dataesr/scientific-paragraphs-categorization huggingface dataset:
All analyses were performed using Stata statistical software version 13. 1. This study was approved by the Stanford Institutional Review Board for human subjects, which waived the need for patient consent. The study sample assembly is shown in Figure  1. We restricted the analysis to patients aged between 40 and 79 years with at least 2 outpatient visits that were at least 1 year apart (n=273 585). We excluded 14 179 patients with pre-existing ASCVD, atrial fibrillation, or heart failure and 27 784 participants with missing risk factor information, missing race/ethnicity, or on lipid-lowering therapy at baseline. The final study population included a total of 231 622 patients with a mean follow-up of 3. 9 years (SD 1. 5). The study population ( Puerto Ricans had the highest prevalence of smoking (8.
2, 25, 26 8] [29] [30]  A cohort of Kaiser Permanente northern California members in 2008 to 2013 concluded that the PCE substantially overestimated 5-year risk across diverse sociodemographic subgroups. 28 In a recent study of the Women's Health Initiative cohort of postmenopausal women aged 50 to 79 years, the PCE overestimated ASCVD risk across racial groups, although risks were better aligned with PCE predictions after inclusion of further Medicare-adjudicated ASCVD events. 27 th these conflicting results, a recent study updated the data used to derive the PCE with newer cohorts and applied revised statistical models, which reduced risk overestimation overall and implausible risk estimates in African Americans in particular, but did not provide revised PCEs for racial groups other than African American or NHW adults.
Our data originate from the EHR, which has inherent limitations including that the data are collected for clinical purposes and not for research. 37 Race/ethnicity was self-reported in the majority of the study sample and inferred in the remainder based on previously validated methods using name lists to infer race/ethnic subgroup. 22 Such approaches may have led to misclassification of race/ethnicity. Incident ASCVD events that occurred outside of the health system may not be fully captured. This underreporting of events may have led to underestimation of observed event rates, although our results are well-aligned with other studies. 20, 28, 34 We attempted to minimize the potential missing information by requiring that patients have at least 2 outpatient visits and stayed in the healthcare system for at least 1 year and by matching EHR records to Social Security Records to ensure that deaths were appropriately captured.
Future risk prediction models should not only include Asians and Hispanics, the 2 largest and growing minority groups in the United States, but also disaggregate by major subgroups. Our study suggests that information routinely captured in clinical care may be useful in risk prediction. Using novel algorithms and continuing to refine the demographic and clinical variables considered will likely improve future risk prediction models for diverse populations. In summary, we used EHR data from a health system in northern California to validate the PCE for estimating ASCVD risk among disaggregated Asian and Hispanic patients. We found that the PCE generally overestimated predicted risk among this diverse patient cohort, but provided adequate discrimination. These findings have important implications for the use of the EHR-based variables in ASCVD risk prediction across diverse populations.
For each cell analysed, we measured three randomly-chosen points in the cytosol to estimate FRET efficiency in this compartment. We did not detect any significant difference between active and kinase-dead AURKA when we compared the ratio of the amplitudes of the red or green auto-correlation curves with the amplitude of the cross-correlation curve (Fig. 3 ). This was in apparent contradiction with previous data acquired in cells where the cytosol was strong enough to perform FRET/FLIM analyses  (Bertolin et al. , 2016) , and where we detected FRET with the GFP-AURKA-mCherry biosensor. Therefore, we concluded that the ratio between the cross-correlation and the green autocorrelation is not sufficiently robust to detect changes due to FRET in the original AURKA biosensor. As FRET cannot be estimated by 2c-FCCS using the original AURKA biosensor, it is possible that mCherry represents a limiting factor for the measurement of FRET via 2c-FCCS, due to its longer dark state compared to the one of EGFP-like proteins  (Dean et al.
