dataesr/scientific-paragraphs-categorization huggingface dataset:
COHORT DESCRIPTION Study population Cohorts of female childhood/adolescent cancer survivors meeting the following criteria were eligible to be included in the pooled study population: a primary cancer diagnosis at <21 years of age, survival ≥5 years from diagnosis, follow-up data on presence and type of subsequent neoplasms, as well as individual detailed accounts of radiotherapy and chemotherapy treatment available for the majority of cohort members. The characteristics of seven cohorts that satisfied these criteria are shown in online supplemental table 1: three cohorts from North America and four from Europe. The cohorts from North America include the CCSS,  32 33  the SJLIFE,  34 35  and the US National Wilms Tumour Study Group (NWTSG). 36 37 ] [44]  The included multi-institutional study groups represent long-standing and well-established research infrastructures to study health and well-being among childhood/ adolescent cancer survivors.
A few specific aspects are mentioned below, as they impact the contribution of the respective study group to this effort. For the SCCSS, data collection on treatment details is ongoing; cohort-wide data is not available yet. Therefore, the SCCSS contributed data from a case-cohort study. The treatment details for survivors in a subcohort of their total cohort and all subsequent breast cancer cases were collected. Similarly, in the NWTSG some aspects of treatment have not been collected for all cohort members. As such, this cohort will be excluded for analyses of chemotherapy treatment dose effects. Overlaps between the North American cohorts (CCSS/ SJLIFE/NWTSG) and the Dutch cohorts (DCCSS LATER/DHL) were checked, and only unique patients were included. The data was prepared by analysts from the individual studies according to a jointly developed harmonised data protocol, and are stored at the Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands (see online supplemental table 1 for an overview per data provider).
Childhood/adolescent cancer diagnosis information and treatment exposures For each individual cohort, details on childhood/adolescent cancer diagnosis and treatment were included in the pooled cohort (table  1 ). For childhood/adolescent cancer diagnosis, the year and month were recorded as well as the ICD-O-3 morphology, behaviour and topography codes. Radiotherapy details included direct in-field exposure (yes/no and cumulative dose) to the following body regions: head/neck, chest, abdomen, pelvis, extremities or total body irradiation. For chest radiotherapy, we also collected data on the specific field(s) that were treated. Each chest radiotherapy field was converted into one of the following field classifications based on the field description by the individual cohorts: whole lung, total body irradiation, mantle, mediastinum, axilla, spine or other chest without axilla (left/right/unknown laterality).
Chest fields Data providers were requested to provide data on each field to the chest area as part of childhood cancer treatment according to the categories listed below, or to provide the coding of fields that they used in their own database. In case the data providers provided their own coding of fields (CCSS/SJLIFE/DCCSS LATER/DHL), these were then recoded by the Máxima team, in close collaboration with the data providers, into the categories listed below. ► Total body irradiation ► Whole lung ► Mantle field ► Mediastinal ► Axilla (both/left/right/unknown) ► Spine* ► Chest without axilla (left/right/other/unknown) Also, the delivered dose of each field was recorded.
Acknowledgements We thank  Giulio J. D'Angio  and  Norman E. Breslow  for their contribution to the set up of the NWTSG. We also thank  Susan Smith  for critical review of the manuscript. on July 13, 2023 by guest. Protected by copyright. http://bmjopen. bmj. com /BMJ Open : first published as 10. 1136/bmjopen-2022-065910 on 7 November 2022. Downloaded from.
The DCCSS LATER is supported by the  Dutch Cancer Society  ( DCOG2011-5027 , CMR, FEvL,  Wim Tissing , principal investigators and  UVA2012-5517 , CMR, LCMK, principal investigators). The FCCSS is funded by the  Fondation ARC  ( PopHARC Grant ) and the  Agence Nationale pour la Recherche Médicale  ( ANR, Hope-Epi Grant ). The SCCSS has been supported by the  Swiss Cancer League  and the  Swiss Cancer Research foundation  ( KFS-02783-02-2011 ,  KLS-3412-02-2014 ,  KFS-4157-02-2017 ,  KLS/KFS-4825-01-2019 ;  KFS-4722-02-2019 ,  KFS-5027-02-2020 ;  KFS-5302-02-2021 ;  KLS-5432-08-2021 ),  Kinderkrebs Schweiz  ( www. kinderkrebs-schweiz. ch)) and  Kinderkrebshilfe Schweiz  ( www. kinderkrebshilfe. ch). The DHL has been supported by  Dutch Cancer Society  ( NKI 2010-4720 ).
