dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods: The suggested Transgene: Apply the following precise protocol, consider: 1. A complete genome of mtDNA from the chimpanzee "Pan Troglodyte JENNY" (GenBank X93335). This circular genome measures 16561 bases, baptize to it "CHIMP". 2. Take the 2 fossil DNA sections of Neandertal extracted by Svante Pääbo starting from the fossil skeleton from the man from Neandertal. The co-ordinates and precise references of these 2 sequences are respectively: i. HVR1-379 bases-S. Paabo in CELL 1997 Jul 11; 90(1): 19-30  [13]. ii. HVR2-345 bases-S. Paabo in PNAS 1999 flight 96 pp. 5581-5585  [14]. 3. Form transgene "G. M. hOmo-NEANDERTAL" by amalgamating the genome  [1]  of the chimpanzee and the 2 sections (2. i and 2. ii) of NEANDERTAL as follows: i. Just after base 15447 of CHIMP, to replace the 376 following bases by 379 bases HVR1 of the NEANDERTAL.
Acknowledgements We especially thank  Dr. Robert Friedmann M. D. practiced nutritional and preventive medicine in  Santa Fe ,  New Mexico , which suggested new research interest on mtDNA genome. We also thank the mathematician Pr. Diego Lucio Rapoport (Buenos aires), the french biologist Pr. François Gros (Pasteur institute, co-discoverer of RNA messenger with  James Watson  and  Walter Gilbert ) and  Pr. Luc Montagnier , medicine Nobel prize winner for their interest in my research of biomathematical laws of genomes.
Data analysis Participants' accuracy was analyzed with logit mixed-effects models using the lme4 package of R  [27]. For each participant group, we ran logit mixed-effects models comparing listeners' accuracy on each word type to chance. Using separate logit mixed-effects models, we also examined the effects of F0 (no F0 cues, F0 cues), foil type (non-word, part-word), L1 (French, English), and two-and three-way interactions on participants' accuracy. We first analyzed the accuracy of all monolingual groups (Groups 1-4 in Table  1 ). The initial model included F0 (no F0 cues, F0 cues), foil type (non-word, part-word), L1 (French, English), and their interaction as fixed effects, and participant and item as random intercepts (models with random slopes did not converge). The factors in this analysis were dummy coded, and in the presence of significant interactions, simple effects were examined by releveling the model.
This material is based upon work supported by the  National Science Foundation  under grant no. BCS-1423905  awarded to  Dr. Annie Tremblay. Support  for this research also comes from a  Language Learning  small research grant awarded to  Dr. Annie Tremblay , and a  Vidi grant  from the  Netherlands Organisation for Scientific Research  awarded to the fourth author (MB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Their results showed an interaction between physiological stressed caused by Varroa and imidacloprid, with imidacloprid possibly affecting the body mass of bees and lower body mass causing decreased flight capacity. An interesting finding is that of  Kessler et al. (2015). Their data generated by choice assays (sucrose laced with imidacloprid or thiamethoxam versus plain sucrose) suggest that bees prefer solutions with imidacloprid and thiamethoxam. Another study investigating effects on food consumption found that thiamethoxam decreased bees' response to higher sucrose concentrations  (Démares et al. 2016). Alkassab and Kirchner (2016)  exposed winter bees to sublethal doses of clothianidin and measured behavioral effects. Chronic exposure to 15 ppb was found to significantly affect long-term memory.
Population-level effects of neonicotinoids on wild bees Nothing was known about the population-level effects of neonicotinoids on wild bees in 2014. As a managed domesticated species, population trend data are available for honeybees, but not for wild bees. One study has attempted to investigate the impact of neonicotinoids on wild bee population trends. Woodcock et al. (2016)  used an incidence dataset of wild bee presence in 10 × 10 km grid squares across the UK. The dataset is comprised of bee sightings by amateur and professional entomologists and is probably the most complete national bee distribution database currently available. Sixtytwo wild bee species were selected and their geographic distance and persistence over an 18-year period between 1994 and 2011 was calculated. Neonicotinoid seed-treated oilseed rape was first used in the UK in 2002, and so the authors calculated spatially and temporally explicit information describing the cover of oilseed rape and the area of this crop treated with neonicotinoids.
