dataesr/scientific-paragraphs-categorization huggingface dataset:
S4. Using the list of locus tags as input, we analyzed the expression of 42 B. subtilis RNA modification genes in all transcriptomic data available in the Colombos database (41 tRNA modification and one involved in rRNA modification)  [37]  (Table  S5 ). Some conditions lead to reductions or increases in most tRNA modification genes and are logically conditions where all translation genes would be expected to be affected, such as those that affect growth rate or transcription inhibition. To further explore the conditions where the response was specific to just a few genes, we focused on the conditions that led to differences in expression for just a subset of genes, as described in the Methods section. As shown in Figure  6 , we found that twelve genes involved in the synthesis of Anticodon Stem Loop modifications (ψ31/32, Q34, ψ38/39, i 6 A37, t 6 A37, k 2 C34, s 2 U34) were up or down regulated in specific stress conditions, opening possible avenues for further exploration.
Up-to-date land cover maps are important for biodiversity monitoring as they are central to habitat and ecosystem distribution assessments. Satellite remote sensing is a key technology for generating these maps. Until recently, land cover mapping has been limited to static approaches, which have primarily led to the production of either global maps at coarse spatial resolutions or geographically restricted maps at high spatial resolutions. The recent availability of optical (Sentinel-2) and radar (Sentinel-1) satellite image time series (SITS) which provide access to high spatial and very high temporal resolutions, is a game changer, offering opportunities to map land cover using both temporal and spatial information. These data moreover open interesting perspectives for land cover mapping based on data combination approach.
However, the usefulness of combining dense time series (more than 30 images per year) and data combination approaches to map natural vegetation has so far not been assessed. To address this gap, this contribution tests the idea that the combined consideration of optical and radar data combination and time series analyses can significantly improve natural vegetation mapping in the Pendjari National Park, a Sahelian savanna protected area in Benin. Results highlight that the combination of Sentinel-1 and Sentinel-2 SITS performs as well as Sentinel-2 SITS alone in terms of classification accuracy. Land cover maps are however qualitatively better when considering the data combination approach. Our results also clearly show that the use of dense/hypertemporal optical time series significantly improves classification outcomes compared to using multitemporal only a few images per year) or monotemporal data.
2018). To address this knowledge gap, this contribution analyzes the complementarity of radar (Sentinel-1) and optical (Sentinel-2) time series for natural vegetation mapping over a Sahelian savanna protected area in West Africa (in this case the Pendjari National Park). Specifically, we here test the idea that the combination of optical and radar data and dense time series analyses can significantly improve our ability to reliably map natural vegetation, particularly subtle differences in savanna vegetation, providing detailed vegetational maps, including measures of agricultural encroachment, that are needed by managers. Savanna landscapes are important for conservation, being known to host a variety of biodiversity hotspots and key conservation areas  (Solbrig 1996). They are characterized by a herbaceous cover with a variable proportion of shrub and woody cover, on a gradient from grasslands to open forests, offering a diversity of habitats for wildlife.
Reference data Ground truth data were collected during a field survey that was carried out in January 2019. About 90 waypoints were recorded across the park using a Global Positioning System (Fig. 1A ). Classes that were considered are: grass savanna, shrub savanna, tree savanna, woodland savanna, forest, water bodies, temporary wetlands, bare ground and rocks vegetation. Temporary wetlands were added as an additional category to account for the unique vegetation characteristics of areas that are inundated during the wet season and part of the dry season; in their dry state, they were characterized by bare ground or very low vegetation ground cover. Polygons were then digitized following a small and homogeneous area around the waypoint using Sentinel-2 and Sentinel-1 imagery. Additional small polygons were digitized using knowledge of the local area, information from Google Earth and Sentinel-1 & -2 data, to obtain at least 300 pixels per savanna type.
