dataesr/scientific-paragraphs-categorization huggingface dataset:
Wall motion pattern was classified as apical ballooning (i. e. typical) or midventricular, basal, or focal type (all designated as atypical), respectively. 18 Left ventricular ejection fraction (LVEF) and left ventricular enddiastolic pressure (LVEDP) were evaluated if assessed. The registry adhered to the requirements of the respective local ethics committee (ClinicalTrials. gov number: NCT01947621).
Statistical analysis Continuous data are given as mean + SD, laboratory values are given as median (interquartile range), and categorical variables are expressed as numbers and percentages. Differences between groups were tested using the Pearson x 2 test or the Fisher's exact test for nominal data, or Mann-Whitney U test for continuous data. A post hoc analysis was performed for comparison between TTS types among 'happy' and 'broken' hearts. One-year survival analysis was performed using the Kaplan -Meier method, and the P-value was calculated with the log-rank test. Statistical analyses were performed using IBM SPSS Statistics, version 21. 0 (IBM Corp. , Armonk, NY, USA). Statistical significance was defined as two-sided P , 0. 05. Graph was compiled with Prism 6 (GraphPad, La Jolla, CA, USA).
Discussion Here, for the first time, we present a systematic analysis of patients diagnosed with TTS after joyful or socially desirable events, contrary to the well-established literature. Our data are derived from the largest study on TTS worldwide examining a plethora of characteristics of 1750 TTS cases, based on the most comprehensive available data thus far in the literature. 18 Interestingly, symptoms such as chest pain and dyspnoea as well as the baseline characteristics and clinical findings upon admission including cardiovascular risk factors and laboratory findings were similar between those with 'happy heart syndrome' and 'broken heart syndrome', respectively, regardless of the nature of the triggering event. It is now recognized that TTS predominantly affects postmenopausal women  20  and is often triggered by an emotional or a physical stressor.
Funding This work was supported by research grants from the  Mach-Gaensslen Foundation ,  Olten Heart Foundation ,  Prof. Otto-Beisheim-Foundation , and  Swiss Heart Foundation  (to C. T. ). J. R. G. has received a research grant from the  Olten Heart Foundation  and a research grant 'Filling the Gap' from the  University of Zurich. Funding to pay the Open Access publication charges for this article was provided by  Christian Templin, MD, PhD , FESC.
3 ] [10] [11] [12]  In a study from Malawi reporting data up to 2012, there was some preliminary evidence to suggest that while the declines in TB were most apparent in those with HIV infection, there might also be a decline in patients who were HIV-negative. 10 This led to suggestions that the overall decrease in HIV-associated TB in the community might have led to reduced community transmission of M. tuberculosis and thus fewer cases of TB in the non-HIV-infected population. We have now collected national aggregate data on the number of PLHIV and the number of ART and TB case notifications stratified by HIV status up to 2015, and we use these data to report on the correlation between ART scale-up and annual national TB case notifications in Malawi from 2005 to 2015, with particular interest in whether there have been changes in HIV-positive as well as HIV-negative TB.
Sources of data, variables and analysis Sources of aggregate data were the national annual reports from the NTP and the HIV Department, Ministry of Health, Malawi. With the changes in ART eligibility criteria during the study period, ART coverage was calculated using the total HIV-infected population as the denominator. This was derived from national epidemiological projections using the Estimation and Projection package and Spectrum software from the Joint United Nations Programme on AIDS (UNAIDS, Geneva, Switzerland)  19  and based on national population estimates obtained from the Malawi National Statistics Office (http://www. nsomalawi. mw/publications. html). Annual TB case notifications were documented and TB case rates per 100 000 popu-lation were derived from national population estimates.
, 2013)  (Fig. 2C ). Most PKC-θ was localized to quiescent regions. However, up to 10% of PKC-θ was tethered to permissive, transcriptionally active and enhancer regions. When cross-referenced to primary T cell microarray data, PKC-θ bound to 217, 122, and 144 transcriptionally upregulated genes in T EM , T AM or both T EM and T AM cells in the ex vivo primary human memory model (E-MEXP-2578; Fig. S1F-J ), and 223, and 316 upregulated genes in T RM , T AM or both T RM and T AM cells compared to naïve cells in another vaccinia-responsive memory CD4 + T cell model (Fig. S2A ; Fig. S2B-F ). PKC-θ localized to enhancers in intronic regions of highly transcribed genes, e. g. NFIC, SLAMF7, YWHAH, RUNX2 and HIVEP3, in ex vivo-bulk and vaccinia-responsive memory CD4 + T cells (Figs S1G,H, S2C,D), some of which represent transcription factors known to shape T cell development.
