dataesr/scientific-paragraphs-categorization huggingface dataset:
In the previous rounds of the task, we considered the large differences in repetitiveness across the datasets as a possible explanation for the variable gains over the baseline obtained by participants. In this perspective, the low system performance observed in the APE15 task and in the APE17 German-English subtask was in part ascribed to the low repetition rate in the data. In contrast, much higher repetition rates in the data likely contributed to facilitate the problem in the APE16 task and in the APE17 English-German subtask, in which most of the participants achieved significant gains over the baseline. For this year's data, values are in line with these two previous rounds. The TER (↓) and BLEU (↑) scores reported in Table  2  are computed using the human post-edits as reference. As discussed in  (Bojar et al.
48 TER, 62. 11/62. 49 BLEU), the top neural systems achieved significant improvements over the baseline  (-3. 24 TER and +5. 54 BLEU in 2016, -4. 88 TER and +7. 58 BLEU in 2017). In 2017, on higher quality German-English data (15. 55 TER, 79. 54 BLEU), the observed gains were much smaller (-0. 26 TER, +0. 28 BLEU). These numbers are not directly comparable since the higher quality 2017 data cover a different language pair and belong to a different domain. Nevertheless, as discussed in Section 4, this year's results confirm the correlation between the quality of the initial translations and the actual potential of APE. Further indications about the difficulty of the two subtasks are provided by Figures  1  and 2 , which plot the TER distribution for the items in the two test sets. As can be seen, the PBSMT test data are more distributed in terms of quality, with 50% of the items in the first five TER bins.
Similar to last year, what makes a big difference between the two test sets is the proportion of "perfect" test instances having TER=0 (i. e. items that should not be modified by the APE systems). For the PBSMT subtask they are 15. 0% of the total, a value similar to the APE17 English-German task in which participants achieved large baseline im-provements. For the NMT subtask, they are 25. 2% of the total: much less than the proportion of the challenging APE17 German-English data (45. 0%) but still a considerably higher value compared to the PBSMT subtask. For these test items, any correction made by the APE systems will be treated as unnecessary and penalized by automatic evaluation metrics. This problem calls for conservative and precise systems able to properly fix errors only in the remaining test items, leaving the "perfect" ones unmodified.
Evaluation metrics System performance was evaluated both by means of automatic metrics and manually. Automatic metrics were used to compute the distance between automatic and human post-edits of the machine-translated sentences present in the test sets (i. e. for each of the target sentences in the PBSMT and NMT test sets). To this aim, TER and BLEU (case-sensitive) were respectively used as primary and secondary evaluation metrics. Systems were ranked based on the average TER calculated on the test set by using the TERcom 5  software: lower average TER scores correspond to higher ranks. BLEU was computed using the multi-bleu. perl package 6  available in MOSES. Manual evaluation was conducted via direct human assessment  (Graham et al. , 2016)  performed by professional translators and proficient translation students, as discussed in Section 6.
Participants Five participating teams submitted a total of 11 runs for the PBSMT subtask and 10 runs for the NMT subtask. Participants are listed in Table  3 , and a short description of their systems is provided in the following. German Research Center for Artificial Intelligence -MLT group. The DFKI-MLT's participation is based on a single APE model that is jointly trained to handle PBNMT and NMT outputs. This was achieved by adding, at the beginning of every MT segment to be corrected, a specific token indicating which type of MT system was used to produce it and from which training corpus the segment pair was extracted. (i. e. the WMT training data, the artificial training data presented in  (Junczys-Dowmunt and Grundkiewicz, 2016) , or the eSCAPE corpus  (Negri et al. , 2018) ). The submitted runs were obtained with two neural architectures.
Apart from this modification, the system follows the transformer-base configuration from  (Vaswani et al. , 2017). The synthetic data provided by  Junczys-Dowmunt and Grundkiewicz (2016)  and the eSCAPE corpus  (Negri et al. , 2018)  were both used during training, the latter being splitted into subsets by means of domain selection algorithms aimed to isolate useful portions for the APE target domain (IT). Final submissions were produced with an ensemble of models trained on the different subsets. Pohang University of Science and Technology. POSTECH's system is a multi-encoder model that extends the Transformer implementation in the Tensor2tensor library in order to model the relation between the original translation produced by the MT system and the ideal translation produced by the human.
