dataesr/scientific-paragraphs-categorization huggingface dataset:
For noncanonical splice site variants, near-exon variants and DIVs, in silico tools Splice Site Finder-like  (Zhang, 1998) , MaxEntScan  (Yeo & Burge, 2004) , NNSPLICE  (Reese et al. , 1997)  and GeneSplicer  (Pertea et al. , 2001)  were used in Alamut Visual software version 2. 13 (Interactive Biosoftware) to predict the impact on splicing, using parameters described by  Fadaie et al. (2019) , and ESEfinder  (Cartegni et al. , 2003)  to predict effects on exon splicing enhancers (ESEs). SpliceAI  (Jaganathan et al. , 2019)  was used via the BROAD Institute web interface tool (https://spliceailookup. broadinstitute. org/#) to further predict splicing effects, using a 10,000-bp (5000-bp upstream and 5000-bp downstream) window. Variants with a predicted delta score of ≥0. 2 (range: 0-1) for at least one of the four predictions (acceptor loss, donor loss, acceptor gain, and donor gain) were considered potential candidates.
| Variant classification All prioritized variants were queried through the Franklin Genoox platform (https://franklin. genoox. com/: Accessed December 10, 2021) to obtain suggested classifications using the ACMG-AMP guidelines to gather annotations and evidence associated with each variant. The ACMG classification system divides variants into five classes: class 1 (benign); class 2 (likely benign); class 3 (variant of uncertain significance i. e. VUS); class 4 (likely pathogenic) and class 5 (pathogenic)  (Richards et al. , 2015). Only variants classified as class 3, 4, or 5 using ACMG guidelines were considered. In addition, all variants were investigated for in public online databases, including the LOVDs (https://www. lovd. nl/: Accessed December 10, 2021) and ClinVar (https://www. ncbi.
nlm. nih. gov/clinvar/: Accessed December 10, 2021). Variant data present in these databases were assessed on the pathogenicity interpretation submitted by previous reports. For novel candidate variants absent from these databases with no previous reports or functional evidence, in silico tools Sorting Intolerant From Tolerant (SIFT) (range 0_1; predicted pathogenic 0-0. 05)  (Sim et al. , 2012)  and MutationTaster (probability value 0-1 where 1 indicates a high security of the prediction; deleterious)  (Schwarz et al. , 2014)  were used to further evaluate predicted pathogenicity of candidate coding variants. A final verdict was made based on a majority vote of allele frequencies, suggested ACMG classifications and previous reports in online databases. Since segregation analysis was not performed for probands included in the study, but are an important criterion following full ACMG guidelines, patients were defined as genetically "possibly solved," "very likely solved," or "unsolved.
| smMIPs sequencing analysis To provide proof of our method and the smMIPs technology of our MD panel, sequencing analysis was performed for a total of 110 of the 379 sequenced probands (from the MD test run and MD run 01 pool A) that were genetically unexplained. These data are presented hereafter. Variant prioritization was performed for all nuclear targets. All 46 (previously known) variants in positive control samples across both sequencing runs, including SNVs plus small and large CNVs, were successfully called, hence reaching 100% concordance. CNV analysis for each NovaSeq 6000 run was performed for all samples included in the run and was normalized using positive control samples that were included in each data set, to exclude samples where false positives were overestimated. Prioritization of variants within the selected parameters, and retaining only variants with an ACMG classification of class 3-5, resulted in 134 variants in 40 genes (Supporting Information: Table  S7 ), comprising two CNVs and 132 SNVs or indels.