7A ). Substrate recognition was also heavily dependent on adjacent histone modifications. For example, butyrylation and propionylation at Lys21, Lys25, Lys28 and Lys29 generally promoted phosphorylation, but malonylation and succinylation at similar residues reduced phosphorylation. Furthermore, lysine methylation exerted status and positional effects on PKC-θ-mediated phosphorylation, such that monomethylation at lysine residues (e. g. Lys20, Lys21, Lys25, Lys28, Lys30, Lys32, Lys34 and Lys35) increased phosphorylation but trimethylated Lys21, Lys25, Lys28 and Lys34 discouraged phosphorylation (Fig. S4H ). Of the three serine residues on H2B:22, Ser32 appeared to be the important regulatory site, given that pre-phosphorylation of Ser32 significantly inhibited phosphorylation of Ser36 and Ser38 on variant H2B_FS (accession number P57053) and variants H2B_1, H2B_1K, H2B_1H, H2B_1M, H2B_1N, H2B_1D and H2B_1L (accession numbers P62807, O60814, Q93079, Q99879, Q99877, P58876 and Q99880, respectively; referred to as and H2B_1/1K/ 1H/1M/1N/1D/1L) (Fig.
Confocal microscopy Jurkat T cells overexpressing vector-only, wild-type PKC-θ or PKC-NLS plasmids were probed with rabbit anti-p65 (Abcam ab7970) or anti-H2B (Abcam, ab10476) primary antibodies (1:100 to 1:200 dilution) and visualized with Alexa-Fluor 488-conjugated goat-anti-rabbit-IgG secondary antibodies (1:1000 dilution; Life Technologies, A11008). Nuclear p65 and H2B fluorescence was detected by confocal laser scanning microscopy. For each sample, Fiji-ImageJ software was utilized to determine Fn/Fc values which were calculated by: Fn/c=(Fn-Fb)/(Fc-Fb), where Fn is nuclear fluorescence, Fc is cytoplasmic fluorescence, and Fb is background fluorescence. Data represent the mean±s. e. m. Channels were overlaid to examine co-localization of the antibody targets. The Pearson's colocalization coefficient (PCC) was calculated with Fiji-ImageJ.
Microarray analysis Affymetrix GeneChip ® Hugene 1 (Jurkat) or Human Genome U133 Plus 2. 0 Arrays (vaccinia) were used for transcriptomics. Affymetrix microarray data including E-MEXP-2578 was robust multi-array average (RMA) normalized in affy  (Gautier et al. , 2004). Pearson correlations were calculated for all peaks and main array probes and visualized using gglot2 and reshape 2. Overexpression was defined as a log 2 0. 5 increase. Gene set enrichment analysis (GSEA) was used with default parameters using gene set permutations and differences of classes with a ≤25% false discovery rate (FDR) and a nominal P-value of ≤0. 05.
Bioinformatics analysis Sequences were trimmed (Cutadapt) and mapped to the human genome (Hg19) using local alignment in Bowtie 2  (Langmead and Salzberg, 2012). Duplicate reads were removed (Picard) and enriched regions were called against the relevant total input using a 0. 01 q-value cut-off and the broad peak option in MACS2  (Zhang et al. , 2008). A cut-off of ≥175 bp for the primary regions was used to remove low complexity reads. The number of reads was counted and minus-average normalized in R software. Minusaverage normalization was performed on the non-log counts using the R MASS and affy packages and adjusting counts using the coefficient from a linear model of the differences in counts and the count average. The code was similar to MAnorm  (Shao et al. , 2012) , except that non-logged values were used.
The nearest ENSEMBL transcript to the peaks was determined using ChIPpeakAnno  (Zhu et al. , 2010)  and HOMER  (Heinz et al. , 2010) , and classified as 5′UTR, exon, intron, promoter (-1 kb from the TSS), upstream (-10 kb to -1 kb from the TSS) or intergenic. Peaks were also annotated to their chromatin marks using Roadmap ENCODE data  (Hoffman et al. , 2013). NFKB ChIP-seq data were from GEO GSM935282. Heatmaps were created using HOMER and Heatplus. Transcription-factor-binding motifs were analyzed using HOMER (mask -200, P<10 -12 )  (Heinz et al. , 2010)  and ISMARA (z-score cut-off of 2)  (Balwierz et al. , 2014)  and CLOVER. Overlapping peaks were calculated in a similar way to read counting, but peaks were first merged and the original peak files treated as reads (with no extension step).
