dataesr/scientific-paragraphs-categorization huggingface dataset:
Satellites as Since direct point observations of irradiances on 1 min basis are not available from current satellites, there is still the need performing an indirect satellite evaluation to derive the 1 min variability status. Having developed such an automated analysis offers then the opportunity to use the already existing long-term observation datasets from satellites and take advantage of their global coverage. Perez et al. (2011)  showed that temporal variability metrics based on clearness index increments can be statistically estimated from the clearness index at the time of the hourly satellite observation and a spatial variability measure of clearness indices in the surrounding 3×3 pixels. This spatial area corresponds to a 30×30 km 2 area. This conclusion was found for various intervals of e.
In the companion paper Schroedter-Homscheidt et al. (  2018 ) and based on the underlying master thesis  Jung (2015) , a reference data base of single hours using 1 min resolved ground observations was created. These individual hours were divided in eight DNI variability classes comprising information on high to low ramping in DNI as well as high to low clear sky index. Clear sky index is defined as ratio of observed irradiance to a cloud-free case and can be seen as a measure of the energy provided in the hour. The individual classes are defined to describe different impacts on ground-based radiation due to cloud cover and cloud features. In  Schroedter-Homscheidt et al. (2018)  this database is used to derive a supervised DNI variability classification for 1 min resolved ground-based DNI time series.
By using the same reference data base as training set, this paper investigates hourly and 15 min resolved satellite-based observations of clouds and their spatial structures to derive DNI variability class information. Combining spectral and textural features was already widely used in cloud remote sensing for detecting clouds in satellite imagery (e. g. Ebert, 1987 or Garand, 1988). Using multi-pixel satellite information to implement cloud type classification was used in the past for nowcasting natural hazards, such as thunderstorms affecting air traffic (as summarized and presented in an own approach by  Berendes et al. , 2008)  or in  Jakob and Tselioudis (2003)  for global climate model development. Irradiance variability patterns from 15 min resolved spectral and textural features were classified by  Jung (2015)  and for spatially higher resolved, but only once or twice per day available polar orbiting satellites, by  Watanabe et al.
Reference database of DNI variability classes This study uses a reference data base for eight irradiance variability classes with different variability structures in 1 min resolved GHI and DNI time series. Fig. 1  illustrates the variability classes with respect to DNI. Black curves provide the 10 min running mean DNI while yellow curves provide the 1 min resolved DNI ground observation. Red boxes mark the hours selected as part of the reference database for each variability class. Please note that only a few hours of each day are selected as other hours are either affected by low sun conditions, show another variability class or are transition hours with changing variability conditions inside the hour. Our previous studies  (Glas, 2014)  have shown that a classification of days into variability classes as e.
g. done by  Kang and Tam (2013)  or  Rauscher (2013)  is not justified due to frequent changes in variability conditions during the day. This was further confirmed e. g. by  Nouri et al. (2019)  who even claim the usage of a 15 min variability classification as necessity for some applications as short-term forecasting in concentrated solar power plants. The variability classes were extensively described in Schroedter-Homscheidt et al. (  2018 ) with respect to clear sky index and variability parameters derived from 1 min resolved ground-based irradiance observations. Annex A in Schroedter-Homscheidt et al. (  2018 ) provides a visualisation of all reference database members. The variability classes are sorted from large to small k cDNI. The clear sky index k c is defined as ratio between actually measured GHI and theoretically expected GHI in the cloud free case, which is taking only Rayleigh scattering, trace gas absorption, and aerosol extinction into account.
Ground-based observations This study uses ground-based measurements from the Baseline Surface Radiation Network (BSRN,  Ohmura et al. , 1998). BSRN stations provide GHI, DNI, as well as diffuse sky irradiance in 1 min temporal resolution. The data base is quality controlled following the procedures described in  Long and Dutton (2012). BSRN stations Cabauw (The Netherlands), Camborne (UK), Carpentras (France), Izana (Spain), Payerne (Switzerland), Sariguen/CENER (Spain), Sede Boker (Israel) are used below as well as DLR's PSA (Spain) station. Furthermore, all classification results are compared also to ground measurements as obtained in the Ener-MENA network  (Schüler et al. , 2016). This includes EnerMENA stations Adrar (Algeria), Cairo (Egypt), Erfoud (Morocco), Missour (Morocco), Oujda (Morocco), TanTan (Morocco), and Zagora (Morocco).