99 BLEU), the original translations in the test set have a similar quality to those of the APE16 and APE17 En-De test sets (see Table  2 ). In spite of this, we observe further improvements compared to last year, in which the winning system was able to beat the baseline by -4. 9 TER and +7. 6 BLEU points. Also this year, all participants managed to beat the MT baseline at least with their primary submission but the top-ranked submission (MS UEdin Primary) achieved larger improvements up to -6. 24 TER and +9. 53 BLEU points. Moreover, three submissions out of eleven outperformed the baseline by at least -5. 0 TER and +8. 0 BLEU points, which suggests a positive trend in terms of technology advancements. This can also be due to the availability of new additional training data (the eSCAPE corpus).
15%) that are close in percentage to substitutions (36. 88%). In terms of raw percentages, the role of shift operations can explain the lower performance of this outlier system, which was probably penalized by a large number of unnecessary reordering actions. As a more general observation, comparing Figures  3  and 4 , we observe that reordering plays a quite different role in the two subtasks. Systems trained and evaluated on PBSMT data learn and apply more substitutions than those built for the NMT scenario. This can be explained by the higher fluency of neural translations which, among the four types of corrections, reduces the necessity of reordering operations. If this hypothesis holds true, the improvements of NMT outputs will mostly depend on other aspects like lexical choice, as suggested by the larger amount of substitutions compared to the PBSMT subtask.
Conclusion We presented the results from the fourth shared task on Automatic Post-Editing. This year, we proposed two subtasks in which the MT output to be corrected was respectively generated by a phrase system (PBSMT subtask) and by a neural system (NMT subtask). Both the subtasks dealt with English-German data drawn from the information technology domain. This evaluation round attracted submissions from five groups, who submitted eleven runs for the PBSMT subtask and ten runs for the NMT one. Participants' systems have a lot in common: they are all neural models based on the Transformer architecture, some of them are based on multi-source methods and they all took advantage of the synthetic corpora released as additional training material. Evaluation results reflect such similarities and the effectiveness of the proposed solutions: top submissions have very close performance which, on both subtasks, shows significant improvements over the baseline.
Acknowledgments The manual evaluation conducted during the automatic post-editing task has been partially supported by the  German Federal Ministry of Education and Research (BMBF)  under the funding code 01IW17001 (Deeplee) and by the  German Research Foundation (DFG)  as part of  SFB 1102 "Information Density and Linguistic Encoding ". We would like to thank the evaluators who participated in the manual evaluation and  Christian Federmann  for helping in data preparation and results extraction (Federmann, 2012).
| Mytilus edulis: assembly and annotation Reads from 18 M. edulis gut libraries (BioSample Accession nos. SAMN08395021-SAMN08395038 in BioProject PRJNA430138) presented here were combined with reads from a further 27 M. edulis gut libraries (BioSample Accession nos. SAMN08395039-SAMN08395065 in Bioproject PRJNA430138) for de novo assembly of a mussel transcriptome backbone. All libraries were derived from the same experiment with the same protocols. We chose to make transcriptome backbones with all libraries for assembly robustness and analysed only the first subset of 18 M. edulis libraries that were relevant for the current question. The backbone was assembled de novo in CLC Genomics Workbench 8. 5. 1 (CLCbio, Denmark) with default parameters, using automatic word and bubble size and a minimum contig size of 200 nucleotides.
SAMN08394988-SAMN08394998 in We used TransDecoder to find the most probable ORFs  (Haas et al. , 2013)  and InterProScan  (Jones et al. , 2014)  to gain more insight into the potential function of differentially expressed M. intestinalis contigs without a BLAST annotation. After removing contigs that had a bivalve (host) or phytoplankton (food) annotation, 44,616 contigs remained (see Table  2  for assembly statistics). The quality of the parasite transcriptome backbone was assessed through BUSCO  (Simao, Waterhouse, Ioannidis, Kriventseva, & Zdobnov, 2015)  (Table  S1 ). The same gene in different organisms may have different functions, but belong to the same gene regulatory network (e. g. ,  Hinman & Davidson, 2007). Understanding the potential pitfalls of inferring function from annotations based on sequence similarity without further experimental evidence on each specific gene for mussel and M.