Acknowledgments The work has been funded by the  Triodos Foundation's Support Fund for Independent Research on Bee Decline and Systemic Pesticides. This Support Fund received donations from the  Act Beyond Trust (Japan) ,  Stichting Triodos Foundation  (The Netherlands),  M. A. O. C. Gravin  van  Bylandt Stichting  (The Netherlands),  Zukunft Stiftung Land-wirtschaft (Germany) ,  Hartmut Spaeter Umweltstiftung  (Germany) and citizens. The authors also thank the  Lune de Miel Foundation (France) , the  David Suzuki Foundation (Canada) , the  SumOfUs community (Germany) , the  Rovaltain Foundation (France) , the  Aurelia Foundation (Germany)  and IUCN for their help in dissemination of these updated WIA findings. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Background: History of assessment of endogenous allergenicity in soybean Presently, the Implementing Regulation EU No. 503/2013 (EC, 2013) asks for the assessment of relevant allergens as part of the comparative analysis and provides a reference to the OECD consensus documents on compositional considerations for new plant varieties. The OECD consensus document on compositional considerations for new varieties of soybean contains a table listing potential soybean allergens (Table  20 in OECD, 2012 ), which was adapted from a review publication  (L'Hocine and Boye, 2007)  and complemented with data from the WHO/IUIS database. However, the significance and relevance of some single potential allergen molecules listed in the table was questioned previously  (Ladics et al. , 2014). While this was on the one hand assigned to the fact that sequences are not available for all potential allergens, also the published scientific evidence of clinical relevance was questioned for others.
However, serum samples from allergic subjects are precious and not easy to come by and this is especially true for food allergies with a relatively low prevalence like allergy to soybean. Due to the restrictions of the human serum IgE-based techniques, standardisation is not feasible and the possibility to compare data from different assessments is limited. The potential allergenic proteins which are quantified in the endogenous allergenicity assessment have usually been described by IgE binding assays previously and they are measured in their native form (i. e. in the raw plant material or protein extracts thereof). The recognition of these potential allergens by human IgE can therefore be considered a pre-requisite. Within the comparative approach, more robust and reliable methods with a better readout are available, which can be standardised more easily and do not depend on limiting human material.
Identification of potential soybean allergens for assessment The table provided in the OECD consensus document on soybean compositional considerations  (Table 20 in OECD, 2012) , as well as the supporting review publication by L'Hocine and Boye (L'Hocine and Boye, 2007) can be used as the basis for the research on potentially relevant soybean allergens. The search can be further complemented using the databases listed in the EFSA scientific opinion on allergenicity (EFSA, 2010), especially the IUIS/WHO database (www. allergen. org), the FAARP database of the University of Nebraska-Lincoln (www. allergenonline. org) and Allergome (www. allergome. org). Table  1  lists the information collected for all potential soybean allergens after applying this search (Table  1 ). A more detailed version of the outcome can be found in the EFSA public consultation document on the guidance on allergenicity assessment of GM plants (Table  C1 , EFSA public consultation document 2016).
Data on single potential soybean allergens as well as on the allergic individuals reported were retrieved, as well as data on possible clinical reactions to the single, purified or recombinantly produced allergen molecules. However, it should be highlighted that one major drawback of the current clinical practice is the fact that data on clinical reactivity is obtained by DBPCFC to (the composite) soybean extract or soybean formulas only  (Ballmer-Weber et al. , 2007; Blom et al. , 2013). While this is the gold standard for assessment and diagnosis of food allergy  (Hoffmann-Sommergruber et al. , 2015) , it does not provide data on reactions to single soybean proteins unless the individual tested is mono-sensitised. Currently, data on allergic individuals reacting to single allergens is mainly restricted to component resolved diagnostics  (Tuano and Davis, 2015) , which allows an analysis of the correlation between levels of serum IgE to single allergens with clinical reactivity to the composite food or food extract.