ACKNOWLEDGMENTS This study was supported by the  HRCI HRB Joint Funding Scheme   2020-007 ,  Stichting Oogfonds Nederland  ( UZ 2020-17 ),  Pro Retina Deutschland, Stichting tot Verbetering van het Lot der Blinden, Stichting voor Ooglijders  and the  Stichting Blindenhulp. The authors would like to thank the  Australian Inherited Retinal Disease Registry and DNA Bank ,  Carmen Ayuso ,  Eyal Banin ,  Marta del Pozo ,  Lubica Dudakova ,  Michael B. Gorin ,  Carel B. Hoyng ,  Petra Liskova ,  Ian M. MacDonald ,  Anna Matynia ,  Marcela Mena ,  Monika Oldak ,  Osvaldo Podhajcer ,  Alina Radziwon ,  Leah Rizel ,  Manar Salameh ,  Francesca Simonelli ,  Heidi Stöhr ,  Jacek P. Szaflik ,  Sandra Valeina  and  Bernard H. F. Weber  for their effort in sample collection. We thank  Ellen A. W. Blokland ,  Bart de Koning ,  Marlie Jacobs-Camps ,  Anita Roelofs ,  Michiel Oorsprong ,  Simon van Reijmersdal ,  Saskia van der Velde-Visser ,  Martine van Zweeden  and the  Radboud Genomics Technology Center  for technical assistance.
Conversely, visualization of the CNV analysis from the previous ABCA4-smMIP sequencing for the same proband indicated that the deletion was present, yet since the density of smMIPs was reduced and only double-tiling was used, the deletion was unconvincing. The homozygous c. 834del p. (Asp279Ilefs*21) variant was identified in one proband (patient 070901), which was reported as heterozygous in prior ABCA4-smMIPs sequencing analysis (Khan et al. , 2020). Revisualization of both datasets show that the c. 834del p. (Asp279Ilefs*21) is present and is homozygous in both datasets, however the MD-smMIPs approach showed coverages of 1,300x surrounding the region in contrast to the 240 reads obtained from the ABCA4-smMIPs approach. Since this is considered a severe variant, this will lead to early-onset STGD1 disease in this individual.
While most of these products were not designed for climate applications  (Levizzani et al. , 2018) , there is now a large number of precipitation datasets spanning >20 years, even at the daily scale  (Roca et al. , 2019). These emerging climate data records (CDRs) are making use of a variety of satellite observations, ranging from infrared geostationary based radiances up to the passive microwave imagers and sounders data from various platforms. The products ingesting microwave data have been shown to outperform the other products on various climatological metrics  (Beck et al. , 2017; Sun et al. , 2018; Maggioni et al. , 2016). Over land, satellite precipitation products that are calibrated against rain-gauge data overall show good performance compared to gridded in-situ datasets in monitoring extreme precipitation, and perform better than reanalysis based products  (Bador et al.
The constellation configuration indeed evolves in time and can be composed of as low as a single platform up to >12  (Kidd et al. , 2021a). As a consequence, any precipitation CDR relying on microwave data is expected to reveal some sensitivity to the time-dependent constellation configuration. In this study, we propose to quantify such an artefact using a full-fledged precipitation estimate algorithm and data-denial experiments. We also propose a way to statistically account for these modifications of the constellation on the daily precipitation distribution monitoring. The estimation algorithm and the satellite data are first introduced along with ground-based evaluation datasets. Then the data-denial methodology is presented together with the variations in time of the constellation. Results are summarized in Section 4 and a conclusion ends the paper.
The GPROF and PRPS Level-2 products GIRAFE also retains the flexible feature of TAPEER regarding the use of more instantaneous rain rate retrievals from Level-2 (L2) PMW algorithms. For the imagers and most of the sounders, the GPROF2017 for GPM V05 database data are used  (Kummerow et al. , 2015). This recent version of GPROF benefited from several improvements (e. g. , on the physical approaches, the cloud and rain profile a-priori databases, the determination of a precipitation detection threshold, among others) compared to the original version  (Kummerow et al. , 1996). In order to also benefit from the dense tropical sampling of the Megha-Tropiques mission  (Roca et al. , 2015) , PRPS L2 using SAPHIR is used  (Kidd et al. , 2021b). We use the most recent PRPS algorithm and database version (PRPS2019 for GPM V06 database).
