dataesr/scientific-paragraphs-categorization huggingface dataset:
As shown herein and also described in the Pfam database  (Finn et al. , 2016) , DUF3794 was often found in association with CBM50. On the other hand, DUF362 is often present in proteins with domains that bind to iron-sulfur clusters, and its coexistence with CBM9_1 in an uncharacterized protein from soil bacteria Sorangium cellulosum was previously observed (UniProt entry S4XJL8). The structure of DUF3459 has been determined (UniProt entries B2IUW9, Q9RX51, Q8ZPF0, Q8P5I2, Q2PS28, M1E1F6, M1E1F3, H3K096), and as observed here, was previously shown to be part of multi-modular proteins comprising GH13 and CBM48 domains (UniProt entries W6LS46, R4KHQ4, C7RTS8). Although not frequently observed, one PUL identified in the poplar hydrolysate-fed beaver droppings culture contained DUF5005 appended to a predicted CBM32 (Figure  6B ).
FUNDING This work was funded by the  Government of Ontario  for the project " Forest FAB: Applied Genomics for Functionalized Fibre and Biochemicals " ( ORF-RE-05-005 ), the  Natural Sciences and Engineering Research Council of Canada for the Strategic Network Grant "Industrial Biocatalysis Network , " the  European Research Council (ERC)  Grant Agreement 322820 to BH and an  ERC Consolidator Grant  to EM ( BHIVE -648925 ). MW was funded by a  University of Toronto   Fellowship and Professor William F. Graydon Memorial Graduate Fellowship. MC was funded by a  Marie Curie International Outgoing Fellowship  within the  7th European Community Framework Program.
These messages, together with other smoking cessation tips, are conveyed to teenagers in an entertaining and engaging way. Statistics of each user's condition and actions are recorded and displayed on the right-hand side of the main screen. These include the maximum number of in-game smoke-free days, number of cigarettes the user has taken, the types of activities and mini-games he or she has played, health condition, etc. This data is useful for analysis and comparison at the end of the game. By looking at this table, the player or physician will be able to tell how well or bad the user did ingame. It can also act as a reminder to the user about what can be done in real-life to fight the urge to smoke and how to react to smoking cues.
A database server will be set up in order for each user to save their account, avatar, and game progress and retrieve them later. The TeenSmoking project team also has plans to set up an online forum or message board for users to interact by posting opinions and questions about the game, smoking cessation, or related topics. This would be a great place for the community to discuss the subject, tell successful stories, and give advice to people looking to quit. The forum could also be utilized by VRMC to post upcoming events, announcements and useful links on the board. When the newest version of the TeenSmoking project goes public, VRMC could organize events or visits to schools and institutions to promote a smoke-free environment with the game. There would also be competitions that give out real prizes to those who play the game and successfully quit smoking within a certain period.
In monitoring active volcanoes, the magma overpressure is one of the key parameters used in forecasting volcanic eruptions. This parameter can be inferred from the ground displacements measured on the Earth's surface by applying inversion techniques. However, in most studies, the huge amount of information about the behavior of the volcano contained in the temporal evolution of the deformation signal is not fully exploited by inversion. Our work focuses on developing a strategy in order to better forecast the magma overpressure using data assimilation. We take advantage of the increasing amount of geodetic data [i. e. , Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS)] recorded on volcanoes nowadays together with the wide-range availability of dynamical models that can provide better understanding about the volcano plumbing system.
However, the model parameter controlling the temporal evolution of the medium and the crustal viscosity was in this study considered as a fixed parameter, thus limiting the interest of considering the temporal evolution of the system. Also, their approach was based on a finite element model which limits the application to real-time forecasting and their joint assimilation of GNSS and InSAR data was still problematic. In this work, our main goal is to test the ability of Ensemble Kalman Filter (EnKF) to quantify the magma pressure evolution at depth from surface displacement data and thus to forecast pressure-based volcanic eruptions. As a first attempt, we focus on a specific dynamical model that well-describes the behavior of several often erupting basaltic volcanoes. We developed an efficient approach to assimilate GNSS and InSAR data into a twomagma reservoir model, which is a simple and generic dynamical model for the magma plumbing system, in order to forecast the overpressures and to constrain two model parameters related to the deepest part of the reservoir system.