Given the aforementioned mentioned challenges, the definitions of renowned, international institutions like WHO can be used as a tool to define allergens. IUIS/WHO describes the IgE-reactivity of at least 5 patient's sera with a specific molecule as a primary criterion for this purpose. In the next step, an expert peer-review process is conducted before a protein is included among allergens in the database. In table 1, the IUIS/WHO primary criteria were applied to the list of potential allergens as a starting point. Considerations for including molecules into current endogenous allergenicity assessments are described on the basis of the further strategy applied by IUIS/WHO, suggesting a peer-review process, as well as on possible methodological limitations (Table  1 ). A more detailed description of this strategy can be found in the EFSA guidance document on allergenicity assessment (EFSA public consultation document, 2016).
Levels varied from 40 to 148 mg of Gly m 5 per g of dry matter (3. 7-fold difference) with more than 60% of the assessed samples falling in the range of 80-110 mg/g. Using a rather similar methodological approach, Chen et al. (  2014 ) quantified Gly m 6 in 469 soybean samples from different origins and cultivars. Results revealed higher variability than in Gly m 5, since Gly m 6 concentrations ranged from 27 to 196 mg per g of dry matter, equivalent to a 7. 3-fold difference, with more than 20% of the tested samples falling in the range of 120-140 mg/g. These data are in agreement with those observed by  Houston et al. (2011)  using a LC/MS 2 /MRM approach, where moderate to high variability of Gly m 5 α-subunit and Gly m 6 subunits was concluded. Wilson et al. (2008)  used a monoclonal antibody raised in animals to assess the endogenous allergen content of Gly m Bd 30K in 138 soybean germplasm collection samples.
The authors concluded that there is a high natural variability between the samples, ranging from approximately 2. 5 to 30 mg/g of extracted protein. The content of the profilin Gly m 3 was assessed in different soybean products  (Amnuaycheewa et al. , 2010). The group concluded that the concentration of allergen might depend on the country of origin as well as on processing of the food products. Currently, publications regarding quantification are not available for all soybean allergens mentioned in the WHO/IUIS database or for all potential allergens included in the OECD consensus document. As described, there are also discrepancies between publications concerning variability of certain allergenic molecules. However, while this might be a technical issue and therefore depend on the method used, it could also be due to different soybean varieties investigated, and also depend on growth sites of the crops and, therefore, environmental factors.
Data interpretation Determination of endogenous allergen levels should be carried out as a part of the comparative analysis of composition  (EC, 2013). Therefore, apart from comparing the GM food to its conventional counterpart, a suitable set of non-GM reference varieties should be assessed in parallel. These varieties can present a set of data on the natural variation of the protein or allergen in question and ideally can provide an upper (and lower) limit of the natural allergen protein content. Any possible changes observed between the GM crop and its conventional counterpart can then, in the next step, be evaluated statistically by taking these varieties and the natural range of endogenous allergenic protein into account. There is a need for better understanding the natural variability of allergens.
Industry and research institutes should be strongly encouraged to collect as many data as possible on allergen natural variability of soybean varieties representative of the ones consumed by the at-risk population. In order for these data to be readily usable and comparable, the methodology used should be standardised and harmonised. A solid database containing a broad range of measurements concerning the natural variation of endogenous allergens is the main basis for interpreting data acquired for a new GM variety. The endogenous allergen content of a new GM variety is less likely to raise an uncertainty the more the natural variation of these proteins is understood. In the case a potential allergenic protein differs statistically significant from its non-GM counterpart regarding the concentration of one or several endogenous allergens and additionally falls outside the range of natural variation, the consequences are uncertain and the biological relevance as well as the impact on human health has to be evaluated in the next step.
It is important to pro-actively include clinicians into the risk assessment process, including into defining appropriate molecules to be measured as well as into the final strategies to be applied for the safety assessment to protect consumers, in particular those at higher risk. The natural variability of endogenous allergens in soybeans traditionally consumed by the population is an invaluable tool to interpret data acquired for new varieties generated by biotechnology. There is therefore a need to generate comprehensive and robust databases for natural levels of endogenous allergens. To this end, the methodology of assessment should be standardised, allowing a harmonisation of the risk assessment process.