The PRPS-SAPHIR rain retrievals are computed based on the relation between the near surface rain rate from the GPM Dual frequency Precipitation Radar (DPR) and the brightness temperature (TB) radiances from SAPHIR held in an apriori database. Both the GPROF and PRPS L2 PMW retrievals are available in the "real time" (L2) and climate (2A-CLIM) database versions. The differences between the L2 and 2A-CLIM lies in the ancillary products used in the initial processing step that associates the model's parameters with the TBs database observations. The 2A-CLIM products make use of the JMA's Global ANALysis (GANAL) and the 2A-CLIM products utilize ECMWF ERA-Interim database. We opted for considering the 2A-CLIM product version in view of its long-term availability, which makes it possible to access certain platforms (e.
g. , TMI) that are not available in the other database. The GPROF and PRPS data were obtained from the NASA Precipitation Processing System (PPS) dataaccess platform. GPROF is a well-documented product and is used in GIRAFE as a reference for the L2 inputs. Yet GPROF is not free from limitations. In general, GPROF indeed tends to overestimate (underestimate) the light (heavy) rainfall amounts. The underestimation of heavy precipitation volumes by GPROF retrieval algorithm is a known artefact of Bayesian approach that averages multiple profiles, which consequently gives less weight to most extreme precipitation intensities in the database  (You et al. , 2020). However, over Central Amazon, overestimation of rain occurrences can be linked to the ice scattering signals from convective clouds (high ice water path contents), which are not always consistently related to the surface precipitation  (Costa et al.
The GEOring archive The ring of geostationary meteorological satellites consists in a fleet of geostationary satellites located at the specific location along the Equator, each providing whole-disk imagery of the Earth, hereafter referred to as the GEOring. The GEOring configuration has been evolving since 2002 as shown in Fig. 1. Infrared observations from the GEOring are used in GIRAFE. Indeed, all sensors feature a ~ 10. 5-μm IR channel with a spatial resolution ranging from 2 to 5 km at nadir. The temporal sampling ranges from 10 to 30 min, including partial scans of the Northern and Southern Hemisphere. Level 1 datasets for the entire time series have been collected from the corresponding agencies (EUMETSAT, NOAA, JMA/JAXA). The open source Python library Satpy  (Raspaud et al. , 2021)  is used to read the various binary data formats and calibrate raw image counts to brightness temperatures.
The GIRAFE algorithm does not require intercalibration of geostationary imagers  (Roca et al. , 2020)  but resampling to the final grid (partial scans to full disk; oversampled to uniformly sampled) and quality control is performed. Given the particular characteristics of each platform, all the geostationary satellite datasets that comprise the five different regions, are processed and quality-controlled independently  (Fiolleau et al. , 2020). Images from older sensors, in particular, can have various defects that affect the quality of the derived rain estimates. Therefore, it is important to identify and exclude these images. For this purpose, an automatic quality control algorithm is independently applied to the entire IR archive  (Szantai et al. , 2011).
Ground based data for validation In order to assess the suite of data-denial experiments from the multiple constellation configurations' periods carried out during two different seasons: June-July-August (JJA) of 2014 and December-January-February (DJF) of 2014/5, for two tropical regions West Africa and Brazil, respectively. Over both regions, a consolidated daily accumulated precipitation database of several gauged-networks is available. Over West Africa, during the monsoon, Niger experiences precipitation from both local convection and organized convective systems. Therefore, the assessments are focused on the AMMA-CATCH observatory (AMMA-CATCH, 1990); specifically over the 1 • Niger site (AMMA-CATCH  Niger, 1990). The 1 • x1 • /daily rainfall amount is estimated from this high-quality rain-gauges thanks to block-kriging.
This dataset has been considered as a reference by several studies under the assessments of satellite precipitation estimates (e. g. ,  Roca et al. , 2010; Kirstetter et al. , 2013; Gosset et al. , 2018). Over Brazil, the analyses were carried out for the whole country during the period of DJF of 2014/5 -the rainy season in much of Brazil. A daily gridded precipitation database (at 1 • /daily of spatial/temporal resolution), is composed of several daily accumulated precipitation observations distributed over the country and are considered as ground reference for the data-denial experiments' assessments. The daily gauged-precipitation observations, recorded from 12 to 12 UTC, used for composing the gridded precipitation fields, were obtained from multiple agencies, primarily from the Centre for Weather Forecasting and Climatic Studies [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)] of the Brazilian National Institute for Space Research [Instituto Nacional de Pesquisas Espaciais (INPE)] and from HYdrogeochemistry of the AMazonian Basin (HYBAM), which are responsible to collect and apply a primary quality control (QC).
