantation, on leaf of Eucalyptus grandis × E. camaldulensis (Myrtaceae), 18 Apr. 2018, A. J. Carnegie (holotype PREM 63266, culture ex-type culture CMW 52484 = PPRI 29907; ITS, EF1-α and Btub sequences GenBank MZ285070, MZ318165 and MZ318163, MycoBank MB 841347). material examined. australia, New South Wales, Mallanganee, on leaf of Eucalyptus grandis × E. camaldulensis, 2018, A. J. Carnegie, PREM 63267, culture CMW 52470 = PPRI 29908; ITS, EF1-α and Btub sequences GenBank MZ285069, MZ318164 and MZ318162. Additional.
Pyrosequencing SNPs in cDNA from tissues and neurospheres of hybrid mice, as well as genomic DNA for methylation analysis after bisulfite treatment, were sequenced using a PyroMark ® Q96 ID instrument (Qiagen). PCR and sequencing primers (Supplementary Table  S1 ) were designed using PyroMark Assay Design Software 2. 0. Biotinylated PCR products were immobilized on streptavidin-coated beads for cleanup and sequenced using PyroMark ® Gold Q96 reagents (Qiagen) following the manufacturer's protocols.
Identification of potential regulatory elements and generation of reporter-gene vectors Potential brain-specific gene regulatory elements were identified from histone modification and CTCF ChIP assay data, as well as DNAse I and ATAC-seq hypersensitivity data for the newborn (P0) mouse brain in comparison to peripheral tissues. These data were extracted from the databases ENCODE3 (https://www. encodeproject. org/), UCSC Genome Browser (https://genomeeuro. ucsc. edu/) and ENSEMBL (https://www. ensembl. org/Mus_ musculus/Info/Index). Seven candidate brain-specific regulatory elements across the Trappc9-Peg13 locus were found; their genomic positions (mouse GRCm38/mm10 genome version) and features are listed in Supplementary Figure  S6. Similarly, the Trappc9 promoter characteristics at exon 1 were noted.
Statistical analysis The data for the promoter-reporter gene assays were analyzed using GraphPad Prism v. 9. 3 software. Data were analyzed for outliers using the ROUT method. The datasets were then analyzed for normality using the Shapiro-Wilkinson test. For non-parametric datasets, Mann-Whitney U-tests were performed in comparisons of the Reg-element datasets to the basic Trappc9 promoter dataset. For parametric datasets, unpaired t-tests were performed.
Alternative exons and transcriptional start site of Trappc9 The core imprinted gene of this cluster on mouse chromosome 15, Peg13, is located within intron 17 of Trappc9 and is transcribed into a non-coding RNA  (Smith et al. , 2003; Wang et al. , 2008). For Trappc9 itself, several alternatively spliced and truncated transcript variants have been described and annotated on ENSEMBL, including alternative first exons (Figure  1A ). Furthermore, while some transcript variants were found to be predominantly expressed from the maternal allele in brain tissue, the truncated variant 203, which ends shortly after Peg13 in intron 17, was identified as a paternal allele-specific transcript in RNA-seq studies  (Gregg et al. , 2010; Hsu et al. , 2018). We set out to confirm these variants using RT-PCR across specific Trappc9 exons.
We readily detected the full-length transcript 202 in brain and other tissues of newborn mice but were not able to confirm the alternative first exon of the 201 transcript (Figure  1B ). Exon 2 of the 202 transcript contains a conserved translational start codon across mammalian species. To further investigate potential alternative transcriptional start sites, we undertook 5′-RACE PCR and sequencing, which revealed several transcriptional start sites within the exon 1 5′-UTR of variant 202 (Figure  1C ), but we did not detect the alternative exon 1 of variant 201. Sequencing of PCR products also showed alternative splicing of exon 5, which was missing in some kidney and spleen cDNAs, in line with ENSEMBL annotations. Additionally, we investigated the alternative, truncated Trappc9 splice forms 206 and 203, which terminate in intron 17 upstream and downstream of Peg13, respectively (Figure  1A , Supplementary Figure  S1 ).
