dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-guided nuclease activity is very specific in P. patens The sgRNAs used in this study were designed to minimize potential off-target cleavage in the P. patens genome (Phytozome 3. 1) using the CRISPOR software. For both the selected targets, no perfect 20-bp matches were found but potential offtarget sequences presenting 3 to 6 mismatches were identified: 9 for sgRNA#1 and 3 for sgRNA#2 (Figure  S4 ). All these potential off-target loci were amplified with surrounding primers (Figure  S5 ) and sequenced in 48 clones transformed with hCas9 and sgRNA#1 and 48 clones transformed with hCas9 and sgRNA#2 that were all previously identified as mutated at the PpAPT locus. No mutation could be detected in the potential off-target sequences for any of the tested clones. CRISPR-Cas9 system increases gene targeting efficiency and single-copy replacement in P.
phytozome. net/physcomitrella_er. php) using the Arabidopsis U6-26 snRNA sequence (X52528;  Li et al. , 2007)  as query. U6 promoter sequence coordinates used for gRNA expression are 5050300-5050621 on chromosome 1. For the design of CRISPR-Cas targets in the PpAPT gene, both strands of the P. patens adenine phosphoribosyltransferase gene (PpAPT, Phytozome # Pp3c8_16590) were searched using the CRISPOR, free software (http://tefor. net/crispor/crispor. cgi), for sequences of the form 5 0 -G-N(18 or 19)NGG-3 0 with respect to the U6 promoter and Cas9 specificity conditions. Two target loci were selected, one in exon 5 (sgRNA#1) and one in exon 3 (sgRNA#2) of the PpAPT gene (Figure  1 ). The sgRNA1 and sgRNA2 cassettes were synthesized as gBlocks â by IDT (www. idtdna. com), PCR-amplified and introduced into pCR â II-TOPO â TA-cloning vectors (www.
Gene knockout assays Moss protoplasts (4. 8 9 10 5 ) were cotransformed with the pAct-Cas9 and psgRNA#1 or psgRNA#2 plasmids. Non-sense mutations in the PpAPT gene confer resistance to the toxic adenine analogue 2-fluoroadenine (2-FA). Regenerating protoplasts were selected on PpNH4 supplemented with 10 lM 2-FA (Fluorochem) to detect clones which had been disrupted at the PpAPT locus. Experiments were repeated three times. For the characterisation of the mutations triggered in the PpAPT gene by the sgRNA/Cas9 system, two sets of primers surrounding the target sites were designed. For target#1, we used the primers PpAPT#15 and PpAPT#19. For target#2, we used the primers PpAPT#5 and PpAPT#14. To check the specificity of our two sgRNAs, we designed primers surrounding all the potential off-target sites identified with the CRISPOR software (Figure  S4 ).
Background: Advanced therapy medicinal products (ATMPs) are innovative therapies that encompass gene therapy, somatic cell therapy, and tissue-engineered products. These therapies are expected to bring important health benefits, but also to substantially impact the pharmaceuticals budget. Objective: The aim of this study was to characterise the ATMPs in development and discuss future implications in terms of market access. Methods: Clinical trials were searched in the following databases: EudraCT (EU Drug Regulating Authorities Clinical Trials), ClinicalTrials. gov, and ICTRP (International Clinical Trials Registry Platform of the World Health Organization). Trials were classified by category of ATMP as defined by European regulation EC No. 1394/2007, as well as by development phase and disease area.
Results: The database search identified 939 clinical trials investigating ATMPs (85% ongoing, 15% completed). The majority of trials were in the early stages (Phase I, I/II: 64. 3%, Phase II, II/III: 27. 9%, Phase 3: 6. 9%). Per category of ATMP, we identified 53. 6% of trials for somatic cell therapies, 22. 8% for tissue-engineered products, 22. 4% for gene therapies, and 1. 2% for combined products (incorporating a medical device). Disease areas included cancer (24. 8%), cardiovascular diseases (19. 4%), musculoskeletal (10. 5%), immune system and inflammation (11. 5%), neurology (9. 1%), and others. Of the trials, 47. 2% enrolled fewer than 25 patients. Due to the complexity and specificity of ATMPs, new clinical trial methodologies are being considered (e. g. , small sample size, non-randomised trials, single-arm trials, surrogate endpoints, integrated protocols, and adaptive designs).