Pixel-wise classifiers Fully and partly cloudy pixels are discriminated. Cloud coverage as the fraction inside a pixel covered by a cloud, expressed in percent, is derived for each altitude level and thin clouds separately. Cloud coverage is calculated at daytime by analysing the dependency between reflectance measured at 0. 6 µm and 0. 8 µm with average reflectances for fully covered and cloud-free pixels in a surrounding box of 58×58 pixels. This size of the surrounding box was defined in the 1980ies and it differs from the later found surrounding boxes of 3×3 and 29×29 pixels for the spatial features in Section 2. 3. 2. This is accepted as the APOLLO cloud retrievals are obtained from the operational CAMS radiation service input dataset which cannot easily be changed. The cloud type discriminates between low-level, medium-level, and high-level reaching water or mixedphase clouds and high-level thin ice clouds.
Automatic classification scheme Based on the cloud parameters introduced as classifiers in Section 2, the typical values of all classifiers are derived and given as Box-Whisker representation showing the median as well as P25 and P75 percentiles for each cloud parameter (Fig. 5 ). The distributions are derived for all hours in the reference database. All indices are scaled to a range of 0 to 1. 5 for better visualization. The classifier values are far from being constant for individual classes, but compared to the other variability classes, the distributions of values within one variability class differ systematically for most classes. Classes 3 and 4 differ less systematically than the remaining classes and may therefore be difficult to distinguish with satellite observations. Nevertheless, these difficult classes 3 and 4 are still considered in the analysis to account for consistency with classification results obtained from ground-based observations as presented in Schroedter-Homscheidt et al.
The class with the lowest distance sum is claimed as result for this satellite observation and for the location of interest which is the central pixel inside the window. In general, the automatic classification is performed every 15 min for each image provided by the satellite. This is advantageous and needed in user applications as e. g. shown by  Nouri et al. (2019). Nevertheless, the reference database was set up on an hourly basis, since daily classifications were found to be not sufficient (e. g. Tomson, 2010). In order to compare automatic, satellitebased classification results versus the visually selected and controlled reference data base, hourly mean automatic classification results are needed. They are derived as the mean of four quarter-hourly values available during each hour.
Comparing the satellite-based and the reference classification For validation the automatic classification scheme is applied to all hours in the reference database (Fig. 7 ). The distributions for all classes peak on or next the 1:1 line. Overall, 55 % of all hours are placed into the same class by using the satellite-based compared to the manual expert-based classification scheme. Hit rate in the satellite-based classification are  70, 37, 48, 50, 51, 31, 60 , 92 % for classes 1 to 8, respectively. There are two groups of classes -class 2 and 6 with low detectability and the other classes with higher detectability. This is very much in line with results of  Watanabe et al. (2016)  who found average classification results of 56 and 61 % for two classification methods and a similar spread of results in their 6 variability classes.
They used MODIS 1 km resolved cloud classifiers, which were averaged over 45×45 km 2 and compared against 2-hourly temporal windows with 1 min resolved global horizontal irradiances. Their result was obtained from the one or two overpass times of the MODIS instrument on board the polar orbiting satellites AQUA and TERRA and using ground observations from Japan. These results are better than those found in a similar classification exercise based on artificial neural networks as discussed in  Schreck (2018). The neural network approach used the same reference dataset as labelled dataset and the same cloud predictors, but achieved only a classification result of 40 % due to a very flat probability function for the intermediate variability classes as result of the neural network. Since a visually based definition of variability classes in the reference database is somewhat arbitrary in transition zones between two classes, a second evaluation is made by counting the classification into a neighbouring class as a success.
