dataesr/scientific-paragraphs-categorization huggingface dataset:
3 billion data points). While the focus of this DTM is seabed mapping, land information is also filled from publicly available sources  [41]. The reader is invited to access this data product through the wwww. emodnetbathymetry. eu data portal. This section is divided into two parts. First, as it is complicated to qualitatively evaluate the behavior of the TIN creation methods used in this paper once the LOD structure has been created, the methods are first applied to a very small dataset extracted from the EMODnet DTM, from which we derive a qualitative visual comparison of the methods. Then, the LOD structure from the Europe-scale EMODnet DTM is created, as an example of the application of the methods to the large-resolution and large world-coverage we envisioned. From this LOD structure, we compute some statistics and error measurements, allowing us to compare the results of each method quantitatively.
Application to a Large Resolution Dataset The full resolution EMODnet DTM has been tesselated following a pyramidal multi-resolution LOD structure using the different simplification methods. A representation similar to the TMS standard  [1]  has been used. At the top of the pyramid (zoom = 0), the full world is contained in two tiles (Eastern and Western). Then, each deeper zoom level doubles the resolution of the previous one (as in Figure  1 ). The tiling of each zoom was set to make each tile contain a footprint of 256 × 256 elevation values. We triangulate the grid of samples in the XY plane, so the base mesh for each tile to be simplified by the different algorithms presented in this paper is composed of 65,536 vertices and 130,050 faces. The parameters used for each algorithm and zoom are listed in Table  3.
Conclusions and Future Work In this paper, we have presented a method to create a tiled LOD data structure out of large-scale regularly-gridded terrain data. We reviewed and updated simplification methods that are well known from the state of the art, in order to efficiently work at the tile level. This allowed us to create a hierarchical LOD structure, where each zoom level is composed of crack-free square sub-tiles. Despite testing specific methods in our results, the modifications made to the three types of simplification methods reviewed (greedy insertion, edge collapse simplification, and point set simplification) are generic enough to be used with any other method from each category. The developed framework has served to obtain a qualitative and quantitative comparison between several simplification methods applied to the problem of terrain visualization, and validated on a European-scale dataset.
Tomographic Analysis of Chromatin Organisation within DNA Repair Foci To better understand the chromatin organization linked to DNA repair we performed dual-axis tomography on semi thin (2500 nm) sections of irradiated cells stained for ChromEMT. The OA-B method is not suitable for this task because of the location of the stain, preferentially at the section surface. The areas for tomography data collection was targeted by confocal imaging using DRAQ5 DNA densities as fiducial markers (Supplemental Figure  S4 ). ChromEMT visualized a 3D-network of heterogeneous fibers and roundish dense bodies. The fibers appear very similar in irradiated areas and non-irradiated EC areas, whereas roundish domains prevailed in HC (Figure  5 , Supplemental Figure  S4 ). The position and shape of 53BP1 IRIF were extracted from confocal fluorescence images, converted into two dimensional masks and overlapped with the slices of the EM-tomograms after adjustment of the magnification (Figure  5  white outline, Supplemental Movie SM2).
Then, the tomogram was cut along the z-axis around the mask contour, the corresponding volume extracted, and the fiber thickness analyzed. To compare irradiated and non-irradiated EC, the IRIF contour masks (shape and area) were randomly placed over non-irradiated EC and volumes extracted, accordingly (Supplemental Figure  S5 ). For the HC analysis a similarly sized mask was used confining only HC-volumes (Supplemental Figure  S6 ). To compare chromatin features quantitatively, we segmented the chromatin elements in the extracted volumes and measured their diameters using custom software developed in-house (see Materials and Methods, Supplemental Figure  S7 ). Diameter distributions obtained from three irradiated and non-irradiated EC areas from three individual cells were compared to each other and to HC areas from the same tomograms.
To compare irradiated and non-irradiated EC, the IRIF contour masks (shape and area) were randomly placed over non-irradiated EC and volumes extracted, accordingly (Supplemental Figure  S5 ). For the HC analysis a similarly sized mask was used confining only HC-volumes (Supplemental Figure  S6 ). To compare chromatin features quantitatively, we segmented the chromatin elements in the extracted volumes and measured their diameters using custom software developed in-house (see Materials and Methods, Supplemental Figure  S7 ). Diameter distributions obtained from three irradiated and non-irradiated EC areas from three individual cells were compared to each other and to HC areas from the same tomograms. The obtained fiber diameter distributions were nearly identical for irradiated areas and non-irradiated EC (Figure  6  green/red).
