dataesr/scientific-paragraphs-categorization huggingface dataset:
05; **p ≤ 0. 005; ***p ≤ 0. 001; ****p ≤ 0. 0001; ns = not significant). a-c and e-h Source data are provided as source data file. overabundance of Bacteroides has been associated with increased rates of inflammation and tumor development  42. Moreover, the expansion of bacteria from the family Alcaligenaceae drives inflammation in ILC-depleted RAG1 -/-mice  43. On the contrary, the enhanced relative abundance of Prevotella in diseased NK WT mice may have beneficial effects, since it correlates inversely with inflammation in a mouse model of inflammation-induced carcinogenesis  42. Altogether, these results demonstrate that the Fig. 6  The lack of c-FLIP-dependent NKp46 + ILC is associated with disease-related alterations in microbiome composition. a-f Feces samples of NK WT and NK Δc-FLIP littermates (Fig.
*p < 0. 05; **p < 0. 01; ***p < 0. 001. A significant effect was dedicated when p < 0. 05 and R 2 > 0. 10 (equivalent to 10% of explained variance). d, e Relative abundance of the average microbiome composition d before and e after DSS administration was determined on the family level. Phylum and families are indicated. f Statistically significant differences on family levels in fecal microbiota composition between NK WT and NK Δc-FLIP mice. Data were analyzed using linear discriminant analysis (LDA) effect size (LEfSe) method (Kruskal-Wallis test with p < 0. 05 and LDA scores > 3. 0). Data are displayed as bar plot ranked according to their effect size and associating them to the genotype of the mice. a-f Source data are provided as source data file. expansion of disease-promoting microbes, such as Proteobacteria, i.
Peripheral blood leukocytes were stained in whole blood samples for 30 min at RT, followed by erythrocyte lysis and fixation using BD FACS TM lysing solution (BD Biosciences). Samples were measured on a LSR Fortessa and FACSCanto flow cytometer (Becton Dickinson) and analyzed by FlowJo software (FlowJo LLC). In vitro stimulation of splenic ILC. Splenocytes were isolated from C57BL/6 mice and cultured for 16 h in RPMI (with 10% FCS and 100 U/ml P/S) supplemented with IL-15 (10 ng/ml, Peprotech), with rmIL-15 (10 ng/ml) and pimozide (5 μM, Calbiochem) or were left untreated. Lineage negative (CD3 -, CD4 -, CD8 -, CD19 -, Gr1 -, Ter119 -) CD122 + NK1. 1 + NKp46 + ILC were analyzed by flow cytometry for their expression of c-FLIP and phosphorylation of STAT5. Detection of apoptotic cells. Apoptotic cells were visualized by flow cytometry by using the FAM-FLICA Caspase 3/7 assay kit from ImmunoChemistry Technologies according to the manufacturer's protocol.
5% (w/v) BSA/2 mM EDTA containing anti-CD16/32 overnight. Subsequently, cells were washed in 200 µl permeabilization buffer (Biolegend) and incubated with fluorochome-labeled anti-cytokine mAbs or isotype-matched control antibodies for 1 h at 4 °C. After washing cells were resuspended in PBS/2 mM EDTA, measured on a LSR Fortessa (Becton Dickinson) flow cytometer and analyzed by FlowJo software (FlowJo LLC). NK depletion. C57BL/6 mice were injected i. p. with 50 µg of NK-depleting anti-Asialo GM1 (Affimetrix). Treatment was repeated every third day. A total of four injections were performed. Control animals were treated accordingly with 50 µg polyclonal rabbit control IgG (Sigma-Aldrich). Statistical analysis. Experimental results were plotted and analyzed for statistical significance with Prism software 5 or 6 (GraphPad Software Inc.
After pooling and normalization to 10 nM, PCR amplicons were sequenced on an Illumina MiSeq platform via 250 bp paired-end sequencing (PE250). 16S rRNA gene analysis. Using Usearch8. 1 software package (http://www. drive5. com/usearch/) the resulting reads were assembled, filtered, and clustered. In brief, merging was performed using -fastq_mergepairs -with fastq_maxdiffs 30 and quality controlling was performed with fastq_filter (-fastq_maxee 1), using a minimum read length of 200 bp and a minimum number of sequences per sample = 1000 as thresholds. Reads were clustered into 97% ID OTUs and representative sequences were determined by use of UPARSE algorithm  66. Taxonomy classification was performed with the Silva database v128  67  and the RDP Classifier executed at 80% bootstrap confidence  68.
