dataesr/scientific-paragraphs-categorization huggingface dataset:
org/funded-project/simonsvariation-in-individuals-project-simons-vip/. The 22q11. 2 UCLA raw data are currently available by request from the PI. Raw imaging data for the Montreal rare genomic disorder family dataset is going to be available on the LORIS platform in 2022. The Cardiff raw data is not publicly available yet, contact the PI for further information. Allen Human Brain Atlas is available from https://human. brain-map. org/. Single-cell expression data is available from the National Center for Biotechnology Information under the accession code GSE97942 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE97942 ). All processed connectomes are available through a request to the corresponding authors. Code for all analyses and visualizations, beta values, and p-values for the 36 FC-profiles are available online through the GitHub platform with Jupyter notebook: https://github.
0 database  (Laske et al. , 2013). For a given source model, the surface displacements at each receiver are computed by summing the contribution of each sub-fault. We search for the spatial distribution of slip amplitude and rake angle independently for each time step. The spatial distribution of the source parameters is obtained using an optimization procedure similar to that of  Pianatesi et al. (2007). We use a heat-bath simulated annealing algorithm, an evolution of the Metropolis-Hastings that lowers the rejection rate by computing the relative probabilities from a set of trial models before a random move is made  (Sen and Stoffa, 2013). To measure the difference between the observed (o) and calculated (c) surface displacements, we use the cost function of  Pianatesi et al. (2007)  to which we add a smoothing constraint: ε = 1 N N i=1 ((o i -c i )/e i ) 2 N i=1 o 2 i + ωΛ (1) where N is the number of observations (2 horizontal components × number of receivers), e is the error associated with the observation, Λ is the Laplacian of the slip distribution and ω is the weight given to the Laplacian (0.
This allows us to investigate its spatiotemporal evolution over the first 12 hours (Figure  3  -The fit to the observations is shown in Supplementary Material S3). Our results show distinct patches that are not moving in space but grow in amplitude over time, and that can be identified 1 to 3 hours after the mainshock. The first one to develop is located near the epicenter of the mainshock, off-shore of Canela Baja (station CNBA). This is also where the 2 largest aftershocks are located (yellow circles on Figure  1 ). This patch seems to nucleate at the southern edge of the co-seismic rupture area inferred by  Melgar et al. (2016). The latter is chosen because it is mostly based on data recording the co-seismic phase only (i. e. , seismological data, high-rate GNSS data, tsunami data), limiting the contamination from very early afterslip by the InSAR data.
This is in line with  Barnhart et al. (2016)  who discuss this question by looking at 26 days after the mainshock and using a similar dataset to ours (i. e. , 9 of the 15 GNSS stations plus InSAR data). They also find some overlap between co-seismic and post-seismic slip but the authors conclude that this result is not robust enough to be interpreted. Finally, we also identify in our models a region where significant slip occurs and that is located south-east of Salamanca (station SMLC). This region is further away from the co-seismic rupture area and is disconnected from the main regions of post-seismic slip. After some tests (see Supplementary Material S5), we conclude that this patch is an unreliable feature and is therefore disregarded in the discussion section. When this region is not taken into account, the final geodetic moment decreases to 8.
series such as those computed at the NGL. Thus, future studies should test the capability to image very early post-seismic slip in near-real-time and investigate what this new piece of information would bring for the forecasting of the spatial distribution of aftershocks. https://doi. org/10. 5194/se-2021-6 Preprint. Discussion started: 5 February 2021 c Author(s) 2021. CC BY 4. 0 License. within the ANR JCJC E-POST (ANR 14-CE03-0002-01JCJC E-POST). CT was supported by the Centre National d'Études Spatiales, the ANR JCJC E-POST and the European Research Council (ERC, under the european union's horizon 2020 research and innovation programme under grant No. 805226). 10 https://doi. org/10. 5194/se-2021-6 Preprint. Discussion started: 5 February 2021 c Author(s) 2021. CC BY 4. 0 License.