Data availability The microarray expression data is deposited in the Gene Expression Omnibus under accession number GSE61172 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE61172) and ChIP-sequencing data under GSE61241 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE61241).
The mitochondrial data allowed us to clarify the phylogenetic relationships among these lineages. They were also used to infer a chronogram that was time scaled based on age estimates for termite fossils (including the oldest Reticulitermes fossils, which date back to the late Eocene early Oligocene). Our results support the hypothesis that the extant Reticulitermes lineage first differentiated in North America. The first divergence event in the ancestral lin eage of Reticulitermes occurred in the early Miocene and separated the Nearctic lineages (i. e. , the NA lin eages) from the Palearctic lineages (i. e. , WE, EE + WA, and EA lineages). Our analyses revealed that the main lineages of Reticulitermes diversified because of vicariance and migration events, which were prob ably induced by major paleogeographic and paleoclimatic changes that occurred during the Cenozoic era.
8S (ITS1R) portions of the nuclear ribosomal RNA gene of Blattella germanica (Genbank accession number: AF321214)  (Mukha et al. , 2002). Similarly, the reverse primer for the 16S gene (16SR1) was designed using the 16S mitochondrial ribosomal RNA gene of Reticulitermes santonensis (synonymous with R. flavipes; Genbank accession number: AF262589)  (Thompson et al. , 2000). The PCR products were purified and sequenced using Big Dye 3. 1 and a 96 capillary ABI 3730xl sequencing system (Applied Biosys tems). Sequences were inspected, corrected and aligned using the Clustal W algorithm implemented in the MEGA v. 6 package. The alignments obtained were further inspected and manually edited. All sequences were deposited in GenBank under the accession numbers provided in Table  1.
Phylogenetic analyses To evaluate possible saturation of the phylogenetic signal in each mtDNA marker dataset, the pairwise proportions of transitions (ts) and transversions (tv) were plotted against the pairwise uncorrected p distances. For the protein coding sequences (COI and COII), we also plotted the proportions of ts and tv occurring at the third codon position against the pairwise uncorrected p distances. The best substitution model for each of the five loci was determined using MEGA v. 6 and employing the Bayesian information criterion (Table  3 ). Phylogenetic trees were estimated utilizing two datasets: (i) the mitochondrial DNA (mtDNA) sequences (COI, COII, and 16S) and (ii) the nuclear DNA (ncDNA) sequences (ITS1 and ITS2). The congruence between the two datasets was tested using the ILD test  (Farris et al.
, 1995) , implemented in PAUP ⁄  (Swofford, 2001) , with 500 bootstrap replicates. For the two data sets, a Maximum likelihood (ML) search and Bayesian inference (BI) were performed using a partitioned dataset (three and two partitions for mtDNA and ncDNA, respectively). Maximum likeli hood trees were calculated using RAxML v. 7. 0. 3 (Stamatakis, 2006) ; bootstrap support was estimated using the rapid hill climbing algorithm  (Stamatakis et al. , 2008)  on 500 replicates. Fifty percent majority rule consensus trees were obtained using Phyutility v. 2. 2. 6  (Smith and Dunn, 2008). Bayesian inference was performed using MrBayes v. 3. 2. 2 (Ronquist et al. , 2012). For both datasets, two searches of four chains each were run for 30,000,000 generations; trees were sampled every 500 genera tions.
Convergence diagnostics were assessed via the variance of split frequencies (<0. 01), PSRF (P1. 00) and ESS (P200). The first 10% of the sample trees were discarded as burnin. The 50% majority rule consensus tree was obtained via the sumt command, and the maximum clade credibility (MCC) tree was selected using TreeAnnotator v. 1. 8  (Drummond and Rambaut, 2007). For the two datasets, the 50% majority rule consensus tree and the MCC tree topology gave identical results. Dating of cladogenetic events was performed using BEAST software v. 1. 8  (Drummond and Rambaut, 2007)  on the mtDNA dataset. The software calculated the tree topology, as well as the posterior probability support and a divergence time estimate for each node. The algorithm was setup to use an uncorrelated, log normal relaxed clock and the birth death speciation process.
