dataesr/scientific-paragraphs-categorization huggingface dataset:
cerevisiae gene ortholog, Gene Ontology (GO), KEGG pathway, and pfam domain, from FungiDB database  [116]. GO enrichment and KEGG pathway enrichment for these genes were then assessed by using Fisher's Exact Test with multiple testing correction of FDR threshold at 5%. Furthermore, conserved domains of each C. neoformans gene were assessed by using the amino acid sequence to search the NCBI Conserved Domain Database (CDD)  [117]. The presence of signal peptide sites was predicted by SignalP 4. 1  [118].
Acknowledgments We would like to acknowledge  Christina A. Cuomo ,  Laurent Cha  ˆtre,  Guilhem Janbon ,  Jean-Yves Coppe  ´e,  Pierre Rocheteau ,  Pierre Henri Commere ,  Christine Schmitt ,  Olivier Gorgette ,  Jacomine Krijnse-Locker ,  Caroline Proux  for their comments on this work and help at different steps of the study.
Within the vast diverse genus Moraxella, at least some species have been shown to be pathogenic, such as M. bovis  59, 60. Also, M. porci and M. plurianimalium that have been isolated from systemic lesions  61, 62. For other OTUs, there is controversial data indicating either beneficial or detrimental roles in the pig GIT, which may depend on the specific strain. In this study, we described many relevant members from the microbiota composition of ASF-resistant animals that deserve further characterization. Nevertheless, it should be stated that, due to both the limitations of the technology used and the scarcity of data related to wild species available in the databases, the complete structure of the GIT microbiota has not been defined, since many taxa remained unidentified especially in warthogs.
Raw sequences were initially filtered to keep only the high-quality-reads (Q > 25). Paired-end joining was done by using fastq join  67, 68  under default values. Classification of taxonomic abundances was done as previously reported  66  using the Quantitative insights into microbial ecology  69  (QIIME) software package (version 1. 9). Sequences were clustered into OTUs at 97% similarity using UCLUST algorithm  70  and the Greengenes database (version 13. 8)  71. Chimeric detection and removal was done with USEARCH 6. 1  70, 72  against the ChimeraSlayer reference database  73. Taxonomic assignment was done with Naïve Bayes classification against RDP database  74  (Release 11, 2016). For each taxon, the Kruskal Wallis test was perform to compare OTU frequencies in sample groups and to ascertain whether or not there are statistically significant differences between the OTU abundance in the different sample groups, P values were FDR-corrected for multiple hypotheses testing.
Single rarefaction was done at 21,134 sequences/sample for alpha-diversity analysis. Alpha diversity and richness estimation through Shannon-Wiener and Chao1 indices respectively, were calculated on rarefied data using alpha_diversity. py script from QIIME. Alpha diversity between groups was compared through two-sample non-parametric t-tests (Monte Carlo method) at maximum depth in rarefied samples (with 999 permutations). UniFrac weighted and unweigthed distances were calculated to assess significant differences across samples  75, 76. Testing the significant differences between grouped samples was done with compare_categories. py script from QIIME  75  using ANOSIM as the method  77. The R statistic is calculated by ANOSIM, where an R value near +1 means that there is dissimilarity between the groups, while an R value near 0 indicates no significant dissimilarity between the groups.
Samples were considered to be significantly different when the accompanying P value was < 0. 05. Distance-based redundancy analysis was done to estimate the strength and statistical significance of sample groupings using Euclidean distance matrices. Functional prediction of the microbiome were done using 16S rRNA gene sequence data using the platform NEPHELE (vs 2. 2. 2) 78 which integrates the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States PICRUSt (vs 1. 1. 3)  24. The closed reference OTU picking approach was done against Greengenes database (vs 13. 8). Then, metagenome predicted functions were inferred using the Kyoto encyclopedia of Genes and Genomes (KEGG)  79  database. Finally, the Statistical Analysis of Taxonomic and Functional Profiles software  25  (STAMP vs 2.
Acknowledgements This study was supported by the  Ministry of Economy and Competitiveness (MINECO)  from the  Spanish Government  (grant number  AGL2016-78160-C2-1-R ). The authors are also grateful to the  Centres de Recerca de Catalunya  ( CERCA) Programme and Global Alliance  for Research on African swine fever (GARA). The authors thank  Frederic Paboeuf  and  Audrey Fougeroux  for providing SPF and domestic pig samples.