In the discussion part, we tackle the effects of the spatial and temporal characteristic of the datasets (i. e. , GNSS-like and InSAR-like data) used during the assimilation and how far-field data affect the performance of assimilation. We also compare the performance of EnKF with a Bayesian-based inversion. Then we present the advantages and limitations of the dynamical model we considered as well as the possibility of applying data assimilation to other types of models. Lastly, we discuss the ease of implementing our strategy and the future of EnKF for real-time volcano monitoring.
High Spatial Resolution vs. High Temporal Resolution Dataset Our results show the huge potential of EnKF in forecasting the overpressures and displacements as well as in estimating the uncertain model parameters. However, having 40 near-field GNSS stations that will provide 80 observations every 2 days is often not the case for most volcanoes. In Figure  10  we show how EnKF performs when the number of observations is varied using two types of dataset: (1) a GNSS-like dataset and (2) an InSAR-like dataset. The GNSS-like dataset is composed of 10 observations (i. e. , five radial and five vertical displacements) located at distance r = 1 -5 km away from the volcano axis and is uniformly spaced every 1 km. The InSAR-like data is an 11 × 11 grid centered on zero-axis (i. e. , r = 0 as the volcano axis) with two components (i.
e. , vertical and radial directions) that have spatial resolutions of 1 km. This provides a total of 242 observations located at distance r = 1 to 5 √ 2 km away from the volcano axis. Each of the datasets is assimilated every 2 days (f obs = 1), consistent with the time interval of the model. We use a prior distribution which is Gaussian and not centered on the true values for the uncertain parameters (Figure  7C ) because it is the most realistic and most critical case. Our findings show that both datasets are able to track the true behavior of the shallow and deep overpressures. However, when it comes to estimating the uncertain model parameters, only the InSAR-type FIGURE 5 | The evolution of the overpressures after performing the state estimation (A,B) given that the initial ensemble of the uncertain parameters (C) are Gaussian, not centered on their true values (black broken lines).
At the time of writing, only the Sentinel-1 satellite has the capability to provide radar data as frequent as every 6-12 days. TerraSAR-X can provide data every 11 days whereas COSMO SkyMed's routine return period can acquire data every 8 days by using different satellites in its constellation. To be more realistic, we then assimilated the InSAR-like dataset every 12 days (f obs = 1/6) and kept the frequency of available GNSS data to 2 days (f obs = 1). This means that for the InSARlike data, the model forecasts are only corrected every 12 days. Figure  11  illustrates that the InSAR-like data failed to recover the true behavior of the overpressures toward the end of the assimilation. More precisely, it failed to recover the exponential part of the system or during the time when the shallow reservoir is refilled by the deep one after an eruption.
As a consequence of the poor overpressure forecast at the beginning, parameter estimation cannot be performed because their resulting posterior distributions are physically meaningless (i. e. , negative radius of the deep reservoir). Performing the resampling option cannot even solve the issue. Take note that in EnKF, uncertain model parameters are only updated by the sample covariance between them and the state variables (i. e. , in this case, the overpressures), and that the model evolution for them is simply an identity. Given the aforementioned results, we know that the advantage of using GNSS data to capture the behavior of the overpressures is its high temporal resolution, in which it is possible to obtain daily observations that can be used for assimilation. InSAR data on the other hand, are less frequent to acquire but provide better spatial information about the surface deformations and constraints on the uncertain parameters.