Most classifiers remain very similar. Only the classifiers 'number of clouds' and 'cloud/cloud free changes are increasing in larger windows -which is obviously expected if using a larger window. The accuracy of classification is better for the 29×29 pixel area. For the hourly mean classification the accuracy versus the reference database is 87, 90, and 92 % for the 9×9, 19×19, and the 29×29 pixel subset, respectively. Therefore, all classifiers are from now on derived from a 29×29 pixel cloud mask subset resulting in a classification accuracy of 92 %. This setting is also in line with findings from cloud type detection by  Ebert (1987). They use 32×32 AVHRR GAC pixels which is the same km pixel range as 29×29 MSG SEVIRI pixels as each AVHRR GAC pixel is about 3×5 km 2 large.
Visual assessment of the satellite-based classification The automatic satellite-based classification result was visually assessed for DNI time series measured at En-erMENA stations Adrar, Cairo, Erfoud, Missour, Oujda, TanTan, and Zagora; BSRN stations Camborne, Carpentras, Payerne, Sede Boker; and DLR's PSA station for the year 2012. The 1 min resolved ground observations of DNI were plotted as a time series with the corresponding satellite-based class coded as background colour inside each hour. Classification errors were identified visually and analysed by comparing these situations with colour composites of SEVIRI data to interpret the meteorological and geographical conditions. Restrictions were found in regions with orographically induced clouds as e. g. at the Plataforma Solar de Almeria (PSA, Spain) where clouds are frequently caused by surrounding mountains.
, 2003). APOLLO is used to derive cloud physical parameters from Meteosat Second Generation (MSG) satellites. This database starts in February 2004 and is available for Europe, Africa, the Middle East and parts of Brasilia. Additionally, satellite-based textural features are added as classifiers in the automatic classification scheme. They describe spatial cloud structures. These classifiers are selected and developed with focus on the needs of solar energy users for the detection of ramping irradiance conditions in either global or direct irradiances. Textural features are derived from a group of pixels around the location of interest and cover a wider geographical region. Such classifiers are therefore less sensitive to parallax errors within the window, which are caused by solar viewing geometry and cloud height in single pixel classifiers.
Acknowledgements The authors would like to thank the personnel from the  BSRN, EnerMENA , and PSA stations. The study received support from the  DNICast  project (grant agreement no. 608623 ) within European Union's  Seventh Programme  for research, technological development and demonstration as well as from the  PreFlexMS  project (grant agreement  654984 ) within European Union's  Horizon 2020 programme. Funding for validating and completing this research was further provided by the integrated project  MetPVNet , project number  0350009A , financed by the  Federal Ministry for Economic Affairs and Energy, Germany.
Materials and methods The specimens of the two Troglopedetes species described here were found in the dark zone of two caves, Tham Don Non, Lang Suan district, Chumphon Province and Tham Kae, La-ngu district, Satun Province (Fig. 1 ). Collembola were collected with aspirators and Berlese extractors. They were stored in 95% ethanol and were mounted on slides in Marc Andre II medium after clearing in lactic acid. Morphological characters were examined using Leica DMLB and Leica DM1000 LED microscope with phase-contrast. Photos of the habitus were taken by a Canon EOS 6D with Canon EF 100mm f/2. 8 Macro lens and optimised by Helicon Remote software. Stacking was performed under Helicon Focus 6. Drawings were made using a drawing tube, and figures were improved with Photoshop and Illustrator CC/PC (Adobe Inc.
Acknowledgements We would like to thank  Anne Bedos  and two anonymous reviewers for many useful suggestions. We also thank  Rueangrit Promdam ,  Areeruk Nilsai , and  Kanchana Jantapaso  for offering help in the field. This work is supported by  Division of Biological Science, Faculty of Science, Prince of Songkla University , the  National Science and Technology Development Agency  ( FDA-CO-2563-11031-TH ), and the  Thailand Research Fund  ( RSA6280063 ).
Materials and Methods Extended experimental procedures are described in the Supplemental Information. Animal strains and procedures. All animal experiments were approved by the French Agriculture and Forestry Ministry. All the mice were on a C57BL/6/J genetic background and maintained in an SPF animal facility. Flow cytometry, fluorescence-associated cell sorting and cell cycle analyses. Single cell suspensions of the most proximal 1/3 of freshly isolated small intestine were used for the cell cycle analysis or FACS sorting of GFP + cells directly in RLT+ lysis buffer (Qiagen) for subsequent gDNA/RNA extraction, using a FACSAria (Becton Dickinson). Flow cytometry analyses were performed by using the FlowJo software (FlowJo LLC). Organoid formation assay and organotypic culture methods. Culture of organotypic structures from Lgr5-GFP + cells or intestinal crypts was performed as previously described  (26).
