dataesr/scientific-paragraphs-categorization huggingface dataset:
We define the smoothness term to capture the edge presence along the common boundary of the two supervoxels where α,β are two nonnegative parameter, and N p is the neighborhood of p. Edge(p) is defined as  (4)  where r q,p is a voxel, such that q and r q,p are symmetric about p. 19www. uvacse. virginia. edu Finally, we solve the supervoxel labeling energy minimization problem using graph cuts  [126] -  [128]. The computing time is about 20 min for each case with Matlab an Intel Core i7-3770K, 3. 50 GHz processor and 16 GB memory system. The most time consuming part is oversegmentation and computing V P,Q in (4). Because we use intensities directly as the feature, we compute the standard scores to put the data in the same scale.
(3) w s (i, j) is a weighting function, which depends on the voxel spacing in each dimension. The term (1 -δ(y i , y j )) penalizes different labels of adjacent voxels, while the intensity term exp((-PCD(x i -x j ))/(2•x) regulates the degree of smoothing based on the local intensity variation, where PCD is a pseudo-Chebyshev distance and x̄ is a generalized mean intensity. D(y i , y j ) allows us to incorporate prior knowledge by penalizing different tissue adjacencies individually (Computation time for one dataset ranges from 4 to 12 min depending on the size of the images, most of the time is needed by the decision forest classifier.
We performed 3-fold cross validation on training dataset to tune the parameters. Extensive experiments suggests employing all tumor samples and randomly selected equal number of non-tumor samples for training the RF classifier yields good training results. For a single patient it takes about an hour and half to complete the whole process as shown in Fig. 11 while 3-fold cross-validation takes only about 15 min. The most time consuming parts are preprocessing and feature extraction which are done offline. All results in this work are obtained using MATLAB 2011a on windows 64 bit 2. 26 GHz Intel Xeon processor, with 12 GB RAM. We process HG and LG patients separately for both Leader Board and Challenge testing phases. There are a few leader board cases that show low scores. Our observation suggests that if the tumor tissue intensities are below the mean intensity of the image, the necrosis tissues are misclassified as non-tumor.
based on a classification forest, which is used such as to produce contextsensitive predictions. The method is based on our work focusing on high-grade glioma [39] , with further technical details available in [51]. The context sensitivity arises from two components in the framework. The first one is that the forest does not operate only on the original input images, but also on initial patient-specific probabilities p′ for each tissue class c. These probabilities are computed at test time for each patient as the posterior probability p ′(c | I(x)) = p(I(x) | c)p(c), based on the likelihood p(I(x) | c) of the multi-channel intensity I(x) given c. p(I(x) | c) and p(c) are estimated based on the training dataset-the likelihood by a Gaussian mixture model, and the prior as a normalized empirical histogram.
Acknowledgments This research was supported by the  NIH NCRR  ( P41-RR14075 ), the  NIH NIBIB  ( R01EB013565 ), the  Academy of Finland  ( 133611 ),  TEKES (ComBrain) , the  Lundbeck Foundation  ( R141-2013-13117 ), the  Swiss Cancer League , the  Swiss Institute for Computer Assisted Surgery (SICAS) , the  NIH NIBIB NAMIC  ( U54-EB005149 ), the  NIH NCRR NAC  ( P41-RR13218 ), the  NIH NIBIB NAC  ( P41-EB-015902 ), the  NIH NCI  ( R15CA115464 ), the  European Research Council  through the  ERC Advanced Grant MedYMA   2011-291080  (on Biophysical Modeling and Analysis of Dynamic Medical Images), the  FCT  and  COMPETE  ( FCOM-01-0124-FEDER-022674 ), the  MICAT  Project ( EU FP7 Marie Curie Grant No. PIRG-GA-2008-231052 ), the  European Union Seventh Framework Programme  under grant agreement no.
600841 , the  Swiss NSF  project  Computer Aided and Image Guided Medical Interventions (NCCR CO-ME ), the  Technische Universität München-Institute for Advanced Study  (funded by the  German Excellence Initiative  and the  European Union Seventh Framework Programme  under Grant agreement  291763 ), the  Marie Curie COFUND program  of the  European Union  ( Rudolf Mössbauer Tenure-Track Professorship  to BHM).