Jude Medical records including pathology reports National Death Index with breast imaging, self-report or next of kin reported NWTSG Clinical records None Annual status reports Medical records review DCCSS LATER Medical records Population-based Netherlands None Pathology reports Cancer Registry Nationwide network and registry of histo-and cytopathology in the Netherlands (PALGA (Dutch Pathology Registry)) FCCSS Hospital clinical files National death certificate data Self-completed questionnaire Pathology reports Long term follow-up National Public and Private visits Hospital and National Health Insurance Database SCCSS Medical records Cantonal cancer registries Self-report Medical records including pathology reports including pathology Cause-of death statistics reports DHL Medical records Population-based Netherlands None Pathology reports Questionnaires sent to Cancer Registry general practitioners CCSS, Childhood Cancer Survivor Study; DCCSS LATER, Dutch Long-term Effects After Childhood Cancer Study; DHL, Dutch Hodgkin Late Effects cohort; FCCSS, French Childhood Cancer Survivor Study; NWTSG, US National Wilms Tumour Study group; SCCSS, Swiss Childhood Cancer Survivor Study; SJLIFE, St.
Other flight patterns were also observed for additional purposes, but the work here focuses on the circles so as to capture and constrain the observations in a single location. The radiative component of this analysis is estimated from observations from BACARDI  (Konow et al. , 2021). This new radiometer package comprises two sets of Kipp and Zonen pyranometers (CMP22) and pyrgeometers (CGR4) mounted to the aircraft fuselage. Together, they provide measurements of the upward and downward solar (sensitive to wavelengths between 0. 2 -3. 6 µm) and terrestrial (4. 5 -42 µm) broadband irradiance at flight level. The respective uncertainty of the measurements from these sensors is 1 % and 4 %. The data used here has been corrected in post-processing for temperature dependence and response time of the sensors as well as aircraft attitude, similar to the methods described by  Ehrlich and Wendisch (2015).
Satellite observations from GOES-16 The cloud properties in this analysis come from the data products of the GOES-16 geostationary satellite Advanced Baseline Imager (ABI)  (Schmit et al. , 2017 (Schmit et al. , , 2018)). GOES-16 is part of the GOES-R series of satellites. Throughout the duration of the campaign, GOES-16 was operated to produce images of the EUREC 4 A research domain at a temporal resolution of 1 minute, when possible, which turns out to be a strong benefit for analyzing the evolution of the clouds. Specifically, we utilize the cloud mask product  (Heidinger and Straka, 2013)  as well as retrieved LWP, r eff  (Walther et al. , 2013)  and cloud top height (z ct ;  Heidinger, 2012)  products. The spatial resolution for the data products used here is 2x2 km at nadir. The cloud mask shows good agreement with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) over the Contiguous United States, but low clouds (cloud tops below 2 km) were the most commonly missed cloud type  (Jiménez, 2020).
(6) Since measurements in cloud-free conditions cannot be obtained simultaneously or in similar conditions to those observed during each flight, simulated cloud-free cases of each flight are calculated using the libRadtran software package  (Emde et al. , 2016). Dropsondes along the flight track provide measured profiles of thermodynamic atmospheric properties (humidity, temperature) used in the simulations  (George et al. , 2021). Estimations of the uncertainties of these simulations are given in Table  1. The largest source of uncertainty is found to be the atmospheric data, particularly for the cloud-free F ↑ ter. Due to the limited spatial resolution of the dropsondes, this method cannot always replicate an observed cloud-free situation. Additionally, because the sondes may pass through clouds on their way down, the humidity and temperature profiles used for the simulation could be biased.