Fluorescence polarisation microscopy Fluorescence polarisation analyses were performed on a SP8 (Leica) inverted confocal microscope with a 63X oil immersion objective (NA 1. 4), and using the Leica Acquisition Suite (LAS)-AF software. As the excitation light coming out of the laser is naturally polarized, an analyser integrated in the Leica emission filter wheel was placed in the emission pathway before the pinhole. The analyser rotates from a parallel to a perpendicular orientation to perform a sequential acquisition of the two corresponding polarised images. Images of GFP polarisation were acquired using a 488 nm argon laser and emission was selected between 500 and 550 nm. Image analysis was performed using the Fiji (ImageJ) software and fluorescence anisotropy was calculated as in  (Tramier and Coppey-Moisan, 2008) , using the following equation: r = (I∥ -I⟂)/( I∥ + 2I⟂), where r is the fluorescence anisotropy, I∥ and I⟂ are the parallel and the perpendicular polarisations and I∥ + 2I⟂ is the total polarisationindependent emission.
FLIM microscopy FLIM analyses were performed in the time domain with a timegated custom-built setup as described in  (Bertolin et al. , 2016)  and driven by the Inscoper hardware (Inscoper). Briefly, cells were excited at 440+/-10 nm and at 480+/-10 nm using a white light laser and emission was selected using a band pass filter of 483/35 nm and of 525/50 nm for mTurquoise2 and GFP, respectively. Fluorescence lifetime was calculated with five sequential temporal gates of 2. 2 nsec each. Mean pixel-by-pixel lifetime was calculated using the following equation: ⟨τ⟩=ΣΔti⋅Ii/ΣIi where Δti is the delay time of the ith image acquired following a laser pulse, and I is the pixel-by-pixel fluorescence intensity in each image. Lifetime measurements and online calculations were performed with the Inscoper software (Inscoper).
2c-FCCS measurements were carried out with the Symphotime software (PicoQuant), integratd into the LAS-AF software (Leica). For each cell, the fluctuation of the fluorescence intensity for both donor-acceptor pairs (green/red or cyan/yellow) in the confocal volume was analysed by monitoring three independent points in the cytosol. These points were analysed sequentially and in non-photobleaching conditions for 30 sec each. Auto-correlation and cross-correlation curves were reconstructed from the fluorescence decay with the Symphotime software and the lifetime integration as previously reported  (Padilla-Parra et al. , 2014 , 2011) , with the Symphotime software. Data were fit using the 3D diffusion model, with one diffusing species  (Padilla-Parra et al. , 2014 , 2011). The ratio between crosscorrelation and green (for GFP/mCherry) or cyan (for mTurquoise2/superYFP) auto-correlation was used to estimate FRET efficiency.
Acknowledgements We thank all the members of the  Microscopy-Rennes Imaging Center (Biologie, Santé ,  Innovation Technologique, BIOSIT, Rennes, France ) for assistance and in particular  X. Pinson  for providing an ImageJ macro for the analysis of fluorescence anisotropy. We also thank  Laurent Deleurme  from the Flow cytometry and Cell Sorting platform ( BIOSIT, Rennes, France ) for assistance, together with  A. Webb  and  S. Ku  for technical help. This work was supported by the  Comité Nationale de la Recherche Scientifique , by the  Ligue Contre le Cancer Comité d'Ille et Vilaine et Comité des Côtes d'Armor  to G. B. and by the  Comité d'Ille et Vilaine, Comité du Maine et Loire et Comité de la Sarthe  to MT, and by the  Infrastructures en Biologie Santé et Agronomie (IBiSA) , région Bretagne and  Rennes Métropole  for the development of the technology for rapid FLIM measurements.
Statistical analysis A Chi-square test was used to determine the possible associations between the ICOSL expression and the clinic pathologic parameters. The effect of ICOSL expression on OS was expressed using Kaplan-Meier curves. The log-rank test was used to compare different survival curves. Multivariate proportional Cox models were employed to evaluate the prognostic significance of the expression of ICOSL, histological types, age, tumor stages, and the expression of HER2, ER, and PR. P-value 0. 05 was considered significant. Statistical analysis was performed using SPSS19. 0 software (SPSS Inc. , Chicago, IL, USA).