For control of the staining specificity, selected grids with sections were incubated on drops of 10 mg/mL RNase A (Sigma-Aldrich, Saint-Louis, MO, USA) in 50 mM Tris-HCl pH 7. 0 at 37 • C for 24 h and washed with distilled water before Tb staining. The solution of RNase was boiled for 5 min to eliminate a risk of possible proteinase contamination and cooled down to 37 • C before use. 4. 3. 5. Transmission EM 100 nm thick sections were collected on Formvar-coated slot grids and imaged in a transmission electron microscope (Jeol 2100Plus, 120 kV). For serial sections, Serial EM software (University of  [55] ) was used. 4. 3. 6. EM Tomography 250 nm thick sections were collected on Formvar-coated slot grids for TEM tomography. Next, 15 nm colloidal gold particles (Sigma-Aldrich, Saint-Louis, MO, USA) were applied on the supporting film to serve as fiducial markers for tomogram alignment.
The tomographic data collection was performed using Tecnai F30 transmission electron microscope (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a US4000 CCD camera (Gatan, Pleasanton, CA, USA). Regions of interests (ROIs) were pre-exposed at 3000 e/Å 2 to minimize the section drift and warping  [56, 57]. The dual-axis tomographic acquisition was done at 12,000× magnification (1. 96 nm/pixel, camera binning 2) using Serial EM software (University of Colorado  [55] ) running a continuous tilt-scheme from -60 • to +60 • and a constant angular increment of 1. 5 •. Tilt series alignment and tomogram reconstruction were performed using ETOMO program of IMOD software package  [58].
Data Analysis The open source programs ImageJ (version 1. 52a, NIH and LOCI, University of Wisconsin, USA) and IMOD (version 4. 5. 0, University of Colorado, USA) were used for general image analysis. Fiber thickness in the tomograms was determined using in-house written software, ImageD (David Eilenstein) (Supplemental Figure  S7 ). Briefly, image grey values were inverted and a 3D median filter applied to remove shot noise. A ROI was defined and selected. For chromatin fiber detection, a threshold was applied automatically inside the ROI and the resulting fibers binarized. Binarized fibers were skeletonized, center-points determined, and the diameter of the fibers analyzed on the basis of the shortest distance between center-points and boundary.
Acknowledgments: The authors wish to thank  Gudrun Becker  and  Henrieke Förster  for cell culture and excellent technical assistance. We are indebted to  David Eilenstein  for the development of the fiber analysis software. We thank  Yannick Schwab  and  Mandy Börmel  for assistance at the  Electron Microscopy Core Facility at EMBL. The support by  Achilleas Frangakis ,  Diana Grewe , and  Fabrizio Porrati at Goethe University of Frankfurt (IFB) / Buchmann Institute for Molecular Life Sciences (BMLS)  and by the  Frankfurt Center for Electron Microscopy (FCEM)  is gratefully acknowledged. We are grateful to our colleagues at  GSI  for operating the accelerator,  Kay-Obbe Voss  for the irradiation at the GSI microprobe, and to  C. Lukas  ( Danish Cancer society, Copenhagen, Denmark ) for providing the U2OS-53BP1-GFP cell line.
Structure of the article The paper is structured as follows. Section 2 is a reminder on mixture models (MM) and hidden Markov models (HMM). In section 3 we describe their nonparametric Bayesian counterparts, the Dirichlet Process MM (DPMM) and the Hierarchical Dirichlet Process HMM (HDP-HMM, or infinite HMM). In the same section, we explain how to automatically calibrate these models, that is how their parameters can be inferred from measurements without human intervention. In section 4 the accuracy of the model is demonstrated on a dataset that has been labeled by humans, as well as on some RIPE Atlas RTT time series where we discuss the matching between routing configurations (from traceroutes) and states learned by the statistical model. We also briefly address the analysis of some CAIDA MANIC measurements.
