dataesr/scientific-paragraphs-categorization huggingface dataset:
Both M 0 and M 1 take the prior range of (21, 1) for the selection coefficients and the prior range of (2, 59) for the change point in the M 1 model. For the M 1 model, these uninformative priors are updated with the constraint that the allele must be segregating at the time of change point. Here, we create 10 7 simulated datasets for each M 0 and M 1 , and apply the rejection algorithm of the CP-WFABC method to retain the best 1000 simulations compared with the observed trajectory. The effective population size is assumed to be N e = 500 as in previous studies  (Wright 1948; Cook and Jones 1996; O'Hara 2005) , with an initial allele frequency of 11% and a minimum frequency ascertainment of 2%. The Bayes factor for M 1 over M 0 is calculated as 0. 952, indicating that the single coefficient M 0 model cannot be rejected in favor of the changing selection M 1 model (Table  4 ).
Experimental evolution of influenza virus with drug treatment The evolution of pathogens within a host is one of the most important cases in which the possibility of fluctuating selection must be consideredas they may experience drastically changing selective pressures due to host immune response, specific drug treatments, and/or pathogenic cooperation or competition  (Tanaka and Valckenborgh 2011; Hall et al. 2011). Thus, how these pathogens adapt to these rapid external and internal changes is of major concern to the biomedical community. The time-serial experimental dataset of influenza A conducted by  Renzette et al. (2014)  and  Foll et al. (2014)  is an interesting case study on the impact of drug treatment on influenza virus evolution. The dataset consists of 13 sampling points from which population-level whole-genome data were collected.
The number of generations per passage is assumed to be 13 (following  Foll et al. 2014) , and the minimum frequency of 2% is set as an ascertainment for observing the minor allele in the data. De novo mutations are assumed to occur at the sampling time point where the SNP was first sampled (except for trajectories whose initial sample frequency was first sampled above 1/N e , which are assumed to be standing variation). This assumption is based on the high mutation rate, large population bottlenecks associated with passaging, and large census population size between passages. A total of 10 7 datasets were simulated for each M 0 and M 1 ; the best 1000 trajectories from the lot were retained using the rejection algorithm described in the Methods section. The uniform prior ranges for the selection coefficients were set as (21, 1), for the change point as (t 0 , 157) where t 0 is the generation when the SNP was first sampled, and with the constraint of segregating alleles at the change point for M 1.
DISCUSSION These simulations demonstrate that the novel CP-WFABC approach presented here is able to detect changing selection trajectories via ABC model choice, and also to estimate a wide range of parameters of interest. Performance was analyzed separately for three categories of allele trajectories according to the nature of the change in selection strength: (1) a change from positive s 1 to positive s 2 , (2) a change from positive s 1 to negative s 2 , and (3) all other changes. The datasets for each possible combination were generated using the Wright-Fisher model with a change in selection strength, using the most general prior ranges for all parameters s 1 , s 2 , CP, and h for diploids, with the only constraint being segregation of the allele at the change point. For both the detection and parameter estimation, CP-WFABC performs best when the change is large, particularly for the second category of change (positive s 1 to negative s 2 ), as shown in the ROC curves (Figure  2  and Figure  S3 ) and the cross-validation graphs (Figure  3 , Figure  4 , Figure  S4 , Figure  S5 , Figure  S6 , and Figure  S7 ).
2014). For haploids with large population sizes, in particular, the estimated values of the single parameter s correlate almost perfectly with the true values (Figure  S5 ). However, when the population size is small for both diploids and haploids, some trajectories that are lost by genetic drift are difficult to estimate, as shown in Figure  3  and Figure  S6  as horizontal lines along negative estimated values. This limitation of constant selection coefficients, however, is due to the simulation conditions of the de novo mutation at the first generation and the minimal ascertainment scheme (minimum frequency of 2% at one of the sampling time points). For real datasets, the conditions are likely to be less stringent, since such uninformative trajectories will not be considered for parameter estimation.
Finally, we utilized this approach to make inference in two very different time-sampled datasets: P. dominula (diploid) and Influenza A virus (haploid). The time-serial medionigra trajectory of P. dominula was reanalyzed to test for a change in selection strength and/or direction during 60 yr of data collection. By assuming h as codominant, the results for N e = 500 indicate that the model M 0 of constant selection cannot be rejected according to the Bayes factor from the ABC model choice algorithm. The selection coefficient from this model is estimated as 20. 15, corresponding with that calculated by  Fisher and Ford (1947). However, when the population size is assumed to be smaller (N e = 50), the Bayes factor result supports M 1 (changing selection) twice as strongly as M 0 (constant selection), but not strong enough to reject M 0 according to the significance level test computed with the distribution of Bayes factors under the null model M 0.