Satellite-based adaptive sampling strategy PROTEVSMED-SWOT took place on board the RV Beautemps-Beaupré between 30 April and 18 May 2018. During the cruise, an adaptive Lagrangian sampling strategy was made possible thanks to SPASSO (Software Package for an Adaptive Satellite-based Sampling for Oceanographic cruises; https://spasso. mio. osupytheas. fr, last access: 3 December 2021). Various satellite data sets were used during PROTEVSMED-SWOT. Sea surface temperature (SST, levels 3 and 4, 1 km resolution, not shown in this study) and chlorophyll a concentrations ([chl a], level 3, 1 km resolution) were provided by CMEMS (Copernicus Marine Environment Monitoring Service, https://marine. copernicus. eu, last access: 3 December 2021). In addition, ocean color composite maps were provided by CLS with the support of CNES.
They were constructed using a simple weighted average over the previous 5 d of data gathered by the Suomi/NPP/VIIRS sensor. The altimetry-derived geostrophic velocities from the AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic data) database were exploited to extract near-real-time daily maps. These were also used to derive the finite-size Lyapunov exponents (FSLEs). The FSLE analysis permits the identification of biogeochemical regions of potential interest. Indeed, FSLEs values often form continuous lines, or ridges, which are used to identify regions of enhanced strain that are to be expected near frontal zones. The first study to show the benefit of using FSLE-derived fronts for biogeochemical studies was probably  Lehahn et al. (2007). Before that,  Abraham and Bowen (2002)  were the first to apply the Lyapunov exponent technique (albeit using finite time rather than finite size) to the ocean, in turn borrowing some ideas from dynamical system theory (see in particular  Boffetta et al.
In situ measurements Physical and biological variables (horizontal velocities, temperature, salinity and abundances of the different phytoplankton functional groups) were measured at high frequency all along the route. In situ systems included a SeaSoar deployed at sea, a vessel-mounted acoustic Doppler current profiler (VMADCP), a thermosalinograph (TSG) and a flow cytometer installed on board. The SeaSoar is a towed undulating vehicle capable of achieving undulations from the surface down to 400 m. Two Sea-Bird SBE-9 (with SBE-3 temperature and SBE-4 conductivity sensors) instruments mounted on either side of the SeaSoar enabled simultaneous measurements of temperature, salinity (from conduc- tivity) and pressure. The conductivity and temperature data were lag-corrected to reduce salinity spiking following the methodology developed by  Lueck and Picklo (1990) ,  Morison et al.
(1994)  and  Mensah et al. (2009) , before conversion to absolute salinity S A and conservative temperature , in accordance with TEOS-10 standards  (McDougall et al. , 2012). Herein, temperature and salinity refer to absolute salinity and conservative temperature. Horizontal velocities between 19 and 253 m were measured with a VMADCP operating at 150 kHz. VMADCP data treatment was performed with the MATLAB software Cascade V. 7 (https://www. umr-lops. fr/ en/Technology/Software/CASCADE-7. 2, last access: 3 December 2021). The sea surface temperature and salinity were measured continuously along the ship route by the underway TSG. The TSG was equipped with two sensors: (i) a CTD Sea-Bird Electronics SBE 45 sensor, which was installed in the wet lab, was connected to the surface water and which continuously pumped seawater at 3 m depth; and (ii) an SBE 38 temperature sensor, which was installed at the entry of the water intake.
Two distinct protocols were run sequentially every 30 min, in order to process the 1164 samples. The first protocol (FLR6) had an FLR trigger threshold fixed at 6 mV and could analyze a volume of 1. 5 cm 3. It was dedicated to the analysis of the smaller phytoplankton such as Synechococcus, which were optimally resolved and counted with this protocol. The second protocol (FLR25) targeted nanophytoplankton and microphytoplankton with an FLR trigger level set at 25 mV and an analyzed volume of 4 cm 3. The data were acquired using the USB software (Cy-toBuoy b. v. ) but were analyzed with the CytoClus software (CytoBuoy b. v. ). The combination of the different variables recorded by the flow cytometer exhibited various clusters of particles (cells) whose abundance (cells per cubic centimeter) and average variable intensities were provided by the Cy-toClus software, which generated several two-dimensional cytograms (e.