Change point detection Quantifying the performance of the HDP-HMM on real RTT time series is not easy since there is no ground truth. The "network state" is not known or vaguely defined. But it was possible to compare the performance of the model on a change point detection task where the goal is to detect significant changes in the delay. While not the primary purpose of the HDP-HMM, detecting change points is simply a matter of segmenting the data and finding changes in the inferred state sequence and this this allows to partially validate the quality of the segmentation obtained. We have benchmarked the HDP-HMM against different change point detection methods on a labeled dataset introduced by  [35]. This dataset is particularly interesting because change points in RTT timeseries have been manually labeled by human experts.
To our knowledge, there are no other RTT time series datasets that are both realistic and labeled. The dataset consists of 50 RTT series of varying length for a total of 34,008 hours of observations. In  [35]  change point detection is performed by minimizing m+1 i=1 C(y τ i -1 +1:τ i ) + β f (m). m is the number of changes, C is a cost function that measures the stability of the delay over a range of successive values, and f (m) is a penalty that prevents overfitting. Different cost functions and penalties are considered. We have compared the performance of the segmentation obtained by HDP-HMM with the best performing changepoint detection methods of  [35]. In our approach a HDP-HMM model is learnt on each timeseries, the most likely hidden state sequence is computed, and changepoints are simply defined as changes in the hidden states sequence.
Dataset. RIPE Atlas offers two types of measurement sources: probes and anchors. Probes are deployed in heterogeneous environments while anchors are restricted to high-availability environments such as data centers, universities, and IXPs (Internet eXchange Points). Anchors tend to be located in well-connected autonomous systems and measurements between anchors represent more stable paths than what may be observed from probes located at the edges of the Internet. On the other hand, anchors are more powerful and perform the so-called anchoring mesh measurements, where various measurements are performed regularly between each pair of anchors. This allows us to collect traceroute results both on the forward and on the reverse path. Our dataset consists of one week of IPv4 RTT measurements between all Atlas anchors and the at-vie-as1120 anchor 2.
Delay is measured every four minutes using three ICMP (Internet Control Message Protocol) pings towards the target anchor. We kept the minimum value of the delay for each time step. Considering the subset of anchors that were online over the time period, we collected 301 series of 2520 data points. We also collected the associated traceroute measurements, both on the forward path, and on the reverse path. Traceroutes are performed every fifteen minutes using three ICMP probe packets for each hop.
In the example of Fig. 7  the average duration of the purple state is of 45 timesteps (= 3 hours) and of 149. 5 timesteps (= 9 hours 58 minutes) for the green state. The standard deviation of the delay in the purple state is of σ = 10. 3 msec while the standard deviation of the green state   if of σ = 4. 1 msec. States with a high variance could possibly be explained by intra-domain load-balancing (since Atlas pings flow ID is not constant), congestion, or in-path devices delaying the processing of ICMP packets. However asserting the cause of such variations and studying the possibility of detecting them from delay measurements is to be done in future works. Figure  9  displays the standard deviation of the RTT against the average duration in a state. In the analyzed dataset the average state duration decreases as the RTT standard deviation increases.
4. 3. 4 Relationship with the AS and IP paths. We hypothesized that the distribution of delay observations is conditioned on the underlying network state, such as the inter and intra-AS routing configuration, as well as the traffic level. As shown in Figure  10 , the majority of the states learned over all the paths in our dataset matches only one AS path and one IP path. For example there are 595 states which always correspond to the same AS path over the 746 states learned. Stated differently only 16% of the states learned can match two AS paths or more. States associated with more than one AS path can  be explained by delay differences too small to be separated into two clusters. Conversely, one AS or IP path can be mapped to several states. For example in Figure  7  we only observe the AS path ASN MARKLEY → GTT BACKBONE → NTT COMMUNICATIONS → ACONET SERVICES in the traceroutes from us-bos-as26167 to at-vie-as1120 and ACONET SERVICES → ACONET → NEXTLAYER AS → NTT COMMUNICATIONS → ASN MARKLEY in the reverse traceroutes (as resolved using the RIPEstat API).
A summary of the time series, as shown in Listing 1, can also be requested by appending /summary to the path. Start and stop time are specified as YYYY-MM-DDTHH:MM where THH:MM is optional and defaults to the start of the day. Additionally to this article we provide interactive notebooks to document and demonstrate the API, and compare various statistical models. Links to interactive Google Colab sessions, as well as the notebooks source and code to facilitate the usage of the API are provided on GitHub  [26].