However, the MA for MACI † was suspended due to the closure of the EU manufacturing site  (10) , and the MA for Provenge † was withdrawn due to the bankruptcy of the MA holder Dendreon  (11, 12). ATMPs hold great potential for reshaping the progression or the disability associated with multiple diseases such as Alzheimer's disease, Parkinson's disease, cancer, muscular dystrophy, and so on  (13, 14) , including an option for curing or reversing diseases known as untreatable today or that are just subject to symptomatic treatments. Maciulaitis et al. (15)  identified 318 clinical trials for ATMPs registered in the EU Drug Regulating Authorities Clinical Trials (EudraCT) database between 2004 and 2010. In addition to Maciulaitis et al. (15) , three studies focused on a specific ATMP segment at the global or UK level.
The Journal of Gene Medicine gives access, through its Gene Therapy Clinical Trials Worldwide database, to the annual number of approved, ongoing, or completed gene therapy clinical trials worldwide  (16). Trouson and Mc Donald (17)  showed the progress in developing stem cells therapies and the challenges facing them. Bisson et al. (18)  identified 41 ongoing cell therapy clinical trials in April 2014 in the United Kingdom; the majority were in an early phase and led by academics. However, the development of ATMPs is a dynamic and fast-growing field; it has progressed greatly since the study by  Maciulaitis et al. (15)  identifying ATMP studies in 2010. The 2010 cut-off date of Maciulaitis et al. is likely outdated, and a more up-to-date study would be valuable for the scientific society, policy decision makers, and payers.
Data collection Two independent researchers retrieved all clinical trials of ATMPs conducted between 1999 and June 2015 using three clinical trials databases: ClinicalTrials. gov, the International Clinical Trials Registry Platform (ICTRP) of the World Health Organization (WHO), and EudraCT. The same combinations of keywords were used for the three databases searches:. For cell therapies: cell therapy, stem cell, cord blood, umbilical cord, bone marrow, cancer vaccine, tissue engineering, engineered cell, tissue engineered, mesenchymal cell, somatic cell, allogeneic cell, viable cell. For gene therapies: gene therapy, recombinant nucleic acid, DNA therapy, cDNA, recombinant DNA, nucleic acid therapy, gene transfer, virus delivery, cancer immunotherapy, RNA therapy, tumour vaccine, genetic therapy, plasmid DNA, oligonucleotides, genetically modified microorganisms, genetically modified organisms, genetically modified cells.
Discussion ATMPs are a new class of biopharmaceuticals. Eight years after the adoption of the regulation, only five products have obtained regulatory approval in the EU and achieved limited success in securing reimbursement  (20). This fact suggests a low impact of ATMPs to date on health insurance budgets but also on patients' health. To identify the number and characteristics of ATMPs in development, we searched three different clinical trial databases and extracted the trials using specific keywords. We believe we have likely captured most of the ATMPs in development. We used the following intervals of time: 1999Á2003, which was the period before the initiation of EudraCT; 2004Á2010, which was the interval of time between the initiation of EudraCT and the end of the first term of the CAT, considered a milestone; and the adoption of the CAT work programme (2010Á2015) that aims to foster development of ATMPs and bring more ATMPs to the market.
Two-thirds of the trials had a recent update date, suggesting that they were still active. These results support our hypothesis that a fast-growing number of ATMPs may ultimately impact the national health insurance budget in Europe. Moreover, 932 studies are currently in avery early phase, such as pilot studies, and were not considered for this research. These studies will continue to fuel the number of ATMPs in development in the coming years. Our results are consistent with the results of Maciulaitis et al. (15) , who identified 318 clinical trials for ATMPs between 2004 and 2010; during this same time period, we identified 333 clinical trials. This 5% increase may be based on the use of multiple databases, or just on differences in ATMP classification. In fact, the study by  Maciulaitis et al.