The 31 acquisitions released from 10 February 2018 to 5 February 2019 with the ascending relative orbit 1 were used. This orbit covers 96% of the park with an incident angle varying from 30 to 36°. We used Level-1 Ground Range Detected High resolution (GRDH) products recorded in interferometric wide swath mode. The images were radiometrically calibrated to convert them to radar backscattering coefficient r 0 using OTB application SAR Radiometric Calibration  (Laur et al. 2004 ). These were then orthorectified to correct for the geometric distortions using the OTB application OrthoRectification (Small and Schubert 2008). The output spatial resolution is 10 m per pixel. The images were subsequently converted from intensity to the logarithm dB scale. We used both polarizations VH and VV, and the ratio VH/VV was computed as a third polarization, because multi-polarization data have often been shown to improve land cover classification accuracy  (Lee et al.
The reference dataset was split randomly into disjoint training (70%) and validating (30%) subsets. The stratified split was performed at the polygon level in order to have an independent set of pixels between the training and the validation steps (no pixels belonging to the same polygon in the training and validating subsets). The random split was repeated 20 times to ensure the results are not biased by a specific combination of training/validating subsets. The same subsets for a given repetition were used for each input, so that the results are fully comparable. The 20 resulting classifications (for each input) were sieved to reduce the salt and pepper effect using the GDAL (GDAL/OGR contributors, 2018) gdal_sieve. For each iteration, the confusion matrix was computed based on the corresponding validation subset and the F-score was extracted as a measure of accuracy, which is the harmonic mean between precision and recall  (Sasaki 2007).
To compare the performances of each input configuration, we used the Z-statistics of the Wilcoxon rank-sum test  (Wilcoxon 1945 ) computed between each pair of distributions of 20 F-scores. The test was run with the SciPy  (Jones et al. 2001 ) library of Python. We also reported the average processing and computational times for each approach and compared them to the average classification accuracy. For the hypertemporal case, all the images of the time series were used. Multitemporal and monotemporal cases were considered for Sentinel-2 only. For the multitemporal case, six dates well distributed in time were extracted from the time series before the gap filling, under the condition that there were 0% cloud cover estimated at L2A. We chose dates  08-02-2018, 05-03-2018, 29-04-2018, 19-05-2018, 26-10-2018, 15-12-2018, 24-01-2019.
However, these authors used L-band from ALOS PALSAR which penetrates better in canopy cover than Sentinel-1 C-band. In addition to that, images were acquired in HH/VH polarization; the polarization has an influence on the radar response to vegetation structure  (McNairn et al. b)  and was shown to impact on the discrimination of woody classes  (Mathieu et al. 2013 ). We did not compare the effect of Sentinel-1 polarizations on the woody classes retrieval, but it could be investigated in future studies. Moreover, as the incident angle of Sentinel-1 is larger than the one of ALOS PALSAR, it could have decreased its ability to distinguish between woody classes because smaller incidence angles are known to penetrate deeper into canopy cover  (Inoue et al. 2002). Symeonakis et al. (2018)  also found an improved overall accuracy (>5%) when combining optical data (Landsat) and L-band radar data (ALOS PALSAR) for land cover classification in a savanna landscape.
These findings suggest that the association of Sentinel-2 with ALOS PAL-SAR-2 could significantly improve savanna subclasses discrimination. We were not able to test this particular assumption in this study as the 2018 ALOS PALSAR-2 mosaic was not yet available. One major drawback revealed by our work relates to the use of Sentinel-1 data when working on a spatial extent covered by different orbits. Sentinel-1 has a wide swath of 250 km, with incident angle varying from 30°t o 46°. Hence, it is difficult to mosaic Sentinel-1 images acquired over different orbits because overlapping areas are viewed with very different incident angles  (Syrris et al. 2019) , considerably impacting the magnitude of the backscattered signal as mentioned above  (Frison and Mougin 1996; Inoue et al. 2002). To be able to work with different orbits one should have a good distribution of reference data over the different orbits, which is not always easy to obtain especially in conservation areas.