Resulting OTU absolute abundance table and mapping file were used for statistical analyses and data visualization using R statistical programming environment (R Core Team (2016)) package phyloseq  69. Statistics were performed with 'R' version 3. 3. 0 (2016-05-03), (http://www. rproject. org) and the packages 'phyloseq'  69  , and 'ggplot2'  70. Non-parametric Mann-Whitney U tests were considered as significant when P values were lower than 0. 05 after multiple testing correction (Benjamini-Hochberg false discovery rate correction). Permutational multivariate analysis of variance test (ADONIS) was used to assess the influence of different variables on the sample variability. The ADONIS tests were computed using 999 permutations and a resulting R 2 > 0. 1 (effect size 10%) and P-values < 0.
Acknowledgements We thank  E. Denks ,  J. Giese ,  C. Kozowsky ,  J. Nichelmann ,  S. Schumann , and  M. Berger  for excellent technical assistance. We are grateful to  Dr. Y. -W. He  for providing c-FLIP fl/fl mice. This work was supported by the  Deutsche Forschungsgemeinschaft Sonderforschungsbereich   SFB854  (project  A22 ) and  DFG priority program 1937  (project  SCHU   2326/2-1 ).
Data availability 16S rRNA gene sequencing data have been deposited in the NCBI (Bioproject Database) under the accession number: PRJNA437582. The source data underlying Figs. 1a-d , 2a-f, 3e-k, 4a-f and i-m, 5a-c, and e-h, 6a-f, 7c and d, 8a-f, i and k and Supplementary Figs. 1, 2, 3a , and e, 4a and e-k are provided as a Source Data file. All other data that support the findings of this study are available from the corresponding author on reasonable request.
Introduction The use of Machine Translation (MT) to translate everyday, written exchanges is becoming increasingly commonplace; translation tools now regularly appear on chat applications and social networking sites to enable cross-lingual communciation. MT systems must therefore be able to deal with a wide variety of topics, styles and vocabulary. Importantly, the translation of dialogue requires translating sentences coherently with respect to the conversational flow in order for all aspects of the exchange, including speaker intent, attitude and style, to be correctly communicated. It is important to have realistic data to evaluate MT models and to guide future MT research for informal, written exchanges. In this article, we present DiaBLa (Dialogue BiLingue 'Bilingual Dialogue'), a new test set of English-French spontaneous written dialogues mediated by MT, 1  obtained by crowdsourcing and covering a range of dialogue topics and annotated with fine-grained human judgments of MT quality.
To our knowledge, this is the first corpus of its kind. Our data collection protocol is designed to encourage spontaneous dialogue between speakers of two languages, using role-play scenarios to provide conversation material. Sentence-level human judgments of the quality of the MT systems are provided by the participants themselves while they are actively engaged in dialogue. The result is a rich bilingual test corpus of 144 dialogues, which are annotated with sentence-level MT quality evaluations and human reference translations. We discuss the potential of the corpus and the collection method for MT research in Sec. 2, both for MT evaluation and for the study of language behaviour in informal dialogues. In Sec. 3 we describe the data collection protocol and interface. We describe basic characteristics and examples of the corpus in Sec.
Related work A number of corpora of informal data do exist. However they either cover different domains or are not designed with the same aim in mind. OpenSubtitles2016  (Lison and Tiedemann, 2016)  is a large-scale corpus of film subtitles, from a variety of domains, making for very heterogeneous content. However the conversations are scripted rather than being spontaneous, and are translations of monolingual texts, rather than being bilingual conversations. The MSLT corpus  (Federmann and Lewis, 2016)  is designed as a bilingual corpus, and is based on oral dialogues produced by bilingual speakers, who understand the other speaker's original utterances. This means that it is not possible to analyse the impact that using MT has on the interaction between participants. Other bilingual taskorientated corpora exist, for example BTEC (Basic Travel Expression Corpus)  (Takezawa et al.