We followed the method described by  Rudnick (1996) , using the scripts freely downloadable from his web page at http://chowder. ucsd. edu/Rudnick/SIO_221B. html (last access: 3 December 2021). The in situ data are considered to be composed of a mean value, a fluctuation and some noise including both smaller-scale variability and instrument error. The fluctuation part of the field statistics was assumed to have a decorrelation length scale of 20 km in both the x and y directions, with a structure orientation of 18. 4 • from north. The correlation length scale was chosen by analyzing the auto-covariance matrix of the σ field and performing several sensitivity tests. The noise-to-signal ratio was assumed to be 0. 05, as in  Rudnick (1996). The interpolated fields are shown superimposed onto the in situ measurements (see Fig.
Statistical analysis Surface temperature and salinity data measured with the TSG were merged with abundance data of the nine phytoplankton groups at each cytometry sampling point along the WE and NS hippodromes. Thus, the final data set consists of 11 vari-ables, each of which contains 215 observations. To deal with this large multivariate data set a principal component analysis (PCA) was applied. PCA consists of summarizing the information contained in the data set by replacing the initial variables with new synthetic variables (called principal components), which are linear combinations of the initial variables and uncorrelated two by two. When applied to our data set, a PCA reveals that the first three components account for 36. 7 %, 18. 1 % and 13 % of the total variance of the data, respectively.
The isopycnals 28. 2 and 28. 9 kg m -3 , separating surface waters from deeper water, are also shown. The bottom and top of the box are the 25th and 75th percentiles, respectively, whereas the central line is the 50th percentile (the median), and the cross is the mean. The ends of the error bars correspond to the 10th percentile (bottom) and to 90th percentile (top). All the SeaExplorer glider transects are considered here (all data acquired from 6 to 15 May 2018). Younger AW corresponds to samples showing salinity between 37. 20 and 37. 84 and temperature between 14. 5 and 17. 4 • C (n = 1657). Older AW corresponds to samples with salinity between 37. 82 and 38. 14 and temperature between 14. 0 and 18. 3 • C (n = 11 760). Mean values of chl a, peak B and peak T of older AW are significantly higher than those of younger AW (t test, p < 0.
Acknowledgements. This work was supported by the  CNES  in the framework of the  BIOSWOT-AdAC  project (https://www. swot-adac. org/,last access: 3 December 2021) and by the  MIO Axes Transverses program  ( AT-COUPLAGE ). The chlorophyll a product is produced by  CLS. The authors thank the SHOM and the crew of the  RV Beautemps-Beaupré  for shipboard operations;  Marc Torner , the SOCIB, and the crew of the  RV García del Cid  for their assistance with the glider operations;  Jean-Luc Fuda  for his help in SeaSoar data treatment;  Louise Rousselet  for discussion about vertical velocity estimations; and  Madeleine Goutx  for the early discussions about the glider FDOM data. The CytoBuoy ® flow cytometer was funded by the  CHROME  project , Excellence Initiative of Aix-Marseille University -A * MIDEX , a  French 11 Investissements d'Avenir program.
SPASSO is operated and developed with the support of the  SIP (Service Informatique de Pythéas)  and in particular  Christophe Yohia ,  Julien Lecubin ,  Didier Zevaco  and  Cyrille Blanpain  ( Institut Pythéas, Marseille, France ). The project leading to this publication received funding from the  European FEDER Fund  under project number  1166-39417. This work was also supported by the  French National LEFE program  ( Les Enveloppes Fluides et l'Environnement)  ( FUMSECK-vv project ,  PI Stéphanie Barrillon  and  Anne A. Petrenko ). Frédéric Cyr received funding from the  Mourou/Strickland mobility program  to work on this project. Roxane Tzortzis  is financed by a  MENRT PhD grant  ( École Doctorale Sciences  de l'environnement -ED 251,  Aix-Marseille University ). Financial support. This research has been supported by the  Centre National d'Etudes Spatiales  (grant no.
Code availability. The LATEXtools are open access and available at https://people. mio. osupytheas. fr/~doglioli/latextools. htm (last access: 3 December 2021)  (Doglioli et al. , 2013). The script for objective mapping is open access and available on Rudnick's web page at http://chowder. ucsd. edu/Rudnick/SIO_221B. html (last access: 3 December 2021).
