dataesr/scientific-paragraphs-categorization huggingface dataset:
We used multi-label and single-label metrics, as well as statistical significance testing. HOMER and RAkEL models outperformed both baselines. They perform statistically better than EmoTxt, when trained and tested on the JIRA data set. The effect size of the performance difference between EmoTxt and the proposed methods is small or small-medium. In general, our models achieved multi-label Micro F-scores, multi-label Macro F-scores and Subset 0/1 Loss up to 81. 1%, 59% and 29%, respectively. When trained and tested on Stack Overflow, our models performed similarly to EmoTxt and no statistical difference was found. This means that it is necessary to perform further comparisons to determine a statistical difference, although they may show that the effect size of the difference is imperceptible or trivial.
We conclude that using either HOMER or RAkEL does not affect the results significantly. Similarly, the size of lexica did not affect performance either. Thus, maybe the quality of lexica annotations rather than size is key in improving performance. In the future, we plan to explore other classification methods. For example, we would like to determine how an algorithm such as Stochastic Gradient Boosting Trees, which has been observed to perform consistently good on different data sets  [67] , could behave in multi-label tasks when used alone (with transformed labels) or as the main classifier of RAkEL and HOMER. We contemplate to investigate algorithms such as Diversified Error Correcting Output Codes  [63]  to deal with the imbalance of specific emotions. In addition, we would like to explore how an embedded version of SMOTE, such as in  [66] , could improve the performance of HOMER and RAkEL.
We would also experiment with algorithms that have been specifically designed for multi-label tasks, such as FastText  [27]. The inclusion of other external resources based on word embedding or other lexica, could help in improving performance. Related to this, the exploration of new extra features for vector enrichment, as well as the inclusion of methods for detecting and managing negations, may improve performance to a great extent. Finally, the use of neural networks along with transfer learning, from another domain, may also contribute to improve the performance of an emotion classifier for texts related to Open Source Software.
[8]songs. Multi-label classifiers can be divided into three groups, following the approach they use[8]: A. Problem transformation Methods transform a multi-label task into multiple single label ones. There are two types of transformation: Binary Relevance and Label Powerset. In the former, each instance annotated with several labels is copied multiple times, each of which is annotated with one label. Label Powerset assigns a unique label to each multi-label combination. Usually, only combinations in the training data are considered. B. Ensemble Algorithms improve upon problem transformation methods by using multiple classifiers, trained on subsets of the original training data. Examples of these methods are HOMER 1 The source code and data sets are publicly available and can be found in: github.
The first is the Automatic Nonlinear Image Matching and Anatomical Labeling (ANIMAL)  (Collins et al. , 1995) , the second is based on the Advanced Normalization Tools (ANTS)  (Avants et al. , 2008)  registration package and the third is a patch-based label fusion technique  (Coupé et al. , 2011) , which despite using only an affine registration demonstrated very good performance. In 2014,  (Xiao et al. , 2014)  proposed a multiatlas method where multiple manually labeled templates were non-linearly registered to the case under study using ANTS and the multiple labels were combined using a majority voting rule. A year later, they proposed other method based on double-contrast patches which used multivariable cross-correlation and label fusion to segment the SN, the STN and RN  (Xiao et al.
Image data In this work, we have used a high-resolution MR dataset to develop the proposed method and to evaluate the results. This dataset contains 5 MR subjects with 0. 3 × 0. 3 × 0. 3 mm 3 high resolution T2-weighted images obtained by 2x interpolation of 0. 6 × 0. 6 × 0. 6 mm 3 acquisitions. The HR images are publicly available at the CoBrALab website (http://cobralab. ca/atlases). These MR images were taken from 5 healthy volunteers (2 males, 3 females, aged 29-57). High-resolution T2-weighted images were acquired using the 3D fast spin echo acquisition, FSE-CUBE (TE/TR=95. 3 ms/2500 ms, ETL=100 ms, 2NEX, and isotropic resolution of 0. 6 mm 3 ). Reconstruction filters, ZIPX2 and ZIP512, were also used resulting in a final isotropic 0. 3 mm 3 dimension voxels. For more details see the original paper  (Winterburn et al.