In our case, 4% of the parks could not be classified when using Sentinel-1 data because they were covered by a different orbit where we did not have ground truth data. To our knowledge, no study has so far reported this issue because most works involving classification using Sentinel-1 data are set on small geographical extents. Another limitation of this study comes from the combination of optical and radar data that was performed at the pixel-level in a relatively simple way (i. e. only by stacking both types of data prior to classification, resulting in a very high number of input variables). Many fusion techniques exist and could potentially lead to better outcomes when using Sentinel-1 and Sentinel-2 sensors in combination. Our study has shown that the combined use of dense optical and radar time series has the potential to substantially improve our ability to map natural vegetation in remote areas such as the W-Arli-Penjari transboundary park.
System preparation The initial coordinates of the D&B-open structure of the human GlyR-α1 were obtained from Dämgen and Biggin, 2019 (Dämgen and Biggin, 2019) (DOI: 10. 5281/zenodo. 3476169). Following the protocol in  Cerdan et al. , 335  2018  (Cerdan et al. , 2018) , the extracellular domain was removed and the transmembrane domain was embedded in a POPC membrane bilayer, surrounded by TIP3 water molecules and 150 mN NaCl using the CHARMM-GUI input generator  (Lee et al. , 2016 (Lee et al. , , 2019)). The simulation box contains 148, 106 atoms, including one protein, 340 POPC molecules, 30, 599 water molecules, 82 Na + , and 122 Cl -. The initial box dimensions are 120 * 120 * 111 Å. To generate a dual membrane setup, we adapted the script makeSandwich. sh provided by  Kutzner et al. , 2011 (Kutzner 345 et al.
Hopkins University (https://github. com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series). [3]  The model is a parametric model whose parameters change from country to country to reflect differences in dynamics. A classical SIR model considers 3 types of individuals: susceptible individuals (S), infected individuals (I) and recovered individuals (R). [4]  Many extensions to this model have been proposed, in particular by adding a compartment of exposed but noncontagious individuals (E) to the model. [5]  Other models distinguish between symptomatic and asymptomatic patients, between patients admitted to intensive care units and patients admitted to regular hospital. [6] [7] [8]  The more complex these models are, the more parameters they depend on. A classical method then consists of fixing the value of some of these parameters to values found in the literature and varying the other parameters in order to obtain simulations with dynamics that are similar to those of the data.
This component is taken into account in the observation model in the form of a cosine function. Once the model is fully defined, its parameters can be estimated for each country using precisely that country's data. The model, the parameter estimation algorithm, the method for model selection as well as several plotting routines have been implemented in the R package covidix. This package is available on Github (https://github. com/MarcLavielle/covidix). An example of how to use covidix can be found here: http://webpopix. org/covidixExample. html. Note that an interactive, easy to use, web application (Shiny v1. 4. 0. 2, 2020) also allows to visualize the data and the fitted model for several countries (http://shiny. webpopix. org/covidix/app2/). The data used in this application are updated frequently in order to be able to follow on a day-to-day basis what the model predicts for several countries.
Results A first version of the covidix package and the web application were available as of April 12 th , with model adjustments on data from eight countries. Nine new countries were added between April 12, 2020 and May 15, 2020. Figure  2  shows that the model is able to accurately capture the different changes in the dynamics of the pandemic i) for countries, such as Switzerland, which controlled the spread of the epidemic well, ii) for countries, such as Italy, which did not react immediately and saw the number of deaths increase very rapidly before taking containment measures which (slowly) decreased this number of deaths, iii) for countries, such as US, that have not taken sufficiently severe measures to significantly reduce incidences, iv) for countries, such as Brazil, where the pandemic has arrived more recently.
On a longer timeline than conventional EEW, the ShakeMap software  (Wald et al. , 1999)  was developed with the primary purpose of providing accurate maps of strong ground motion. These maps, typically available within 5-10 min after an earthquake, allow disaster risk managers (DRMs) to make preliminary assessments of the shaking impact. ShakeMap generates maps of ground shaking using earthquake source parameters (location, magnitude and the finite fault if available), intensity measurements (PGA, PGV, …), ground motion models (GMMs) and Vs30 maps as proxy to account for site amplifications. Ground motion interpolation is performed at points of the map which do not have ground motions recorded (e. g. ,  Worden et al. , 2018). The ShakeMap software has not been designed for EEW --typically shakemaps become available only when the first location and magnitude estimation are available.