3 ) 3 (3) w s (i, j) is a weighting function, which depends on the voxel spacing in each dimension. The term (1 -δ(y i , y j )) penalizes different labels of adjacent voxels, while the intensity term exp((-PCD(x i -x j ))/(2•x) regulates the degree of smoothing based on the local intensity variation, where PCD is a pseudo-Chebyshev distance and x̄ is a generalized mean intensity. D(y i , y j ) allows us to incorporate prior knowledge by penalizing different tissue adjacencies individually (Computation time for one dataset ranges from 4 to 12 min depending on the size of the images, most of the time is needed by the decision forest classifier.
We propose to implement a decision support system so called FGRAPEDBN able to (1) capitalize the heterogeneous fragmented knowledge available including data and expertise and (2) predict the sugar (resp. the acidity) concentrations with a relevant RMSE of 7 g/l (resp. 0. 44 g/l and 0. 11 g/kg). FGRAPEDBN is based on a coupling between a probabilistic graphical approach and a fuzzy expert system.
Air temperature, rain fall, relative humidity, sunshine hours are well known to affect the grape ripening, mainly sugar concentration  [8]  and total acidity  [7]. Anthocyanins level  [9]  are also important to predict but not measured by winegrowers inside the vineyard. The decision of the optimal time to harvest mostly relies on the expert knowledge and the evaluation of the grape maturity  [5]. In a context of optimization and sustainable "considerations" a big challenge for such an agri-food system is to propose robust mathematical predictive tools relying on knowledge integration  [10]. Nevertheless it is not an easy task as regards to this specific domain. Indeed difficulties remain to develop and implement integrative mathematical tools for several reasons detailed in  [11]. In the case of grape maturity, above the complexity of the reactions involved, several factors are to be emphasized: Data handling is time consuming and limited (one year for one experimental condition), available knowledge is fundamental to handle but expressed in different forms (equations, expert opinions, databases.
Moreover, the conditional probabilities are time-invariant (first order homogeneous Markov property) meaning that P(X(t)|U(t)) = P(X(2)| U(  2 )) for all t in  [1, σ]. Hence to specify a DBN, we need to define the intra-slice topology (within a time slice), the inter-slice topology (between two time slices), as well as the parameters (i. e. conditional probability functions) for the first two time slices. The structure of a model can be explicitly built on the basis of knowledge available in the literature and parameters can be automatically learned without a priori knowledge on the basis of a dataset (known as parameter learning). The techniques for learning DBNs are generally extensions of the techniques for learning BNs. Different methods exist to learn about the structure or the parameters from substantial and/or incomplete data  [29, 30].
; a ijr i )  [29, 30]. If we have an available experimental database in which event (X i (t) = x k | U i (t) = x j ) occurs N ijk times, the posterior variable (θ ij |database) then follows a Dirichlet distribution (θ ij |database) ~Dir(N ij1 þ a ij1 ;. ; N ijr i þ a ijr i ) and the expected a posteriori gives as estimation: y ijk ¼ N ijk þ a ijk S r i k¼1 N ijk þ a ijk ð12Þ where α ijk = 1/r i inducing an uniform prior distribution over θ ij allowing to take into account the lack of data. Parameters may be then updated with a simulated database stemming from the results of the previous fuzzy model simulation, i. e (see  [18] ): y ijk ¼ N S ijk þ a 0 ijk S r i k¼1 N ijk þ a 0 ijk ð13Þ where a 0 ijk corresponds to the previous sum of N ijk +α ijk and N S ijk corresponds to the number of occurrences inside simulated database.
The decision support system for grape berry maturity prediction Two models are developed and coupled to integrate the maximum of knowledge available. A first expert model so called FGRAPE formalized the expert memory of what happens during the ten past years and its consequences on the grape berry maturity kinetics. It is then coupled to a dynamic Bayesian network (DBN) expressing the dynamic of the system on the basis of conditional laws extracted from the data basis representing 4 years of climatic conditions. DBN parameters are updated using the results of simulation of FGRAPE. It is achieved using Eqs 12 and 13 presented above. It leads to the decision support tool proposed in this paper and so called FGRAPEDBN.
