dataesr/scientific-paragraphs-categorization huggingface dataset:
| MRI data acquisition and analysis Standardisation of data acquisition across sites was performed based on previous suggestions  (Glover et al. , 2012; Kloppel et al. , 2015). 3T-MRI data were acquired on two different scanner systems (Philips Achieva at Leiden and Vancouver and Siemens TIM Trio at London and Paris). Diffusion-weighted images were collected with 42 unique gradient directions (b 5 1,000 s/mm 2 ) with eight images with no diffusion weighting (b 5 0 s/mm 2 ) (Siemens) and one image with no diffusion weighting (b 5 0 s/mm 2 ) (Philips). Acquisition parameters were TE 5 88 ms, TR 5 13 s and voxel size 2 3 2 3 2 mm (Siemens); TE 5 56ms and TR 5 11s and voxel size 1. 96 3 1. 96 3 2. 75 mm (Philips). The diffusion data were preprocessed using standard FSL pipelines  (Smith et al.
, 2004). The T1 scan was segmented into gray and white matter using the VBM8 toolbox (http://dbm. neuro. uni-jena. de/vbm/) and combined to create an improved anatomical scan for DTI data registration. Prior to analysis, we screened each DTI dataset for artifacts, signal drop-out and motion. Data were then corrected for distortions caused by eddy currents and motion using eddy_correct in FSL, and vector gradient information updated accordingly. The no-gradient (B0) image (an averaged B0 image was used for the Siemens data) was then skull-stripped using the Brain Extraction Tool (BET) and manually corrected. For registration purposes, we also applied BET to the structural T1 image. To improve the quality of the brain mask, we combined and dilated a thresholded segmented image with an eroded brain-extracted T1 mask, which was then applied to the original brain-extracted T1 image.
We then linearly registered the resultant T1 image to the B0 image using FLIRT  (Jenkinson & Smith, 2001)  with standard parameters. Diffusion tensors were fit to the corrected data using dtifit and fractional anisotropy (FA) axial diffusivity (AD) and radial diffusivity (RD) values derived. We modeled within-voxel crossing fibers using a Bayesian probabilistic method implemented in Bedpostx  (Behrens et al. , 2003). Probabilistic tractography was performed for a series of tracts using probtrackx  (Behrens, Berg, Jbabdi, Rushworth, & Woolrich, 2007) ; more information can be found at Table  1. For the sensorimotor network, this included tracts in the right hemisphere connecting the primary motor cortex (M1) and the motor thalamus; the premotor cortex (PMC) and the motor thalamus; and the primary somatosensory cortex (S1) and the somatosensory thalamus (for completeness we also included the equivalent left-sided tracts from our previous study in the final analyses).
Mean fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) values were extracted from the mask of each tract for each participant weighted by the contribution of each voxel by the number of streamlines that pass through it (as opposed to assuming an equal contribution from each voxel). FIG URE  1  Study workflow. After processing data from the DTI sequence, tractography was employed to discern 15 tracts bilaterally, the mid corpus callosum and posterior corpus callosum so that, altogether, we derived 32 tracts. From each tract we extracted information about axial (AD) and radial diffusivity (RD) as well as fractional anisotropy (FA). Hence, we included 96 diffusivity measures in our PCA. The first 3 principle components, each of which contains information on all 3 diffusivity measures (AD, RD, FA) explained 56% of the variability in the data.
| Diffusivity patterns (PCA) across both controls and HD gene-carriers We first examined diffusivity patterns, i. e. , PC (principal components) across all 32 tracts of the combined control and HD gene-carrier groups. PCA revealed that the first three principal components The similarity with the PCA of controls alone suggests that the overall variability in the data forms patterns similar to the patterns of correlated natural variability within the healthy white matter of the controls. Nonetheless, within the joint analysis there were notable differences in the mean PC scores. On the first and third PCs, the early HD group had significantly different mean scores compared to the preHD and control groups (see below). For PC1, there was a pattern across all tracts which demonstrated an inverse relationship between FA and the two diffusivity measures, AD and RD, i.