, 2002) , SLDB (Spoken Language DataBase)  (Morimoto et al. , 1994)  and Field Experiment Data of  Takezawa et al. (2007) , which is the most similar corpus to our own in that it contains MT-mediated dialogue. However these corpora are restricted to the travel/hotellery domains, therefore not allowing the same variety of conversation topic as our corpus. Human judgments for the overall quality are provided for the third corpus, but only at a very coarse-grained level. Feedback about the participants' perception of MT quality is therefore of a limited nature in terms of MT evaluation; sentence-level evaluations are not provided.
Data collection and protocol We collected the dialogues via a dedicated web interface, allowing participants to register, log on and chat. Each dialogue involves two speakers, a native French speaker and a native English speaker. Each writes in their native language and the dialogue is mediated by two MT systems, one translating French utterances into English and the other translating English utterances into French. Participants Participants are volunteers recruited by word of mouth and social media. They participated free of charge, motivated by the fun of taking part in fictional role-play. Users provide basic information: age bracket, gender, English and French language ability, other languages spoken and whether they work in research or Natural Language Processing (see Tab. 1 for basic statistics).
MT systems We compare the quality of two MT model types (see Sec. 4. 4). Within a dialogue, the same model type is used for both language directions, and the same number of dialogues is mediated by each model type. Both models are neural encoder-decoder models with attention  (Bahdanau et al. , 2015) , implemented using Nematus  (Sennrich et al. , 2017). The first model (BASELINE) is trained to translate sentences in isolation. The second (2TO2), is trained to translate sentences in the context of the previous sentence, as in  (Tiedemann and Scherrer, 2017)  and  (Bawden et al. , 2018). This is done by concatenating each sentence with its previous sentence, separated by a special token, and translating both sentences at once. In a post-processing step, only the current sentence is kept. Note that if the previous sentence is spoken by the same speaker as the current sentence, then the original previous sentence is prepended.
If the previous sentence is spoken by the other speaker (in the opposite language), then the MT output of the previous sentence is prepended to the current sentence. This means that the previous sentence is always in the same language as the current sentence, and also corresponds to the context seen by the current speaker. Training Data and MT setup The systems are trained using the OpenSubtitles2016 parallel corpus  (Lison and Tiedemann, 2016). The data is cleaned, tokenised and truecased using the Moses toolkit  (Koehn et al. , 2007)  and tokens are split into subword units using BPE  (Sennrich et al. , 2016b). The data is then filtered to exclude poorly aligned or truncated sentences, resulting in a training set of 24,140,225 sentences. Hyperparameters are given in App. B. During the dialogues, the participants' text is first split into sentences and preprocessed in the same way as the training data.
Machine translations Each sentence is translated automatically into the other language for the other participant. A single type of MT system (BASELINE or 2TO2) is used for all sentences within a dialogue. The use of two different systems is relevant to our analysis of the human evaluations produced by dialogue participants (Sec. 4. 4). The choice of MT system does of course affect the quality of the MT. However, the corpus will remain relevant and useful as a test set and for analysing human language behaviour in this setup independently of this choice. Figure  3 : A dialogue extract with baseline MT output ("MT:", in grey) and human evaluation ("Eval:").
Human judgments of MT quality As described in Sec. 3, participants evaluated the translations from a monolingual (target language) perspective. We provide a preliminary analysis of these judgments to show that they are a good indicator of MT quality and that such a protocol is useful for comparing MT models in a real setting. Overall MT quality Although an in-depth linguistic analysis is beyond the scope of this paper, we look here at global trends in evaluation. 6 ifferences between models are shown in Fig. 4. They show unsurprisingly that MT quality is dependent on the language pair; translation into English is perceived as better than into French, approximately half of all EN→FR sentences being annotated as medium or poor. 7 There is little difference in perceived quality between the BASELINE and 2TO2 for FR→EN.