Two MCMC chains were run for 100,000,000 generations, starting from a random tree; sampling took place every 500 generations. As the loci examined are all part of the mitochondrial genome, data partitioning was the same as in the BI analysis (unlinked site models and a linked clock model). Chain stationar ity and convergence were reached when ESS P 200. The MCC tree was determined after discarding the first 10% of the trees obtained (burnin). For calibration purposes, the following outgroup taxa were added to the dataset: Coptotermes michaelseni, C. lacteus, C. acinaciformis, Heterotermes tenuis, H. tenuior, H. longiceps, Heterotermes sp. , and Schedorhinotermes breinli (Genbank accession numbers are provided in Supplementary Table  S1 ). Two calibration points based on fossil records were applied to the analysis: the old est known Reticulitermes fossils, R.
Phylogenetic analyses ML analysis using the mtDNA sequences (Fig. 1 ) revealed two main lineages of Reticulitermes termites. The first lineage included all the western European (WE) taxa, and the second lineage was divided in two sublineages. The first sublineage grouped together taxa from eastern European (EE) and western Asian (WA) whereas the second sublineage comprised all the taxa from eastern Asian (EA). In this analysis, North American taxa did not group together but instead clustered in multiple lineages at basal positions in the tree. With some exceptions, the BI tree (Fig. 1 ) was congruent with the ML tree and robust posterior probabilities provided support for the WE, EE + WA, and EA lineages. The ML and BI analyses conducted on the ncDNA dataset were less informative than those conducted on the mtDNA dataset.
The ncDNA based phylogenetic trees indeed contained numerous deep nodes that were poorly supported or not supported at all (Fig. 2 ). For instances, these analyses did not support the sister relationship between the EE + WA and EA lineages. They also failed to support the WE lineage and the phylogenetic subdivision within the WA lineage. In addition, the topologies of these trees were not congruent with trees obtained on the mtDNA dataset (ILD test p < 0. 01). For this reason, analyses on the full dataset combining mtDNA and ncDNA sequences were not conducted. Interestingly, however, analyses performed on the ncDNA dataset clarified the phylogenetic status of the North American taxa, which all grouped together in a well supported lineage (NA lineage) (Fig. 2 ). Altogether, analyses conducted separately with the mtDNA and the ncDNA datasets suggested that NA lineage was the first to diverge in Reticulitermes genus followed by WE lineage before EE + WA and EA lineages.
Such relationships between the main lineages of Reticulitermes termites were also supported by the BEAST analysis conducted with the mtDNA dataset (Fig. 3 ). The topology within the WE lineage was very well supported and in perfect agreement with previous studies  (Clément et al. , 2001) (Figs. 1  and 3 ). Similarly, the topology within the EE + WA lineage was similar to that found in previous studies  (Ghesini and Marini, 2012; Luchetti et al. , 2007; Uva et al. , 2004; Velonà et al. , 2010). In this lineage, a well supported group composed of the three R. urbis samples had a sister relationship with the WA lineage, and the later was further subdivided in two groups: WA. 1 and WA. 2. The WA. 1 group provided confirmation that a close relationship exists between the northern samples previously called ''R.
Dating cladogenetic events A chronogram was built solely using the mtDNA dataset (Fig. 3 ). Fossil based calibration points allowed to retrieve the time estimates of cladogenetic events that mostly occurred between late Paleocene and late Miocene, although a few events also occurred during the Pliocene (Fig. 3 ). Interestingly, our time estimates were consistent with those obtained by other phylogenetic studies in termites  (Bourguignon et al. , 2015; Lee et al. , 2015). For instance, the divergence of the lineages leading to Reticulitermes genus and to the group comprising both Heterotermes and Coptotermes genera was estimated to have occurred 66. 6 Ma (51. 2 87. 0 Ma 95% high posterior density  [HPD] ) in the present study, whereas the same event has been estimated to have occurred about 60 Ma by  Bourguignon et al.
It is particularly necessary for the eastern European and western Asian lineages (EE + WA), for which only three of ten taxa (i. e. , R. urbis, R. balkanensis, and R. clypeatus) have been described at the species level. Furthermore, some of the names given to these undescribed taxa are misleading and should be avoided in future studies. For instance, the name ''R. lucifugus Turkey", which has been given to distinct taxa in the EE + WA lineage, should no longer be used since it refers to the ''true" R. lucifugus species complex, which belongs to the WE lineage (Figs. 1 4 ). It is also worth noting that introgressive hybridization might occur among Reticulitermes taxa. In this study, introgression is indeed suggested by the sample collected in Iran (R. sp. 6, IRA), which exhibited a different position in the phylogenies obtained using the mtDNA versus the ncDNA dataset (Figs.