In order to exploit the advantages of both dataset, we jointly assimilated the GNSSlike and InSAR-like data. Figure  12  shows how the evolution of the overpressures is well captured and how the uncertain model parameters converged to their true values. In fact for this case, in as early as ∼6 months of jointly assimilating GNSS and InSAR, it may already be possible to forecast the long term overpressure values that can later tell whether a critical overpressure-unique for each volcano-will most likely be achieved. Retrieving the three-dimensional (3D) displacement vector using InSAR is not always possible and most of the time, only the line-of-sight (LOS) displacement is available. In the Supplementary Materials, we show that the joint assimilation of GNSS and InSAR in either ascending or descending LOS view can still capture the temporal behavior of the overpressures as well as estimate the two uncertain model parameters, thereby allowing the possibility of near-real time forecasting.
Including Far-Field Data While InSAR data can cover up to hundreds of kilometers with one swath, most volcanoes are small in size and thus volcano deformation signals may cover only a small portion in the image. In Figure  S3  we plot the radial and vertical displacements as a function of the distance from the volcano axis given the values of the parameters in Table  1. As one goes farther away from the volcano axis (i. e. , r is increased), the deformation signal weakens and almost decays to zero. Decomposing the source of deformation, we find that the near-field signals are mostly related to the shallow reservoir whereas at farther distances (i. e. , >16 km and >10 km for the radial and vertical displacements, respectively), the signals became dominated by the deep one. Given this, one can infer that far-field data can bring more information about the deep reservoir but note also that as one goes farther away from the volcano axis, the signal-to-noise ratio also weakens.
Thus, when assimilating InSAR data, it is important to know the effect of including far-field data in order to avoid spikes in the root mean square error (RMSE) between the forecasted and the synthetic surface deformation associated with the use of InSAR especially when coupled with GNSS data as previously performed by  Gregg and Pettijohn (2016). To do so, we generated an 11 × 11 grid InSAR-like dataset with two components (i. e. , vertical and radial directions), giving a total of 242 data points. However, unlike in the previous section where we assimilate near-field observations that are equally spaced every 1 km, here, we use non-uniform spacing and intentionally limit the number of far-field points to avoid overwhelming the dataset with noise. In Figure  13A , we plotted the location of these observations.
Spatially correlated noise can be present, especially in the case of InSAR data where atmospheric noise are usually embedded in the data. The spatial correlation can change the results depending on the quality, quantity and the distribution of the data points. Bekaert et al. (2016)  suggest that neglecting InSAR covariance should be avoided as it may result to treating spatially correlated atmospheric noise as part of the signal. In the future, InSAR variance-covariance shall be applied when dealing with real case data. Furthermore, the approach presented by  Brankart et al. (2009)  and  Ruggiero et al. (2016)  can be considered.
Models are selected based on the Metropolis-Hastings rules, which always accept models that fit better to observations than the previous iteration and randomly accept those that do not fit to avoid being trapped to a local minima  (Segall, 2013). For example, at t i , a set of a d and Q in values are drawn from their initial distributions, generating model forecasts using the two-chamber model. These model forecasts are then compared to the GNSS and/or InSAR data and are always accepted if the fit is better than the last sampling iteration, creating the so-called Markov-Chain. The sampling iteration can be performed thousand to million times in order to build a full posterior distribution. In our case, we performed 11,000 MCMC sampling iterations at each time-step and burned-in the first 10,000 so that in the end we have a similar ensemble size (e.
In fact, similar deformation behavior has been observed at other basaltic volcanoes: Kilauea and Mauna Loa in Hawaii  (Lengliné et al. , 2008 , Westdahl Volcano (Lu et al. , 2003) , and Axial Seamount Volcano  (Nooner and Chadwick, 2009)  such that it can be FIGURE 10 | Influence of the spatial density of observations on the assimilation: GNSS (10 observations that are assimilated every time-step, f obs = 1, with distance to the volcanic center ranging from 1 to 5 km) vs. InSAR-like dataset (242 observations that are assimilated every time-step, f obs = 1, with distance to the volcanic center ranging from 1 to 5 √ 2 km). (A,B) illustrates the estimated overpressures and uncertain parameters, respectively, given that the initial conditions of the uncertain parameters are similar to those of in Figure  5C.