By connecting the centers of these dodecagons, a triangle-square-rhomb tiling is formed on next-larger scale as emphasized in black in Figure  6b. Recently, a systematic statistical analysis of the BaTiO 3derived OQC tiling has been conducted for a system of 8100 vertex positions as determined from an atomically resolved large-scale STM image. [21] In this issue, Zollner et al. repeat this analysis for an even larger dataset of almost 20 000 vertices. [24] he comparison of both systems to the statistics of the ideal NGT regarding tiling element ratio and frequency of shared edges clearly reveals that the OQC is the first realization of this ideal dodecagonal model system. To the best of our knowledge, these are the largest real space datasets of any quasicrystal analyzed so far. First of all, an almost perfect match of the OQC tiling element ratios of 2.
Additionally, close to the first-order spots of the Pt substrate pairs of spots occur in twelve directions. In addition to this characteristic dodecagonal pattern, the sixfold symmetry of the substrate leads to a backfolding of intense QC spots from neighboring surface Brillouin zones of the hexagonal substrate, as has been explained in great detail recently. [24] he backfolding at the substrate superimposes a sixfold symmetric pattern to the original dodecagonal diffraction of the OQC. An example for a backfolded spot, which is extraordinary intense at the given electron kinetic energy, is marked by the black open circle in Figure  9a. An alternative approach to the momentum space can be obtained from FT of atomically resolved STM images as shown in Figure  9b  for a dataset with 20 000 atomic positions.
Acknowledgements The authors are grateful to  Tom Ishimasa  for fruitful discussions. The authors thank  Jürgen Kirschner  for supporting the momentum microscope experiments and  Katayoon Mohseni  and  Holger L. Meyerheim  for our fruitful collaboration related to the σ-phase approximant. The authors acknowledge the  Paul Scherrer Institut, Villigen, Switzerland  for the provision of synchrotron radiation beamtime at the PEARL beamline of the SLS. The authors also thank  Alessandro Coati  and the staff of the  SIXS beamline at the SOLEIL Synchrotron  for their assistance during the experiments. Funding by the  Deutsche Forschungsgemeinschaft  through the collaborative research center SFB 762 ( Functionality of Oxide Interfaces , projects  A3 ,  A7  and  B8 ) is acknowledged.
Search Strategy Two independent reviewers conducted a systematic review of related articles published between 1991 and 2015 in the MEDLINE (via PubMed), Cochrane Library, EBSCO, Web of Science and Scopus databases. This systematic review respected the PRISMA standard. The following keywords were used for the search: "temporalis muscle flap donor site" or "temporalis muscle flap morbidity" or "temporalis muscle flap complication" or "temporalis muscle flap hollowing" in combination with "reconstruction" or "rehabilitation" or "management" or "restoration". Gray literature was also searched.
1. Introduction 1. 1. On the need for novel, efficient and fast approaches to single-crystal X-ray diffraction data analysis The availability of faster detectors and brighter laboratory and large-scale facility X-ray sources allows the collection of a full single-crystal X-ray diffraction (XRD) data set within seconds (or even less) to minutes and the automatic collection of data sets (especially in the macromolecular crystallography field) increases dramatically the number of collected crystal structure data sets. In situ single-crystal diffraction will become far more common in the next few years, just as in situ powder diffraction has become more widespread in recent decades. Moreover, the novel approach named 'serial crystallography' at X-ray free-electron laser (XFEL) facilities will further increase the amount of data and crystal structure data sets to be analysed  (Standfuss & Spence, 2017 and references therein).
Experimental and data analysis details 3. 1. The in situ single-crystal diffraction experiment A suitable crystal of Y zeolite for single-crystal measurements was obtained by the hydrothermal method described in the literature  (Warzywoda et al. , 1999; Ferchiche et al. , 2001)  and detailed in the supporting information. The experiments were performed at the ESRF Synchrotron in Grenoble, on the Swiss-Norwegian Beam Line (SNBL; BM01A) equipped with a 2D Pilatus 2M detector. The single crystal was mounted on a 0. 2 mm capillary and sealed inside a 0. 3 mm capillary. The external capillary was connected to a gas line and a vacuum pump and left at 300 K under vacuum for the outgassing. One first data set, to test the sample and to be used as a reference, was collected under vacuum at 300 K. CO 2 was injected into the capillary to a pressure of 10 kPa and the temperature was lowered to 200 K, while measuring a complete single-crystal data set (360 ' scan with an exposure time of 2 s per frame) every 2 K.