6 Manual labeling: The 6 structures of interest (we assigned different labels for left and right structures) were manually segmented in the 15 library cases using ITK-SNAP software  (Yushkevich et al. , 2006). Taking benefit from the symmetric properties of the brain, the library size was duplicated by including the axially mirrored cases which lead to a total library size of 30 cases. All the images were manually segmented by a trained person (AB) and supervised by an expert in brain anatomy (EL) (see Fig. 2 ). 7 Non-linear registration: A non-linear deformation was estimated between the cropped region of every subject and a reference cropped MNI152 template. The non-linear deformation was estimated using the Advanced Normalization Tools (ANTs)  (Avants et al. , 2008)  using cross correlation metric and pyramidal framework at 8 × 4 × 2 × 1x scales and 200, 200, 200 and 0 iterations at each scale.
New cases preprocessing The proposed method can be applied to both HR data and standard resolution data. Depending on this, the preprocessing pipeline is slightly different. When dealing with HR data the preprocessing steps were the same than for the library construction (steps 1 to 5). However, when standard resolution the preprocessing pipeline is the following: 1 Denoising: We used the Spatially Adaptive Non-local Means Filter  (Manjón et al. , 2010b). 2 Registration: Affine registration to the Montreal Neurological Institute (MNI152) space using the Advanced Normalization Tools (ANTs)  (Avants et al. , 2008). This registration was estimated using as reference the standard 1 mm 3 resolution T2w MNI152 template. 3 Inhomogeneity correction: Intensity inhomogeneity correction using the N4 bias field correction  (Tustison et al.
, 2010). 4 Intensity standardization: The images were intensity normalized using a histogram matching method (Nyúl and Udupa, 1999). 5 Cropping: A standard resolution bounding box surrounding the different structures was applied to crop the region of interest at 1 mm 3 resolution. 6 Superresolution: The cropped data must be upsampled to produce HR 0. 5 × 0. 5 × 0. 5 mm 3 resolution data. This is performed using a patch-based super-resolution technique called LASR  (Coupé et al. , 2013). 7 Non-linear registration: To achieve a better match between the different subjects' anatomy, a non-linear deformation was estimated between the cropped regions of every subject and the reference cropped MNI152 template. The non-linear deformation is estimated with the Advanced Normalization Tools (ANTs)  (Avants et al.
High resolution results In Table  1  we show the results of the proposed method for each structure and the overall performance. As can be noted, left and right results are consistent for each structure (being the STN the less stable probably due to its small size). To evaluate the impact of the proposed error corrector, we performed a 3-fold validation. The 30 library cases were split in 3 subsets of 10 cases. Two sets (20 cases) were used to train the error corrector and the remaining set was used to test the error corrector. This operation was repeated 3 times. Therefore, the 30 cases were used to estimate the results of the whole dataset. For each training set the automatic segmentations were produced using only a library of 18 cases from this training set. Results are summarized in Table  2.
Method's meta-analysis A direct comparison of the proposed method with related methods is not possible due to the use of different datasets. Therefore, we performed a method meta-analysis to locate then proposed method in its approximated context. We analyzed the proposed method pBrain with other related methods (both LR and HR results are provided). Specifically, we analyzed the methods proposed in  (Xiao et al. , 2012) , three variants proposed by  (Haegelen et al. , 2013) , another method from  (Xiao et al. , 2014) , two variants of the proposed method in  (Xiao et al. , 2015)  and finally the MIST method  (Visser et al. , 2016). We used published values provided by the authors in the corresponding papers. This analysis is only indicative since each method was evaluated in different conditions with different datasets and labeling protocols.
Clinical data validation When evaluating a new pipeline one aspect that sometimes is not analyzed is how the proposed method generalizes to other data sources different from the training/test dataset. To evaluate this aspect, we automatically analyzed 7 clinical cases from the Hospital La Fe of Valencia (Spain). Written informed consent was obtained for the use of the data and the hospital institutional ethical board approved its retrospective use. This dataset was composed of 7 Parkinson patients (4 males, 3 females) with an age range of 55-75 years. They were acquired in Phillips Achieva 3T MR scanner using a HR 3D T2 Spin Eco sequence (matrix size was 320 × 320 × 575 voxels at a resolution of 0. 78 × 0. 78 × 0. 4 mm 3 ). The images were automatically segmented using the proposed pBrain pipeline and the results were compared with manually annotated labels from our expert.
