dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, unless data at family level include 'relation to family head' or individual level data provide the number of children for females, it is neither possible to capture single mothers nor know the number of children of each female in the household. Cross-tabulations of position in the household and marital status can only tell us if there are divorced or widowed daughters VIII of heads in complex households. However, given various assumptions, some IPUMs data files include inferred variables relating to different family units inside the household. This is mainly available from 1910 for US censuses and from the 1960s for a few other countries.
Stark broadening parameters, full wi dth at half maxi mum of s pectral line and shift, have been calculated for 3 spectral lines of Ar VII, for broadening by electron, proton, and He III i mpacts. For calculations, the semiclassical perturbation approach in the impact approxi mation has been used. The results are provi ded for temperatures from 20 000 K to 500 000 K and for an electron density of 10 17 cm -3. Obtained results will be included in the STARK-B database which is also included in Virtual atomic and molecular data center (VAMDC).
ON THE IMPLEMENTATION OF RES ULTS IN THE STARK-B DATAB AS E The presented in Table  1  Stark b roadening parameters for Ar VII spectral lines, will be also implemented in the STARK-B PHYSIC S database  (Sahal-Bréchot et al. , 2015 , 2017) , intended for the investigations, modelling and diagnostics of the plasma of stellar atmospheres, diagnostics of laboratory plasmas, and investigation of laser produced, inertial fusion plasma and for plasma technologies. The STARK-B database contains Stark widths and shifts calculated by authors of this article and their coauthors, by using the SCP co mputer code for spectral lines, and published in more than 150 publications. Actually, in this database are SCP data for the following elements and ionization degrees: Table  1. This table gives electron-, proton-, and doubly charged helium-impact broadening parameters for Ar VII spectral lines, for a perturber density of 10 17 cm -3 and temperatures fro m 20 000 to 500 000 K.
We note as well that STARK-B database is one of the databases which are in the Virtual Ato mic and Molecular Data Center -VAM DC  (Dubernet et al. , 2010; Rixon et al. , 2011; Dubernet et al. , 2016). VAM DC is created in order to enable an efficacious search and mining of ato mic and mo lecular data scattered in different databases and to make more convenient their adequate use. It has been the principal aim of a FP7 founded project of the same name  (Dubernet et al. , 2010) , which started on July 1 2009 and lasted 42 months. During this project an interoperable e-infrastructure for ato mic and mo lecular data upgrading and integrating European (and wider) A&M database services has been build, and a foru m of data producers, data users and databases developers has been created. Currently in VAM DC are 30 databases with atomic and molecular data, including STA RK-B, and can be accessed and searched through VAM DC portal: http://portal.
CONCLUS ION We have performed a SCP calculat ion of Stark b roadening parameters for three mult iplets of Ar VII. Stark broadening parameters -widths and shifts, have been calculated for collisions of Ar VII ions with electrons, protons and doubly charged helium ions. The obtained data will be imp lemented in STARK-B database. There is no neither theoretical, nor experimental data for Stark broadening of Ar VII spectral lines and we hope that the obtained results will be of interest. for a number of problems in astrophysical, laboratory, laser produced, inertial fusion and technological plasmas.
M SE Stark line widths, in some cases together with line shifts, of the following emitters are in STARK-B PHYSIC S database: Ag II, Al III, Al V, Ar II, Ar III, Ar IV, As II, Au II, B III, B IV, Be III, Bi II, Bi III, Br II, C III, C IV, C V, Cd II, Cd III, Cl III, Cl IV, Cl VI, Co II, Co III, Cu III, Cu IV, Eu II, Eu III, F III, F V, F VI, Fe II, Ga III, Ge III, Ge IV, I II, Kr II, Kr III, La II, La III, Lu III, Mg III, Mg IV, Mn II, Mn III, N II, N III, Na III, Na VI, Nb III, Ne III, Ne IV, Ne V, Ne VI, Ne VII, Ne VIII, O III, O IV, P III, P IV, P VI, Pt II, Ra II, S IV, Sb II, Sc II, Se II, Se III, Si IV, Si V, Si VI, Si XI, Sn III, Sr III, Ti II, Ti III, V II, V III, V IV, Xe II, Y II, Zn II, Zn III, Zr II and Zr III.