This dataset of the medionigra morph thus demonstrates the difficulty of detecting and evaluating a change in selection when the assumption of a known and constant effective population size is not fulfilled. Depending on the assumed population sizes, the conclusions may indeed change, as this study demonstrates. Furthermore, if the population size fluctuates, this method has the caveat of detecting a change in N e Ã s rather than only in selection strength. Next, CP-WFABC was applied to SNP trajectories of interest from an experimental dataset of influenza A virus. The ABC model choice test was conducted on the trajectories identified as 1) being positively selected and 2) as outliers from the single-s WFABC method. For the SNPs in the presence of drug, the Bayes factor for six out of nine trajectories favored the changing selection model M 1.
Simulated data with constant selection and with changing selectionWe generated simulated datasets of different effective population sizes using the Wright-Fisher model for two scenarios: (1) trajectories of constant selection with only s and h (for diploid models) as parameters, and (2) trajectories of changing selection with s1, s2, CP, and h (for diploid models) as parameters. For selection coefficients, uniform priors of (21, 1) were used. The uniform prior of CP was set to occur between the second generation and the second-to-last generation (2, T21), where T is the number of generations of the population in the time-serial data. The dominance coefficient h for the diploid model was randomly drawn from one of three values: complete recessiveness, codominance, or complete dominance [0, 0.
and continued through to 1999 (Cook and Jones 1996; Jones 2000). Despite this phenotypic time-serial data having been analyzed previously from various angles (O'Hara 2005; Mathieson and McVean 2013; Foll et al. 2015) , it is still relevant to consider a model of changing selection in time, as Wright (1948)  originally suggested. The recent reconsiderations of the dataset tend to favor a lethalrecessive model with an effective population of N e = 500 (Mathieson and McVean 2013; Foll et al. 2015)however, the biological question of how the medionigra morph could have reached the initial frequency of 11% in the dataset remains unanswered with this conclusion of constant strong negative selection. Wright asserted that the trajectory of the medionigra morph during this period could be explained by fluctuating selection with "no net selective advantage or disadvantage.
" Although this alternative hypothesis has been considered as a random fluctuation of selection by estimating selection coefficients between every sampling time point (see O'Hara 2005), the quantitative plausibility of a directional change-in-s model over a single-s model lacks thorough investigation. Thus, we reanalyzed this dataset using the CP-WFABC method in order to investigate the possibility of changing selection in the medionigra morph during the 60-yr data collection.
LncRNA expression in ETV6/RUNX1-driven BCP-ALL To study the role of lncRNAs in the molecular pathogenesis of human BCP-ALL, we profiled a cohort of 64 primary BCP-ALL patient samples using a previously published microarray platform that simultaneously detects lncRNA and mRNA transcripts  [26]. This patient cohort consisted of 25 ETV6/RUNX1-positive, 7 TCF3/PBX1positive, 15 high hyperdiploid and 17 normal karyotype BCP-ALLs. Using 14055 probes that showed expression above the background, we established an ETV6/RUNX1specific lncRNA signature consisting of 596 lncRNA transcripts (434 up-and 162 down-regulated) that showed significant differential expression between ETV6/RUNX1positive BCP-ALL and other genetic BCP-ALL subclasses (adj. p-value<0. 05, Figure  1A , Supplementary Table  S1 ). In our dataset, average lncRNA expression was lower as compared to mRNA transcripts (Figure  1B ), a notion that has previously been reported in other studies  [27].
Identification of ETV6/RUNX1 specific lncRNAs by RNA sequencing of human BCP-ALL cell lines To further validate our initial findings and to evaluate the ETV6/RUNX1-positive BCP-ALL cell line REH, as a suitable in vitro model system to study the functional relevance of ETV6/RUNX1-specific lncRNAs, we analyzed publically available non-stranded poly-A RNA sequencing data from 13 human BCP-ALL tumor lines including REH and 12 other non-ETV6/RUNX1 cell lines (SEM, SUP-B15, NALM-6, NALM-19, RCH-ACV, RS4;11, KASUMI-2, KOPN-8, MUTZ-5, MHH-CALL-2, MHH-CALL-3 and MHH-CALL-4). More specifically, using differential expression analysis of sequence count data (DESeq2  [28] ), we identified 50 lncRNAs that showed a significant differential expression between REH and the other non-ETV6/RUNX1 cell lines (Generalized Linear Model; adj.