Discussion In this paper we have presented a novel pipeline for Parkinson's disease related structure segmentation. The proposed pipeline is able to work with both HR and standard resolution MR T2 images. It is based on a state of the art multiatlas label fusion segmentation framework that uses a library of manually annotated templates to segment the substantia nigra, the red nucleus and the subthalamic nucleus. We have compared our proposed pipeline with related methods previously proposed in bibliography. Although a direct comparison is not possible due to the use of different labeling protocols and datasets (and field strengths) we showed that the proposed pipeline produces results on pair and many times improving the results of related methods. We believe that one of the reasons for the improved performance of the proposed framework is the fact that it works at high resolution (0.
The proposed pipeline is also very efficient with mean running times around 5 min (around 6 times faster than a similar reported method). Our results on a small clinical dataset of patients with Parkinson`s disease reveal that the proposed method apparently provides similar results to those obtained in the training dataset, despite the fact that this dataset is solely formed with healthy cases with different MR machines/parameters. This suggests that the proposed method can be readily applied in different research and clinical conditions, although a much larger validation will be required. Structural brain imaging has been shown to be very useful for the accurate diagnosis of Parkinson's disease  (Heim et al. , 2017). In addition, the neurodegenerative process underlying the disease is thought to start at least years before motor symptoms  (Ziegler et al.
Acknowledgements The authors want to thank  Dr. Mallar Chakravarty  for making accessible the HR MRI data used in the proposed pipeline. This research was supported by the  Spanish   DPI2017-87743-R grant  from the  Ministerio de Economia, Industria y Competitividad of Spain. This work also benefited from the support of the project DeepVolBrain of the  French National Research Agency  ( ANR-18-CE45-0013 ). This study was achieved within the context of the  Laboratory of Excellence TRAIL   ANR-10-LABX-57  for the  BigDataBrain  project. Moreover, we thank the  Investments for the future Program IdEx Bordeaux  ( ANR-10-IDEX-03-02 ,  HL-MRI  Project),  Cluster of excellence CPU  and the  CNRS.
In particular, we do not yet include ambipolar drifts, which are known to favour neutral-ion chemical routes. In Section 2, we introduce the CHEMSES code and we give details of our numerical set-up. In Section 3, we show the results of the numerical experiment: the effects of turbulent dissipation on the average thermodynamical and chemical state of the gas and its H 2 excitation diagram. In particular, we show how excitation can affect some of the chemistry. In Section 4, we explore in detail the role of shocks in our simulation, and how we can recover some of the results of the previous section with a well-chosen collection of 1D planar shocks. In Section 5, we discuss our conclusions and prospects for the future.
The DUMSES hydrodynamics solver DUMSES, 1 mainly written by S. Fromang, originates from RAM-SES, a magnetohydrodynamics (MHD) code with adaptive mesh refinement  (Teyssier 2002; Fromang, Hennebelle & Teyssier 2006). DUMSES uses the MHD solver of RAMSES on a regular grid (i. e. without the adaptive mesh part). We use Van-Leer slopes to estimate the right and left states of the Riemann solver, for which we approximate the fluxes by the Lax-Friedrich prescription (see  Toro 1999). The regular grid makes it much easier to implement new physics in DUMSES, especially dissipation and diffusion terms, which require easy access to neighbouring zones. Therefore, it makes it simpler to quickly explore and validate a variety of new methods. We added in DUMSES the treatment of dissipative terms (viscous momentum diffusion, thermal diffusion and chemical diffusion) as cell-centred differences, which makes them second-order accurate in space.
The Paris-Durham shock code makes use of a highly modular set of chemical reactions including two-body gas phase reactions, photoionization, photodissociation, H 2 formation on grain surfaces, cosmic ray induced ionization, desorption, secondary photon ionization and dissociation, grain sputtering and erosion. The code uses DVODE  (Brown, Byrne & Hindmarsh 1989)  as its time integration engine. The population of excited levels of H 2 is followed in a time-dependent fashion, coupled to the fluid dynamics, which allows direct predictions for the H 2 line intensities.