Assembly of the sequencing reads resulted in a single circular contig of 20,222 bp. The viral genome was sensitive to various type II restriction endonucleases; digestion of the genome with the single-cutters XbaI and BglII resulted in a linearized product, which migrated in the agarose gel as a single sharp band, consistent with the genome being a circular dsDNA molecule. The SPV1 genome has a GC content of 38. 3%, which is similar to that of various Sulfolobus genomes (e. g. , 35% for S. islandicus  [30] ). The SPV1 genome contains 45 open reading frames (ORFs), which are tightly arranged and occupy 89. 1% of the genome (Fig. 4 ). The ORFs are generally short, with median length of 103 codons. Homology searches using the BLASTP program  (31)  revealed that less than one-third (ϳ27%) of SPV1 gene products are significantly similar (E-value cutoff of 1eϪ03) to sequences in the nonredundant protein database (Table  1 ).
The oligonucleotide primers used for 16S rRNA gene amplification are A21F (F stands for forward) (5=-TTCCGGTTGATCCYGCCGGA-3=) and U1525R (R stands for reverse) (5=-AAGGAGGTGATCCAGCC-3=). The primers S38A_399F (5=-CCGGAGACCAGGATACCAACTAG-3=) and S38A_1065R (5=-TCTACAAAGGCGG GGGAATAAG-3=) were designed based on the obtained sequences. The 16S rRNA gene sequence of Sulfolobus sp. S38A was assembled. The following collection strains were analyzed for the ability to replicate the SPV1 virus: S. islandicus LAL14/1 (30), REY15A (29), and HVE 10/4 (29), S. solfataricus P1 (GenBank accession no. NZ_LT549890), P2  (56) , and 98/2 (57), and S. acidocaldarius DSM 639  (58). Aliquots of virus preparation were added to the exponentially growing cultures of these strains, and after incubation for 2 days at 78°C, the cultures were diluted 1:50 and further grown for 2 days.
For TEM and cryo-EM, the samples were prepared and analyzed as described previously  (13). The pictures were recorded using a Falcon II direct electron detector (FEI, USA). Extraction and analysis of SPV1 DNA. Nucleic acid was isolated from purified viral particles as described previously  (11). SPV1 genome libraries were prepared with the Nextflex PCRFree kit (Bioo Scientific), and samples were sequenced by Illumina Miseq with paired-end 250-bp read lengths (for both ends) (Genomics Platform, Institut Pasteur, France). An average coverage of 3,000 was obtained. The sequence was assembled using CLC Genomics Workbench software package. ORFs were predicted using GeneMark. hmm v3. 25  (61)  and RAST v2. 0  (62). The in silico-translated protein sequences were used as queries to search for sequence homologs in the nonredundant protein database at the National Center for Biotechnology Information using BLASTP  (31)  with an upper threshold E value of 1eϪ3.
Searches for distant homologs were performed using HHpred (32) against different protein databases, including PFAM (Database of Protein Families), PDB (Protein Data Bank), CDD (Conserved Domains Database), and COG (Clusters of Orthologous Groups), which are accessible via the HHpred website. Transmembrane domains were predicted using TMHMM  (63) , whereas the secondary structure was predicted using Jpred  (64)  and PsiPred  (65). Analysis of SPV1 structural proteins. The highly purified SPV1 virions were analyzed for protein content by using 4 to 12% gradient NuPAGE Bis-Tris precast gels (Thermo Fisher Scientific). Protein bands were stained with Coomassie blue using InstantBlue (Expedeon). Stained protein bands were excised, and digested "in gel" with trypsin. The digested peptides were analyzed at the Proteomics Platform of the Institut Pasteur by nano-LC-MS/MS using an Ultimate 3000 system (Dionex) as described previously  (11).