Validation of the HDP-HMM model at large scale on RIPE Atlas In Section 4. 2 we show that the HDP-HMM model is at-least as good as classical change point detection methods on a labelled RTT change points dataset. This however, does not tell us whether the model fits well RTT data from a statistical point of view. In this section, we propose to compare the likelihood of the time series (with respect to their inferred model) with the likelihood of time series simulated according to an HDP-HMM model. If the models fit well the data, we can expect that the likelihood of the data with respect to the model should follow the same distribution as the likelihood of synthetic data generated by the model. To do so, we consider 100k time series of one week duration (2520 data points) from the anchoring mesh measurements.
As proof, it has been implemented on an Internet-wide operational measurement infrastructure, RIPE Atlas, with a publicly available Web API. We have shown that this method can accurately detect moments when abnormal events occur on the Internet. In the future we would like to automate this detection, and in particular to locate anomalies (infrastructure failures, etc. ) in a precise way. This will require the use of other methods exploiting the diversity of the measured paths and tomographic approaches or using a preliminary timeseries filtering strategy. We will also work on a real-time processing of measured data to detect novelties in RTT series with HDP-HMMs in an almost instantaneous way, based on some recent sequential approaches to inference in HDP-HMMs.
Background: Patients with lower-limb trauma requiring immobilization have an increased risk of venous thromboembolism (VTE). While thromboprophylaxis for all patients seems not effective, targeted thromboprophylaxis in high risk patients may be an appropriate alternative. Therefore, we aimed to develop and validate a risk assessment model for VTE risk: the TRiP(cast) score (Thrombosis Risk Prediction following cast immobilization). Methods: In this prediction model study, for development, data were used from the MEGA study (case-control study into the etiology of VTE) and for validation, data from the POT-CAST trial (randomized trial on the effectiveness of thromboprophylaxis following cast immobilization) were used. Model discrimination was calculated by estimating the Area Under the Curve (AUC).
For model calibration, observed and predicted risks were assessed. Findings: The TRiP(cast) score includes 14 items; one item for trauma severity (or type), one for type of immobilization and 12 items related to patients' characteristics. Validation analyses showed an AUC of 0. 74 (95%CI 0. 61À0. 87) in the complete dataset (n = 1250) and 0. 72 (95%CI 0. 60À0. 84) in the imputed data set (n = 1435). The calibration plot shows the degree of agreement between the observed and predicted risks (intercept 0. 0016 and slope 0. 933). Using a cut-off score of 7 points in the POT-CAST trial (incidence 1. 6%), the sensitivity, specificity, positive and negative predictive values were 76. 1%, 51. 2%, 2. 5%, and 99. 2%, respectively. Interpretation: The TRiP(cast) score provides a helpful tool in daily clinical practice to accurately stratify patients in high versus low-risk categories in order to guide thromboprophylaxis prescribing.
Because of the high prevalence of lowerlimb trauma and the significant impact of VTE in terms of morbidity, mortality and resource expenditure, targeted thrombosis prevention will have a major impact on public health [7À11]. To personalize thromboprophylaxis treatment in patients with lower-limb immobilization, two specific VTE risk assessment models (RAMs) have been developed  [12, 13]. Furthermore, two studies published a list of predictors in which case thromboprophylaxis should be considered  [14]. In 2015, the Leiden-TRiP(cast) (for Leiden-Thrombosis Risk Prediction for patients with cast immobilization score) was developed in the Netherlands  [13] , using data from a large population-based case-control study  [15]. It includes 19 items with scores ranging from 1 to 5 and was retrospectively validated in two independent datasets.
Study methods Fig. 1  shows the study flow-chart that presents all analyses which have been performed throughout the study. Two previous risk prediction models for VTE following cast immobilization (the Leiden-TRiP(cast) score and the TIP score) were used to create a final risk score entitled the TRiP(cast) score, note: without "Leiden". (Step 1, Fig. 1 ). The Leiden-TRiP(cast) score was developed using data from the MEGA study whereas the TIP score was developed by a group of experts using the Delphi method. Following development, the TIP score was validated in the MEGA study  [13]. Thereafter, score performances were compared by the AUC, sensitivity, and specificity. Both scores had a comparable discriminative value, and many similar predictors. The main difference was the Trauma component from the TIP which was lacking in the Leiden-TRiP(cast) score.