Proportionately, the 79% of cells with mono-allelic and biased paternal expression approximate the bulk neurosphere expression bias of 87% paternal transcripts (Figure  2 ). In in vitro differentiated neurons, the cellular heterogeneity was reduced as almost all cells showed monoallelic or paternal bias of Peg13 expression (Figure  3B ), much in line with the paternal bias of 89% in brain tissue as a whole (Figure  2 ). For Trappc9 the allelic expression varied considerably in single NSCs. We observed mono-allelic maternal or paternal Trappc9 expression in 10% of the cells respectively, while 29% displayed equal bi-allelic transcription (Figures  4A-C ). Around half of the NSCs showed biased bi-allelic expression: 21% predominantly maternal, 31% predominantly paternal (Figure  4B ). Considering all single-NSC categories in proportion, the data are in line with the bulk neurosphere analysis (Figure  2 ) as no clear overall parental allele bias was observed in either dataset.
In differentiated neurons, the proportion of cells with equal bi-allelic expression was reduced to 11%, while another 11% showed mono-allelic maternal and 17% mono-allelic paternal Trappc9 expression (Figure  4B ). Taking into account the proportions of neurons with biased bi-allelic expression (33% maternal, 28% paternal bias), the single-neuron dataset depicts an overall equal bi-allelic Trappc9 expression, which is in contrast to the maternal expression bias of 78% that was observed in whole-brain lysate (Figure  2 ). This discrepancy might be due to the brain lysate containing additional cell types (e. g. , astrocytes, microglia, oligodendrocytes) and a wider range of neuron types from different brain sub-regions as compared to the in vitro differentiated neurons, which were derived from hippocampal neurospheres.
Neither of the two genes displayed an overall maternal expression preference in the population of single neurons, which is in contrast to the data obtained from brain lysates. However, our neuron culture is not fully representative of all the cell types that would be included in a brain tissue lysate, since we actively selected against dividing glial cells by adding Ara-C to the culture. Furthermore, since the neurons were differentiated from hippocampal NSCs, our culture is likely to contain only a limited range of neuronal cell types. Data from the Allen Brain Map (https://portal. brain-map. org/) in situ hybridization atlas and single-cell transcriptomics indicate medium levels of Trappc9 and Ago2 expression in many neurons of the cortex and hippocampus, with lower levels occurring in some types of neurons as well as astrocytes and oligodendrocytes.
, 2022). Further experiments will be required to determine whether the silencer elements we identified within the Trappc9 locus might function in an allele-specific way and contribute to the brain-specific imprinted expression bias of this gene. A number of Trappc9 transcript variants have been annotated on ENSEMBL, including two alternative promoters and truncated transcripts, one of which has been described as specifically expressed from the paternal allele in RNA-seq data  (Gregg et al. , 2010; Hsu et al. , 2018). Our attempts to confirm such truncated transcript versions with primer combinations that span transcript-specific and shared exons were unsuccessful. Trappc9 and Peg13 are transcribed in the same direction and Peg13 is an unspliced long non-coding RNA located in intron 17 of Trappc9.
Funding This project was funded by a  Wellcome Trust  grant ( 099795/ Z/12/Z ), a  University of Liverpool-A*STAR   Research Attachment Programme (ARAP, Singapore ) PhD studentship and a PhD studentship from the  King Saud University, Saudi Arabia. Work in G. K. 's lab was funded by the  UK Biotechnology and Biological Sciences Research Council  ( BBS/E/B/000C0423 ) and  Medical Research Council  ( MR/S000437/1 ). This work was supported by funding from the  National Research Foundation, Singapore  ( NRFF-2015-05 ) to  DMM's lab.
To assess internal data, looking for new opportunities for additional value creation is the best way to capitalize on Orange's knowhow. This data also provides opportunities for different actors and sectors to incubate multiple data testing beds. Exploring more ideas reveals more about the potential for mobile data. In this article, we focus on the ability of data from mobile services to understand population behaviors and patterns of communication, to create a ground-truth dataset to support observation of Covid-19, and eventually to develop a predictive model. 1 The large volume of mobile data can be transformed into smart data with AI-and with a human centered vision, 2 can become "data for good. " This article focuses on the use of mobile data to combat against Covid-19. Orange has collaborated closely with health and medical research institutes in France (INSERM 3 ) since March 2020 to assess the value of the service to help predict the spread of epidemics.