Details of the data analysis Data reduction was performed using CrysAlisPro (Rigaku, Oxford Diffraction). The data were treated at first by SHELXL  (Sheldrick, 2015)  with the SHELXLE interface (Hu ¨bschle et al. , 2011) and then by TOPAS-Academic  (Coelho, 2012)  in single-crystal diffraction batch analysis mode. Simulated hkl files were generated by TOPAS giving the desired occupancies (from linear or polynomial fitting or from the refined data) as input to the structure model and obtaining the calculated profile as output. PCA of simulated, experimental data and refined occupancy values was carried out by RootProf  (Caliandro & Belviso, 2014). Graphics were generated using CCDC Mercury  (Macrae et al. , 2008)  and Vesta  (Momma & Izumi, 2011). The 200 and 300 K refined crystal structures are given as CIFs in the supporting information.
Location of extra-framework species Individual structural refinement of in situ single-crystal data was performed using SHELX. The data revealed that the cell parameters increased while decreasing the temperature, as CO 2 was filling the pores (Fig. S1 ). The crystal structure was at first solved and refined from three data sets, collected at the temperatures 200, 254 and 300 K, revealing that not only CO 2 , but also Na + ions and a residual water molecule moved noticeably during the experiment. The refinement of all the atom positions of Na + , CO 2 and water is difficult to handle in a consistent way for all the data sets. Such a refinement became unstable in batch mode and the resulting atomic shifts were difficult or impossible to interpret. A structural model was then built from the SHELX refinement of one data set out of five (every 10 K), considering all positions of CO 2 , water and Na + atoms found from manual structure solution.
4. 3. 1. PCA directly applied to X-ray simulated and experimental diffraction data. Besides the above-described manual (SHELX) and automatic (TOPAS TA) traditional approaches of structure refinement and analysis, the efficiency of multivariate methods in following structural changes occurring during in situ experiments  (Palin et al. , 2015 (Palin et al. , , 2016) )  was exploited by applying PCA to these single-crystal data. As a preliminary test, since the application of PCA to in situ single-crystal XRD data is unprecedented, a simulated experiment was carried out and analysed by PCA (case 1 as defined in Section 2) as described for polycrystalline samples by  Palin et al. (2015 Palin et al. ( , 2016)). Simulated hkl data were generated starting from the refined structure and the occupancies of a subset of atoms varied.
As a first consideration, it must be pointed out that PCA can be used to estimate the quality of the data when tracking the changes occurring during an in situ experiment. In fact, as widely discussed by  Palin et al. (2015)  for powder diffraction data, the PC1 versus PC2 scores must show a parabolic behaviour if an occupancy variation is observed within a data set. This is clearly also visible in simulated data (Figs. S5-S8C ) for the single-crystal case. This trend is still observable in the more complex simulation, close to real data (Fig. S12 ), and is also observed in the real data (Fig. S13 ), even if the experimental errors induce deviations from the theoretical behaviour. The presence of such parabolic trends ensures a good quality of the data and a correct recovery of the dynamic trends within the in situ XRD data set.
PCA was then used in a different way to obtain insight into relationships between atom occupancy changes during CO 2 adsorption. In this case, PCA was performed using the results from the structure refinement as the input data matrix. In particular, CO 2 , Na + and water occupancy changes were analysed by PCA and the common trends obtained. PCA clearly indicates which atoms change their occupancies in a synchronized way. This information was obtained from the plots shown in Fig. 4. Finally, the possibility to exploit an in situ single-crystal experiment to obtain thermodynamic data by a structure-free PCA-based approach was also demonstrated. PCA was able to estimate, with a large discrepancy with respect to the traditional approach, the enthalpy and entropy of adsorption, but the trend was obtained and the order of magnitude investigated.
ExAC Database Searches Version 0. 3. 1 ExAC was queried for 'EMD' and 'BANF1' via the ExAC Browser (Beta), selecting only for variants that passed the quality assurance filter  (Lek et al. , 2016; ExAC database, 2018). The data was narrowed to include in-frame deletions and missense, synonymous and splice variants, and exported in the. csv file format. To identify variants associated with broadly defined 'psychiatric disease' (schizophrenia, bipolar disorder, Tourette syndrome), we compared two variant call files from ExAC version 0. 3. The first file  (ExAC. r0. 3. sites. vep. vcf ) contained all ExAC variants; the second file (ExAC. r0. 3. nonpsych. sites. vcf) contained all variants not found in the psychiatric cohort. By subtracting the 'nonpsych' variants from the full cohort using the UNIX 'comm' tool in a shell script, further selecting for emerin and BANF1 alleles, and manually curating the final list to handle the consequences of minimal and multi-allelic representation, we were able to generate a file of emerin and BANF1 variants unique to the psychiatric cohort.