DISCUSSION Emerging evidence suggests that lncRNAs are critically involved in a variety of human tumor entities  [20] [21] [22] [23]  and the identification of cancer-associated lncRNAs might reveal new prognostic biomarkers or novel therapeutic strategies for the treatment of human cancer. In this study, we characterized the lncRNA expression signature associated with ETV6/RUNX1positive BCP-ALL, one of the most prevalent genetic subtypes of childhood leukemia. A previous study by Fernando and colleagues [24] also evaluated lncRNA expression signatures in genetic subtypes of human BCP-ALL using 14 ETV6/RUNX1, 15 TCF3/PBX1 and 15 MLL-rearranged leukemia specimens. Although their study was not only focused on the identification of subtype specific lncRNA signatures, the ETV6/RUNX1 specific expression of lnc-BTBD10-3 (LINC00958) reported in their study, could be confirmed in our dataset.
25 μl Forward Primer (5μM), 0. 25 μl Reverse Primer (5μM)). qRT-PCR was performed on a LightCycler 480 (Roche) and data were analyzed using qBasePLUS software (Biogazelle)  [38]. Relative expression values were calculated using the ΔCtmethod. For normalization, the expression of at least three reference genes was combined to calculate a normalization factor. The primer sequences are listed in Supplementary Table  S8.
Microarray profiling RNA samples were profiled on a custom designed Agilent micro-array covering all protein coding genes and 23042 lncRNAs as described by Volders et al. [26]. A total of 100 ng input RNA was used to complete the profiling according to the manufacturer's protocol (Onecolor Microarray-Based Gene Expression Analysis, Low Input Quick Amp Labeling, Agilent Technologies). Normalization of the expression data was performed using the VSN-package (BioConductor release 2. 12) in R bioconductor. Expression values were further subjected to background subtraction and probes detecting a 10% higher expression as compared to the negative controls in at least 60% of one genetic subtype of BCP-ALL, were retained for further analysis. Following the normalization of geneexpression microarray data and background correction, differentially expressed genes between ETV6/RUNX1positive BCP-ALLs and other subgroups were determined using the limma in the R bioconductor.
RNA-seq analysis Publically available RNA sequencing data were obtained from the Cancer Cell Line Encyclopedia for a panel of 13 human BCP-ALL cell lines including one ETV6/RUNX1-positive (REH) and 12 ETV6/RUNX1negative (SEM, SUP-B15, NALM-6, NALM-19, RCH-ACV, RS4;11, KASUMI-2, KOPN-8, MUTZ-5, MHH-CALL-2, MHH-CALL-3, MHH-CALL-4) human BCP-ALL cell lines. Total RNA was extracted from REH and 697 cells, as well as from REH cells treated with LNA gapmeRs against four different lncRNAs and a non-targeting gapmeR using the miRNeasy mini kit (Qiagen) according to the manufacturer's instructions. The RNA quality was evaluated using a RNA Nano chip on a Bioanalyzer (Agilent technologies). For library preparation, the Illumina TruSeq RNA sample Prep Kit (San Diego, CA, USA) was used according to the manufacturer's protocol.
Library quality was checked using a DNA-1000 chip on a Bioanalyzer (Agilent technologies). Briefly, 100 ng of total RNA was poly-A purified, fragmented and first-strand cDNA reverse transcribed using the Truseq protocol. Next, second-strand cDNA synthesis, end repair, addition of a single A base, adaptor ligation and PCR amplification was performed and enriched cDNA libraries were using an Illumina NextSeq 500 sequencer instrument. RNA sequencing data have been deposited to the GEO repository (GSE79873). Reads were aligned to the reference genome hg38 with STAR-2. 4. 2a and differential expression analysis was performed using DESeq2  [28].