Flux Vision is the name of the data analytics solution set up to create these insights, and also the business unit that handles it. Flux Vision is managed by Orange Business Services. Orange Flux Vision fully respects privacy and abides by GDPR and e-privacy. The information provided as output is in the form of population statistics, fully anonymized and aggregated. The Flux Vision solution guaranties that individuals cannot be traced nor identified, even via reverse engineering and even during data processing. Flux Vision serves many customers in many markets in Europe and Africa where Orange operates mobile services (Flux Vision, https://www. orange-business. com/en/prod ucts/flux-vision). Orange adapted the Flux Vision data production tool and accelerated the collaboration with its partners.
The principle of Flux Vision is to count the mobile phones present in each area during a 1-hr timespan. The network event data set used to conduct this simple count is processed on the fly and immediately deleted. Flux Vision only retains a completely anonymized set of statistical data. Standard statistical adjustments, including an extrapolation process to obtain representative population figures, is executed based on observations made about Orange network users over the whole of France (both French residents and international visitors). The indicators are calculated every day based on the 24 million users of the Orange mobile network. This includes consumer and business customers, as well as international visitors moving around France and temporarily using the networks via roaming agreements.
In parallel with action taken in France, Orange along with all major mobile operators agreed to provide data from affiliate countries to support the European Commission's initiatives through the JRC (Joint Research Center). Orange was the first European telecom operator to provide data from France, then Slovakia and Romania, which enabled the JRC to publish its first report based on Orange's data ("On the Role of Mobile Positioning Data Derived Mobility to Explain the Initial Spread of Covid-19: a Case Study, European Commission, Joint Research Centre," May 15, 2020). The health crisis has strengthened many actors' perception of the value of mobility data from operators, in Orange's case through Flux Vision. To respond to the requests linked to this episode, the Flux Vision indicators were adapted in the space of a few days.
Orange data specialists provided guidance to local operations in these countries and leveraged GMSA's support and influence to create a multidisciplinary project team. As a result Flux Vision could then deliver tailored analyses to health authorities and dedicated NGOs to enable pandemic modeling efforts. 4. 1. DRC: Leveraging GSMA and an initial project with the UN/World Food Program Orange and GSMA were planning to hold a major seminar in Kinshasa (DRC) in March 2020 when the Covid-19 pandemic struck. Avirtual event took place instead to help DRC State and health authorities, the regulator, the UN and other NGOs to consider the use of mobile data and the benefit of such assets to monitor the emerging pandemic. The DRC vice-minister for health led the discussion and welcomed the strong engagement of Orange.
The Flux Vision solution was ready to be launched in less than 3 months e4-4 Pascal Chambreuil et al. thanks to preparation already in place with the UN WFP to improve the delivery of food during the Ebola crisis. Flux Vision delivered data in mid-April and at the end of May 2020. A "control room" was set up by the DRC authorities to manage activities around the pandemic response. Orange was an active member, continuing to provide mobility indicators until the end of December 2020. These data services were offered free of charge to the DRC with the national Orange operating company donating data and central Orange Business Services (OBS) contributing analysis and visualization through its Flux Vision platform. With the support of GSMA Orange committed to involve local resources to process the statistics provided by Orange and delivered valuable insights to health authorities.
Orange Flux Vision's documentation and resources explaining how the platform works. This sort of capacity building effort is an essential precursor to mobile big data projects, engaging the consumers of insights in the process and allowing them to shape the application and delivery to maximize utility. Second, following on from developing an understanding on the demand side, in order to make a longterm contribution to a sustainable ecosystem it is also vitally important to enhance the technical skills of local stakeholders on the supply side. Engaging local companies and contractors and providing assistance with the data analytics, visualization, and interpretation of outputs will help to create a healthy and productive market for data-drive insights. Third, it is crucial to adopt best-practice in data privacy and governance from the outset-including accountability in the use of data, processes for data privacy and governance, and identification and mitigation of risks associated with handling customer data.