| Differential expression analyses Differential expression analyses and GO enrichment analysis were performed in the R statistical environment version 3. 4. 1 (R Core Team 2017), after the reads from each library were mapped back to the respective transcriptome backbone, using the RNA seq tool in CLC Genomics Workbench 8. 5. 1 (CLCbio, Denmark), with the length and similarity fraction parameters set to 0. 5 and 0. 8, respectively. The resulting count data were further analysed using the DESeq2 package  (Love, Huber, & Anders, 2014)  from BioConductor  (Huber et al. , 2015) , where we selected the 18 transcriptomes of mussels and 11 of parasites that belong to the experimental groups described in this article. To address the different questions for mussel hosts, we first analysed the complete mussel RNAseq data set to examine the difference between all infected vs.
all control mussels and then focused on a subset including only the infected mussels to differentiate between sympatric and allopatric infections. For the parasite, we only contrasted  interaction effects were tested by Wald tests. For each main and interaction effect, we used the fdrtool for false discovery rate correction with the Benjamin-Hochberg method  (Strimmer, 2008a (Strimmer, , 2008b)). We consider contigs differentially expressed if the adjusted p-value (p adj ) <. 05 and the absolute log2fold change (LFC) ≥1. 5. To gain further insights into the functions of differentially expressed contigs for mussels, we performed GO enrichment analyses for biological processes of the main and interaction terms based on the GO annotations from Trinotate using the R-package topGO  (Alexa, Rahnenf€ uhrer, & Lengauer, 2006).
intestinalis  (Romero, Novoa, & Figueras, 2015). Consequently, apart from the direct response to wounding, the upregulation of genes associated with cell division processes may also be linked to apoptosis by replacing apoptotic cells. Other immune genes that were differentially expressed in infected mussels (Figure  2a ) were either annotated by host-virus interaction according to the UniProt database (BUB1, SAMDH, importin subunit alpha-1)  (Foulger et al. , 2015; Masson et al. , 2014;  The UniProt Consortium, 2017), or were related to response to F I G U R E 4 Functionally coupled genes between hosts and parasites. The upper panel shows the sums of reactive oxygen species (ROS)producing genes and ROS antagonists of hosts (six contigs numbered  23,071, 41,162, 50,761, 5,027, 106,242 and 14,158  for ROS; seven contigs numbered  1, 728, 38, 961, 14, 694, 116, 190, 90, 969, 61, 914, 105, 058  for ROS antagonists) and parasites (two contigs numbered 8,928 and 8,821).
ACKNOWLEDGEMENTS This work was supported by the  German Research Foundation  (DFG) Special Programme 1399 on host-parasite co-evolution (grant number  We4641/1-2  to K. M. W. ). The authors wish to thank  David Thieltges  and  Anouk Goedknegt  for their help in the field,  Kaibil Escobar Wolf  for his technical assistance in preparation and during the experiment,  Nancy Kuehne  for guidance with library preparation and running Illumina and  Lars Harms  for his support with bioinformatic work on the servers. We thank three anonymous reviewers and  Camille Bonneaud  for their comments, which greatly helped improve this manuscript.
DATA ACCESSIBI LITY Raw RNAseq data are available in the BioProject PRJNA430138 on NCBI with BioSample Accession numbers  SAMN08394988-SAMN08395065 , which is linked to the Short Reads Archive study number  SRP131446 ,  Accession  numbers  SRR6513662-SRR6513728. Mussel M. edulis and parasite M. intestinalis assemblies and corresponding annotation tables are available upon request.
Following HIER at pH 9. 0, the primary antibody against CD31 (DIA-310, Dianova, Hamburg, Germany) or Ki67 (ab16667, Abcam, Cambridge, MA) was applied at a concentration of 1:250 and 1:100 respectively, followed by application of a polymer detection system (DS9800, Novocastra Bond Polymer Refine Detection, Leica Biosystems). For all IHC stains and TUNEL, the chromogen was 3,3 diaminobenzidine tetrachloride (DAB), and sections were counterstained with haematoxylin. For quantification of CD31, Ki67 and TUNEL staining, whole slide digital images were generated on a scanner (Pannoramic 250 Flash III, 3DHistech, 20x/ 0. 8NA objective, Budapest, Hungary) at a resolution of 0. 2431 µm per pixel. Staining quantification was performed with QuPath 0. 1. 2 software (Centre for Cancer Research & Cell Biology, Queen's University Belfast, UK).