PC coefficients represent correlations between diffusivity measures and their contributions to each pattern of diffusivity. We investigated these associations in a subset of representative tracts for each PC, plotting diffusivity metric values against PC scores with individual data points color-coded for group membership. As discussed earlier, there was considerable overlap in distribution across the groups (Figure  4 ). For PC1, all three groups displayed correlations between PC1 scores and RD and AD in sensorimotor (bilateral PMC and both thalamus and putamen, respectively) and visual network (bilateral IFOF) tracts plus bilateral ILF (Figure  3 ; Table  2 ), suggesting that both AD and RD contributed strongly to PC1 regardless of the type of tract (Figure  3 ). Diffusivity values and mean scores were highest in earlyHD (Figure  4 ).
Many applications in biomedical natural language processing rely on sequence tagging as an initial step to perform more complex analysis. To support text analysis in the biomedical domain, we introduce Yet Another SEquence Tagger (YASET), an open-source multi purpose sequence tagger that implements state-ofthe-art deep learning algorithms for sequence tagging. Herein, we evaluate YASET on partof-speech tagging and named entity recognition in a variety of text genres including articles from the biomedical literature in English and clinical narratives in French. To further characterize performance, we report distributions over 30 runs and different sizes of training datasets. YASET provides stateof-the-art performance on the CoNLL 2003 NER dataset (F1=0. 87), MEDPOST corpus (F1=0. 97), MERLoT corpus (F1=0.
Introduction Many applications in biomedical Natural Language Processing (NLP), including relation extraction or text classification, rely on sequence tagging as an initial step to perform more complex analysis. To support text analysis in the biomedical domain, we present Yet Another SEquence Tagger (YASET), an open-source multi-purpose sequence tagger written in Python. YASET aims at providing NLP researchers with fast and accurate implementations of cutting-edge deep learning sequence tagging models. YASET is built using Ten-sorFlow  (Abadi et al. , 2015) , an open source software library for numerical computation. The code is licensed under the version 3 of the GNU Gen-eral Public License 1  and is freely available online 2. The main contributions of this work are: • a fast and accurate implementation of a state-of-the-art sequence tagging model based on Long Short-Term Memory Networks (LSTMs)  (Hochreiter and Schmidhuber, 1997).
Related Work Several open-source implementations for sequence tagging based on neural network architectures have become available over the last years. Lample et al. (2016)  provide a Python implementation 3  for the two models presented in their paper. They are implemented with Theano  (Al-Rfou et al. , 2016) , a Python library for deep learning. NeuroNER 4  (Dernoncourt et al. , 2017 ) targets non-expert users and is based on Lample's Bi-LSTM model. The authors intended to make the tool easy to use by providing automatic format conversion from the brat format  (Stenetorp et al. , 2012)  to the input format and from the output format to the brat format. The tool produces several plots during training for performance analysis. It is implemented in Python and makes use of the TensorFlow library.
Both implementations of the Bi-LSTM model suffer from a very long training time which makes them cumbersome to use. YASET offers a faster implementation of the model by allowing mini-batch training and by using the pipeline API of TensorFlow. Rei and Yannakoudakis (2016) released a Python implementation 5 of different models presented in their works  (Rei and Yannakoudakis, 2016; Rei et al. , 2016; Rei, 2017). One major difference with YASET resides in the possibility to use a language modeling objective during training. Recently,  Yang and Zhang (2018)  introduced NCRF++ 6 , a tool presented as the neural version of CRF++ 7 and implemented with Py-Torch  (Paszke et al. , 2017). The tool is very close to YASET, with the possibility to define handcrafted word features and to perform nbest decoding.
Another type of neural network model, based on Convolutional Neural Networks (CNNs) for character level representation, is presented in  Ma and Hovy (2016). The authors implemented the architecture with PyTorch. The code is freely available online 8. The same type of architecture is implemented in the tool released by  Reimers and Gurevych (2017). It is implemented with  Keras (Chollet et al. , 2015)  and is freely available online 9. Outside the peer-reviewed scientific environment, many other implementations are freely available online. However, we will not review them in this paper.