Data The input data used in this study consist of 3-component earthquake waveforms data from 2016 Central Italy sequence  (Chiaraluce et al. , 2017)  recorded by 39 stations in the epicentral area and its surroundings. The dataset has been selected because of the dense network of stations in the area (Fig. 3 ), the large number of earthquakes and the importance of the sequence from the seismological perspective. We use earthquakes in the study area bounded by latitude [42°, 43. 75°] and longitude [12. 3°, 14°] which occurred from 1 January 2016 to 29 November 2016. All the events occur within crustal depths in the range 1. 6km < D < 28. 9km. Using these criteria, 915 earthquakes with magnitude M≥3 have been used (Figs  1  and 2 ). In the same study area there are 86 recording stations from the networks IV and XO available.
In this study, we have selected a subset of 39 stations, which had at least 700 earthquakes recorded on the station (Fig. 3 ). The selected stations were all belonging to the IV network. Together with the event data, we have also selected 1037 examples of noise data, recorded from 30th August to 30th September 2016 recorded at all the selected stations. The criterion adopted for selecting the noise data consisted of windowing the continuous waveforms with a start time at least 180 seconds after and 20 seconds before the origin time of any earthquake in the area. The waveform data were downloaded using the INGV FDSN web services for HN* (acceleration) and HH* and EH* (velocity) channels, where * ∈ [E,N,Z]. The data were processed to remove the instrument response. Velocity data were differentiated to acceleration.
The target data consisted of the IMs associated to each recording: peak ground acceleration (PGA), peak ground velocity (PGV), spectral acceleration (SA) at 0. 3 s, 1 s and 3 s periods. For the stations that had no data, the IMs were calculated using USGS ShakeMap software with the latest configuration for Italy  (Michelini et al. , 2019). ShakeMaps have been calculated using the IMs from all available stations. This has been done to ensure no missing output data (target variables), and it is expected to be beneficial in the learning stage of the ML procedure. This approach, however, can introduce some error since the ShakeMap prediction may be incorrect given the uncertainties in the estimation of the ground motion. We note, however, that  Michelini et al. (2019)  have shown that nearly no systematic bias in the prediction of the maximum IMs was observed with the new ShakeMap configuration giving us good confidence that the values inserted as target values are statistically relevant.
3) and PSA(1. 0), yet they are slightly higher for PGA. Concerning the standard deviation, we find that the values are comparable for the two models, and are in agreement with those reported by  Bindi et al. (2011)  who reported a standard deviation between 0. 34 and 0. 38 in log 10 unit. When applied on the 3 largest events in the sequence (using 5-fold cross-validation), with magnitudes of M=[5. 9, 6. 0, 6. 5], the CNN model performance deteriorates (Fig. 8 ), compared to the model performance on events with smaller magnitude. These results are due to the unbalanced dataset with few training examples at large magnitude (Fig. 2 ), which makes learning at larger magnitudes difficult. More specifically, the poor performance for the M 6. 0 event can be also attributed to the use of only 24 station waveforms in the input data, as no temporary stations had yet been installed in the area before the August 24, 2016, M 6.
Discussion In this study, we have shown that a CNN model can be used to predict accurately earthquake IMs at recording stations using only raw, multi-station waveforms with a 10 s time window starting at the earthquake origin time. That is, from the very first recordings at stations near the epicenter, it is possible to predict accurately the IMs at farther stations that have not yet recorded the earthquake signal or its maximum values. To this end, we exploit the 3C station data (i. e. , waveforms) and the pattern of the waveforms of the recording network without any knowledge concerning the earthquake location or magnitude. Input data consist of the recorded acceleration waveforms, while the target variables are PGA, PGV and SA at 0. 3 s, 1 s and 3 s periods. The CNN generally performs equally well regardless of the IM type.