of 12 Current Protocols in Mouse Biology High-resolution ultrasound system with imaging station (e. g. , VisualSonics Vevo), including mouse handling thermally regulated table with real-time monitoring of key physiologic parameters (respiration, heart rate), rectal temperature probes, and anesthesia line connected to anesthesia face mask Ultrasound transducer, 25 to 55 MHz (e. g. , MS550D for abdominal scanning) 3D motor assembly (see Fig. 1C ) Computer running ultrasound imaging software (e. g. , Vevo LAB VisualSonics imaging software version 3. 0. 0) 1. Place anesthetized mouse on the animal platform in a similar way as described in Basic Protocol 1, steps 14 to 17, for injection, and visualize liver tissue, vessels, and abdominal organs. As in human HCC, tumors formed after Huh-7-Luc cell injection appear as nodules that are more echogenic (brighter) than surrounding normal tissue  (Figs.
of 12 Current Protocols in Mouse Biology 3D reconstruction is performed by superimposing these pictures on a z-axis using the Vevo LAB VisualSonics imaging software version 3. 0. 0. 4. Observe tumor image from distal to proximal, and note length and position of the transducer at the midpoint. 5. Scan a distance equal to the tumor length plus 3 to 4 mm on both sides. 6. Review images and save the scan only if the whole tumor was scanned. 7. Return mouse into its home cage. 8. Use the software study management function for 3D volumetric analysis. Load desired 3D scan file for analysis, and initiate the volumetric analysis function. 9. Trace tumor/tissue border around the perimeter of the tumor, and left-click to anchor specific points. When the entire border is traced, right-click to close the circle 10.
of 12 Current Protocols in Mouse Biology (1000 mm 3 ) by 5 weeks after injection (Fig. 5A ). Sensitivity to doxorubicin of Huh-7 cells was compared using direct intratumoral (Fig. 5B ) versus intraperitoneal injection of doxorubicin with increasing doses. The effect of intratumoral 4 mg/kg doxorubicin injection was more pronounced than in mice treated by intraperitoneal injection and in the control saline-treated group. A dose-dependent decrease in tumor size was observed by this local route of delivery (Fig. 5B ). All data were extracted from longitudinal follow-up of tumor development. At each time point the volume of the tumor was measured as described in Basic Protocol 2. Doxorubicin-treated mice were compared to their respective controls. Results are expressed as mean of the tumor volume at each time point among mice of a given group.
Acknowledgments We are grateful to members of the  Mouse Clinical Institute (MCI-ICS)  for their help and helpful discussion during the project. This project was supported by the  French National Center for Scientific Research (CNRS) ; the  French National Institute of Health and Medical Research (INSERM) ; the  University of Strasbourg  and  Centres Européen de Recherche en Biologie et Médecine ; French state funds through the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir Bou About et al. 11 of 12 Current Protocols in Mouse Biology ( ANR-10-IDEX-0002-02 ,  ANR-10-INBS-07 PHENOMIN );  ARC, Paris and Institut Hospitalo-Universitaire, Strasbourg  (Ther-aHCC IHUARC IHU201301187); the  Foundation of the University of Strasbourg and Roche Institute (HEPKIN) ; and the  European Union   Horizon 2020 research and innovation program  under grant agreement nos.
667273  and  671231. This work has been published under the framework of the LABEX  ANR-10-LABX-0028_HEPSYS  and benefits from funding from the state managed by the  French National Research Agency as part of the Investments for the Future Program. Funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. All protocols were reviewed and approved by the  Institutional Animal Care and Ethical Committee of the French Research Ministry  and conform to French and European regulations regarding the care and use of laboratory animals (authorization number: 2018032911411188; 14314).
Study design and data collection This is a retrospective multicenter analysis. Data were provided and approved for this study by the acute leukemia working party (ALWP) of the European Society for Blood and Marrow Transplantation (EBMT). The study protocol was approved by the institutional review board at each site and complied with country-specific regulatory requirements. All patients provided written informed consent authorizing the use of their personal information for research purposes. Eligibility criteria included patients with de-novo or secondary AML who relapsed after allogeneic SCT from an HLA-matched sibling or unrelated donor, and were given a second SCT from the same or a different donor, in any disease status, between the years 2006 and 2016. Patients were required to engraft after SCT1, to relapse before SCT2 and to be given SCT2 within 300 days after relapse.
Patients were divided into three groups based on the donor selected for SCT2: same donor group, different HLA-matched donor group, and second haplo-donor group. Haploidentical donors were defined as two or more mismatches from a related donor. There was no haploidentical unrelated donor. The EBMT database does not include enough information on kinship of haploidentical donors. Most unrelated donors were 9-10 HLA matched. Variables collected included recipient and donor characteristics for both transplants, disease features, transplant related factors including drugs and total doses used in the conditioning regimen, and outcome variables.