The insets in (A) provide a closer look on the overpressures at the beginning of the assimilation. The light blue and red shades correspond to the spreads (1σ ). Note that for the overpressures, the spreads are difficult to discern since they are very small when compared to the scale of the plot. The black broken lines represent the true values. accepted as generic for this type of frequently erupting volcanoes. However, except for Axial Seamount, the time constant derived at other places are larger than the one observed at Grímsvötn  (Reverso et al. , 2014). This parameter is expected to only influence the impact of the temporal frequency of available observations. In the case of Grímsvötn, it is indeed expected to be more restrictive regarding the importance of a high temporal resolution dataset.
Evensen (2003)  have shown that the model error can be FIGURE 11 | Influence of the frequency of observations on the assimilation: GNSS (10 observations that are assimilated every time step, f obs = 1, with distance to the volcanic center ranging from 1 to 5 km) vs. InSAR-like dataset (242 observations that are assimilated every 12 days, f obs = 1/6, with distance to the volcanic center ranging from 1 to 5 √ 2 km). Since parameter estimation is not possible to perform when InSAR dataset is used (see text), only the estimated overpressures are presented. Note that the initial conditions of the uncertain parameters are similar to those of in Figure  5C. The insets provide a closer image of the overpressures at the beginning of the assimilation. The light blue and red shades correspond to their spread (1σ ).
Another important challenge of the assimilation approach is the availability of frequent data. Although for GNSS we can obtain daily observations, InSAR data are less frequent and are still dependent on the quality of interferograms produced. Also, in reality, the 3D-displacement field vector from InSAR are not always retrievable due to the satellite's acquisition geometry. Furthermore, while we only used deformation data alone, the observation vector can include gas emission and seismic data for a more deterministic approach in forecasting (i. e. , seismic data in particular can be used to estimate the timing of an eruption), as long as they can be related to the dynamical model used. Interestingly, when it comes to near-real time monitoring, it may be possible to use inversion and data assimilation jointly in order to accommodate vast amount of incoming data.
CONCLUSIONS AND PERSPECTIVES Our work presents a simple yet efficient model-data fusion strategy using data assimilation (i. e. , EnKF) that can be applied to real-time volcano monitoring. Synthetic GNSS and/or InSAR data are assimilated to a simple yet generic dynamical model (i. e. , two-chamber model) to mainly forecast the overpressures-one of the key parameters when assessing volcanic eruptions. The EnKF method is tested on two synthetic cases: (A) state-estimation and (B) state-parameter  parameters are also estimated along with the state variables (overpressures). Based on the specific case considered in this study, frequent but spatially sparse observations like GNSS are more likely to recover the true evolution of the overpressures than with an infrequent but spatially dense dataset (e.
g. , InSAR). Although, using an InSAR-like data will better constrain the uncertain model parameters. While  Gregg and Pettijohn (2016)  pointed that the assimilation of InSAR creates spikes in the RMSE between the forecasted and the synthetic displacements when coupled with GNSS, our strategy presents a successful joint assimilation of these datasets for the first time, allowing to exploit both the high temporal characteristic of GNSS and the high spatial characteristic of InSAR. An important point to consider is the use of far-field data. While far-field displacements can provide more information about the deep reservoir, they can generate noisier and less accurate forecasts because of their weaker signal-to-noise ratio. Future work must be dedicated to strategically balance them with near-field observations when used for assimilation (i.
Interestingly, it may also be possible to combine both techniques in which MCMC will be used to first constrain the non-evolving model parameters followed by applying EnKF in order to forecast only the state variables (e. g. , overpressures). The strategy that we have developed here aims to give a preliminary assessment of EnKF as a tool to assess volcanic unrest. While our framework is simple, it offers a great potential in using the method toward a more deterministic approach in eruption forecasting and better understanding of the magma plumbing system. The use of more sophisticated physics-based models as well as other types of datasets such as gas emission and seismic data are highly encouraged for future studies. In terms of the real case application of the strategy to Grímsvötn volcano, additional uncertain model parameters such as the depth of the deep reservoir and the hydraulic strength between the two reservoirs should be accounted.