Output i ðweek t Þ ¼ Output i ðweek tÀ1 Þ þ k Â X j¼1;7onDt Index outputi FGRAPEðdayÞ ð 17Þ Where X j¼1;7onDt Index outputi FGRAPEðdayÞ is the sum on the 7 days contained in the week t of the values of the index predicted day by day by the fuzzy rules of FGRAPE for each output i (sugar or acidity) and k is a constant adjustment parameter fixed for each year based on expert criteria about soil considerations and global climatic impacts (equal to 0. 8 for 2006, 1 for 2007, 0. 9 for 2008 and 0. 7 for 2009). A coupling between DBN and fuzzy logic: FGRAPEDBN DBNs are very useful when few things are known about the phenomena of system but they need substantial database to estimate parameters. In our application, this database is really hard to acquire (1 year, 1 kinetic) which is generally the case in our domain.
Fuzzy logic is then used to translate another source of knowledge, the expert knowledge expressed in the form of qualitative heuristics, into a data basis directly usable by the DBN. FGRAPEDBN represents the scientific knowledge about the principal conditional links that can be established between the grape maturity indexes studied and the climate conditions. The prediction of the decision support tool starts by an initialization of S and Ac on the basis of measurements achieved during the first week of the maturation, followed by simulations week by week of the dynamic Bayesian network based on predictions of the outputs for the week-1 (see  Fig 4). On the basis of the conditional probabilities learnt using the data basis presented in Eq 12, FGRAPE is used to update the parameters of the DBN model upon a methodology proposed in Table  3.
Nevertheless, a DBN used alone, would have not predicted with a good accuracy the whole system as regards to the data available. In this sense, the integration of FGRAPE inside DBN implementing FGRAPEDBN, thus improves the RMSE of the whole coupled system and is a relevant way to reduce the uncertainty by the way of an integration of qualitative expert knowledge. Parameter learning in FGRAPEDBN, for a known network structure, performs in polynomial time. However Inference in a dynamic Bayesian networks (see Eqs 7 and 15) is NP-hard  [14]. The computational complexity of FGRAPEDBN does not stem from our methodology but from the chosen formalism of representation namely DBNs. Moreover, this coupling approach allows to reduce the uncertainty on the system by knowledge integration.
Acknowledgments We would like to thanks all the experts and winegrowers of Tour and Angers who have participated to this adventure. We also thanks  InterLoire , the  Institut Français de la Vigne et du vin-IFV Tours , the  Institut Français de la Vigne et du vin-IFV Angers , the  Chambre d'Agriculture d'Indre et Loire-Groupement de Développement Viti-Vinicole (GDVV) , the  Cellule Terroir Viticole , the  INRA Angers-Unité Vigne et Vin , the  ESA Angers-Laboratoire GRAPPE.
are observed environmental conditions from time 1 to τ. All DBN parameters are initialized and updated by means of Eq 12, from an experimental database containing the monitoring of maturation from 2006 to 2009 on 26 parcels. Model simulations may then be ¼ S x xPðXðtÞ ¼ xjfOðt 0 Þ ¼ oðt 0 Þ; 8 t 0 2 ½1; tgÞ ð18Þ where P(X(t) = x|{O(t') = o(t'),8t'2[1,τ]}) are marginal probabilities, t is on the order of week, X is total acidity (resp. sugar) and Ac(1),S(1) are initial concentrations;, O(t) = {HR(t),Pl(t),Ins(t), T(t)}, t2[1,τ].
In silico Arc analysis A multiple sequence alignment was performed using Clustal Omega software  [24]  and viewed in Jalview 2  [25]. Consensus was calculated based on an algorithm analysing the physicochemical properties of sets of amino acids in the multiple sequence alignment  [26]. MeDor, a meta-predictor of protein structure  [27] , was used to analyse the primary hArc sequence. MeDor outputs graphical displays of various prediction methods including a hydrophobic cluster analysis (HCA) plot  [28]. Horizontal clusters indicate α-helices and vertical shapes mainly correspond to βstrands. Publicly available structure models of hArc were obtained from MODBASE  [29] , which contains theoretically calculated models based on homology. Reliability was measured using five different methods with the following thresholds; MPQS (ModPipe Quality Score): 1.
MS/MS data were acquired in centroid mode. Only multiply charged precursor ions (2 + , 3 + , 4 + ) were selected for MS/MS. Data analysis was performed using MaxQuant Software (version 1. 0. 13. 13) for filtering the data and creating. mgf files, needed for searching in MASCOT version 2. 2. 03 (Matrix Science). The data were searched against a speciesspecific (Homo sapiens) Uniprot database with a list of common contaminants appended. The data were searched with the following modifications: carbamidomethyl (C; fixed) and oxidation (M; variable). Termini were postulated based on peptide ladders of increasing amino acid length, either all starting or all ending at the same residue (for N-and C-termini respectively).