Type 2 Diabetes (T2D) Knowledge Portal Since we were unable to determine the clinical status of variant carriers in ExAC, we searched the open-access Type 2 Diabetes Knowledge Portal for EMD variants. ' Associations across all datasets' for each ExAC EMD variant were obtained using the variant ID or reference SNP ID (rs) as the search term in the T2D portal 1. The only protein-coding variant with significant and 'high-impact' associations (p. D149H) was identified solely within the 13K Exome Sequence Analysis cohort, which includes samples from 13,007 individuals (half with T2D, half controls) representing five ancestries: European (∼5,000 individuals), African-American, East Asian, South Asian and Hispanic (∼2,000 individuals each). All p. D149H-related statistics come from the analysis reported in the T2D Knowledge Portal.
D57Y are non-conservative substitutions visible on the 'front' and 'side' surfaces (Figure  1 ); the role of the 'front' surface is unclear, but may influence binding to histones (Montes de  Oca et al. , 2005). ExAC variants p. H7Y, p. D9N, p. D9H and p. S22R probably affect the integrity of the 'top' surface, but are different from residues (e. g. , V11, A12, P14, E83, D86, A87, F88) that contact the Ig-fold of lamins A/C  (Samson et al. , 2018). Variants p. D9N and p. D9H are potentially more disruptive than an in vitro mutation (p. D9A) that reduced FIGURE 1 | Wildtype BANF1 residues affected by novel ExAC missense variants. Crystal structure  (Umland et al. , 2000)  of the BANF1 dimer showing wildtype residues affected by novel ExAC variants (RCSB Protein Data Bank ID: 1CI4). 'Top' and 'Bottom' views were obtained by rotating the 'Front view' 90 • along its long axis out of, and into, the page respectively.
Three Emerin ExAC Variants (p. K37del, p. R203H, p. D149H) Reported in Laminopathy Database We anticipated little overlap with the laminopathy database, since severe childhood-onset disorders such as EDMD1 are excluded from ExAC  (Lek et al. , 2016). Indeed, only three emerin ExAC variants were previously reported in the laminopathy database: p. K37del, p. R203H and p. D149H. Variant p. K37del causes the loss-of-function EDMD1 phenotype even though the resulting protein is expressed at normal or near-normal levels  (Ellis et al. , 1998). Loss of residue K37 destabilizes the LEM-domain  (Samson et al. , 2017) , reduces affinity for BANF1  (Essawy et al. , personal communication)  and accelerates the rate of emerin self-association as filaments in vitro  (Samson et al. , 2017). Finding two individuals with p.
R221L) were identified at frequencies of 10-83 per 10,000 individuals in specific populations. Note that these frequencies may be skewed by multiple factors including distribution artifacts and potential association with disorders (e. g. , cardiomyopathy or type 2 diabetes) that are enriched in ExAC. Variant p. D149H, Identified in 0. 8297% of East Asians, Associates With a 'Healthy' Lipid Profile Variant p. D149H was identified 58 times in ExAC with allele frequencies of 0. 0665% overall and 0. 8297% in East Asians (Table  2  and Supplementary Table  S2 ). For most disorders, ExAC does not link individuals to clinical phenotypes. However, ExAC includes type 2 diabetes cohorts for whom clinical data is accessible via the T2D Knowledge Portal 6. To determine if any variants associated with a metabolic phenotype(s), we searched within 100 kb of EMD (see Materials and Methods).