Radiomics relies on non-invasive biomarkers based on advanced imaging analytics 2 and has been shown to be able to unravel tumor phenotype in multiple studies  [3] [4] [5] [6]. The typical radiomics workflow involves imaging of the patient, identification of the gross tumor volume (GTV), conversion of images to higher dimensional data (i. e. radiomic features) and the subsequent integration and mining of these data for model building. Thus, radiomics allows to build diagnostic, prognostic and predictive models for clinical outcomes using imaging data which is acquired as a part of clinical routine 7. However, several challenges have to be faced during radiomic signature development such as issues in reproducibility, standardisation in both the image acquisition, the handcrafted feature extraction and the statistical model building, and other limitations and pitfalls 8.
8 for two out of three testing cohorts, the PMH cohort being statistically worse than the other two, with a median AUC of 0. 62. The 3D-CNN, the 2D and 3D CNN+Clinical and the ANN achieved very good results on the MAASTRO cohort, the other two cohorts being worse. In general, models were always able to transfer good validation prediction performance to the MAASTRO testing cohort, while for the CRO cohort the performance depended on the architecture. For the PMH cohort no network architecture yielded a median AUC above 0. 66. Performing time-to-event analysis. Several authors  [25] [26] [27]  have underlined the issue of censored data when performing binary outcome classification and have proposed extensions to deep learning models that are able to incorporate time-to-event information and thus censoring.
The same holds for the ANN, although results were on average slightly worse than for the CNNs and the CNNs+Clinical. Again, the PMH dataset was found to be worst for all models. Of interest is also the fact that when performing the binary masking experiment the HCI for the 3D-CNN and for the 3D-CNN+Clinical decreased by no more than 0. 03 (i. e. not significantly). A more substantial drop (yet still not significant in terms of confidence intervals) was observed for the 2D-CNN and the 2D-CNN+Clinical. In addition to the assessment of the discriminative power according to HCI, we also analysed patient stratification capability of the time-to-event networks. To do so, we found an optimal threshold to split the CV cohorts into high-and low-risk patients and then applied this threshold to the three independent testing cohorts.
Methods Patient cohorts. For this study, 4 different cohorts totalling 1037 patients with head and neck cancer were used. All patients selected for this study received either radiation alone or chemo-radiation as main treatment and have a follow-up time larger than 2 years. All patients which had a metastasis at time of diagnosis were excluded. • Canadian benchmark cohort: consists of 298 patients treated at 4 different hospitals in Quebec, Canada. Out of 298 patients, 294 were used for this study as few patients had to be excluded due to data corruption or non-clear identification of the RTSTRUCT corresponding to the CT. This cohort was used in the benchmark study by Diamant et al. 22  and in a traditional radiomics study 5 on which the former was based. All patients received either radiation alone or chemo-radiation as main treatment modality.
Time-to-event analysis. Although binary classification models as the ones built in  5, 17, 22  achieve good prediction results, they are limited by the fact that time-to-event information is discarded during model training. Especially in some situations, for instance when predicting death or metastasis occurrence for older patients, it is fundamental for treatment customisation to know how fast the event would occur. Additionally, binary classification models usually are trained on a specific time point (e. g. 2-year OS) so they would need to be re-trained to make predictions for a different one  27. To overcome these issues we extended Diamant's network to include timeto-event information by incorporating the survival model by Gensheimer et al. 27. Their discrete-time survival model has been implemented in Keras  37  (https:// github.
0, as patients with e. g. a low value for metastasis-free probability should be patients with a short time-to-event. Implementation details. All the code needed for data pre-processing and running the models was written in Python 3. 6. The networks were built and optimized with Tensorflow 2. 2. 0  40  using the high-level library Keras  37. Training and testing was carried out on three different graphical processing units: a NVIDIA P6000 with 24 GB, a NVIDIA P5000 with 16 GB and a NVIDIA Titan V with 12 GB of memory. The input shape of the 3D CT of 256 × 256 × 256 and the batchsize of four were chosen so that the networks could fit also on the smallest graphical processing unit available. For the 2D-CNNs a batchsize of 32 was used. The weights and biases of the networks were optimized using the Adam algorithm  41  with a constant learning rate of 3 × 10 -4 and 2 × 10 -4 for the 2D and 3D binary classification CNNs and of 6 × 10 -4 and 3 × 10 -4 for the 2D and 3D time-to-event CNNs.