Global Learnings: How to Serve the Anonymized Mobility Data in a Quick Service Mode to Support Crisis Management in Europe and Africa Once Orange group made the decision to provide mobility data to different crisis taskforce institutions in France and in other Orange countries, Orange Flux Vision adapted production to provide the necessary mobility analytics quickly for France and the European Union (JRC) in the first delivery. The required data provision to support the response to the crisis was classified as urgent and prioritized above planned deliveries from the second day after the decision was made in France (Figure  1 , France movement evolution), and all other deliveries followed. Whilst facilitating the rapid provision of mobility and attendance information was important, protecting the privacy of each Orange customer was a key objective.
The demand from different entities involved in Covid-19 crisis management varies from one sector to another based upon their objectives, upon the situation on the ground, and upon the actions that they plan to carry out. Orange Flux Vision qualified each demand and then interpreted this into a mobility data vision. This exercise consists of enabling the technical production team to adapt the outputs to meet the application and to help the recipient to understand what kind of data would be available. This enables the end user to quickly assess data as soon as it is available, and arrive at the right decisions and actions without losing time. In Spain and Belgium, national taskforces were combining anonymized and aggregated data from Orange with other operators' inputs to t perform national observation.
In France, Spain, and Belgium, which were already covered by Flux Vision, the data is updated daily with a 1-day lag in the case of presence data and 3 days in the case of mobility data, and delivered to numerous partners to support a variety of apps. The Spanish Ministry of Economic Affairs and Digital Transformation asked Orange to provide data to their national institute for statistics. This data has been updated every week since April 20. In Belgium, the first mobility data was provided on March 20 and is now updated daily for the Belgium Federal Task Force. In the MEA region, Orange decided to deliver the statistical data to local authorities requesting it, which can communicate it to the World Bank according to their terms. Orange Flux Vision mobility data deliveries have been set up for Orange in the Democratic Republic of Congo, Morocco, Sierra Leone, and Burkina Faso.
Nature reservations, parks, or regions that care about the preservation of their environment could set up an observation center to look over the population flows and volumes, requesting mobile data analytics accordingly. Some water supply companies intend to predict consumption, in order to improve their provisioning plans. Real-time mobility and attendance data become central data elements that they should take into account in their algorithm. Both locating optimal recharging points for electric cars and exploring the CO 2 emissions by transport are also interesting use-cases that Flux Vision starts looking into alongside specific customers. Third-party data combined with mobility data and artificial intelligence could bring more benefits. The limits of mobility data have not been reached.
Acknowledgments. The authors acknowledge the supports from  Orange Business Service and Orange Group. A special recognition to Flux Vision team for all the contribution made by each member to handle the C19 crisis requests. The authors appreciate our collaboration with all Orange affiliates ( Belgium, France, Romania, Slovakia, Spain , and  Republic Democratic Congo). The authors also thank  GSMA Data  for Development (specifically  Charlie Harrison and Hilary Camp ) for their lecturing and constructive advice. Funding Statement. All mobile data analytics served by  Orange (Flux Vision mobile data analytics)  to provide information with diverse countries' government bodies and sanitary services with regard to Covid-19 are executed on demand and supported by  Orange Group. The funder has a role in data collection and decision to publish, or preparation of the manuscript.
Data Availability Statement. The data that support this commentary are available Flux Vision, Orange Business Service, Orange Group. Restrictions apply to the availability of these data, which were used under license for this study. Data are available on demand from OBS (https://www. orange-business. com/en/products/flux-vision) with the permission of Flux Vision. Supplementary Materials. To view supplementary material for this article, please visit http://dx. doi. org/10. 1017/dap. 2021. 31.
■ METHODS AND POLYMER MODELS All the results presented in this paper are based on DFT. Equilibrium structures and their total energies are obtained from the Quantum-Espresso software package 27 (versions 6. 5) by using the PWSCF module pw. x. The energy barriers and reaction pathways are calculated by the climbing image NEB  26  method, as implemented in the Quantum-Espresso distribution. Quantum-Espresso is publicly available through the official site [http://quantumespresso. org (accessed on 21 June 2021)]. The NEB method being a static one, we neglect here temperature effects on the free energy barriers as we do not add entropic contributions. The estimation of entropic contributions to reaction barriers, which might be non-negligible especially for reactions with very low or no barrier, should be performed with dynamical methods which are clearly out of the scope of the present paper.