(15)  was based solely on the EudraCT database; therefore, it reflected the situation only in Europe. In contrast, we used two international databases (ICTRP and ClinicalTrials. gov) in addition to EudraCT. Consistent with Maciulaitis et al. , our findings show that the vast majority of ATMP sponsors were noncommercial (73. 2%). In addition, we showed that 53. 8% of Phase III trials were sponsored by commercial sponsors, whereas only 20% of Phase I and I/II trials had commercial sponsors. In fact, non-commercial sponsors may have a limited budget and limited experience to achieve the development of their products. Products initially developed by non-commercial sponsors may either be moved to a spin-off organisation or be licensed to a pharmaceutical industry to achieve their development in later phases and obtain MA.
There are some limitations of our study. First, the trials initiated before the implementation of the regulation may not be registered in the databases. However, very few products that would qualify as ATMPs were in development at that time, limiting the potential for this fact to alter the outcome of this research. Second, selection bias is possible, actually in ClinicalTrials. gov and ICTRP, as the investigational products were not classified and we did the classification following the definition in the regulation. The double-blind classification of ATMPs should have prevented significant misclassification.
Background: Improved performance of crossbred animals is partly due to heterosis. One of the major genetic bases of heterosis is dominance, but it is seldom used in pedigree-based genetic evaluation of livestock. Recently, a trivariate genomic best linear unbiased prediction (GBLUP) model including dominance was developed, which can distinguish purebreds from crossbred animals explicitly. The objectives of this study were: (1) methodological, to show that inclusion of marker-based inbreeding accounts for directional dominance and inbreeding depression in purebred and crossbred animals, to revisit variance components of additive and dominance genetic effects using this model, and to develop marker-based estimators of genetic correlations between purebred and crossbred animals and of correlations of allele substitution effects between breeds; (2) to evaluate the impact of accounting for dominance effects and inbreeding depression on predictive ability for total number of piglets born (TNB) in a pig dataset composed of two purebred populations and their crossbreds.
One of the major genetic bases of heterosis is dominance  [7, 8]. At the level of gene action, dominance is due to interactions between alleles at the same locus  [9]. In pedigree-based genetic evaluation, dominance is rarely included because large-scale datasets that comprise a high proportion of full sibs are required to obtain accurate estimates and because the computational complexity is high  [10]. With the recent availability of single nucleotide polymorphism (SNP) information and the development of genomic selection, estimation of the dominance effects of SNPs has become more feasible  [11, 12]. Genomic evaluation has been successfully used in purebred  [13, 14]  and crossbred populations  [15] [16] [17]. However, these studies generally ignore the dominance effects. A number of studies have been carried out on genomic evaluation including dominance effects using either simulated  [18]  or real purebred data  [9, 12].
Animals and genotypes We begin this section with a short presentation of the data used in the study, with the aim of defining the notation for the methodological developments that follow. For this study, all datasets were provided by the Danish Pig Research Centre. Data from three Danish pig populations were analyzed simultaneously: Landrace (L), Yorkshire (Y) and their reciprocal crosses  (LY). Only data on TNB data for the first parity of sows in the three populations were used. In total, there were 2126, 2218 and 5143 genotyped sows with own records on TNB for L, Y and LY, respectively. Instead of using original records, corrected phenotypic values of TNB were used as dependent variables for the trivariate GBLUP model, because the pre-correction for non-genetic effects, such as herdyear-season, month at farrowing, and service sire was more accurately achieved on a larger dataset (293,339 L, 180,112 Y, and 10,974 LY).
Among the crossbred animals, 7407 LY had a Landrace sire and a Yorkshire dam, while 3567 LY had a Yorkshire dam and a Landrace sire; L and Y populations were from nucleus farms and LY from a commercial farm. The litters of purebred sows were both purebred and crossbred litters. The relationship between LY-L and LY-Y are comparable since, in both cases, parents of the F1 animals are in the purebred datasets; further details about the model used for the pre-correction are in  [17]. All the purebred sows had first farrowing dates between 2003 and 2013, while the crossbred sows first farrowed between 2010 and 2013. Only five of these purebred L and Y sows were dams of the LY. The pedigrees for both purebred and crossbred sows were available and all crossbred animals were traced back to their purebred ancestors until 1994 by the DMU Trace program  [23] , as was done for the larger dataset used for pre-correction.