) J. Schroet. ) and the parasitic weed Striga hermonthica (Del. ) Benth. While some WCA farmers strive to intensify their production using fertilizer, many are unable to afford/access fertilizer, especially women. Therefore, identification of germplasm that can cope with low soil fertility and simultaneously produce stable yields is crucial. Kountche et al. (2013)  reported the existence of genetic variation for Striga resistance in pearl millet and demonstrated a significant response to recurrent selection in a Striga-resistant genepool. Further screening of genetic resources for Striga resistance is required so that promising materials can be integrated into the genepool to assure longer-term selection gains or adaptation of the genepool to further environments. The objectives of this study were therefore to: (1) extend passport data of different WCA accessions within the GCP pearl millet reference collection; (2) find sources of Striga resistance in pearl millet; (3) determine relationship patterns of pearl millet accessions in WCA.
The respective significance levels were Bonferroni-adjusted. The Additive Main Effect and Multiplicative Interaction (AMMI) analysis was performed based on lattice-adjusted entry means per location for grain yield, in order to assess patterns of genotype by environment interaction. The principal component analysis (PCA) was based on centred adjusted means across environments of all above-mentioned traits having heritabilities higher than 70% to avoid strong environmental bias on the PCA. All calculations were performed within the R-environment v. 2. 14. 2 and v. 3. 2. 3 (R Development Core Team, 2012). Mixed model analysis was conducted using the ASReml v. 3. 0 package for the R-environment  (Butler et al. , 2009)  and the AMMI was calculated using the agricolae package v. 1. 2-8 (de Mendiburu, 2016).
Acknowledgements The financial support from the  Global Crop Diversity Trust , Trust grant no:  GS08024 , is gratefully acknowledged. HD  Upadhyaya (ICRISAT-India ) kindly provided the seed from the  ICRISAT genebank in Patancheru, India. In addition, the authors would like to thank the following individuals for technical assistance during seed multiplication and accession characterization:  A Abarchi ,  H Adamaou ,  D Lankoande ,  T Boye ,  A Boubacar  ( ICRISAT-Niger ),  S Muhtah  ( LCRI-Nigeria ),  M Mariko  ( IER-Mali ). The Ph. D. stipend for FT Sattler was funded by the  German Ministry of Economic Cooperation and Development (BMZ ,  GIZ  Project No:  13. 1432. 7-001. 00and Contract No:  81170266 ). Finally, we thank the  McKnight Foundation Collaborative Crop Research Program  for the discretionary research funds provided to  BIG Haussmann.
luchuensis, A. aculeatus, A. brasiliensis, A. carbonarius, A. niger, A. tubingensis). Additional species from the section Nidulantes were included because of the high divergence of the genome sequence of A. nidulans from the other Aspergillus genomes, and A. sydowii because of its marine life-style and being a pathogen of Gorgonian corals  [17]. We demonstrate that this combined set of genomes provides a highly valuable dataset for comparative and functional genomics. This study was performed as a global consortium effort with different researchers addressing different topics as subgroups of the consortium. Where possible, experimental data were generated to examine inferences from the genomic differences and to provide an unprecedented comparative analysis of variation and functional specialization within a fungal genus.
General comparison of the genomes and phylogenomics The genome sequences were generated at the Joint Genome Institute (JGI) and compared to other fungal genomes in JGI's MycoCosm database  [15]  with respect to genome size, structure, and gene content (Table  1 , Fig. 1 , Additional file 1). All Aspergillus genomes are similar in size  (29) (30) (31) (32) (33) (34) (35) (36)  and GC content (48-53%), while smaller genomes are present in the Onygenales (Table  1 , Additional file 1A). The number of predicted genes in the aspergilli is in the range of 9113-13,553, which is generally higher than that observed for the Onygenales (<10,000; Additional file 1B). Larger differences were found for the repetitive portion of the genome, which is in the range of 100 kb to 1. 3 Mb for the aspergilli. Genomes of the Aspergillaceae have an average repeat content of 2-3% (Additional file 1C).
This comparison was based on the family (and subfamily where possible) designations set by Table  2  Gene numbers related to degradation of different plant-based polysaccharides detected in genomes of selected ascomycete fungi Only families that can be assigned to a specific polysaccharide, based on currently available data, are included. Highest numbers for any of the polysaccharides are highlighted in green the CAZy database (www. cazy. org) [59]. Exceptions to this were, for example, the lower number of (hemi-) cellulases for A. carbonarius compared to other species of the section Nigri and that A. versicolor is particularly enriched in inulinases compared to other species of the section Nidulantes (Table  2 ). Interestingly, the two most established enzyme producers of this genus, A. niger and A.