Guilt-by-association analysis Spearman correlation coefficients were calculated between lncRNAs and all protein coding genes using expression data of BCP-ALLs profiled on the custom designed Agilent array. The correlation values of each lncRNA and all protein coding genes were subsequently ranked from the highest to the lowest value and used as input for pre-ranked gene set enrichment analysis (GSEA), using the c2v5. 0 MsigDB collection as gene set database (http://www. broad. mit. edu/gsea/) [40].
ACKNOWLEDGMENTS This work was supported by a grant from the  Belgian Foundation Against Cancer  (grants  2010-187  and  2012-199 ), the  Fund for Scientific Research Flanders  (research projects  GA00113N ,  3G065614 ,  G. 0C47. 13N  and  31500615W  to PVV), the  Cancer Plan-Action  29 from the  Belgian Federal Public Service of Health  (PhD grant to FG) and Kinderkankerfonds (a non-profit childhood cancer foundation under Belgian law). The computational resources ( Stevin Supercomputer Infrastructure ) and services used in this work were provided by the  VSC (Flemish Supercomputer Center) , funded by  Ghent University , the  Hercules Foundation  and the  Flemish Government -department EWI.
The descent of each female was divided in two halves in order to be preserved in 70 % ethanol either as eggs or as freshly hatched (< 15 day-old) larvae. Ticks were washed with PBS (phosphate buffered saline) buffer before proceeding to DNA extraction using the DNeasy Blood & Tissue Kit (QIAGEN, Hilden, Germany) according to the manufacturer's instructions. Ehrlichia ruminantium infection was detected using the seminested PCR targeting the PCS20 genomic region  [14]. A template of one field-collected A. variegatum specimen that had previously shown to be infected by E. ruminantium  [4]  was used as a positive control. PCR-products were purified and sent for sequencing (EUROFINS, Ebersberg, Germany). The newly-generated sequences were submitted in the GenBank database under accession numbers KX356089-KX356091.
Additional file Additional file 1: Alignment of the sequences generated in the present study with those of Ehrlichia spp. references strains retrieved from the GenBank database. (TXT 2 kb) Fig. 1  Phylogenetic relationships between the sequences generated in the present study and sequences for reference strains (GenBank accession numbers in parentheses) using maximum likelihood. A discrete Gamma distribution with invariant sites (G = 2. 38 and I = 6. 55 %) was used to model evolutionary rate differences among sites. Branch numbers indicate percent bootstrap support (5,000 bootstraps). The scale-bar is in units of substitutions/site. BF refers to Burkina Faso.
For example,  Clare and Weninger (2008)  and  van der Plicht et al. (2011)  demonstrated that a multiplication of 14 C dates by CPDF at a single site can fill or confirm the suspicion of a hiatus. A critical analysis of the state of regional radiocarbon databases is therefore essential, not only, as recently applied by  Flohr et al. (2016) , with a selection of radiocarbon dates but also by a systematised intra-site stratigraphic and taphonomic evaluation such as that recently conducted on the Dikili Tash and Sidari sites (Greece)  (Lespez et al. , 2013 (Lespez et al. , , 2016;; Berger et al. , 2014). We consider that this approach is the most reliable way to observe the degree of continuity of human occupation and thus to establish its possible links to local hydrogeomorphological dynamics during RCCs.
In Sidari, a CPDF analysis is used to better specify and compare the chronology of hydrosedimentary and pedological activity in the two sites. A local database integrating Sidari 1 and 2 sites has been compiled. It integrates 33 radiocarbon dates from three main geomorphological contexts: channel fills, floodplain overbank deposits, and palaeosol. 14 C dates have been performed using guidelines set out by  Johnstone et al. (2006). BP calendar ages, including 1 s error range, were summed using a Microsoft Excel macro. Regarding the site of Dikili Tash, geomorphological studies have focused on the tell and its surroundings  (Lespez et al. , 2013 (Lespez et al. , , 2016 ) (Fig. 6 ). The proximity of a swamp (Tenaghi Philippon) gave us the opportunity to follow a strategy of complementary palaeoenvironmental analyses, centred on the study of pollen, non-pollen palynomorphs (NPPs) and fire signal in order to reconstruct the history of the local and regional vegetation and of fires.
These results suggest that data provided by continental hydrology, soils, and vegetation dynamics studied on the micro-regional scale may be good records of potential impacts of climate and of development of human societies. These new data establish the necessity of always reasoning, from contextualised data, not to overplay temporal CPDF- type constructions, which are sometimes too schematic and occasionally disrupted by bias related to the organic material used for the 14 C. Indeed, we observe a constant time lag between chronocultural and morphological data (from 100/150 years), whose origin is probably to be found in the old wood effect (almost a predominance of oak in charcoal assemblages). The Sidari 2 Mesolithic occupation centred on the 9. 2 ka event is associated with a short intermediate RCC pedogenic episode.
