dataesr/scientific-paragraphs-categorization huggingface dataset:
Preprocessing To generate the templates library, all 50 selected T1-weighted images were preprocessed using the following steps: 1. Denoising: All images were denoised using the Spatially Adaptive Non-Local Means (SANLM) Filter  (Manjón et al. , 2010b)  to enhance the image quality. The SANLM filter can deal with spatially varying noise levels across the image without explicitly estimating the local noise level which makes it ideal to process data with either stationary or spatially varying noise in a fully automatic manner. This method has been included in several software packages already such as VBM8, CAT12 (http://www. neuro. uni-jena. de/vbm) or the Connectome Computation System  (Xu et al. , 2015).
MNI space registration: The template library and the subject to be segmented have to be located in the same stereotactic space. A spatial normalization based on a linear affine registration to the Montreal Neurological Institute (MNI152) space was performed using ANTS software  (Avants et al. , 2009). The resulting images in the MNI space have a size of 181 × 217 × 181 voxels with 1 mm 3 voxel resolution. The transformation matrix was estimated using the inhomogeneity corrected image in previous step but applied the denoised image without IH correction. Although, N4 removes most of the inhomogeneity it sometimes does not remove it completely (especially on 3T cases). Therefore, N4 was used only to improve the linear registration parameter estimation. 4. Fine Inhomogeneity correction: Once the data is in MNI space we used the inhomogeneity correction capabilities of SPM8  (Ashburner and Friston, 2005)  toolbox.
We found this model-based method to be quite robust once the data were in the MNI space (especially for 3T images). 5. Intensity normalization: As the proposed method is based on the estimation of image similarities using intensity-derived measures, every image in the library was normalized. We used a tissue-derived approach to force mean intensities of WM, GM, and CSF to be as similar as possible across subjects of the library in a similar manner than  Lötjönen et al. (2010). Mean values of CSF, GM, and WM tissues were estimated using the Trimmed Mean Segmentation (TMS) method  (Manjón et al. , 2008)  that robustly estimates the mean values of the different tissues by excluding partial volume voxels from the estimation process and by using an unbiased robust mean estimator. Finally, a piecewise linear intensity mapping  (Lötjönen et al.
Manual Labeling Manual labeling at different scales was performed by a trained expert using ITK-SNAP software. Details of intracranial cavity mask and macrostructures (cerebrum, cerebellum, and brainstem) can be found in the corresponding original papers  (Manjón et al. , 2014; Romero et al. , 2015). Lateral ventricles and subcortical structures were manually segmented from scratch using ITK-SNAP software using the 3 orthogonal views to avoid any inconsistency in 3D. Lateral ventricles label were thresholded using a threshold of 100 over the intensity-normalized images to get a consistent label definition (note that choroid plexus was not included in our lateral ventricles definition). All subcortical structures were segmented according to the current common definition criteria with the exception of hippocampus that was segmented using EADC protocol  (Frisoni and Jack, 2011).
The volBrain Online System Most of the developed pipelines for MR image analysis are packages that need to be downloaded, installed and configured. Installation step can be complicated and thus may require an experimented person not always available in a research laboratory or clinical context. In addition, the users have to be trained to use the software and computational resources have to be allocated to run it. These requirements can make complex the use of these packages, especially the most recent and sophisticated ones since they usually require high hardware requirements. Furthermore, multiplatform versions and support has to be deployed to the community of users. We have tried to overcome all these problems by deploying our proposed pipeline through a web interface (http://volbrain.
upv. es) providing not only access to the software pipeline but also sharing the computational resources of our institution. Thus, using the volBrain pipeline does not require any installation, configuration or training. The volBrain volumetric analysis system works remotely through a web interface using a SaaS (Software as a Service) model to automatically provide a report containing volumetric information from any submitted case. The volBrain interface is supported by an XAMPP web server on a Windows 7 system which has been developed in AJAX (HTML + Javascript + PHP) + MySQL. The segmentation pipeline is executed on dedicated cluster running Windows Server 2012 R2 Datacenter using a Matlab compiled version of our pipeline (the proposed method was fully implemented in MATLAB© 7. 8. 0 by using MEX code).