The respective loss functions used were binary cross-entropy and the custom loss function surv_likelihood by Gensheimer et al. 27. Prior to the augmentations, all images were isotropically re-sampled to a 1 × 1 × 1 mm 3 grid. Specifically, the binary masks where re-sampled in 3D using in-house code for shape-based interpolation  42. The full CTs where interpolated in 3D using linear interpolation. Moreover, all CT values were windowed to -500 and +500 Hounsfield Units and then re-scaled from -1 to +1 (standard image input). The linearly interpolated CTs were then masked with the shape-based interpolated binary masks to create the re-sampled masked CTs which were used as input to the networks. For the binary masking experiment, we set all values inside the GTV which were bigger than -1 to +1 , i.
e. we used re-sampled binary masks as input (see Supplementary Fig. S1 ). To decrease over-fitting, a weight decay term of 1 × 10 -4 was used for all models. Additionally, for the 2D and 3D CNNs and the 2D and 3D CNNs+Clinical several augmentations were implemented by adapting a multithreaded augmentation pipeline which is freely available online  43  (https:// github. com/ MIC-DKFZ/ batch gener ators) to our workflow. This allowed us to use a slightly wider range of augmentations and especially to perform the augmentations online (during the training), in contrast to Diamant's study. To be specific, we used random cropping to 128 pixels in each dimension with a central random shift of maximal 20% of the original 256 × 256 × 256 image. We also used mirroring of the image with 50% probability, rotations of maximal 60 degrees and elastic deformations of the image of up to 25% of the size of the cropped image, both with a probability of 10% for the binary classification 3D-CNN, 20% for the time-to-event 3D-CNN and 50% for all 2D-CNNs.
Finally, for all our models we also used dropout with a dropout_rate of 25%. For the CNNs all these hyper-parameters were found by performing a manual grid search meaning that we repeated the 3-fold CV on the Canadian benchmark data set until we were satisfied with the mean validation AUC or HCI which was achieved with given parameters. On the other hand, training for the ANN took much less, so after manually finding a range of parameters leading to good results we performed an automatic grid search over 16 different combinations of hyper-parameters (number of neurons in the hidden layer, weight decay, dropout probability, learning rate) to find the ones performing best on average for the 3-fold CV. For all CNN+Clinical models we manually explored some hyper-parameters around the best hyper-parameters for the CNNs and found that the ones leading to best average CV were the same as for the CNN.
For all our network architectures we took the 3 models achieving the best average CV performance and used them for testing without any further change. To get a single prediction out of three we applied the validation models on test data and then averaged the resulting three predictions to obtain a single model averaged prediction per patient. A schematic view of the workflow is shown in Fig. 1. When doing binary classification the 3D-CNN and 3D-CNN+Clinical were trained for 500 epochs, using Keras callbacks to save the model's weights when an improvement in the AUC was observed. One 3-fold CV took on average 11 h. The 2D-CNN and 2D-CNN+Clinical were trained for 500 epochs, one 3-fold CV taking on average 30 minutes. The ANN was trained for 5000 epochs, one 3-fold CV taking on average 10 minutes.
Acknowledgements We would like to thank  Philipp Wesp ,  Balthasar Schachtner  and  Micheal Ingrisch  from the  Department of Radiology of the University Hospital  of the  LMU Munich  for the help in setting up the MIC-DKFZ augmentation pipeline. Special thanks go to  Leonard Wee  and  Andre Dekker  from  MAASTRO , and to  Scott Bratman  from PMH for answering our inquiries about the datasets. Elia Lombardo acknowledges  Moritz Rabe  for the support for the figures shown in this work and  Sophie Duque  for the precious help and contribution to the shape-based interpolation code.
Data availability Three out of four cohorts used in this study can be found on The Cancer Imaging Archive (TCIA)  24  : Canadian benchmark dataset  23  : https:// doi. org/ 10. 7937/ K9/ TCIA. 2017. 8oje5 q00. MAASTRO dataset  30  : https:// doi. org/ 10. 7937/ tcia. 2019. 8kap3 72n. PMH dataset  31  : https:// doi. org/ 10. 7937/ tcia. 2019. 8dho2 gls. The CRO dataset is the result of a collaboration and can be obtained upon request.