Consequently, 8227 L, 9851 Y and 5143 LY individuals were in the pedigree. The dataset of pre-corrected TNB records for genotyped individuals is termed "full genomic dataset" throughout the whole paper, and it should not be confused with the larger dataset used to do the pre-correction. For the "full genomic dataset", purebred sows were genotyped with the Illumina PorcineSNP60 Genotyping BeadChip  [24] , while the crossbred sows were genotyped with a 8. 5 K GGP-Porcine Low Density Illumina Bead SNP chip  [25]. SNP quality controls (such as: call rate for individuals ≥80%; call rate for SNPs ≥90%; minor allele frequencies ≥0. 01; etc. ) were applied on the same dataset in a previous study  [26] , which provides more details. Then, for the crossbred individuals, imputation from low density to moderate density was done by using a joint reference panel of the two pure breeds  [26]  using the software Beagle version 3.
y L = 1µ L + f L b L + Z L a L + W L d L + e L , y Y = 1µ Y + f Y b Y + Z Y a Y + W Y d Y + e Y , y LY = 1µ LY + f Y b Y + Z LY a LY + W LY d LY + The above equations can be reformulated to genotypic values of individuals instead of SNPs, in order to be compatible with the classical GBLUP model and animal breeding software, such as BLUPF90  [33]  and DMU  [34] : Note that u and v are vectors of genotypic additive and dominance effects and therefore cannot be directly compared to breeding values and dominance deviations in the pedigree-based genetic evaluation. In addition, f is a vector of genomic inbreeding coefficients and b is a population-specific inbreeding depression parameter per unit of genomic inbreeding, respectively. Note that there is potentially inbreeding depression at the level of the crossbred animals, although, first, the numeric values of the vector f should be smaller since crossbred animals have a higher level of heterozygosity, and second, the estimates of the inbreeding depression parameters (b) do not need to be identical across the three populations, which thus gives considerable flexibility.
(7) Var(v) = var             v L v L,Y v L,LY v Y ,L v Y v Y ,LY v LY ,L v LY ,Y v LY             = D ⊗   σ 2 D L σ D L D Y σ D L D LY σ D Y D L σ 2 D Y σ D Y D LY σ D L D LY σ D Y D LY σ 2 D LY   = D ⊗ D 0 , across the three breeds   W =   W L W Y W LY     and W ′ = W ′ L W ′ Y W ′ LY. Then, the (co)variances of dominance genotypic effects of SNPs are: This approach, which is an extension of Vitezica et al. [22] , makes it possible to estimate (co)variances of genotypic effects of SNPs in purebred and crossbred populations under a genomic model with additive and non-additive (dominance) inheritance. Matrices Z and W, their crossproducts and the inverses of G and D were built using own programs. Genetic parameters were estimated by using average information REML with software airemlf90  [33].
Scenarios Variance components, genetic correlations of breeding values between purebred and crossbred performances (r PC ) within each pure breed and correlations of allele substitution effects for purebred (r αP L ,αP Y ) and crossbred (r αC L ,αC Y ) performance between two pure breeds were first investigated using the full genomic dataset. To explore the effects of using genomic information and the inclusion of dominance deviation on the genetic evaluation of crossbred performance in the trivariate model, three different scenarios were compared.
Nogen The statistical model was a trivariate BLUP model, similar to Eq. (  3 ), but the dominance deviation was excluded. Instead of using a genomic relationship matrix, a single relationship matrix A was constructed across the three breeds, assuming that they form a single population. Thus, the genetic (co)variances of additive genetic effects u were: where A 0 were variance components associated to genetic additive effects and not the genotypic additive effects in Eq. (5). Pedigree-based inbreeding depression was also included in the model. The pedigree-based inbreeding coefficients were calculated as in  [41]  using the software inbupgf90  [33].
Gen_AM The statistical model was similar to Eq. (  3 ), but without dominance deviations. Genomic information was used to construct the additive genomic relationship matrix. Var(u) = A ⊗   σ 2 A L σ A L A Y σ A L A LY σ A Y A L σ 2 A Y σ A Y A LY σ A L A LY σ A Y A LY σ 2 A LY   = A ⊗ A 0 , construct the additive and dominance genomic relationship matrices. To explore the impact of genomic information and dominance effects on genomic evaluation for crossbred performance, the full genomic dataset was split into training and validation populations and the predictive ability for crossbred animals in the validation population was investigated in different scenarios. The farrowing date of January 1, 2013 was used as the cut-off date to divide recorded purebred and crossbred sows into training and validation populations.