This job dispatching is done by using a queuing system consisting of a main queue (FIFO) for the incoming jobs and a process queue (FIFO) for each cluster machine. The web-cluster communication is bidirectional and it is implemented by several PHP daemons running on the web server and the cluster machines. Jobs are assigned to a machine using database entries that are periodically consulted by the daemons on the web server and the cluster. The cluster also notifies its current status by updating these entries. The files from the web server are transferred to the processing machines by secured FTP and the results from the processing machines are also sent to the web server which storages the results and send the corresponding reports to the users (see Figure  2 ). The output produced by the volBrain pipeline consists in a pdf and csv files sent to the user by email.
These files summarize the volumes and asymmetry ratios estimated from their data. If the user provides the sex and age of the submitted subject, population-based normal volumes and asymmetry bounds for all structures are added for reference purposes. This normality bounds were automatically estimated from the IXI dataset which contains almost 600 normal subjects covering most of adult lifespan. Furthermore, the user can access to its user area through volBrain website (see Figure  3 ) to download the resulting Nifti files containing the segmentations at different scales (both in native or MNI space). Figure  4  shows an example of a volumetry report produced by volBrain (note that a screenshot of the results is included for quality control purposes).
Accuracy A leave-two-out procedure was performed for the 50 subjects of the library (i. e. , excluding the case to be segmented and its mirrored version). Dice's kappa  (Zijdenbos et al. , 1994)  was then computed by comparing the manual segmentations with the segmentations obtained with our method. The proposed method was also compared with two publically available software packages for subcortical brain structures labeling  (Freesurfer, Fischl et al. , 2002)  and FSL-FIRST  (Patenaude et al. , 2011). Both methods were run on the CBRAIN interface (http://mcincnim. ca/neuroimagingtechnologies/cbrain/) with their default parameters  (Tarek et al. , 2014). As can be noted on Table  1 , volBrain obtained the best DICE coefficients for all the considered structures. Moreover, the improvement was statistically significant for all the structures and for the two methods compared.
Reproducibility A very important feature for a measurement method is its reproducibility. To measure the reproducibility of the different compared methods, we used a reproducibility dataset of the brain segmentation testing protocol website (https://sites. google. com/site/brainseg/). This dataset consists of a test-retest set of 20 subjects scanned twice in the same scanner and with the same sequence (SSS). To measure the reproducibility of the two repeated sets we used the Percent Volume Difference (PVD) and the Percent Volume Overlap (PVO)  (Morey et al. , 2010)  defined as follows: PVD(C 1 , C 2 ) = 100 2 | C 1 0 -C 2 0 | C 1 0 + C 2 0 (11) PVO(C 1 , C 2 ) = 100 2 |C 1 -C 2 | 0 C 1 0 + C 2 0 where C 1 and C 2 represent the segmentations 1 and 2 respectively. Note that the reference to compute the percentage is set to the mean of both segmentations to avoid any order influence.
SSS Dataset This reproducibility dataset consist in a subset of the OASIS (www. oasis-brains. org) dataset consisting in 20 subjects (age = 23. 4 ± 4. 0 years, 8 females) who were scanned using the same pulse sequence two times (1. 5 T Siemens Vision scanner, TR = 9. 7 ms, TE = 4 ms, TI = 20 ms, flip angle = 10 • , slice thickness = 1. 25 mm, matrix size = 256 × 256, voxel dimensions = 1 × 1 × 1. 25 mm3 resliced to 1 × 1 × 1 mm 3 , averages = 1). The three compared methods were run on this dataset but the comparison was done only on a subset of 18 subjects since FIRST did not produce valid results for two of the 20 cases. Since PVD and PVO measures do not distribute normally we represent the results using the median and the interquartile interval and we used the Wilcoxon rank test to measure the statistical significance of the differences between methods.