Dynamic light scattering Dynamic light scattering (DLS) was performed on a Malvern Zetasizer Nano ZS with a HeNe laser at 633 nm. Temperature scans and size measurements were carried out at a fixed scattering angle of 173 • (back scatter). Temperature scans were run from 4 • C to 90 • C at a rate of 2 • C/min. Purified protein preparations were diluted to 1 mg/ml (22 μM) in the indicated buffers and additives. Data analysis was performed on intensity and volume size distribution curves and the molecular mass and Z-average size was calculated using Malvern DTS software. The intensity size distribution, the first order result, is weighted according to scattered intensity of each particle fraction whereas the volume size distribution represents the relative proportion based on the mass or volume, which is derived from the intensity size distribution via the Mie theory.
), corresponding to the N-terminal region of α-lactalbumin, was treated in the same way as PS1 and used as a negative control. Regeneration of anti-GST antibody surfaces was accomplished with a 30 μl/min injection of 10 mM glycine/HCl, pH 2. 1, for 2 min. All data generated were subtracted from the reference surface (no anti-GST antibody immobilized). BIAevaluation software (Version 3. 2; GE Healthcare) was used for analysis of the sensorgrams. Dissociation constants (K d ) were calculated by fitting the sensorgrams to a 1:1 Langmuir binding model via non-linear regression analysis.
ACKNOWLEDGEMENTS The pET vectors and the tobacco etch virus (His-tagged TEV) protease expression vector were gifts from  Gunter Stier  ( EMBL, Heidelberg, Germany ). We thank  Jarl Underhaug  ( University of Bergen ) for assistance in data analysis,  Joanna Kirkpatrick  ( Proteomics CoreFacility, EMBL, Heidelberg, Germany ) for the mass spectrometric analysis, and  Peter Gimeson  ( Malvern Instruments Ltd. ) for help with DSC.
FUNDING This work was supported by the  Western Norway Regional Health Authority  [grant number  911666  (to C. M. and C. R. B. )]; the  Research Council of Norway  [grant number  FRIBIO 214012  (to A. M. )]; the  Spanish Ministry of Science and Innovation  [grant number  BFU2013-44202  (to J. M. V. )]; and the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ).
Online-delivered interventions can be a low-cost and nonstigmatizing way of delivering therapy and self-management support, and they are easily accessed. Online-delivered therapy is effective for psychological distress  [19] , although low levels of engagement can lead to poor patient outcomes  [20]. Furthermore, established generic computerized cognitive behavioral therapy packages, such MoodGym  [21]  and Beating the Blues  [22] , may not be acceptable for people with comorbid physical health problems  [23]. A solution that includes disease-specific content is needed to maximize the health benefit. Our searches of electronic reference databases suggests that interest in Internet-delivered interventions for patients with long-term conditions including CHD is increasing, though data on their effectiveness are scarce.
Phase 1: Development of the Intervention We identified existing evidence-based interventions for common physical (ie, chest pain, palpitations, breathlessness, fatigue) and associated psychological (ie, depression, anxiety, stress, subclinical distress) symptoms of CVD. We searched electronic databases-Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Embase, Medline, and PsycINFO-for relevant systematic reviews and randomized controlled trials of interventions for CHD symptoms or distress. We also obtained relevant resources currently used in, or recommended for use by, Southwark IAPT service; this service is participating in the Pathfinder study  [18]. National Institute for Health and Care Excellence (NICE) guidelines and high-quality websites, including NHS Choices [34] and the British Heart Foundation (BHF)  [35] , were reviewed.
Data Collection A focus group was conducted in the meeting room in the National Institute for Health Research (NIHR)/Wellcome Clinical Research Facility at King's College Hospital. Participants were compensated with a payment of a £20 Love to Shop voucher and travel expenses were reimbursed. The group was facilitated by EB using a topic guide (see Multimedia Appendix 1) informed by literature review and expert opinion-study team and collaborators-to guide the discussion. Topics included current coping strategies (ie, their own or, if not personally relevant, any strategies they are aware of) for chest pain, fatigue, breathlessness, and distress; barriers and facilitators to healthy living (ie, diet, exercise, alcohol); and medication adherence. An example SilverCloud intervention (ie, the online intervention for depression currently being used in 11 NHS IAPT services) was demonstrated by a member of the SilverCloud team and participants were asked whether they thought a similar intervention would be useful to them and, if so, what adaptations would be needed.