Penicillium). When comparing specific enzymes, highly variable gene numbers were observed for GH5_7 and GH26 endomannanases, GH32 endoinulinases, GH28 endorhamnogalacturonases, GH43 α-L-arabinofuranosidases, and GH115 α-glucuronidases (Additional file 11). Only some species contain genes encoding CE15 glucuronoyl esterases, GH45 endoglucanases, and PL11 rhamnogalacturonan lyases. While most of the feruloyl esterase encoding genes are included in the CAZy database, comparison of genes encoding these enzymes also revealed high variability, confirming the considerable diversity in feruloyl esterase activities recently reported for a smaller set of aspergilli  [60]. This diversity is in part due to the absence of specific orthologs in some species, resulting in differences in the overall gene set per species (Additional file 11).
These substrates provide useful comparative datasets because both contain cellulose, but differ in that wheat bran is rich in xylan, while sugar beet pulp contains large amounts of pectin and xyloglucan. The results demonstrated a large variation in enzyme production between the aspergilli, even for species within the same section (Fig. 5 ). Most species produced predominantly celluloseactive and xylan-active enzymes during growth on wheat bran, but the ratio between these two enzyme groups varied strongly in some species (Fig. 5a ). Some unusual profiles were also observed, such as the high relative amount of different pectinases produced by A. fischeri, the low amount of xylanases for A. carbonarius, and the high amount of amylases for A. oryzae. On sugar beet pulp, nearly all species produced high amounts of pectinases, appropriate to the composition of the substrate (Fig.
CYPomes and supporting cytochrome P450 reductases CYP genes are also of interest for secondary metabolism. More than 2300 CYP sequences (PF00067) were identified in the 19 aspergilli examined (data not shown). These numbers are comparable with analysis of the CYPome for A. nidulans in the previous CADRE genome annotation  [124]. Some of the CYP families were further analyzed in the present study relating to their possible use as antifungals and roles in fungal secondary metabolism, namely: CYP51 (sterol 14α-demethylase)  [125] , CYP61 (22-sterol desaturase)  [126] , CYP56 (dityrosine synthesis)  [127] , CYP53 (benzoate 4-hydroxylase)  [128] , CYP630 (oleic acid ω-hydroxylase)  [129] , Ppo (fatty acid oxygenases or oxylipin generating dioxygenases)  [130] , and a supporting enzyme CYP reductase (CPR) as well as CYP505, CYP-CPR fusion protein (fatty acid hydroxylase)  [131].
The aspergilli were also exposed to selected antimycotics that interfere with cell wall (caspofungin) and cell membrane (voriconazole, amphotericin B) composition and structures. Stress sensitivities were tested in both liquid and surface cultures and the complete stress dataset with photographs is also available in the Fungal Stress Database  [133]. A more extensive description of the data can be found in Additional files 3, 15, 16, and 17.
Abiotic and biotic stress determinants were colored in green for resistance (r), red for sensitive (s), no color for insensitive (-) and not determined (nd) different abiotic and biotic stressors: 2,4-diacetylphloroglucinol (DAPG), Hydrogen peroxide (OX), nitric oxide (NO), calcofluor white (CFW), caspofungin (CA), voriconazole (VO), amphotericin B (AmB), and Aspergillus-Pseudomonas fluorescens co-cultivation (Biotic). Species used in this study are framed in black. b Qualitative descriptive statistics of the exoproteome of four selected Aspergillus strains. Exoproteomes were enriched from filtered culture supernatants (Aspergillus MM, 30 °C) and analyzed by shotgun proteomics (LC-MS). Proteins were identified using draft genomic databases of the respective strains. For prediction of subcellular localization of the proteins the programs "SignaIP" and "WoLF PSORT" were used.
Heavy metal stress Cd(II) tolerance was clearly correlated with the presence of a Pca1-type Cd(II) transporter in the studied genomes (Fig. 9 , Additional files 3 and 15). Not surprisingly, A. sydowii, A. fumigatus, A. wentii, A. versicolor, and A. terreus, all harboring a Pca1 ortholog, were the most tolerant to Cd(II). Interestingly, the important opportunistic human pathogen A. fumigatus also possessed high Cd(II) tolerance, but it was remarkably sensitive to oxidative, Photographs on the tress tolerance/sensitivity phenotypes were taken from the Fungal Stress Database (http://www. fung-stress. org/). Further details of the phylogenetic and evolutionary calculations and more information on the stress response proteins can be found in Additional file 15 osmotic, and cell wall integrity stress when compared to other aspergilli (Fig.
The Aspergillus sugar transportome Sugars are not only important nutrients and structural precursors but are also signaling molecules that trigger changes in gene regulation. Sugar transport and sensing are therefore crucial functions in fungal cells and considerable progress in understanding the molecular bases of these processes has been made in studies on yeast  [137]. Most sugar transporters belong to the Major Facilitator Superfamily (MFS)  [138, 139]  of secondary transporters (secondary active transporters and facilitators), which currently comprises 82 named classes  [140, 141]. In order to investigate the sugar transporter complements of diverse filamentous fungi, we focused on those proteins from Aspergillus species that were classified as members of the PFAM family of "Sugar and other transporters" (PF00083).