DISCUSSION We proposed a novel pipeline that is able to automatically segment the brain at different scales in a robust and efficient manner using a library of expert segmentations used as priors and an enhanced version of our patch-based label fusion scheme. We have shown that the proposed pipeline is able to provide state-of-the-art results at different levels (intracranial cavity, brain macro-structures and subcortical structures) in a very efficient manner. The proposed pipeline was compared with two wellestablished software packages (Freesurfer and FIRST) for subcortical structure segmentation. The volBrain pipeline was found to significantly improve the accuracy (according to the used protocol) compared to both methods. We should also remark that volBrain platform is one of the first few platforms to provide hippocampus segmentation based on EADC protocol that will be the reference definition on AD in the next years.
e. , 10% of failure rate) while both volBrain and Freesurfer worked for all the 20 cases. The high failure rate of FIRST can limit it use in clinical context  (Kempton et al. , 2011). Finally, volBrain pipeline was also found more computationally efficient than Freesurfer since it takes few minutes to produce the results compared to several hours in the case of Freesurfer (we have to note that Freesurfer provides full brain segmentation and cortical thickness in this time) and similar than FISRT (only for subcortical segmentation without lateral ventricles). Moreover, an online web-based platform has been deployed that gives access to the whole scientific community not only to the software pipeline but also to our own computational resources (we limit the number of cases that a user can submit daily to 10 in order to share our system to the higher possible number of users).
CONCLUSION In this paper, we present a novel pipeline based on our stateof-the-art non-local label fusion technology to segment brain anatomical structures at different scales. The proposed pipeline has been compared with state-of-the-art related packages showing competitive results in term of accuracy, reproducibility and computational time. Besides, we hope that the online nature of the proposed pipeline will facilitate the access of any user around the world to the proposed system making their MRI data analysis simpler and more efficient. We plan to extend the volBrain capabilities in the near future to segment all the brain structures including cortical areas and to add regional cortical thickness measurements in the final report. excellence CPU, LaBEX TRAIL (HR-DTI ANR-10-LABX-57), and the CNRS multidisciplinary project "Défi ImagIn.
ACKNOWLEDGMENTS We want to thank  Sebastian Mouelboeck  for his help to improve the robustness and reproducibility of the platform. We want also to thank  Jose Enrique Romero Gomez  and  Elena Carrascosa  for their help developing the volBrain web interface. This work benefited from the use of ITK-SNAP from the  Insight Segmentation and Registration Toolkit  ( ITK ) for 3D rendering. We also want to thank  CBRAIN team (especially Marc Rousseau)  for their help during experiments and for providing a really nice system to the community. This research was partially supported by the  Spanish  grant  TIN2013-43457-R  from the  Ministerio de Economia y competitividad. This study has been also carried out with financial support from the  French State , managed by the  French National Research Agency (ANR)  in the frame of the  Investments for the future Programme IdEx Bordeaux  ( ANR-10-IDEX-03-02 ) by funding  HL-MRI  grant, Cluster of.
The proportion of patients with 'mild', 'minimal', or 'no diarrhoea' increased from 33% before treatment to 75% during treatment; 75% were unconcerned about the impact of diarrhoea on daily life. Satisfaction with flushing control amongst patients with significant flushing at treatment initiation was 73%. Conclusions: Lanreotide treatment was associated with improvements in symptoms as well as a range of PROs in patients with neuroendocrine tumours and carcinoid syndrome (ClinicalTrials. gov: NCT01234168).
Trial design and interventions SymNET was a multinational, observational, non-interventional study conducted between October 2010 (first patient enrolled) and December 2012 (last patient visit) at 45 secondary or tertiary care centres in eight countries (Czech Republic, France, Hungary, Israel, Italy, Poland, Spain, and UK) (ClinicalTrials. gov: NCT01234168). Patient recruitment and PRO assessment were conducted prospectively during a single study visit; in addition, patient data were collected from medical records. The study did not impact on usual clinical management. Patients' clinic attendance and the exact prescription of lanreotide or any other concomitant medications was unrestricted and in accordance with routine clinical practice. The decision to prescribe lanreotide was made prior to, and independently from, the decision to enrol patients in the present study.
Statistical analyses Estimated sample size, based on an anticipated PSD of 60% and precision of 5. 5%, was 305 patients, assuming a two-sided 95% confidence interval (CI). For PSD-associated factors, assuming alpha = 5% and power = 80%, estimated sample size was 323 patients to detect an odds ratio (OR) ≥2 and a probability of exposure to any given level of a prognostic factor of ≥0. 5. In order to ensure 323 evaluable subjects, 340 were to be included in the study. However, because of slow recruitment, the protocol was amended to reduce patient numbers and enrolment ended in December 2012, at which point >80% of the target number (i. e. , >250 patients) had been enrolled (see Supplementary Information for protocol amendments). The primary analysis population was all patients who were enrolled, provided consent and had data available for the relevant endpoints.