In this study, for TNB, models with dominance deviations did not improve the genomic evaluation of crossbred performance with regard to both predictive ability and unbiasedness, but the inclusion of an inbreeding depression effect in the models significantly improved predictive ability. Phenotypic variances were larger for purebred animals (11. 76 for breed L and 9. 99 for breed Y) than for crossbred animals (7. 30 for LY). This could be the reason why the estimated additive genetic variances for purebred performance (σ 2 AP ) were larger than those for crossbred performance (σ 2 AC ). However, compared to results in a previous study that used a much larger Danish purebred and crossbred dataset  [17] , both estimated additive genetic variances and phenotypic variances in the current study were smaller, which is due to three reasons.
(1)  The dataset in the current study was a genotyped subset of the population used in the previous study. Purebred genotyped individuals were pre-selected and their performances were more homogeneous than that of the whole population. The preselection process resulted in a loss of about 15% of the purebred phenotypic variation. However, the genotyped crossbred animals were an almost random sample of the whole population and there was only a small loss of about 5% of phenotypic variation for crossbred animals. (2)  The phenotypic values for TNB in the current study were pre-corrected for fixed and non-genetic random effects. This pre-correction led to a loss of about 11 and 17% of phenotypic variation for purebreds and crossbreds, respectively. (3) During the pre-correction, some genetic variation may have been allocated to other random effects (e.
g. service boar effects), in particular because TNB is a lowly heritable trait. The estimated heritabilities of TNB for purebred performance (H 2 P ) were slightly lower than those previously reported (0. 11 and 0. 09 for breeds L and Y, respectively)  [17, 22, 43]. Large standard errors of H 2 P implied that the current dataset was not large enough. The consistent standard errors across scenarios indicated that even when genomic information was included, the uncertainty of H 2 P did not decrease. Taking the standard errors into account, the estimated H 2 P across scenarios were not very different. Compared to the results of  [17] , the lower H 2 P found in the current study was due to the sharp decrease in additive genetic variances (σ 2 AP ). The ratios of estimated dominance genetic variances to additive genetic variances in the current study (5 to 11%) were generally a little smaller than in other studies on TNB.
Our results suggested that using a small set of genotyped animals and pre-corrected data to implement genetic evaluation for crossbred performance was less powerful than using the whole dataset, which is similar to the conclusions for purebred performance  [43]. The regression coefficients obtained with the Vitezica model were less than 1, which suggests that variations in total genetic effects could be overestimated (inflated). In terms of unbiasedness, there was no clear trend among the scenarios examined, regardless of whether genomic information was included or not. Overall, unbiasedness was not a problem in the current study because the regression coefficients in all scenarios did not significantly differ from 1. Inbreeding depression for litter size in pigs is a wellknown phenomenon  [50, 51] , and we found that inclusion of inbreeding effects in the model improved predictive abilities of crossbred animals.
To be able to compare solutions, we identify three generic steps for kNN computation on MapReduce: data pre-processing, data partitioning and computation. We then analyze each step from load balancing, accuracy and complexity aspects. Experiments in this paper use a variety of datasets, and analyze the impact of data volume, data dimension and the value of k from many perspectives like time and space complexity, and accuracy. The experimental part brings new advantages and shortcomings that are discussed for each algorithm. To the best of our knowledge, this is the first paper that compares kNN computing methods on MapReduce both theoretically and experimentally with the same setting. Overall, this paper can be used as a guide to tackle kNN-based practical problems in the context of big data.
As the amount of data or their complexity (number of dimensions) increases, this approach becomes impractical. This is why a lot of work has been dedicated to reducing the in-memory computational complexity  [8] -  [12]. These works mainly focus on two points:  (1)  Using indexes to decrease the number of distances need to be calculated. However, these indexes can hardly be scaled on high dimension data. (2) Using projections to reduce the dimensionality of data. But the maintenance of the accuracy becomes another problem. Despite these efforts, there are still significant limitations to process kNN on a single machine when the amount of data increases. For large dataset, only distributed and parallel solutions prove to be powerful enough. MapReduce is a flexible and scalable parallel and distributed programming paradigm which is specially designed for data-intensive processing.