MEGA study To assess the performances of all three scores, we used data from the MEGA study. Details of this study have been published previously  [15, 18, 19]. In short, 4956 consecutive patients aged 18À70 years with a first deep vein thrombosis (DVT), pulmonary embolism (PE), or both were recruited from six anticoagulation clinics in the Netherlands between 1 March 1999 and 31 August 2004. The diagnosis of DVT or PE was confirmed by (Doppler) ultrasonography, ventilation/ perfusion scan, angiography, or spiral CT scan. The control group (n = 6297) consisted of partners from participating patients and other controls who were identified using a random digit dialing method; controls were frequency matched to cases with respect to sex and age. All participants completed a questionnaire on risk factors for VTE that included questions on (potential) risk factors such as trauma, immobilization (including cast immobilization and location), (orthopedic) surgery, current use of (any) medication, and comorbidity in the past year before VTE.
Validation of the final TRiP(cast) score in the POT-CAST trial For the main external validation analysis of the TRiP(cast) score, we used data from all patients who were included in the intentionto-treat analysis of the POT-CAST trial (n = 1435 patients) with a cast immobilization of the lower-leg. Demographics were summarized as means § standard deviation or proportions as appropriate. To account for missing data, we used multiple imputation techniques. Ten imputations were performed, and results were pooled according to Rubin's rules  [20]. The TRiP(cast) score was thereafter calculated in all patients. To assess model discrimination, the AUC was estimated in both the complete cases (n = 1250) and imputed data sets (n = 1435). Furthermore, the sensitivity, specificity, PPV and NPV were calculated for several dichotomized cut-off scores.
POT-CAST (validation) population Among the 1435 patients included in the POT-CAST study, the TRiP (cast) score could be calculated for 1250 patients (complete predictor data). Data were imputed for 185 patients. Patient characteristics are summarized in Table  3. In brief, 49. 9% were males and the mean age was 46 § 16. 5 years. The median BMI was 25. 8 § 4. 5 kg/m 2. Among all patients, 9. 8% had a family history of VTE, 2. 5% had active cancer or cancer history within 5 years and 9. 5% received oral contraceptives or hormonal therapy. The majority of patients had a fracture: 1279/1435 (89. 1%). Ninety-four patients had an Achilles tendon rupture (6. 6%) and thirty-five patients had an ankle distortion (2. 5%). 7. 0%, 8. 8% and 84. 2% of patients were classified as having a high, intermediate or lowrisk trauma, respectively.
TRiP(cast) score performance The distribution of the TRiP(cast) score among patients with or without VTE is displayed in Appendix Fig. A1. The TRiP(cast) score performed well with an AUC of 0. 74 (95%CI 0. 61À0. 87) in the complete dataset and an AUC of 0. 72 (95%CI 0. 60À0. 84) in the imputed data set. Table  4  shows test statistics for each dichotomized cut-off of the TRiP (cast) score. For example, using a cut-off score of 7 points to stratify individuals into a low versus high-risk category (low-risk 50. 7% and 49. 3% high risk), the sensitivity was 76. 1% and the specificity was 51. 2%. Based on an incidence of VTE of 1. 6% (incidence in POT-CAST), the PPV of the test (cut-off >=7 points) was 2. 5%, and the NPV 99. 2%. The predicted risk (absolute VTE risk) was calculated by exp (6. 677,015 + 0.
Differentiation between a low and high-risk group for symptomatic VTE The AUC of the TRiP(cast) score in untreated patients in the POT-CAST trial (n = 716) was 0. 66 (95%CI 0. 49À0. 83) whereas for LMWH treated patients (n = 719) the AUC was 0. 80 (95%CI 0. 67À0. 94). 50. 7% (n = 728/1435) of all patients had a TRiP(cast) score of <7, and were classified as low-risk patients (mean observed symptomatic VTE risk of 0. 8%) whereas 49. 3% (n = 707/1435) of patients had a TRiP(cast) score of 7, who were classified as high-risk (mean observed symptomatic VTE risk of 2. 5%). Across patients in the low-risk subgroup, 0. 4% (1. 3/360) of patients treated with LMWH developed symptomatic VTE as compared with 1. 1% (4. 2/367. 8) in the untreated group, for a RR of 0. 30 (95%CI 0. 03 À 2. 60) (absolute numbers represent mean values across 10 imputed datasets, hence, the non-integers).