Source of Uncertainty Wavelength Range Parameter Uncertainty Atmospheric Profile (dropsonde) Solar F ↓ < 1 % F ↑ 3 % Terrestrial F ↓ 6 % F ↑ 8 % Surface Temperature (± 3 K) F ↓ < 1 % F ↑ 2. 5 % Table 2. The flights from EUREC 4 A that are used in this analysis are given here with their respective flight IDs (HALO-mmdd) along with the times when HALO was in the circling flight pattern and GOES-16 data were available. The amounts of time and number of GOES images are for cloudy and cloud-free sky, but a total amount of time and number of GOES images for cloudy sky only is given as well. The general descriptions of the flights come from notes in the flight reports. Flight partial flights are usable in some cases. Additional filters were applied regarding the SZA and the presence of clouds above the aircraft (e.
g. cirrus). To prevent complications in the case of low sun conditions and to avoid the need to consider cirrus in this study, the data were limited to SZAs less than 70°and to flights with cloud-free conditions above the flight altitude of HALO. Furthermore, the maximum mean z ct for an observed scene is limited to 4 km to avoid the inclusion of deeper convective clouds, which have properties and associated relationships that should be considered separately. Scenes without clouds below 155 HALO are also excluded. To enable the combined use of BACARDI and GOES-16 data, spatial collocation needs to be assured with both observations covering the same footprint at the same time. BACARDI has a 180°field of view (FOV), but radiation from different directions is weighted with the viewing zenith angle.
Overview of cloud data The radiative data in this study was recorded by the BACARDI instrument and the cloud properties were obtained from GOES-16 satellite data products (see Sects. 2. 1 and 2. 2). A set of histograms depicting the distributions of the data is given in Fig. 3. The mean α ce is 0. 086, while the mean ∆F ter is 6. 35 Wm -1. The mean ∆F sol is -77. 64 Wm -1 , which demonstrates that the solar part of the cloud radiative forcing of shallow trade-wind cumuli is significantly stronger than the forcing from the terrestrial part. However, due to the influence of SZA on ∆F sol , the mean value is provided only as an estimate for the purposes of comparison. The mean cosine weighted cloud fraction is 0. 204, which is larger than the value of 0. 087 reported by  Mieslinger et al. (2019)  from Advanced Spaceborne Thermal Emission and Reflection Radiomater (ASTER) imagery of tradewind cumuli.
In those instances where certain relations were unclear, it could be possible that the addition of other information or ways of categorizing the data could be the key to extracting more clear results. Here, the results should be constrained to specific cloud types observed during EUREC 4 A, which are largely a mixture of shallow cumulus and low stratiform clouds. This could be further extended into categorization based on the description of four cloud types given in  Schulz et al. (2021). Also, using I org as the parameter for defining the organization of the cloud field may do so correctly in a macrophysical sense, but it fails to capture other important qualities about the clouds, such as the optical thickness, which other methods for describing cloud morphology capture more effectively  (McCoy et al.
Discussion and Conclusions Bringing together a comprehensive characterization of trade-wind clouds based on their macrophysical and microphysical properties and interactions is a challenge, one component of which is the lack of representative observational data sets. The recent EUREC 4 A campaign provides a solid database for this task. In this study, we assess how different cloud properties (cloud fraction, LWP scene , LWP cloud , r eff , z ct and I org ) affect the cloud radiative forcing of shallow trade-wind clouds and to what degree these parameters matter relative to each other. Using irradiance observations from the BACARDI broadband radiometer onboard HALO, we calculate the solar and terrestrial cloud radiative effects, here represented by α ce and ∆F ter. The cloud properties are obtained via 1-minute collocated cloud products from GOES-16.
Acknowledgements. This work was supported by the  Deutsche Forschungsgemeinschaft (DFG)  within the  HALO SPP 1294  (project numbers  422897361  and  316646266 ). We thank the  Max Planck Institute for Meteorology  for designing and coordinating the EUREC 4 A campaign and the  German Aerospace Center (Deutsches Luft und Raumfahrtzentrum, DLR)  for campaign support. We also thank the  NOAA/NESDIS  and  University of Wisconsin-Madison/CIMSS team  for providing the dedicated EUREC 4 A GOES-16 data set. We are grateful to  H. Deneke  and  M. Klingebiel  for helpful discussions and support that contributed to this work. 19 https://doi. org/10. 5194/acp-2021-812Preprint. Discussion started: 15 October 2021 c Author(s)  2021. CC BY 4. 0 License.
Data availability. Time series of the BACARDI observations are published on AERIS by  Ehrlich et al. (2021). All other data produced for this study from BACARDI are available upon request from the leading author. The dropsonde data used in this study are published on AERIS by  George (2021). The source for the GOES-16 data set used in this study is the National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite, Data, and Information Service (NESDIS) and the University of Wisconsin-Madison/Cooperative Institute for Meteorological Satellite Studies (CIMSS).