GSE123006 Fluorescent immunohistochemistry on paraffin-embedded tissue and organotypic cultures. Tissue dissection and immunohistochemistry on 5-μm-thick sections of paraffinembedded tissue and organotypic cultures were performed essentially as described previously  (28). All experiments were performed on formalin-fixed tissues with epitope retrieval in boiling 10 mM sodium citrate (pH 6. 4).
Acknowledgments This work was supported by  ITMO Cancer (Plan Cancer   2009-2013 EPIG201311  to P. J. and M. W. ),  SIRIC Montpellier Cancer  (grant  INCa_Inserm_DGOS_12553  to P. J),  ARC  ( SL220110603456  to P. J. ),  ANR  ( ANR-14-CE14-0025-01  and  ANR-17-CE15-0016-01  to P. J. ),  INCa  ( 2014-174  and  INCA_2018-158  to P. J. ), the  Labex EpiGenMed  (an " Investissements d'avenir" program   ANR-10-LABX-12-01  to P. J. ), the  PJ team is "Equipe Labellisée Ligue contre le Cancer" , and the  European Research Council  (Consolidator grant  n°615371  to M. W. ). M. B. was supported by  Ligue Nationale contre le Cancer. We acknowledge  M. Zenati ,  S. Barahoui  and  E. Sidot  for contribution to the collection of data,  J. Pannequin  and  T. Bouschet  for reagents, protocols and advices,  C.
A wide variety of methods have been proposed and evaluated for attenuation correction of PET data in PET-MR imaging. Current techniques that have been fairly widely used for brain attenuation correction are as follows: 1) Using a CT scan measured independently on a PET-CT scanner. This requires the development of a process to import a registered CT-derived μ-map into the PET-MR reconstruction pipeline. 2) Using a segmentation algorithm applied to an MR image acquired on the PET-MR scanner that assigns different tissue types to a small number of different tissue classes (e. g. soft tissue, bone, air). Attenuation factors are then applied to each class to generate a μ-map. To account for bone, a bone atlas can be directly incorporated into the MR-derived μ-map or the underlying MR sequence has a very short echo time (e.
g. ultra-short echo time (UTE) or zero echo time (ZTE)) to generate signal from bone that can be included as an additional tissue class. Algorithms to do this are available on current PET-MR systems  [1] [2] [3] [4]. 3) Atlas-based approaches that estimate a patient-specific μ-map by performing registration of patient's anatomic MR images to a CT based atlas derived from a database of CT images  [5]  or a dataset of paired MR and CT images  [6, 7]. Currently, only the method described by Wollenweber et al. [5]  is implemented on a commercial PET-MR scanner. A review of these methods, and other techniques, can be found in work by Mehranian et al. [8] , with additional examples given by Lui et al. [9]  and García-Pérez et al. [10]. For neuroimaging, the problem of attenuation correction in PET-MR for adults with normal anatomy is widely considered solved.
Converting HUs to 511 keV LACs for attenuation correction using a separately acquired CT scan To investigate how best to scale CT images from a PET-CT scanner for attenuation correction of PET data from PET-MR, we measured the difference between HUs and LACs derived from images acquired on GE and Siemens PET-CT systems. The CT electron density phantom was scanned using the standard brain protocol on a Siemens Biograph mCT and a GE Discovery 710 PET-CT scanner at 120 kVp. The Siemens brain CT protocol is acquired in helical mode at 8 mAs, with a pitch of 1. 35 and slice thickness of 3 mm. The images were reconstructed with FBP using Siemens standard soft tissue filter. The phantom consists of a cylinder made from 'solid water' (an epoxy resin material designed to mimic the attenuating properties of water) into which cylindrical samples of known density are inserted.