12a ). 5. 1 Sidari/Dikili Tash and the early Holocene northern Greece/southern Balkan regional pattern The increase in erosion and fluvial activity observed on both archaeological sites around 8. 2 ka has also been observed elsewhere in northern Greece such as in the Lake Prespa  (Panagiotopoulos et al. , 2013)  and Lake Doirani  (Zhang et al. , 2014) (Figs. 2, 12a)  areas. This confirms the trends of increase in soil erosion and sediment transfer to the wetland around 40-41 • N during the 8. 2 ka event. At the regional scale, these continental results seem consistent with the Adriatic climate data from northwestern Greece to the Po Valley in northern Italy. The confrontation with the nearest multi-proxy marine records (MD 90-917 in the central Adriatic sea) and northern Aegean Sea consolidates the regional climate-environmental mechanisms previously described  (Rohling et al.
Acknowledgements. We thank  Paléomex-ArcheoMed  ( CNRS INEE ) for their financial and logistic support. This paper has benefited from the financial support from the  ENVOL  programme of the  MISTRALs  project. Many thanks to the French missions of the foreign office (MAE) in Greece (site of  Dikili Tash ;  P. Darcque , and  Z. Tsirtsoni ) and in Cyprus (site of Khirokitia;  A. Le Brun ). We are also grateful to the  Collège de France , " chaire des Civilisations de l'Europe au Néolithique et à l'âge du Bronze " and the 8th  Ephorate of Prehistoric and Classical Antiquities, Corfu (G. Metallinou). We finally thank the  Laboratoire de Mesure du Carbone 14 ,  UMS 2572 , and ARTEMIS in Saclay for 14 C measurements by SMA in the frame of the national service to CEA,  CNRS ,  IRD, IRSN and Ministère de la Culture et de la Communication.
In contrast, several genes were predicted to code for enzymes that could be involved in recently identified alternative pathways  (8). While one ack gene coding for an acetate kinase was identified, two putative ycf genes coding for acyl-coenzyme A (CoA): acetoacyl-CoA transferase were detected. These genes do not have homologues in C. acetobutylicum and may have a key role in the conversion of butyryl-CoA to butyrate in C. tyrobutyricum, consistent with observations made by other authors  (9). Further studies are required to investigate this alternative butyrate formation pathway, and the genomic data obtained in this study will allow the performance of efficient genetic strategies to confirm this hypothesis and increase the production capacities of the strain. Nucleotide sequence accession numbers.
Possible candidate genes for DD/ID in 6q21q23 Although the duplicated regions are large, we conducted a gene content study to search for candidate genes that may explain the ID/learning problems in presented families. Gene lists were generated using Nexus BioDiscovery Copy Number 7. 5 and the data were uploaded and analyzed with QIAGEN's Ingenuity® Pathway Analysis (IPA®, QIAGEN Redwood City, www. ingenuity. com). A list of genes and proteins related to ID was generated using the Knowledge Base of IPA. In case of the 6q duplication in family 1, we did not find any candidate gene that may explain the ID when duplicated, which may support our hypothesis that the 6q duplication seems to be benign if present alone. However, the proximal region that was duplicated in family 2, but not in family 1 seems to have an interesting gene content.
It contains four OMIM genes: MARCKS, HDAC2, HS3ST5 and FRK, all involved in brain function and development  [27] [28] [29] [30]. Interestingly it was shown that MARCKS expression is significantly elevated (45 %) in the hippocampus of mice, which exhibit impaired hippocampus-dependent learning  [27]  and neuron-specific over-expression of HDAC2 decreased dendritic spine density, synapse number, synaptic plasticity and memory formation  [31]. Since all four genes are involved in neural development, one could speculate that if their expression is dosage dependent, their duplication may play a role in neurological phenotypes like ID, DD and brain malformations. To our knowledge, so far, there are no cases of whole gene duplications in MARCKS, HDAC2 and HS3ST5 in control populations (neither in Decipher Population CNVs, Toronto Database of Genomic Variants, nor our in house controls), and there is only one family published by Pazooki et al.