This contrasts with EN→FR, for which the number of sentences marked as perfect is higher by +4% for 2TO2 than for BASELINE. An automatic evaluation with BLEU 8   (Papineni et al. , 2002)  shows that the contextual model scores mildly better than the baseline, particularly for EN→FR. We retranslate all sentences with both models and compare the outputs to the reference translations: for  Types of errors encountered The evaluation results for each problem type are shown in Fig. 5. The few number of problems classed as other indicates that our categorisation of MT errors was sufficiently well chosen. The most salient errors for all language directions and models are in word choice, especially when translating into French, with approximately 16% of sentences deemed to contain a word choice error.
As with the overall evaluations, there are few differences between BASELINE and 2TO2 for FR→EN, but significant differences are seen for EN→FR: 2TO2 models perform better, with fewer errors in most problem types, except word choice. A notable difference is the lower frequency of coherence-related errors for 2TO2. Coherence errors also appear to be less serious, as there is a lower percentage of translations labelled as poor (as opposed to medium). These results are encouraging, as they show that our data collection method is a viable way to collect human judgments, and that such judgments can reveal fine-grained differences in MT systems, even when evaluating on different sentence sets. In spite of the errors, the translation quality is in general good, especially into English, and participant feedback is excellent concerning intelligiblity and dialogue flow.
Conclusion The corpus presented in this paper therefore provides a wide range of opportunities for future research into the type of language used in this setting and the way in which participants handle breakdowns. The protocol for data and MT judgment collection presented provides a useful framework for future evaluation of MT quality, and the corpus itself can be used as a test set to guide manual evaluation of new models. Our preliminary analyses of sentence-level human judgments show that the evaluation procedure is viable, and we have observed some interesting differences between two types of MT model used in our experiments.
Indeed, following the extensive experimental programs implemented during the last century both in the field of nuclear structure and of the nuclear reactions, we posses now databases that make possible practically all applications currently available. Further, the development of new applications that may bring benefits to the society depends on the improvement of these databases. Therefore the field of nuclear data represents nowadays a quest for reliability and precision. In this context we developed the GAINS (Gamma Array for Inelastic Neutron Scattering) setup  [1]  at the GELINA  [2]  (Geel Linear Accelerator) neutron source with the definite goal of measuring neutron inelastic cross * e-mail: alnegret@tandem. nipne. ro sections with low uncertainty while controlling and understanding as well as possible all experimental details in order to gain confidence and reliability.
The setup is dedicated to measurements of nuclei that constitute structural materials for present and future facilities. Particularly in the nuclear reactors, inelastic scattering plays a key role during the process of neutron thermalization as it is the main energy loss mechanism. A second setup dedicated to neutron inelastic scattering on actinides, GRAPhEME, is also operational at GELINA; a separate contribution from the current proceedings presents an overview of this setup  [3]. The experimental program of both facilities was driven to a large extent by the experimental issues listed in the HPRL (High Priority Request List) database maintained by the Nuclear Energy Agency [4] but also by other scientific cases, as it will be detailed in the present overview.
The setup. Evolution timeline The current version of GAINS consists of 12 HPGe detectors centered around the sample under investigations at angles of 110 • , 125 • and 150 • with regard to the neutron beam provided by GELINA, the pulsed time-of-flight facility operated by the Joint Research Center of the European Commission in Geel, Belgium. The current flight path is 100 m. The neutron beam has a diameter of 6. 1 cm, it is pulsed at a repetition rate of 800 Hz and each pulse has a duration of about 1-2 ns. These characteristics combined with the specific energy resolution of the HPGe detectors allow highly precise cross section measurements using the γ-spectroscopy and the time-of-flight techniques. The primary experimental results consist of the angle-integrated γ-production cross sections and, using the level scheme of the studied nucleus from an external database, we infer the level and the total inelastic cross sections.
Neutron inelastic scattering as a source of background for neutrinoless double-β decay experiments One last horizontal investigation we wish to mention exploited several of our experimental datasets to perform an assessment of possible sources of background affecting the experiments attempting to observe neutrinoless doubleβ decay. Such experiments are normally conducted in deep underground laboratories where neutrons and particularly their inelastic scattering can produce background that would mimic the signature of neutrinoless double βdecay events. Neutrons can originate from the interaction of cosmic muons with the environment or from (α,n) reactions triggered following the α decay of the uranium or thorium from the rocks. The study was presented in Ref. [8]. We identified a 2041-keV transition in 206 Pb and a 2035-keV transition in 56 Fe tht could represent sources of background in the attempt to detect the specific signature of the neutrinoless double β-decay of 76 Ge.