ACKNOWLEDGMENTS The authors would like to thank  Dr. Bernard Valette  and  Dr. Jean-Michel Brankart  for their helpful discussions about the inversion method and data assimilation, respectively. We are also grateful for the scientific comments of  Dr. Marie-Pierre Doin  and  Dr. Jean-Luc Froger. Furthermore, we also acknowledge our two reviewers and  Dr. Valerio Acocella  for their constructive comments that helped to improve our work.
130  This assay is especially formulated with robust lytic capacity to overcome 3D MCTS obstacles such as tight cell-cell junctions and the presence of the extracellular matrix. 130  The relatively simple workflow and data analysis make this ATP assay scalable to high-throughput screenings of drug-loaded nanocarrier efficacy. 125  Qualitative assessment of proliferating or dead/necrotic cells can be performed by Confocal Laser Scanning Microscopy (CLSM) imaging following spheroid incubation with various live/dead reagents. 46, 118, 131  Preliminary spheroid dissociation into single cells is instead required for a quantitative measurement by Fluorescence-Activated Cell Sorting (FACS). 70  Staining cell dispersions of dissociated spheroids with fluorescently labeled Annexin V (AnnexinV-FITC) and propidium iodide (PI) has also been largely used to detect early apoptotic and dead/necrotic cells, respectively.
Cross-linking between chromatin sites are precisely the events sampled by Chromosome Capture data (3C, 4C, 5C, HiC)  [3, 8] , and single cell HiC confirms that positions of cross-links can vary between cell types and phases  [9]. In that context, TADs represent average chromatin conformations, characterized by a higher numbers of binding molecules compared to non-TAD regions. Over the past ten years, polymer models have been used to analyze statistics hidden in Chromosome Capture (CC) data and to characterize the decay of the encounter probability with the genomic distance s. For example, the EP between two monomers A, B for a (linear) Rouse polymer decays with s -3/2 , thus the exponent is 3/2  [10]. A range of decay exponents lower than 3/2 can be produced by other polymer models, where stable transient loops are formed between segments  [11].
We present here a general computational and algorithmic procedure to estimate parameters of a randomly cross-linked polymer model following the 5C protocol. The procedure consists in constructing an ensemble of polymer models from the EP of 5C by randomly cross-linking monomers and in resolving the difficulty of assigning the minimal number of sparse interactions between monomer pairs. These interactions can be directly interpreted as binding molecules. The construction of the polymer model starts with the Rouse model  [10] , which consists of beads linearly connected by harmonic spring. We started with the coarse-grained Rouse polymer that describes accurately the statistics of the chromatin below a scale of few Mbp  [28, 29]. To further constrain monomer interactions, we determine monomer connectivity from the 5C data of mammalian X chromosomes.
New policy can be informed,  60  including in other fields such as hydropower mitigation, flood risk management and nature conservation which all revolve around the same river channel and hydrological regime and should be well integrated. How? Depending on the target group, products can have very different formats (e. g. qualitycontrolled raw data, newspaper articles, technical recommendations, Facebook posts, short movies for environmental education). 6 Careful pre-discussion with the future users is needed as there is a risk of high costs and low benefits. Quality-controlled raw data from ProME must be archived in a central searchable database that is accessible to all programme partners. Apart from the analyses within ProME, the data base should allow for an independent learning process by different stakeholder groups.
Central storage of and access to data requires clear responsibilities and standardized procedures for database management, quality assurance and quality control. Data sharing must be actively promoted, i. e. by financial incentives in the funding process. Data ownership has to be clarified. Positive and negative outcomes must be shared. 23 Processing, analysis and dissemination requires allocated resources (see principle 1) and can be done by the programme partners or via mandates to external specialists, e. g. for knowledge transfer.