The primary endpoint (PSD) was also analyzed for the per-protocol (PP) population (all those enrolled with no major protocol deviations). Descriptive methods were used for all other endpoints on PRO measures or physician-recorded changes in CS symptoms. PSD-associated factors were explored first using univariate and then multivariate logistic regression analyses. Post hoc analyses were conducted to evaluate physician-recorded changes in diarrhoea characteristics according to PSD, and patient-reported QoL according to PSD. Missing data were not replaced, and all endpoints were calculated based on patients with available data.
Discussion Data from this large real-world study demonstrate that lanreotide Autogel/Depot not only improved physician-rated symptoms in patients with CS and diarrhoea, but also a variety of subjective PROs including patient satisfaction with diarrhoea and with flushing. Satisfaction rates for diarrhoea control were slightly lower in those with versus without other contributing factors to diarrhoea. It was also shown that factors associated with even better satisfaction were the presence of stool leakage and primary tumour outside the small bowel. These data were obtained in patients mostly receiving standard doses at standard dosing intervals. PROs are an important component of monitoring drug efficacy and patient management, particularly for symptomatic conditions such as CS and functional gastrointestinal disorders  [21].
9 months (range 3-215)  [27]. There are some important limitations that should be considered when interpreting the SYMNET study data. Firstly, the PRO measures in this study were completed only at one visit. These data were intended to provide an accurate "snapshot" of current patient experience in routine practice. They do not provide longitudinal information. Secondly, the study intended to capture experience in a general NET population in the real world, which would have variable treatment duration, dose and dosing interval for lanreotide. These results do not reflect those that would likely be observed in a more restricted and homogenous clinical study population. Finally, inter-patient and within-patient variability are inherent to subjective PROs; however, the main PRO data were corroborated by the objective data from medical records on CS symptoms and the validated QoL questionnaires.
One Dicer knockout cellular clone (Dicer KO) was selected for further analysis. LentiCRISPRv2-Ago2 expression plasmid expressing single-guide RNA targeting exon 2 of Ago2 gene was generated by annealing and cloning the following primers: caccgtcaagccagagaagtgcccg and aaaccgggcacttctctggcttgac into the lentiCRISPRv2  (36). HEK293T cells were transfected either with lentiCRISPRv2-Ago2 or empty lenti-CRISPRv2 as control using Lipofectamine LTX (Life technologies). Forty-eight hours later, viral particles were harvested and used to transduce HeLa cells. Cells were selected in medium supplemented with puromycin for 15 days. Cells transduced with empty lentiCRISPRv2 were used as control cells. Data acquisition and data analysis were performed on the Cochin Cytometry and Immunobiology core Facilities.
Data analysis Reads of low quality were discarded, adapter sequences were trimmed and sequences were sorted according to the specific barcodes used in each condition of IP using cutadapt software (https://cutadapt. readthedocs. org/en/ stable/). Cleans reads were then mapped to HIV-1 NL4-3 sequence (GenBank ID: AF324493. 2) or hg19 UCSC H. sapiens genome. In a first analysis, trimmed reads of at least 15 nucleotides length were mapped using Bowtie software (http://bowtie-bio. sourceforge. net/index. shtml with the following parameters: -a -n1 -best -strata). HITS-CLIP clusters were defined using Piranha software  (39)  with a 50 nucleotides bin, following a negative binomial distribution. Enrichment was determined by P-values < 0. 05. In a second analysis, we aim to rule out reads that could correspond to sncRNAs from viral origin present in the IP.
Viral mappers not present in the human alignment were selected. Exclusively viral reads were mapped on each of HIV-transcripts using the -a option of Bowtie2. To identify HIV-1 clusters that are enriched in the IP, read coverage throughout the HIV genome was calculated, normalized by the number of hits and sequencing depth. For the calling of chimeras in HITS-CLIP data using hyb  (40) , HITS-CLIP reads were pre-processed selecting Flexbar preprocessing, trimming reads from 3 end with a minimum read length of 17, then chimeras where called using hyb default parameters against a custom database of human and viral RNAs.