The peptide masses were searched against annotated SPV1 proteins using Andromeda  (66)  with MaxQuant software, version 1. 3. 0. 5  (67). Analysis of SPV1 lipids. Lipids were extracted from highly purified SPV1 preparation and from uninfected cells of Sulfolobus sp. S38A as described previously  (68). The lipid extracts were dissolved in chloroform-methanol-H 2 O (65:24:4 [vol-vol-vol]) and separated by thin-layer chromatography on silica gel 60 plates (Merck) using chloroform-methanol-H 2 O (65:24:4 [vol-vol-vol]) as the solvent. The lipids were visualized by molybdate  (68). Accession number(s). The SPV1 genome sequence has been deposited in the GenBank database (accession no. KY780159). The Sulfolobus sp. S38A 16S rRNA gene partial sequence has been submitted to the GenBank database (accession no.
Genes encoding structural proteins are shown in boldface type. b The coordinates on the complementary strand are shown in parentheses after c (for complementary strand). c Length is shown by the number of amino acids (aa). d TMD, transmembrane domain. e wHTH, winged helix-turn-helix; RHH, ribbon-helix-helix; SAM, S-adenosyl-L-methionine. f The number of identical to total number of aligned amino acids and percent identity shown in parentheses are shown before the commas. The E values are shown after the commas. g Accession numbers beginning with "PF" may be found in the Pfam database, http://pfam. xfam. org/.
ACKNOWLEDGMENTS This work was supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  685778 , project  VIRUS-X. We are grateful to  E. V. Koonin  and  T. G. Senkevich  for their help in collecting environmental samples from hot springs in Beppu, Japan, to  M. Duchateau  ( Proteomics Platform, Institut Pasteur ) for help with proteomics analyses, and to  M. Nilges  and the  Equipex CACSICE  for providing the Falcon II direct detector.
a Genes encoding structural proteins are shown in boldface type. b The coordinates on the complementary strand are shown in parentheses after c (for complementary strand). c Length is shown by the number of amino acids (aa). d TMD, transmembrane domain. e wHTH, winged helix-turn-helix; RHH, ribbon-helix-helix; SAM, S-adenosyl-L-methionine. f The number of identical to total number of aligned amino acids and percent identity shown in parentheses are shown before the commas. The E values are shown after the commas. g Accession numbers beginning with "PF" may be found in the Pfam database, http://pfam. xfam. org/.
2 99. 4 99. 5 97. 1 97. 6 97. 6 96. 7 98. 7 98. 7 98. 7 99. 9 HHpred hit 2n25 2dmd COG3600 PF04434 g PF14947 3m8j PF12441 PF10102 PF10102 PF14580 1rzu Zn finger DNA-binding protein C2H2 Zn-binding protein GepA-like uncharacterized conserved protein SWIM Zn finger protein DNA-binding protein, wHTH DNA-binding protein, HTH; conserved in Sulfolobales Transcriptional regulator, CopG-like RHH Structural protein Structural protein Leucine-rich repeat protein Glycosyltransferase, GT-B fold 1 2 1 2 171 234 65 110 39 83 40 107 63 36 131 57 64 62 65 96 112 62 208 34 84 147 115 43 400 357 187 64 310 77-592 599-1303 1300-1497 1494-1826 1813-1932 1929-2180 c(2305-2427) c(2414-2737) c(2771-2962) c(2946-3056) c(3064-3459) c(3590-3763) c(3750-3944) c(4186-4374) c(4364-4561) c(4548-4838) c(4838-5176) 5475-5663 5660-6286 6283-6387 6380-6634 6781-7224 7205-7552 c(7810-7941) c(7967-9169) c(9211-10284) c(10289-10852) c(10846-11040) 11083-12015 ORF01-171 ORF02-234 ORF03-65 ORF04-110 ORF05-39 ORF06-83 ORF07-40 ORF08-107 ORF09-63 ORF10-36 ORF11-131 ORF12-57 ORF13-64 ORF14-62 ORF15-65 ORF16-96 ORF17-112 ORF18-62 ORF19-208 ORF20-34 ORF21-84 ORF22-147 ORF23-115 ORF24-43 ORF25-400 ORF26-357 ORF27-187 ORF28-64 ORF29-310 (Continued on next page).
Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10 4 genes) with characteristics of gene regulatory networks observed in vivo.