As expected, model performance improves when longer time windows (e. g. , 15 s time windows) are used as input. Because of the Gutenberg-Richter distribution of earthquake magnitudes, our dataset is severely unbalanced with a much larger frequency of smaller events than larger ones. As shown in Fig. 8 , this makes it difficult to predict the IMs of larger events. A possible solution to this problem could follow from the use of data augmentation (e. g. ,  Chollet, 2018 ) by introducing more training data for the larger magnitudes after applying random transformations to the existing larger events. Another alternative can consist of calculating synthetic seismograms at the same recording stations for additional (hypothetical) larger events in the same area. Another approach towards mitigating problems due to small datasets is the use of transfer learning methods  (Pan and Yang (2010) ).
These methods allow the CNN model to subspecialize by training on a smaller dataset, using a pre-trained model which was previously trained on a larger dataset. Following  Chollet (2018) , we could even use a training network developed elsewhere and for different purposes, but with a very large dataset, and use it to our goal of IM prediction. Similarly, the network developed in this study could be adopted as a pre-trained model for other areas where IM predictions are required. To test the methodology, we have used raw waveforms without any preliminary data cleaning, in order to simulate real-time analysis where missing or erroneous data due to equipment malfunctioning or data transmission problems is common. To compensate for these deficiencies, we found that replacing missing data with zero values and the adoption of ShakeMap predicted target data values is suitable to ensure that the target data exist for all cases during learning.
We are confident, however, that by using a bigger dataset, with different spatial distribution of epicenters, the CNN model will likely be able to learn the path and site characteristics of each station and for each zone where the earthquakes occur. This aspect will be addressed in future studies encompassing larger areas and datasets. Our results with earthquake and noise only data (Fig. 9 ), have shown that the methodology is capable to predict the ground motion adequately. This fact combined with the translational invariance characteristics of convolutional neural networks suggests that the technique could be used in a real-time setup with data streamed continuously to predict the IMs seamlessly at farther stations as the earthquake nearest stations start recording the first earthquake generated signals.
We have found that the CNN model was able to predict with satisfying accuracy the IMs for stations which had no input data when training. Comparison between the CNN results and those obtained with the  Bindi et al. (2011)  ground motion model, calibrated with earthquakes in Italy, has shown that the CNN model does not suffer from prediction bias while featuring similar variance of the residuals. The performance of the CNN model degrades for large magnitudes (M>5) because of the small number of earthquakes (5) available for training. We have also observed that the CNN model performs well when noise only data were included in the dataset. This results gives us good confidence that the proposed analysis could be implemented in a real-time analysis configuration. Although the technique is not strictly designed for earthquake early warning, we found that it could provide useful estimates of ground motions within 15-20 sec after earthquake origin time depending on the data transmission infrastructure, latencies and processing time requirements.
Data and Resources Earthquake catalogue and waveform data have been downloaded through the INGV FDSN web services (http://terremoti. ingv. it/en/webservices_and_software). Waveforms have been downloaded and processed using python library Obspy  (Beyreuther et al. (2010)  ). IMs for the stations with no data have been calculated using USGS ShakeMap 4 (http://usgs. github. io/shakemap/sm4_index. html). The CNN model has been developed using the Keras Python Deep Learning library  (Chollet et al. (2015) ). The code and the data of this paper will be available on https://github. com/djozinovi/CNNpredIM.
The data had been split randomly into training (80%) and test (20%) subsets. For evaluation of the performance of the CNN model 5-fold cross-validation has been used, which splits the randomly permuted dataset into 5 equally-sized disjunct subsets, and uses each of them as the test set to the model trained on the remaining 4 subsets. The batch size used for optimisation was 5, and the mean squared error (MSE) function was used as the loss function in the model. Hyperparameter values for model optimization were based on Kriegerowski et al. (2018) with minor modifications. The model was trained for 12 epochs (the training history plot is shown in Fig. 5 of the electronic supplement). The training took approximately 3 minutes on an Nvidia GTX 1060 6GB for a 10 second input window.