HITS-CLIP mapping of Ago2 binding regions on HIV-1 RNA To precisely map the Ago2 binding regions on the HIV-1 RNA genome, we employed a high-resolution approach using high throughput sequencing of RNA isolated by CLIP. As high level of specificity during the IP is essential for HITS-CLIP, several antibodies against endogenous Ago2 and tagged versions of Ago2 protein were tested. Immunoprecipitation of the GFP-tagged Ago2 (GFP-Ago2) using an anti-GFP antibody and wash buffers supplemented with 4 M urea resulted in the most specific HIV RNA enrichment relative to control (data not shown). HITS-CLIP experiments were thus performed from GFP-Ago2 transfected 293T cells infected with VSVg pseudotyped HIV-1. Endogenous CAp24 staining and FACS analysis indicated that 75-90% of transfected cells were infected.
Ago2 HITS-CLIP analysis To assess the quality of our HITS-CLIP experiments, we first uploaded our sequencing reads to the CLIPZ software, specifically dedicated to HITS-CLIP data analysis  (44). The number of distinct reads mapping to the human genome and the quality of the sequences were similar to other Ago2 HITS-CLIPs analyzed with the CLIPZ tool (data not shown). The alignment of reads in the input and in the HITS-CLIP experiments showed that whereas rRNAs, tRNAs and mRNAs were the predominant reads in the input (45%, 12% and 17%, respectively), a 2. 5-fold enrichment of mRNAs, but not of snoRNAs, snRNAs, rRNAs or tRNAs, could be observed in the HITS-CLIP (Figure  2B ). We did not observe any enrichment in miRNAs likely because our HITS-CLIP procedure favored long reads over short ones.
We then sought to identify Ago2 binding sites on the viral gRNA. For this, HITS-CLIP data were analyzed in two different ways. First, trimmed reads longer than 15 nucleotides were aligned to the viral genome with a maximum of one mismatch allowed. Ago2 binding sequences were identified by overlapping reads and by identifying statistically significant peaks above background (cutoff of P-value < 0. 05). The three HITS-CLIP experiments were highly reproducible and led to the identification of statistically significant clusters representing regions of HIV-1 RNA genome that were bound by Ago2 (Supplementary Figure  S1 ). These sequences could either correspond to sncRNAs from viral origin that are loaded into Ago2 complexes or to Ago2 target sites on the viral genome. To identify Ago2 binding sites on the HIV-1 genome, we then refined our analysis: small trimmed reads of miRNA sizes (between 18 and 30 nt) were discarded and only long reads of 50 nucleotides were considered.
The use of an anti-miR-92a inhibitor fully restored RLuc expression of the psiCHECK-2 miR-92a reporter plasmid, indicating that this specific downregulation depends on endogenous miR-92a-mediated loading of Ago-RISC. In sharp contrast, the miR-21 binding sequence did not repress RLuc expression, likely reflecting the low level of endogenous miR-21 expression in HEK293T cells  (45). The other HIV cluster sequences tested showed either no significant changes of RLuc expression or a 2-to 3-fold increase compared to empty vector (data not shown), the latter possibly reflecting effects on RLuc transcripts expression or stability. In order to verify that downregulation of RLuc expression observed for clusters 2, 13, 16, 21, 25 and 30 was dependent on Ago proteins, the reporter assay was conducted in Ago downregulated cells.
Given the potential role of Ago proteins in regulating alternative splicing of cellular transcripts  (13, 15) , we further assessed the effect of donor sites D1, D2, D3 and D4 when present in the context of the spliced junction sequences (Supplementary Table  S2 ). Contrary to what we observed for the splicing donor sites alone, HIV-1 splice junction sequences inserted at the 3 end of the RLuc gene had either no significant effect or stimulated RLuc expression (Figure  4B ). We also re-analyzed our Ago2 HITS-CLIP data using 'hyb' bioinformatics pipeline that allows the mapping of chimeric RNAs in order to specifically detect clusters overlapping HIV-1 spliced junctions. HIV-1 reads containing unspliced junctions largely outnumbered reads corresponding to spliced transcripts (Table  2 ). Mutations of the splicing donor sites of cluster 2, 13 and 16 were also tested in the RLuc reporter assay.