Variables were included in the multivariate model if they were conceptually important or if they differ in term of distribution between the three groups. Results are expressed as hazard ratio (HR) with 95% confidence interval. To test for a center effect, we introduced a random effect or frailty for each center into the model. All P values were two-sided and values <0. 05 were considered statistically significant. Statistical analyses were performed with SPSS 24. 0 (Inc. , Chicago) and R 3. 4. 1 software packages (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www. R-project. org/).
Measurement of Reactive Oxygen Species (ROS). On the fourth day of UVB irradiation of rabbit corneas, the animals were sacrificed and the eyeballs were enucleated, the anterior eye segments were immediately frozen in light petroleum chilled with an acetone-dry ice mixture, and sections were cut on a cryostat and transferred onto glass slides. The unfixed cryostat sections were incubated with 5 μM dihydroethidium (DHE, Sigma, Saint Louis, MO, USA) for 15 min at 37 °C. Sections were examined with a Leitz Orthoplan light microscope equipped with a Leica DC 500 digital camera, and the intensity of staining was measured using the Fiji ImageJ Program  [23]. 2. 4. The Determination of the Corneal Thickness. Changes of corneal optical properties after the injury and during healing were evaluated by measuring the central corneal thickness (taken as an index of corneal hydration) (see  [24]  in detail).
It contains 5,520 sentences, among which 1,227 sentences are negated. Each occurrence of the negation, the cue and its scope are annotated, as well as the focus of the negation if relevant. In this corpus, cues and scopes can be discontinuous. The I2B2/VA-2010 challenge  (Uzuner et al. , 2011)  featured several tasks using US clinical records. One task aimed the detection of statements and of their scope. Medical concepts had to be associated with the corresponding statement: present, absent, possible, conditional, hypothetical or not associated with the patient. Mipacq  (Albright et al. , 2013)  is another corpus with clinical data in English annotated with syntactic and semantic labels. Each detected UMLS entity has two attribute locations: negation (true or false) and status (none, possible, HistoryOf or FamilyHistoryOf ).
EXPERT SYSTEMS Among the rule based systems dedicated to the negation detection, NegEx  (Chapman et al. , 2001)  pioneered the area. It uses regular expressions to detect the cues and to identify medical terms in their scope. It was later adapted to various languages including French  (Deléger and Grouin, 2012). ConText  (Harkema et al. , 2009) , derived from NegEx, covers more objectives: negation, temporality, and the subject concerned by this information in the clinical texts. It has been adapted to French  (Abdaoui et al. , 2017). In another work, medical concepts may receive additional labels (positive, negative or uncertain)  Elkin et al. (2005a). Özgür and Radev (2009) ;  Øvrelid et al. (2010) ;  Kilicoglu and Bergler (2010)  exploit lexical, grammatical and syntactic information to detect speculation and its scope.
ANNOTATION LAYERS These two corpora are Part-of-Speech tagged and lemmatized with TreeTagger  (Schmid, 1994). For the creation of the reference data necessary for machine learning, both corpora were annotated manually to mark up negation and speculation cues and their scope. However, the 200 annotated clinical cases did not include enough examples of speculation for a machine learning models to train properly. Therefore, speculation detection is either trained and tested with ESSAI alone or with ES-SAI and CAS. Table  2  presents an annotated sentence from CAS: No dyspnea. The corpora also includes two additional columns (sentence number and token position).
WORD VECTOR REPRESENTATIONS In the recent years, several models have been introduced to generate vector representations of words helping machine learning approaches to better capture their semantics. The models used in the negation/speculation detection task are the following ones. word2vec  (Mikolov et al. , 2013)  is a predictive model to learn word embeddings from plain text. The embeddings can be calculated using two model architectures: the continuous bag-of-words (CBOW) and Skip-Gram (SG) models. In this work, we use use the SG model; it treats each context-target pair as new observation, which is suitable for large datasets. fastText  (Bojanowski et al. , 2017)  addresses the Word2vec's main issue: the words, which do not occur in the vocabulary, cannot be represented. Hence, this algorithm uses subword information: each word is represented as a bag of all possible character n-grams it contains.