GSEA for transcriptome analysis of LNCaP-AR xenografts following VTP treatment LNCaP-AR xenografts were established in intact SCID mice by injecting 2 million cells as described previously  (16)  and, once established, were treated with VTP at 9 mg/kg WST11 followed by 100 mW laser fluence. Tumors were collected at 3, 6, or 24 hours, 1 week and 8. 5 weeks post VTP and RNA was isolated following the standard protocol using TRIzol (Fisher Scientific). Expression profiling was performed using Illumina HT-12 Expression BeadChip array and the data was analyzed using Partek Genomics Suite (Partek Inc. , St. Louis, MO). The microarray data then underwent secondary analysis by GSEA (17) using gene sets from the Hallmark and C2, Canonical Pathways collections (Molecular Signature Databases v6. 0 (MSigDB); Broad Institute: http://software.
Discussion The effective adoption of prostate cancer screening has led to earlier detection of small, clinically significant prostate cancers amenable to the newly developed treatment strategies for partial gland ablation which are well tolerated and associated with fewer adverse side effects than aggressive radical therapies such as surgery and radiation. Positive oncologic outcomes in clinical studies of VTP has led to the recent approval of TOOKAD® Soluble for the treatment of low-risk PCa and highlights the potential of VTP to serve as an alternative to active surveillance or radical therapies  (9) (10) (11). To potentially extend VTP treatment to larger cohorts of patients, we have recently launched a VTP clinical trial (NCT03315754) for patients with localized PCa of intermediate risk, Grade Group 2 [Gleason score 7 (3+4)].
Acknowledgments Authors thank  Thompson Family Foundation  (KK, SL, SJ, AJS, SLR, KSM, AS, JAC),  MSKCC Molecular Cytology Core Facility  and the  Laboratory of Comparative Pathology. Contributions from core facilities were supported in part by  NIH/NCI Cancer Center Support  Grant  P30 CA008748. LH is supported in part by a  Cancer   Center Support Grant  from the  National Institutes of Health/National Cancer Institute  ( NIH/NCI) made to Memorial Sloan Kettering Cancer Center  [ P30 CA008748 ], the  MSKCC SPORE in Prostate Cancer  ( P50CA092629 ), the  Sidney Kimmel Center for Prostate and Urologic Cancers ,  David H. Koch  through the  Prostate Cancer Foundation , and  Oxford Biomedical Research Centre Program in UK.
Methods Data on HIV patients in French Guiana has been available since 1989. Clinical, biological and epidemiological data was entered by specific trained research technicians in the DMI2 government software until 2008, and in eNADIS/DATAIDS since then, as described elsewhere  [7, 8]. The data was aggregated by year. Given the small number of patients in the early years, this led to large fluctuations between years. Therefore, to avoid this "noise" we limited our analysis to the 1996-2016 period. This allowed us to obtain, for each year, the proportion of patients by CD4 count strata at the time of diagnosis, by CDC stage, by CD4 count strata at the time of treatment initiation, and the nadir CD4 count. This was stratified by sex, age group, and foreign/French status.
Statistical analysis The main definition of "advanced disease" used was having CD4 counts < 200/mm 3. To be more thorough we also looked at the CDC stage, the CD4 nadir, the CD4 count at the time of treatment initiation. The data was plotted. In order to test for statistical trend in the curves we used regression models regressing the dependent variable on time, in practice testing the hypothesis that the slope in time was 0. The dependent variables were normalized when necessary to ensure homoscedasticity. Data was analyzed using STATA 13 software (College Station, Texas, USA).
Ethical and regulatory aspects Patients included in the FHDH give written informed consent for the use of their data for research and publication of research results. Their identity is encrypted before sending the data to the Institut National de la Recherche Médicale (INSERM), which centralizes data from Regional Coordination for the fight against HIV (CORE-VIH) throughout France. This cohort has been approved since November 27th 1991 by the Commission Nationale Informatique et Libertés (CNIL) and has led to numerous international scientific publications.
Numerical code Our simulations are performed with a modified version of the RAMSES code  (Teyssier 2002) , a code using the Godunov method to solve the magnetohydrodynamics (MHD) equations  (Fromang et al. 2006). We use a 256 3 and 512 3 grid with no adaptive mesh refinement. For a 1 kpc 3 box, that corresponds to a physical resolution of ∼ 4 or ∼ 2 pc. The development of the shearing box also lead us to use a planar decomposition (along the z-axis) of the cubic domain, each partial domain being attributed to one core. We use sink particles  (Bleuler & Teyssier 2014)  to follow the evolution of the dense gas and model star formation.