[12]. The method generates a pseudo-CT image through the registration of a database of aligned MRI and CT image pairs with a T1-weighted MRI image acquired as part of the PET-MR scanning protocol. Hair is not included in the pseudo-CT image-all pixels located outside the boundary between air and the patient's scalp are assigned an LAC value of zero. For each subject, an image was generated of the percentage difference between a static PET image attenuation-corrected using the CT-derived μ-map from a GE PET CT scanner and an image reconstructed using a pseudo-CT generated by MaxProb.
Acknowledgements The authors wish to thank all staff of the King's  College London and Guy's and St Thomas' PET Centre  that helped out with work presented in this paper. We also wish to thank  Dr. Paul Schleyer  (who is now with Siemens Healthineers, Knoxville, TN,  USA ) for his assistance in developing the method for attenuation correcting scans on the mMR using a separately acquired CT scan and  Prof Sally Barrington  for advice about the clinical impact of attenuation correction inaccuracies present in PET-MR images. This work was supported by the  KCL and Guy's and St Thomas' PET Centre , the  Wellcome/EPSRC Centre for Medical Engineering  [ WT 203148/Z/16/Z ], the  National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London.
Acknowledgments We thank the contribution of CNRS in the framework of the  UMI Takuvik , the  Canada Excellence Research Chair on Remote sensing of Canada's new Arctic frontier (M. Babin) ,  NSERC   Canada Discovery grant  ( RGPIN-2017-04505 ) (J. Lavaud), the  Sentinel North program of Université Laval  ( Canada First Research Excellence Fund)  and the  research network Québec-Océan  for their financial support;  J. Larivière  and  M. Béguin  for their technical support;  M. Simard  and  V. Richard  for their support with HPLC;  P. Massicotte  and  N. Schiffrine  for their help with statistical analyses and diatom culturing, respectively;  M. -H. Forget  for her management support, and  Profs. A. Juhl  and  M. Gosselin  for kindly providing us with the N. frigida strain and the  XE-PAM , respectively.
Risk of a cardiac event for patients with a mean heart dose of <10 Gy, 10 to 20 Gy, or >20 Gy was 4%, 7%, and 21%, respectively, adjusted for competing risk. 13 Retrospective analyses have highlighted the V40-55 dose to pulmonary artery  9  and high dose to the left atrium and superior vena cava  3  as being associated with early mortality. In addition, changes in electrocardiograms (ECGs) were observed owing to the large dose to the left atrial wall. 6 etrospective analyses are limited by the lack of availability of segmentations of cardiac substructures. This limitation can be overcome to an extent by using analysis methodologies that do not require a cardiac segmentation in the analysis. Our previous work used a technique called image-based data mining to analyze the plan dosimetry for 1101 patients with NSCLC without the need for segmentations.
Methods and Materials Data for 1161 patients with NSCLC treated with curativeintent radiation therapy (55 Gy in 20 fractions) were extracted from the treatment planning archive from a single institution. Institutional approval was granted to use the data (research ethics committee reference: 17/NW/0060). Five patients were selected to act as template patients with representative anatomy. Images of the cardiac substructure delineation for the 5 template anatomies are included in Figure  E1. These template anatomies were selected as follows: (1) the tumor was not in proximity to the heart (ie, the planning target volume should not overlap the heart) and (2) the heart volume was representative of distribution of heart volume in the study patients (template 1, 919 cm 3 ; template 2, 641 cm 3 l template 3, 738 cm 3 ; template 4, 632 cm 3 ; template 5, 735 cm 3 ).
There is also incomplete information regarding patient comorbidities. For a subset of these patients, we have an ACE-27 score; however, at the time these patients were treated this was not routine. ACE-27 captures severe comorbidities, and we do not have detailed data on cardiac comorbidities. Prospective data collection is required to better understand the interaction between cardiac dose and a patient's underlying cardiac comorbidities. This understanding will allow personalized cardiac risks to be calculated, individualized radiation dose constraints, and targeted follow-up of patients. To further this understanding, we are recruiting patients to a prospective cardiac biomarker study funded by Yorkshire Cancer Research (Research ethics committee: 18/NW/0706). Detailed comorbidity data will be collected as well as prospective ultrasound CT angiogram alongside circulating cardiac biomarkers.