Indeed, the main purpose of the present study is to demonstrate the role of shocks in multidimensional turbulent dissipation. Future work on 1D steady-state shocks can strive to refine the observationally relevant microphysics. We refer to the resulting code as CHEMSES, which thus joins the group of multidimensional codes that couple MHD and chemistry, such as ASTROBEAR  (Poludnenko, Frank & Blackman 2002) , PLUTO  (Mignone et al. 2007) , KROME  (Grassi et al. 2014)  and NIRVANA  (Ziegler 2005 (Ziegler , 2018)). Our code is one of the few that control the dissipation and diffusion physics exactly. To our knowledge, it is the only code that considers the time dependence of the H 2 level populations. The way we interfaced DUMSES with Paris-Durham makes it easy to validate CHEMSES against steady-state shock code computation (see Appendix A1).
AC K N OW L E D G E M E N T S We thank the anonymous referee for his thorough and detailed comments, which considerably improved the presentation of these results. Many thanks to  S. Fromang  for providing us with his version of DUMSES, from which CHEMSES was developed. The simulations were run on the jade machine at IDRIS under the project number  c2012046804. This work was partly supported by the  Programme National 'Physique et Chimie du Milieu Interstellaire' (PCMI)  of  CNRS/INSU  with  INC/INP  co-funded by  CEA  and  CNES. VV acknowledges support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (MagneticYSOS project , Grant Agreement No  679937 ). The research leading to these results has received funding from the  European Research Council , under the European Community's  Seventh Framework Programme , through the  Advanced Grant MIST  ( FP7/2017-2022 , No  742719 ).
CURVED SENSOR An innovative solution, based on advances in manufacturing technologies consists of curving the sensor. This is performed by the start-up CURVE-One, a specialist in curving any sensor to a required shape. For our project, the sensor curvature will be used to compensate for the curvature of the observed object: the macaque cortex. To quantify the desired curvature of the image sensor, the first step consisted in the calculation of the radius of curvature of various macaque cortices. To the best of our knowledge, similar calculations have not been reported in the literature. We have had access to a public database of Magnetic Resonance Imaging (MRI) data (from the Cerimed), from which we extracted via an open-source software (Brain VISA Anatomist) the data points corresponding to the surface of the brain included in the recording chamber.
2(A) ). The source of the large deviations around the average seems to be mainly due to local irregular features present at the surface of the cortex. While we found an average value of the curvature, what is more relevant for us is to repeat the calculations on the data from the two animals: Ziggy and Hip we will be working with. As shown in Fig. 2(B ), the values calculated from their cortex surface are included in the average range of the radii curvatures calculated previously. In conclusion, for the rest of the study, we have set the target object curvature to be equal to 29 mm corresponding to a value included in the average value obtained for theses two animals (28 ± 1 mm). Now that the object curvature is determined, the characterisation of the optical system should be performed. We carried various tests on our laboratory test bench; indeed, we replicated the original setup with as the object of interest: a 3D printed artificial brain (printed from white resin).
ACKNOWLEDGMENTS The authors would like to thank  Joel Baurberg  and  Xavier Degiovanni  for their technical assistance. We also thank  Kep-Kee Loh ,  Olivier Coulon  and  David Meunier  from  INT  for their invaluable help processing the brain MRI scan data. We would also like to acknowledge  H2020 ATTRACT  and  CNRS 80 Prime  for financial support.
Pharmacogenomics (PGx) studies how individual gene variations impact drug response phenotypes, which makes PGx-related knowledge a key component towards precision medicine. A significant part of the state-of-the-art knowledge in PGx is accumulated in scientific publications, where it is hardly reusable by humans or software. Natural language processing techniques have been developed to guide experts who curate this amount of knowledge. But existing works are limited by the absence of a high quality annotated corpus focusing on PGx domain. In particular, this absence restricts the use of supervised machine learning. this article introduces PGxCorpus, a manually annotated corpus, designed to fill this gap and to enable the automatic extraction of PGx relationships from text. It comprises 945 sentences from 911 PubMed abstracts, annotated with PGx entities of interest (mainly gene variations, genes, drugs and phenotypes), and relationships between those.