Input Pipeline Minimizing the memory footprint was one of the core objectives during the development of the tool. YASET leverages the pipeline API of TensorFlow to build a lightweight and fast input pipeline. Input instances are stored in the binary TensorFlow format. This avoids the need to store the whole dataset in memory. Mini-batches are extracted randomly and fed to the network. Training instances can be bucketized according to their lengths. This possibility can speedup training with large corpora. Buckets boundaries are automatically computed. To assert effective randomization during training, buckets will contain enough instances for several mini-batches.
Metrics Model performance is measured on the development instances at the end of each iteration. Two metrics are currently implemented in YASET: accuracy and CoNLL. The former measures the fraction of correctly predicted labels among all the predicted labels. The latter is an entity-based metric which outputs precision, recall and F1-measure scores. Precision measures the fraction of correctly predicted entities among all predicted entities. Recall assesses the fraction of correctly predicted entities among all the entities that should have been detected. F1-measure is the harmonic mean of precision and recall. The implementation of the CoNLL metric is inspired by the official evaluation script used during the  CoNLL-2003 shared task (Sang and Meulder, 2003)  and by the Python portage of the script by Sampo Pyysalo 11.
Experiments We demonstrate the performance of YASET by applying the model to four different corpora selected to cover a variety of languages, text genres, sequence types and annotation densities. We focus our effort on biomedical texts, using Med-Post  (Smith et al. , 2004) , a corpus of biomedical abstracts annotated with Part-of-Speech (PoS) tags, the NCBI Disease corpus  (Islamaj Dogan and Lu, 2012) , a dataset of biomedical abstracts annotated with disease related entities and MERLoT  (Campillos et al. , 2018) , a corpus of clinical documents written in French which were annotated with two different Named Entity Recognition (NER) tag sets (Protected Health Information (PHI) and biomedical entities). We also apply YASET on the CoNLL 2003 English NER corpus  (Sang and Meulder, 2003) , a classic benchmark corpus for NER in the general domain.
Presentation of the Corpora We use the corpus partition provided with the dataset distributions when available. For NCBI and CoNLL 2003, models are trained on the train sets and evaluated on the test sets while the development sets are used to determine early stopping. For MERLoT and MedPost, partition details are outlined below. The code used to preprocess and analyze the corpora is available online 13. The MedPost corpus is a collection of tokenized MEDLINE abstracts annotated with PoS tags. It contains 6,701 sentences (≈182,000 tokens). The corpus is divided in 13 files of different sizes, from which we extracted one file to serve as development set (tag mb. ioc). The tag set contains originally 63 unique entities that we grouped in a coarser set of 51 entities. Grouping affected punctuation signs, which were assigned a unique PUNCT tag.
The NCBI corpus contains 793 MEDLINE abstracts with 11,350 annotations of diseases and disease modifiers. There are 4 different entities. Train and development sets contain 6,594 sentences (≈151,000 tokens). MERLoT is a French clinical corpus with several levels of annotations. It contains 500 medical reports with a total of 25,087 sentences (≈177,000 tokens). We used it for two tasks. The 13 https://github. com/strayMat/ bio-medical_ner first one is de-identification with a tag set comprising 11 types of PHI (e. g. names, dates). The second one is medical NER with 19 entity classes (e. g. anatomy, disorder). The medical entity annotations include nested entities. Because this study aims to experiment on a variety of datasets using the same model, we did not attempt to extract several levels of nested entities.
Hyper-parameter Selection We used Hyperopt 14   (Bergstra et al. , 2011 (Bergstra et al. , , 2013) )  to select a set of hyper-parameters that performed well on the MERLoT de-identification dataset. Due to heavy computation times, we re-used this set of hyper-parameters on the other datasets. Parameter search space includes the number of units of the character Bi-LSTM, the number of units of the main Bi-LSTM, the dimension of the character embeddings and the dropout rate. The retained setup uses character embeddings of size 24, 32 units for the character-level Bi-LSTM, 64 units for the main Bi-LSTM and a dropout rate of 0. 5.