4 Discussion and Conclusions Bringing together a comprehensive characterization of trade-wind clouds based on their macrophysical and microphysical properties and interactions is a challenge, one component of which is the lack of representative observational data sets. The recent EUREC 4 A campaign provides a solid database for this task. In this study, we assess how different cloud properties (cloud fraction, LWP scene , LWP cloud , r eff , z ct and I org ) affect the cloud radiative forcing of shallow trade-wind clouds and to what degree these parameters matter relative to each other. Using irradiance observations from the BACARDI broadband radiometer onboard HALO, we calculate the solar and terrestrial cloud radiative effects, here represented by α ce and ∆F ter. The cloud properties are obtained via 1-minute collocated cloud products from GOES-16.
One of the challenges of all synchrotron facilities is to offer the highest performance detectors for all their specific experiments, in particular for X-ray diffraction imaging and its high throughput data collection. In that context, the DiffAbs beamline, the Detectors and the Design and Engineering groups at Synchrotron SOLEIL, in collaboration with ImXPAD and Cegitek companies, have developed an original and unique detector with a circular shape. This detector is based on the hybrid pixel photon-counting technology and consists of the specific assembly of 20 hybrid pixel array detector (XPAD) modules. This article aims to demonstrate the main characteristics of the CirPAD (for Circular Pixel Array Detector) and its performance -i. e. excellent pixel quality, flat-field correction, high-count-rate performance, etc.
, 2014). However, up to now, there was no detector available at the beamline which could combine high-speed acquisition (higher than 500 Hz to follow transformations in the studied materials), short acquisition time (to perform time-resolved acquisition in stroboscopic experiments) and very large area (more than 100 to record full structural information with an angular resolution in the order of 0. 01 ) with increased flexibility of use (with two degrees of freedom). For this reason, the Detectors group and DiffAbs beamline on one side, and the Cegitek company (formerly imXPAD) on the other, developed an original high-speed 2D detector based on a silicon sensor hybrided into an XPAD3. 2 readout chip  (Pangaud et al. , 2007) , with a particular geometry covering a very wide angular range. This 2D detector, named CirPAD, for Circular hybrid Pixel Array Detector, is basically an assembly of 20 XPAD-S70 modules  (Medjoubi et al.
e. SLOW and FAST settings. The SLOW setting is optimized to work with lower energy photons (down to $ 4 keV) but at the cost of the maximum count rate (8. 4 Â 10 5 photons s À1 pixel À1 ; see x3. 4), while the FAST setting allows a maximum speed of the detector (2. 5 Â 10 6 photons s À1 pixel À1 ; see x3. 4), but with increased electronic noise and therefore higher minimum threshold settings (down to $ 5 keV). Also, the CirPAD, like all XPAD detectors, can be controlled with either proprietary Cegitek software or with a TANGO LIMA controls device (https://lima1. readthedocs. io/ en/latest/) that connects directly to the Cegitek server via the corresponding Application Programming Interface (API). Both interfaces provide full access to the detector and allow the detector to be operated in four different acquisition modes: * Standard Mode: the detector counts all the incoming photons during user-defined time.
between modules 2 and 3 are shown in Fig. 10(a). All the structures and artefacts on the raw images, such as the higher counting in the guard ring regions, correction for virtual pixels and sensor inhomogeneities, have been efficiently corrected and removed from the final image. Due to the size and geometry of the CirPAD, it is quasi-impossible to obtain a flat and uniform illumination of the whole detector surface. Therefore, a remaining gradient can be observed after flatfield correction. This gradient can also be corrected by plane or second-order polynomial levelling, in a similar manner to what is done in scanning probe microscopy. The resulting pixel-to-pixel signal variations have been significantly reduced from = 2. 9% in the case of the raw data to = 0. 36%. The overall homogeneity of the complete detector is very high, with a pixel-to-pixel signal variation within the AE1% range for all pixels, as shown in the histograms in Fig.