The authors thank the team of operators of the  GELINA facility  for the preparation of the neutron beam and to the technical staff of  JRC-Geel  for their support during the data taking campaign. This work was partially supported by the  European Commission  through the  ANDES (EURATOM  Contract number  FP7-249671 ),  EU-FRAT (EURATOM  Contract number  FP7-211499 ), and CHANDA (EURATOM contract number  FP7-605203 ) projects.
Based on the large database of Alzheimer's Disease Neuroimaging Initiative, conversion from mild cognitive impairment to Alzheimer's disease was predictable with an accuracy of 0. 7-0. 79 [25]. Structural measures in the temporal lobe and its subregions such as the hippocampus or the entorhinal cortex seem to be highly indicative for progression of the disease  [26] [27] [28] [29] [30] [31] [32]. It was early suggested that automation of volumetric assessment could pave the way for clinical implementation of prognostic consulting in the clinical setting  [33]. Moreover, wavelet features from the MRI have been applied for classifying patients with Alzheimer's dementia and mild cognitive impairment  [34] [35] [36]. Despite the considerable attention attracted by neuroimaging in the past decade  [37] , clinical use of the EEG for diagnosis and prognosis of dementia was emphasized more than 30 years ago  [38].
MRI Volumetry. e automated segmentation was performed using a set of 30 hand labeled atlases (83 regions each, for a list of the regions, see Supplementary material) made publicly available by Hammers et al. [81]. After brain extraction using the brain extraction tool in the functional MRI of the Brain Software Library (available from http://fsl. fmrib. ox. ac. uk), all participants were diffeomorphically registered using advanced normalization tools (available from http://stnava. github. io/ANTs/) to each atlas. e final segmentation was obtained by using majority voting to fuse the registration outcomes for each subject. e result was a labeled volume, containing labels for the 83 cortical and subcortical structures. e volume of the segmented regions was normalized by the sum of all volumes, that is, we calculated the percent each region took in relation to the sum of all segmented regions.
EEG Registration. EEG was recorded in a quiet room using a BrainCap with a 10-20 system and a BrainAmp (Brain Products GmbH, Germany) 16-bit analog-to-digital converting amplifier. e sampling rate was 500 Hz. Of the 32 recorded channels, one was used to monitor the lower vertical electrooculogram and one was used to measure electrocardiographic activity. Two channels were positioned at the earlobes for rereferencing purposes to remove the bias of the original reference, which was placed at FCz. Data analysis was conducted for a subset of 17 electrodes: F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T7, T8, P7, P8, Fz, Cz, and Pz. Impedances were kept below 10 kΩ. e two EEG sessions were arranged to take place at the same time of the day. For most participants, EEG was performed within the same time range around noon (1 pm).
Data Preparation. Data were preprocessed with Brain Vision Analyzer (Version 1. 05. 0005, Brain Products GmbH). In order to rereference all channels, a new reference was built by averaging the signal of earlobe electrodes. Butterworth zero-phase filters were used for a high-pass filter from 1 Hz (time constant 0. 1592 s, 48 dB/oct), and an additional notch filter (50 Hz) was applied. An automatic artefact detection was carried out in order to exclude artefacts. Maximal allowed voltage step per sampling point was 50 μV (values which exceeded this threshold were excluded within a range of ±100 ms), maximal allowed absolute difference on an interval of 200 ms was 200 μV, and lowest allowed absolute difference during an interval of 100 ms was 0. 5 μV (values which exceeded this were marked with a surrounding of ±500 ms).
e preprocessed data were exported into a generic data format and imported to Matlab (release R2017b, Mathworks, Massachusetts, USA). e data were segmented into 1000 ms segments (i. e. , 500 sampling points) for each participant and each condition. For each condition (learning/recall), the segment started 100 ms before stimulus onset and ended 900 ms afterwards. If the segment overlapped with a marked artefact, it was excluded.