Embeddings Pre-trained word embeddings were shown to boost the performance in various NLP tasks  (Collobert et al. , 2011; Mikolov et al. , 2013a)  and specifically in NER  (Lample et al. , 2016; Dernoncourt et al. , 2017). For each corpus, we used pre-trained word embeddings created with datasets that were consistent in genre and domain with the corpora used in this work. All word embedding models were computed using word2vec. For CoNLL 2003, we trained a model on Google News. For NCBI disease and MedPost, which both comprise abstracts from biomedical articles, we trained a model on the PubMed corpus. For MERLoT, we trained a model on the corpus from which MERLoT is extracted. It contains about 138,000 clinical notes written in French  (Campillos et al. , 2018). For each of these word vector models, we computed low-dimensional representations by applying Principal Component Analysis (PCA) on the original high-dimensional word vectors (300 di-mensions).
Corpus Specific Results Reimers and Gurevych (2017) report that neural network model training is highly nondeterministic and is subject to the random seed choice. Because of this variability during the training phase, it is crucial to report results on numerous trainings. Therefore we ran 30 experiments for each task presented in this work. We report F1score statistics in Table  2. We also plot F1-score distributions for each task in Figure  4. Our experiments show that performance varies across datasets, reflecting the heterogeneous difficulties of the tasks, inherent to the nature of the labels and the quality of the annotations. We notice that the standard deviations are similar for all NER tasks. It suggests that the variability of the score is independent from the task and mainly due to the model architecture.
Performance According to Training Data Size The development of a labeled dataset for training annotation models is often a heavy investment in time and resources. Having some insight on the performance of the model for different training data sizes is crucial. Thus, we investigate the impact of this parameter on model performance. Focusing on the de-identification task of MERLoT, we split the training set into 20 subsets of equal sizes. Then, we built sub-training sets of growing size, that we refer as chunks. resulted in 20 chunks where small chunks are subsets of the bigger one. Finally, we train YASET 6 times on each chunk, measuring how the performance improves with the size of the chunks. Figure  5  shows the progression of the F1-score according to the number of tokens. We observe that the performance improves logarithmically with every chunk added in the training data as shown in Table  4.
Conclusion and Future work We propose an easy-to-use annotation tool implementing a state-of-the-art Bi-LSTM-CRF neural architecture. We apply the tool to PoS tagging and NER on clinical, biomedical and general domain texts. By running multiple experiment for each dataset, we confirm previous observations that neural network training is highly nondeterministic and dependent on the random seed choice. Although we did not search for optimal hyperparameters for each task, we obtain high performance in all our experiments, suggesting that tailoring hyper-parameters for a specific task improves only lightly the performance of the model and the neural network architecture implemented in YASET is robust with regards to the performance obtained. Concerning the impact of the training dataset size on model performance, we confirm the intuition that adding more data allows to improve the performance of neural network models.
However, result analysis suggests that the growth is logarithmic with the training data size. One limitation of our model is its inability to directly handle nested entities such as those found in the MERLoT medical dataset and commonly used in the biomedical and clinical domains. When filtering these nested entities, specific classes are heavily impacted, including anatomy, disorder, localization, and medical procedure entities. Several strategies have been proposed to handle such cases. Campillos et al. (2018)  present a 3layer CRF model that annotates different non-overlapping clinical entities at each layer. Ju et al. (2018)  present a dynamic end-to-end neural network model capable of handling an undetermined number of nesting levels. Katiyar and Cardie (2018)  model the task as an hypergraph whose structure is learned with an LSTM network.
Acknowledgements The authors thank the  Biomedical Informatics Department at the Rouen University Hospital  for providing access to the LERUDI corpus for this work. This work was supported in part by the  French National Agency for Research  under grant CABeRneT  ANR-13-JS02-0009-01  and by  Labex Digicosme , operated by the  Foundation for Scientific Cooperation (FSC) Paris-Saclay , under grant  C ÔT.