Using the data of  Vacca et al. (1996) , we predicate the following relation between S * and the mass of the star: S * = 2 β S 0 M M 0 α 1 + M M 0 α-γ β β. (21) We then have the asymptotic relations S * ∼        2 β S 0 M M 0 α if M M 0 , 2 β S 0 M M 0 γ if M M 0. ( 22 ) The results of this empirical model are given in Table  3  and Fig. 3. The evolution of HII regions is then computed with the radiative transfer module RAMSES-RT  (Rosdahl et al. 2013). Comparisons between analytical models and simulations results for the expansion of HII regions with RAMSES-RT have been done by  Geen et al. (2015). RAMSES-RT has been validated by the STARBENCH comparison project (Bisbas et al. (  2015 )), which tested the D-type expansion of an HII region with different codes. Other works have recently studied the role of HII regions in star formation  (Butler et al.
Acknowledgements. This work was granted access to HPC resources of CINES and CCRT under the allocation A0010407023 made by  GENCI  ( Grand Equipement National de Calcul Intensif ). This research has received funding from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013 Grant Agreement no. 306483 ). We thank the referee for his helpful report.
Characterization techniques The elemental composition of the samples was analyzed using an inductively coupled plasma (ICP) optical emission spectrometer (Thermo ICAP 6300 DUO). Structural analysis of the different catalysts was carried out at room temperature by X-Ray diffraction (XRD) technique using a Bruker D8 Advance diffractometer, with a copper anode Cu Ka radiation. A count time of 2 s per step with a resolution of 0. 02 was used. Identification of the crystalline phases was made with the Joint Committee on Powder Diffraction Standards (JCPDS) files, using EVA software from Bruker. The specific surface area was calculated by the BrunauereEmmette-Teller (BET) method, by nitrogen adsorption at low temperature (À196 C), using an Ankersmit Quanta Sorb Junior apparatus. Before the adsorption, the uncalcined samples were degassed at 60 C for 45 min and the calcined samples at 130 C for 30 min.
Acknowledgments The  "Universit e du Littoral-Côte d'Opale" (ULCO, France)  and the  Lebanese University  ( UL, Lebanon ) are gratefully acknowledged for financial supports. The authors would like to thank the  "Agence universitaire de la Francopho-niedR egion du Moyen-Orient" (AUF)  and the  ULCO  for research fellowship. The  Interreg V project "DepollutAir" (France, Wallonie, Flandres)  funded by the  European Union  is also gratefully acknowledged for financial support. The authors would also like to thank  Karine Deboudt  and  Xavier Secordel  for SEM analysis.
Statistical Analysis As we expected patterns of mate copying to be weaker in males than females, we a priori decided to triple the sample size relative to equivalent studies on female mate copying. All statistical analyses were performed with the R software (version 3. 3. 3; R Core Team, 2017). For each treatment, the difference from random choice was tested with a binomial test. Mate-copying and firstcourt scores were analysed in a generalized linear mixed model (GLMM) with binary logistic regression with the package lme4  (Bates, Maechler, Bolker, & Walker, 2015). All models included air pressure as a fixed effect as previous studies in D. melanogaster showed that weather affects social learning in females  (Dagaeff et al. , 2016). We also included the colour of the female copulating in the demonstration as a fixed effect, as well as 'block', i.
Acknowledgments We thank  Nathalie Parthuisot  for her help in fly care. This work was supported by the  'Laboratoires d'Excellences (LABEX) '  TULIP  ( ANR-10-LABX-41 ), as well as  ANR funded Toulouse Initiative of Excellence 'IDEX UNITI'  ( ANR11-IDEX-0002-02 ). Work of E. D. and S. N. was also supported by the Soc-H 2  ANR  project ( ANR-13-BSV7-0007-01 ) to E. D. S. N. 's salary was provided by Soc-H 2 and a  Marie Curie   PRESTIGE grant  ( PRESTIGE-2014-1-0005 ). M. A. and S. N. performed the experiments together; S. N. carried out data analysis, participated in the design of the study and drafted the manuscript; G. I. participated in the design of the study; E. D. participated in designing the study, data analysis and helped with the manuscript. All authors gave final approval for publication.