The word is padded using a set of unique symbols which helps singling out prefixes and suffixes. The full sequence is added to the bag of n-grams as well. The vector now denotes every char n-gram and the word vector is the sum of its char n-gram vectors. Since the char n-gram representations across words are often shared, rare words can also get reliable representations. These two word embedding models are trained using the Skip-Gram algorithm, 100 dimensions, a window of 5 words before and after each word, a minimum count of five occurrences for each word and negative sampling. The training data are composed of the French Wikipedia articles and biomedical data. The latter includes the ES-SAI and CAS corpora, the French Medical Corpus from CRTT 2  and the Corpus QUAERO Médical du francais 3   (Névéol et al.
CONCLUSION and FUTURE WORK The interest for the automatic detection of speculation and negation in English with supervised machine learning has increased in the recent years. Yet, the lack of data for other languages and for specialized domains hampers the further development of such approaches. In our work, we presented new bodies of biomedical data in French annotated with negation and speculation (cues and their scope). Prior to the dissemination to the research community, the French clinical trial protocols corpus will be finalized through the integration of new data and the computation of the inter-annotator agreement. The French CAS corpus will be distributed as more clinical cases are manually annotated, as we need more speculative sentences to train supervised learning models on this dataset.
Another contribution of our work is the study of different types of word vector representations and recurrent neural networks for the detection of negation and speculation. There has not been much work of this type on French corpora, especially for the biomedical domain which contains specific negation and speculation phenomena. We showed that a CRF layer yields better performance than softmax on exact scope match. Finally, the models have been applied in a cross-corpus context. Besides, we plan to improve our neural network performance by providing richer feature. In particular, recent embedding techniques, such as BERT or ELMO  (Devlin et al. , 2018; Peters et al. , 2018)  may provide more accurate representation of the sentences. Moreover, in order to provide more accurate features, we plan to move from TreeTagger, which makes a substantial number of mistakes on our datasets, to a POS tagger/lemmatizer dedicated to French biomedical texts.
eu) [4, 5]  has established itself in the last 20 years as a major software suite dedicated to the analysis of regulatory regions, with five public servers supporting more than 500 eukaryote and 9000 prokaryote genomes. With a major focus on usability and accessibility to users with and without formal bioinformatics training, RSAT provides tools to retrieve sequences, perform motifs analysis, evaluate TF motif quality, compare and cluster motifs, convert file formats, etc. Here we describe Var-tools, a subset of tools included in RSAT that enable users to analyse regulatory variants and assess their putative impact on TF binding sites.
Dataset : experimentally-determined regulatory variants in red blood cells The regulatory activity of 2756 red blood cell variants has been systematically measured using Massively Parallel Reporter Assays (MPRA)  [25]. MPRA is a high-throughput assay in which a library of putative regulatory elements, each followed by a unique barcode, is inserted into a plasmid, then transfected into a cell, and quantified through the abundance of barcodes. These variants are known to be in strong linkage disequilibrium (LD) with 75 variants associated with common traits of this cell type. Three sliding windows per variant (left, right, and center) were synthesized, barcoded and used for to study the effect of slight changes in their genomic context. For each sequence mRNA/DNA ratio was computed to obtain a quantitative evaluation of the regulatory effect a sequence variant.
Evaluation of variation-scan The variant dataset was used as input for variation-scan ; the variants assessed in the red blood cell assay were annotated with the Ensembl GRCh37 human genome release, and given as input to convert-variations followed by retrieve-variation-seq. Since three sliding windows were used for each variant in the MPRA, the corresponding windows were merged before computing a background model using the create-background-model tool. According to the original study  [25]  , binding sites for the following TFs were enriched in the sequences of interest: GATA1, KLF1, DHS, TAL1, ETS, FLI1 and AP-1. Therefore, a total of 48 PSSMs annotated as related to these TFs were retrieved from the non-redundant RSAT motif collection  [24]  , and given as input to variation-scan. A negative control set of motifs was created using the RSAT tool permute-matrix  [4]  ; five pemuted motifs were created for each of the 48 motifs, generating a collection of 240 control motifs.