Data Collection Participants were given access to the adapted intervention (ie, the version including modifications made in response to findings of the above studies) via a password, which allowed access from any computer for 2 weeks. Data concerning patterns of usage of, and satisfaction with, the intervention, as well as clinical data were collected electronically via the secure SilverCloud system. All data were anonymized. Usage data collected over 2 weeks were as follows: number of sessions per user, time spent per session, and total time spent using the intervention. A session was defined as a period of 5 minutes or more of a user being logged onto the system. Log-ins for less than 5 minutes were also recorded. Session time estimation will always be an imperfect calculation, as users may be interrupted or take breaks within a session, and may not formally log out of the system.
It is sometimes assumed that older or less-educated people will not be able to access or use technology. In this work we recruited people with a range of educational backgrounds and self-reported levels of confidence using a computer; an important finding was that no participant reported problems either using or accessing the intervention. This is a preliminary indication of the feasibility of the intervention in this population. Finally, the focus group data indicated that users would appreciate a tailored approach. In future, we plan to develop a supporter role to facilitate the intervention. The original SilverCloud intervention employs such a supporter who is able to provide tailored encouragement and feedback, for instance making suggestions regarding which elements of the intervention the user may find helpful.
Acknowledgments The authors would like to thank the people with CVD who participated in this work. We would also like to thank  Melanie Dupin , BHF support group coordinator, for helping to recruit participants and  Professor John Chambers  of  Guy's and St Thomas' NHS Foundation Trust  who provided invaluable cardiology support. This work was funded by  NHS England  under their  Small Business Research Initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This independent research was also supported by the  National Institute for Health Research/Wellcome Trust King's Clinical Research Facility , the  NIHR Biomedical Research Centre and Dementia Unit at South London, Maudsley NHS Foundation Trust, and King's College London.
Determination of RAR/RXR binding regions from ChIP-seq data Reads from ChIP-seq samples were aligned using Bowtie (29) version 0. 12. 9 with options -n 3, -e 70 and -m 1. We used the UCSC Genome Browser  (30)  to produce visualizations of the aligned reads along the chromosomes and gene annotations. We performed peak calling using a dedicated procedure in order to deal with the ChIP-seq time series data: (i) call peaks independently for each sample, with a really permissive threshold; (ii) consider the set of positions on the genome which are the summit of a peak in at least one condition; (iii) define a link between any two summits that are closer than 100bp; (iv) compute connected components of this graph; (v) define, for each connected component, a binding region, as a location of 500 bp wide centered at the mean of the summits in the connected component.
Individual libraries were then sequenced on Illumina GAII. In addition, total genomic DNA was sequenced to obtain a reference input profile. Sequencing data, ChIP-qPCR validation data (Supplementary Figure  S1 ) and detailed bioinformatics analyses can be found in Supplementary Experimental Procedure. An example of the binding profiles for RAR and RXR around the Hoxa locus is shown in Figure  1A. We performed peak calling on each ChIP-seq sample and merged the results so as to determine a fixed set of genomic regions across all tested conditions (see Supplementary Experimental Procedure). The defined binding regions, considered as a genomic feature, do not depend on one particular condition anymore, and we analyzed their occupation status along the time series. To be able to compare occupancy of the identified binding sites in the various sampled conditions, statistical tests were performed to detect significantly occupied regions relative to the input control.
RAR/RXR binding sites coincide with loci targeted by selfrenewal associated transcription factors The motif enrichment analysis of our data set by algorithms composing the MEME-ChIP suite also highlighted several binding motifs for transcription factors, including NRs, known to be associated with self-renewal/pluripotency (i. e. POU5F1, SOX2, KLF4, TCFCP2L1, STAT3, ZFX, ES-RRB, NR5A2) or differentiation (i. e. SOX17, NR2F1, NR2F2) (Supplementary Table  S2 ), thereby agreeing with the previous observation made by Mahony and colleagues in RA-treated mES cells on a smaller set of RAR binding regions  (22). To confirm the prediction obtained by motif enrichment analysis, we comparatively analyze the binding profiles of these factors with RAR/RXR binding region data set. Using published data sets listing binding regions of transcription factors or cofactors known to play an important role in pluripotency and/or self-renewal (e.