2019) makes it hard for humans or machines to get a full understanding of the state of the art in this domain. NLP (Natural Language Processing) techniques have been consequently developed and used to structure and synthesize PGx knowledge  5, 6. Previous works mainly investigated rule-based approaches  [7] [8] [9]  and unsupervised learning  10, 11  , because of the absence of an annotated corpus. Supervised learning has also been experimented  [12] [13] [14] [15] [16]  , but without appropriate corpora, most studies build train and test sets on the basis of PharmGKB, which is the reference database for this domain  17. Because it is manually curated, PharmGKB provides a high quality referential for such a task. Annotations provided by PharmGKB (i. e. 2 associated entities and the identifier of the PubMed article in support) result of the consideration by human curators of various knowledge sources: article text; tables and figures; and curator's own knowledge of the domain.
This manuscript first presents PGxCorpus itself; its construction, in Methods; and a baseline experiment, in Technical Validation. Position in regards to existing corpora. In domains closely related to PGx, corpora have been annotated with biomedical entities, but only few of them include relationships (see Hahn et al. 6  for a panorama, plus  [18] [19] [20] [21]  ). More relevant corpora are related to pharmacovigilance or genetic traits, and then focus on drug-adverse response or SNP-phenotype associations. To our knowledge, no corpus includes annotations of all three PGx key entities, i. e. drugs, genomic variations and drug response phenotypes; and no corpus annotates PGx relationships between these entities. Developed for pharmacovigilance, EU-ADR  22  is a corpus composed of three disjoint subcorpora of 100 PubMed abstracts each.
Data Records Statistics on the preparation of PGxCorpus. PubMed has been queried with our initial query (Box 1) in July 2017, to retrieve 86,520 distinct abstracts, split out in 657,538 sentences. Statistics of pre-annotations obtained with PubTator and PHARE on these sentences are provided in Tables  6  and 7 , respectively. After filtering malformed sentences and sentences that do not contain at least one genomic factor and one drug, we obtain 176,704 sentences, out of which we randomly pick 1,897 sentences that are subsequently manually annotated. This number of sentences is chosen in regards to constraints of the distribution of the annotation task. These sentences come from 1,813 distinct abstracts. The first phase of manual annotation, by 11 annotators, took roughly four months. The mean number of sentences annotated by an annotator is 344.
The NER is achieved with a variant of a Convolutional Neural Network (CNN) model, whereas the RE is processed with a multichannel CNN (MCCNN). Both models are detailed in the Supplementary Methods section of the Supplementary Material. Source code of the experiments is available at https://github. com/practikpharma/ PGxCorpus/. In a related work  44  , we used a preliminary, partial and naive set of annotations to test the feasibility of extracting relations and incorporating them in a knowledge network. This included only 307 sentences (out of 945), annotated with a simplified schema of only 4 entity types and 2 relation types. The associated model for RE was simplistic, since it only aimed at empirically demonstrating feasibility. The baseline experiment reported here considers all sentences of PGxCorpus and has been done with more advanced annotation schema and models.
48A BRAT server48, enabling a user friendly online visualization of the annotations: https://pgxcorpus. loria. fr/ • A Git repository of the whole project that also includes the annotation guidelines and programmatic code of the baseline experiments presented in Technical Validation https://github. com/practikpharma/ PGxCorpus/. Considering hierarchies: (yes or no) no yes is Associated With Δ 30. 89 51. 71 (4. 02) ↳influences Δ 36. 55 46. 45 (5. 17) ↳causes Δ 41. 91 41. 91 (13. 35) ↳decreases Δ 29. 47 29. 47 (9. 85) ↳increases Δ 17. 94 17. 94 (15. 20) ↳treats 39. 97 39. 97 (12. 60) isEquivalentTo 79. 76 79. 76 (7. 69) Macro average 45. 67 49. 56 (4. 51) PGx relations only(Δ), no hierarchy 54. 04 (3. 31).