G28A, p. E35K, p. F39del) Four LEM-domain variants were identified in ExAC: the cardiomyopathy-associated variant p. K37del (discussed above) and three unique novel variants: p. G28A, p. E35K and p. K39del (Supplementary Table  S2 ). WT LEM-domain residues affected by ExAC variants are depicted via surface view (Figure  3A ) and ribbon diagram (Figure  3B ; PDB code 2ODC;  Cai et al. , 2007). All four WT residues are surface-exposed (Figure  3A ) on the large α-helix (Figure  3B ). Residues K35, K37, and F39 each stabilize the α-helix (Figure  3C ). Deletion of residue K37 destabilizes the LEM-domain  (Samson et al. , 2017) ; we therefore expect the same or worse for variant p. F39del, which is predicted to enhance disorder more than p. K37del (Figure  4 ). Missense variant p. E35K, which replaces a negative charge (Glu) with a positive charge (Lys), is predicted to increase disorder for residues 21-29 and 35-49 (Figure  4 ) and thus has the potential to destabilize the LEM-domain.
Variant p. G28A is positioned to alter the 'left' surface of the LEM-domain (as viewed in Figure  3A ). A more-disruptive substitution, p. G28R, is reported for three individuals in the dbSNP database (Praxis fuer Humangenetik Teubingen; ID: rs1064797380). The functions of this surface are unclear, since BANF1 binds the other ('right') side (as viewed in Figure  3A ) via direct contact with LEMdomain residues Y34 (colored yellow; Figures  3A, B ), T13, G24, P25, V27, S29, T30, L33, K36, and K37  (Cai et al. , 2007; Samson et al. , 2018). Intriguingly, this 'mystery' surface of the LEMdomain includes α-helical residues Y34 through R47, which together with disordered residues L48-S53 comprise the 'LEMpeptide, ' which mediates emerin self-association (Figure  2B ) and is largely protease-resistant in the context of emerin filaments (Figure  2D ;  Samson et al.
Methods This was an ecological study. The CC mortality rates were estimated through the national mortality database from the Health Surveillance Department of the Ministry of Health. 13 opulation data were obtained from the national census and from projections from the Brazilian Institute of Geography and Statistics (IBGE, in the Portuguese acronym). 14 The mortality rates were corrected in order to reduce the deficiencies in the quality of the reports. The indicators used reflected aspects of health care and socioeconomic development. All of the recorded deaths from 2008 to 2012 were analyzed. Mortality data in Brazil are based on death certificates using the International Classifications of Disease (ICD-10) codes. Age-specific mortality rates by federal units were calculated using as numerator the number of deaths, and as denominator the population for the same age, federal unit, and year.
Palavras-chave ► câncer do colo do útero ► mortalidade por câncer ► fertilidade ► índice de desenvolvimento humano ► disparidades em saúde Data on Human Development Index (HDI) were obtained from the 2000 report of the Brazilian office of the United Nations Development Program (UNDP-Brazil). 10  This index is a composite factor that combines life expectancy, education, and per capita income. A high HDI is associated with a better human development level. The UNDP-Brazil estimates the HDI by municipalities, by federal units, and by regions. Data on the availability of radiotherapy centers were used as a measure of access to cancer care and were obtained from the Brazilian National Commission of Nuclear Energy (CNEN, in the Portuguese acronym). 16 Data were available only for the year of the analysis (2015).
The coefficients from the regression model and their 95% confidence intervals (CIs) were used to determine the direction of the effect for each factor on the CC mortality rates. A negative coefficient indicated a decreasing effect, whereas a positive coefficient meant an increasing effect on the mortality rates. The effects were concluded at a level of significance of 5%. In the main multivariate regression analysis, factors with a p-value < 0. 05 were included. We have also performed a secondary analysis including only factors with a p-value 0. 02. All of the analyses were performed using the software Stata, Version 13. 0 (StataCorp LLC, College Station, TX, USA). The Institutional Review Board of the School of Medicine of the Universidade Estadual de Campinas (Unicamp, in the Portuguese acronym), Campinas, state of São Paulo, Brazil, approved the present study as part of a research project evaluating the CC control program in Brazil, in collaboration with the International Agency for Research on Cancer (IARC).
22 The HDI was negatively associated with mortality rates in the univariate analysis. The HDI is a broad index often used to predict the level of development of regions. 8, 10 A relation between low HDI and high CC incidence and mortality rates was demonstrated in a study that analyzed data from the GLOBOCAN database. 8 In our study, in the multivariate analysis, the HDI was not significantly associated with mortality rates. As low fertility is recognized as a consequence of socioeconomic development,  19  probably the role of the HDI on mortality rates is through affecting fertility, or even by enhancing screening programs. The indicators used to measure access to health care performed better in the South and Southeast regions, and were associated with mortality rates in the univariate analy-sis.