Acknowledgments We would like to thank  Omar Rota Stabelli  for fruitful discussions and his help with the phylogenetic analyses. We also thank  Mario Marini  for the termite samples from Crete and  Kirsten Copren  for the samples from California. We wish to acknowledge  Jessica Pearce  for the English revision of the manuscript, and  Guillaume Baudouin  for his help in drawing the graphical abstract. This work was supported by a  French ANR  grant obtained by  F. Dedeine  ( EvoSymTer 08 JCJC 007601 ) as well as an  Italian RFO UNIBO  and  Canziani  funding obtained by  B. Mantovani  and  A. Luchetti.
80⁰ N, 85. 54⁰ W), Lamentos (10. 74⁰ N, 85. 63⁰ W) and Aromal (10. 73⁰ N, 85. 60⁰ W; Fig. 1 ). The age of the pastures (i. e. time since conversion) was at least 45 yr. Qualitative data on the past and present land uses of these sites have been obtained from landowners and managers and are reported in Appendix S1. The pastures are flat and crossed by a number of non-permanent streams lined with riparian vegetation. We selected a high number of individuals and species of isolated trees to minimize the risk of bias due to phylogenetic correlation amongst traits. We used several inclusion criteria for trees: (1) crown not overlapping with the crown of another tree; (2) no substantial part of the crown covered by a liana (so that the effects of trees could be separated from lianas); (3) located in an area with environmental conditions comparable to the rest of the site (areas that had hydromorphic soil or recent burning were excluded).
Survey and properties of the regeneration assemblage In July 2013, we conducted a survey of the regenerating woody vegetation under each selected isolated tree. We quantified and identified all seedlings, saplings and young adults of trees, tall shrubs (only shrub species that can grow > 2 m height were considered) and woody lianas located in the Sampling area. Individuals sprouting from the roots of the trees (as visually assessed by inspecting the first few centimetres of the tap root) were excluded. The minimum height for a plant to be included was 10 cm, to exclude seedlings germinated in the year of data collection, which have not yet experienced the full annual climatic cycle. We set no upper height limit for inclusion. However, we did not select isolated trees where the relative size of trees with overlapping crowns created any uncertainty about whether the isolated tree established before the woody regeneration located under its crown.
Acknowledgments We gratefully acknowledge the help of  Roberto Espinoza  in the field, of  Daniel Perez Aviles ,  Maria Gabriela Gei  and  Roberto Espinoza  for information about functional traits, of the staff of the experimental forest station of Horizontes (particularly  Vanessa Brenes Hernández and Pedro Alvarado Joares)  for their logistical help and of  Milena Gutierrez ,  Roger Blanco  and  Maria Marta Chavarria Diaz  of the  Área de Conservación de Guanacaste. We thank the owners of the farms for letting us collect data on their properties. Ian Harris  and  Andrew Packwood  provided assistance with GIS. Thanks to  Monika Wulf ,  Meelis Pärtel  and five anonymous reviewers for their advice on the manuscript. This study has been supported by the  European Commission  under the  FONASO Erasmus Mundus Joint Doctorate Programme  and by the  Coalbourn Trust.
The impact of temperature, origin (geographical or clonal), and their interactions on LGR, SP, and FI were assessed with linear mixed models using the "lme4" package, version 3. 0. 3 (Bates et al. 2011). In these models, temperature, population origin (geographical or clonal), and test repetition were treated as fixed factors and the MLG was rated as random factor. SP was square-root-transformed to satisfy the assumptions of homoskedasticity and normality. The effects of temperature, origin (geographical or clonal), and the interactions temperature 9 origin and temperature 9 origin 9 test repetition were tested with a Wald's test. When needed, pairwise comparisons of least square means (LSMeans; the "lsmeans" package version 3. 0. 3) have been performed using the Tukey's method for P-values adjustment.
This trade-off between intra-epidemic transmission (i. e. , aggressiveness) and interepidemic transmission has been recently demonstrated  (Pasco et al. 2016). Finally, a trade-off between the number of sporangia produced and their size could also be involved. 13_A2 isolates indeed produced fewer sporangia, but bigger ones than 6_A1 (data not shown), resulting in a probably lower fitness deficit than would be expected based on spore production alone, especially at low temperatures. High temperatures could be more penal than low temperatures to P. infestans fitness We observed a strong effect of temperature on the three life-history traits measured, for isolates of each population. The optimal temperature range for the development of P. infestans was reported between 15 and 22°C (Mizubuti and Fry 1998;  Maziero et al.