A growing number of tools now allow live recordings of various signaling pathways and protein-protein interaction dynamics in time and space by ratiometric measurements, such as Bioluminescence Resonance Energy Transfer (BRET) Imaging. Accurate and reproducible analysis of ratiometric measurements has thus become mandatory to interpret quantitative imaging. In order to fulfill this necessity, we have developed an open source toolset for Fiji-BRET-Analyzer-allowing a systematic analysis, from image processing to ratio quantification. We share this open source solution and a step-by-step tutorial at https://github. com/ychastagnier/BRET-Analyzer. This toolset proposes (1) image background subtraction, (2) image alignment over time, (3) a composite thresholding method of the image used as the denominator of the ratio to refine the precise limits of the sample, (4) pixel by pixel division of the images and efficient distribution of the ratio intensity on a pseudocolor scale, and (5) quantification of the ratio mean intensity and standard variation among pixels in chosen areas.
This homemade toolset is a complete open source solution for Fiji [free software for scientific image analysis,  (Schindelin et al. , 2012) ], available for the scientific community together with a step-by-step tutorial (https://github. com/ychastagnier/BRET-Analyzer). The BRET-Analyzer toolset includes classical processing of the D and A images by cleaning the background, and pixel by pixel division of the A/D images. We worked mainly on two parameters. First we defined a donor threshold computed as a function of the donor signal intensity in a given area. This step allows defining the frontiers between the sample luminescence per se and the light spread on neighboring pixels. Second we selected and combined different threshold processes independently in each sub-area, depending on the local donor intensity.
MATERIALS Source Code The data were analyzed using a toolset made for Fiji (https:// fiji. sc) (Schindelin et al. , 2012). We here provide a stepby-step tutorial as Supplementary Material. The toolset and documentation details of how to use it are also publicly available at (https://github. com/ychastagnier/BRET-Analyzer), and will be regularly updated. Moreover, the tools can be freely downloaded, modified and improved to fit future research needs. TurboReg, the plugin used to align stacks of images can be downloaded at (http://bigwww. epfl. ch/thevenaz/turboreg/) (Thevenaz et al. , 1998).
Example Luminescent Images The donor and acceptor fluorescent images used here to exemplify the use of BRET-Analyzer corresponds to an hippocampal neuron from primary cell culture transfected with an intramolecular BRET-based sensor for ERK activity [YEN  (Goyet et al. , 2016) ], except in Figure  5 , where mGlu5-NanoLuc and Venus-Homer intermolecular BRET was recorded. Raw images are available at https://github. com/ychastagnier/BRET-Analyzer.
ACKNOWLEDGMENTS We are grateful to  Vincent Compan ,  Laurent Fagni ,  Elise Goyet , and  Vincent Ollendorff  for helpful reading of the manuscript. This work was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (JP , grant agreement No. 646788 ), the  Agence Nationale de la Recherche  ( JP ,  ANR-13-JSV4-0005-01 ) and the  Région Languedoc-Roussillon  ( JP, Chercheur d'Avenir 146090 ).
The baseline ITER burning plasma equilibrium is designed to place the divertor strike points deep into the 'V-shaped' region formed by the high heat flux handling vertical targets (VT) and the reflector plates (RP). The divertor plasma performance under these conditions has been extensively studied in the past two decades with the SOLPS4. 3 plasma boundary code suite. However, during tokamak operation, inaccuracies in the control of the vertical plasma position, or a requirement to avoid damaged monoblocks, could force the strike point position further down the VTs, or even directly on the RPs. In this paper, we present the results from the first SOLPS-ITER modelling in which the consequences of strike point displacements on the divertor plasma behaviour and surface heat loading are assessed.
At the outboard divertor the plasma condition is not significantly affected by the downward displacements, nor are the power fluxes to the outer RP. Finally, the heat load profiles computed with SOLPS are used in input for a finite element thermal analysis, considering the full cooling geometry, to assess the response of the VTs and RPs under the conditions exploited in the displaced scenarios. This thermal model, based on a simplified treatment not requiring a full 3D description of the divertor monoblock plasma-facing units, constitutes a new module for the SOLPS-ITER code suite.