Most, but not all, members of this family catalyze sugar transport, with their substrates including hexoses, pentoses, disaccharides, alpha-glucosides, inositol, and quinic acid as well as organic and inorganic ions. Using the annotated genome data from eight previously available Aspergillus species, a total of 940 putative PF00083 transporters were identified after manual filtering and reannoation to yield greater consensus to the conserved 12 transmembrane domain topology. From phylogenetic analysis, the vast majority of sequences were found to fall into eight major clades (A to H; Fig. 10 ). Although support for the very basal branches is generally low, the subclades within them have high bootstrap support. This initial analysis was extended to include an additional 1422 sequences corresponding to the PF00083 complements of the novel Aspergillus genomes, along with those of two fungi for which experimental evidence of function has been obtained for a number of PF00083 members: N.
crassa (Sordariomycete) and S. cerevisiae. For comparison, the complements of P. chrysogenum, T. marneffei, T. reesei, and two animal pathogens (Histoplasma capsulatum and Microsporum canis) were also included. Additional files 20 and 21 present a summary and details, respectively, of the numbers and distribution of PF00083 members found in each of the clades and the function(s) identified for some members of those clades. Considerable diversity in the numbers of PF00083 members was found, with a threefold difference existing between those species having the largest and smallest complements (A. sydowii vs. A. clavatus). Even species from the same sections do not have very similar numbers of loci, for example: members of the Nigri range from 83 to 113. Of the Nidulanti, A. sydowii  (180)  and A.
versicolor  (171)  have the highest numbers of loci whereas the number present in A. nidulans  (113)  is similar to that observed in A. niger and P. chrysogenum. The three genome-sequenced A. niger strains (consisting of an enzyme producer, a citric acid producer, and a laboratory strain) all have similar PF00083 complements. Perhaps surprisingly, T. reesei (Sordariomycete), a wellknown producer of plant cell wall degrading enzymes, only has 64 members. Interestingly, A. clavatus has a very similar complement size (63) but there is a notable enrichment of T. reesei members in clade D and a reduction in clade G compared to A. clavatus. A. terreus (123) and A. wentii (120) also have similar numbers of PF00083 members but clade D is threefold enriched in the former, while A. wentii, along with A.
NrtA displays low affinity, high capacity while NrtB is a high affinity, low capacity protein. In species with a single NNP, the protein more closely resembles NrtA rather than NrtB using a Smith-Waterman algorithm for pairwise comparison (Additional file 25). Nitrite transport can also be performed by proteins of the formate-nitrite transporter (FNT) family (PF01226; TCDB 2. A. 44)  [140]. Bioinformatic analysis in the present study revealed that the majority of the aspergilli contain one gene encoding an FNT family protein, but a few species (A. flavus, A. oryzae, A. sidowii, A. fischeri, and A. versicolor) have two such genes. In A. nidulans, the FNT protein NitA has been shown to be a high affinity but low capacity nitrite transporter compared to NNP proteins and is expressed under the same conditions as the NNP proteins  [191].
The aspergilli also contain one to six members of the proton-dependent oligopeptide transporter (PTR) family (PF00854: TCDB 2. A. 17)  [140]  that includes the Arabidopsis thaliana nitrate transporter, Nrt1. 1 (CHL1). However, absence of the nitrate binding residues identified in Nrt1. 1 (H356 and Y388)  [192, 193]  implies that nitrate transport by PTR family proteins in the aspergilli is unlikely. The aspergilli usually possess three or four members of the ammonium transporter/methylammonium permease/ Rhesus factor family (AMT/MEP/Rh family) (PF00909;   [194]. MepA and MepB display higher substrate affinity than MeaA  [194]. The inorganic nitrogen transport capability emphasized by the present study most likely reflects the natural habitat of Aspergillus species in the soil where nitrate, nitrite, and ammonium form the principle inorganic salts of nitrogen.