This procedure allows the orientation of the detector to be finely tuned and the exact geometry which will be used for subsequent measurements to be obtained. The comparison of the reconstructed diffractogram and the theoretical positions of the XRD peaks is shown in Fig. 13. The quality of the data is evaluated using the whole powder pattern matching method  (Le Bail et al. , 1988)  and structural refinements  (Rietveld, 1969) , performed using the powder diffraction software FullProf  (Rodrı ´guez-Carvajal, 1993; Roisnel et al. , 2000). The background adjustment was made with a series of selected points and, after indexing the pattern, the peak shapes were fitted by a specific profile function (Npr = 7; Thompson-Cox-Hastings pseudo-Voigt convoluted with axial divergence asymmetry function)  (Finger et al.
A low number of defective pixels has been measured: 0. 09% of dead pixels for the FAST calibration mode and close to 0. 02% for the SLOW one, and only a few hot pixels (0. 0001%) regardless of the mode. Flat-field imaging and mapping of all the defective or noisy pixels have been performed, and both will be used to correct the data sets. The limit of the count rate has been measured and reaches 2. 5 Â 10 6 and 8. 4 Â 10 5 photons s À1 pixel À1 for FAST and SLOW calibration, respectively. The diffraction images obtained from the CirPAD using the LaB 6 reference powder allowed the architecture of the detector, including the electronics, the geometry of the 20 modules and their synchronized readout to be verified. From this diffraction image, the metrology of each module has also been realized such that the obtained intensity versus scattering angle diffractogram is in very good agreement with the expected reference data set.
This makes this detector adapted to a number of synchronized time-resolved pump-probe X-ray diffraction experiments, with the major advantage of giving access to extended q-range diffractograms. The use of the CirPAD and its great interest for materials science and physical chemistry scientific applications, including highlights/scientific cases showing the capabilities of the CirPAD for the structural characterization of polycrystalline, textured or amorphous materials, will be the subject of a second article. = azimuth on the XRD ring), the XRD rings appear as vertical lines (2 = constant). The data can be converted into 1D diffractograms (intensity versus scattering angle 2) [Fig. 16(c) ] by performing the data regrouping (see also Fig. 13 ). The wide range ($ 120 ) available for rotation around the z axis (R z , see label 6 in Fig.
Acknowledgements The authors thank  Region Centre-Val de Loire  for its financial involvement in the development of the CirPAD. We also thank the Direction of SOLEIL, in particular,  Andrew Thompson , for support of the project. The authors gratefully acknowledge Pierre Delpierre from the imXPAD company,  Louis Hennet  from  CEMHTI/ICMN, Orleans, France, and Stephanie Hustache  and  Kadda Medjoubi  from SOLEIL for their contribution in the initial XPIX project. Johan Simoncini  from  Cegitek  is acknowledged for his technical input. The authors thank  Alain Lestrade  (Alignment and Metrology Group of SOLEIL), and  Sylvain Bonnin ,  Carlos de Oliveira ,  Pascal Prout  and  Jessy Bouillaud  ( Mechanical Workshop of SOLEIL ) for their technical support in all the setting-up steps. The MKS company contributed to the development and the setup of the frame for uniting the six-circle diffractometer and the CirPAD.
[14] [15] [16]  Through the more C-terminal RUN domain, DENND5A interacts with sorting nexin 1, a protein involved in protein trafficking between endosomes and the trans-Golgi network. 17, 18  No specific protein interaction or other function has been observed with the PLAT domain (Polycystin-1, Lipoxygenase, Alpha-Toxin) of DENND5A, a little-studied beta sheet protein module involved in protein-protein and proteinlipid interactions. 19  Full-length DENND5A also interacts with GTP-Rab11, but no specific binding site has been identified. 20  The domain structure and protein partners of DENND5A suggest its involvement in intracellular membrane trafficking pathways, but these pathways yet to be identified. Here, through physician-initiated contact and searching of disease variant databases, we identify a cohort of 15 individuals from 14 families with 21 unique DENND5A variants.
Recruitment We originally identified four individuals, one deceased, from two consanguineous families with homozygous DENND5A variants. 9  Another two homozygous individuals were described by Anazi et al. in 2017. 21  Neither study provided a detailed phenotypic analysis. Here we identified an additional 13 individuals: five were brought to our attention by physicians who had performed diagnostic whole exome sequencing on patients and discovered DENND5A variants; five were identified through physician-submitted GeneMatcher  22  and ClinVar  23  database entries who performed whole-exome sequencing for similar clinical purposes; and another three were identified through word-of-mouth of the study between clinicians that already completed the survey and their colleagues with other DENND5A EE patients.