All Aspergillus genomes also contain a highly conserved H2B histone. As previously reported  [238] , in the aspergilli, as in other ascomycetes, the extant H2A histone corresponds to the H2AX variant, rather than the canonical H2A form. This was confirmed for all aspergilli in the databases, where the diagnostic motif SQEL is conserved at the carboxy terminus. In all aspergilli, as reported previously for A. nidulans and N. crassa  [239, 240] , the H2AX gene is transcribed divergently from the H2B gene. In addition, all analyzed Aspergillus genomes have an H2A. Z ortholog. While nothing is known about the role of this histone in any of the Pezizomycotina, the cognate gene is not essential in N. crassa, S. cerevisiae, or S. pombe  [241, 242]. 0 0 0 FungiJGICTBE1792 AN6339 Pkinase Pod6 0 0 0 0 FungiJGICTBE4614 AN1560 Pkinase PlkA 0 0 0 0 FungiJGICTBE4599 AN4717 Pkinase PkaB 0 0 0 0 FungiJGICTBE3865 AN0822 Pkinase KfsA 0 0 0 0 FungiJGICTBE1403 AN4936 Pkinase Prp4 0 0 0 1 FungiJGICTBE4249 AN7537 Pkinase Ppk33 0 0 0 1 FungiJGICTBE2868 AN3101 HiskA PhkB 0 0 0 1 FungiJGICTBE1795 AN3102 HisKA PhkA 0 0 0 2 FungiJGICTBE4245 AN6044 Pkinase NpkA 0 0 0 1 FungiJGICTBE4202 AN3065 Pkinase CmkB 0 0 0 1 FungiJGICTBE3486 AN0699 Pkinase Cak1 0 0 0 1 FungiJGICTBE668 AN4479 HisKA NikA 2 0 0 0 FungiJGICTBE164 AN11032 Pkinase SepL 1 0 0 1 FungiJGICTBE3739 AN4385 Pkinase SepH 1 0 0 1 FungiJGICTBE3573 AN10800 BCDHK_Adom3 pkpA 1 0 0 1 FungiJGICTBE2991 AN11101 Pkinase Gin4 1 0 0 1 FungiJGICTBE3299 AN5728 Pkinase Stk22 0 1 1 1 FungiJGICTBE2936 AN10515 Pkinase PrK1 0 1 1 1 FungiJGICTBE598 AN1665 Pkinase stk51 1 1 1 0 FungiJGICTBE878 AN2412 Pkinase CmkA 1 2 2 0 FungiJGICTBE3318 AN7563 Pkinase ChkC 1 1 1 0 All analyzed genomes contain an H3 gene, with complete amino acid sequence conservation for all species.
Genome annotation Genome assembly scaffolds were masked with Repeat-Masker  [257] , RepBase library repeats  [258] , and repeats frequently detected (>150 times) by RepeatScout  [259]. A combination of gene predictors was run on the masked assemblies: ab initio Fgenesh  [260]  and Gene-Mark  [261] ; homology-based Fgenesh +  [260]  and Genewise  [262]  seeded by BLASTx  [263]  alignments against the NCBI NR database; and transcriptome-based assemblies. In addition to protein-coding genes, tRNAs were predicted using tRNAscan-SE  [264]. Predicted proteins were functionally annotated using SignalP  [265]  to predict signal peptides, TMHMM  [266]  for transmembrane domains, InterProScan  [267]  for protein domains, and protein alignments to NCBI NR, SwissProt, KEGG  [268]  for metabolic pathways, and KOG  [269]  for eukaryotic clusters of orthologs.
Pfam domains  [272]  were assigned using v. 22 of the database and a TimeLogic Decypher machine and an evalue of 10 -4. The phylogenetic tree was generated from a separate cluster run which differed from the previous one by the addition of non-ascomycete outgroups Rhizopus oryzae and Batrachochytrium dendrobatidis (JGI portals Rhior3 and Batde5, respectively; omitted from Fig. 1 ). Protein sequences with single copies in each organism were aligned using MAFFT  [273] , trimmed to well-aligned regions using Gblocks  [274]  with default parameters. RAxML  [275]  was then run using the PROTMIXWAG model and B. dendrobatidis set as an outgroup to generate the phylogenetic tree shown in Fig. 1. All branches received maximum support, except for that between A. niger strains NRRL2 and ATCC 1015 (73%; Fig.
Genes involved in asexual and sexual reproduction The annotation pipeline of putative genes involved in asexual and sexual sporulation involved a two-step procedure of identification and annotation. Genes previously identified from A. nidulans were used as references (Additional files 2 and 5). The identification step of asexual genes was performed by searching the reference protein sequences against Aspergillus overall protein sequences with phmmer of HMMER 3. 0 (http:// hmmer. org/). Hits that passed MSV, Bias, Vit, and Fwd filters (see HMMER User's Guide, http://eddylab. org/) were then subject to annotation involving BlastP comparisons against the database of non-redundant protein sequences  [277]  (Additional file 2). The identification of sexual genes was performed by searching protein databases available at the JGI (http://genome.