Preliminary assessment of GIRAFE/GPROF The current implementation of the GIRAFE product is a prototype product that will evolve with the use of a reprocessed set of L2 rain-rate estimations in the future. The current GIRAFE product is a microwave and IR only product and does not include any adjustments of the GPROF dataset using radar observations. Similarly, unlike most of the daily gridded satellite products  (Roca et al. , 2019) , no calibration against rain-gauges is performed. As a consequence, such an experimental product is likely to well reflect the L2 related uncertainty. We provide a preliminary assessment of the protype product below. The conditional mean rainfall for the quasi-global land masses over the 2006-2019 period averages to 8. 9 mm day -1 and to 15 mm day -1 for the oceanic regions.
The median of the distribution is mainly slightly negative and bounded by -8% and + 1. 25%, for PER99 over ocean and PER03 over land, respectively. Fig. 7  summarizes the constellation-period configurations' continuous and categorical statistical performances, through the Kling-Gupta Efficiency (KGE) and Heidke Skill Score (HSS) metrics, respectively  (Gupta et al. , 2009; Wilks, 2011; Kling et al. , 2012). The land and ocean performance distributions are presented as a function of precipitation, for the period of JJA of 2014 and over a quasi-global (55 • N/S) domain. The daily precipitation distributions, from each constellation configuration (PER99, PER00, …, PER07), are assessed via pixel-by-pixel comparison considering the 'golden era' configuration as a reference database (REF). In general, this analysis reveals how can the different constellation configurations modify the precipitation distribution, affecting the representation of both the detection and intensity of precipitation according to the surface type condition.
Time-dependent uncertainty bootstrapping The results of the previous section are now put back into a physical time line. For each JJA season from 2006 to 2019, the relevant period is first identified thanks to Fig. 5  data. The corresponding GMM coefficients are used to simulate the GIRAFE histogram that would account for the constellation change. For each day of JJA for each year, for each grid point, 1000 random samples are taken from the GMM uncertainty model. The anomaly is added to the actual grid point value and a "bootstrapped" histogram is built. The procedure is repeated for each day of the season. The final histogram is divided by 1000 to ensure consistency with the raw dataset. Ocean and land are processed separately to account for the previously highlighted differences in the impact of the constellation change.
Conclusions A prototype version of a climate-oriented global precipitation algorithm is used to explore the impact of the changing passive microwave constellation configuration on the precipitation estimate. The experimental product exhibits the well-known precipitation climatological features and compares well with a limited validation dataset, but for the very high daily accumulation that do not show as good performances as for the moderate accumulation. Data denial experiments are performed to mimic the evolution of the passive microwave constellation configuration over the last decades. The resulting differences are analyzed with respect to a reference period that correspond to the most populated constellation (in 2014). The differences are shown to depend on the period and the number of platforms available as well as on the rain accumulation.
Acknowledgements We thank  M. Gosset  and  T. Fiolleau  for interesting discussions on tropical precipitation and satellite observations. We also thank  T. Sikorski  for his help with Fig. 1. The authors also extend their gratitude to  D. Vila  for his support in accessing the Brazilian ground observations. We thank the anonymous reviewers for their many insightful comments and suggestions, which improved the clarity and quality of the manuscript. This study benefited from the  IPSL mesocenter ESPRI  facility which is supported by  CNRS ,  Sorbonne Université ,  UVSQ ,  CNES ,  Ecole Polytechnique  and national research infrastructures  Climeri-France  and  DATA TERRA. This study was performed by the  EUMETSAT Satellite Application Facility on Climate Monitoring , and we acknowledge the financial support by the  EUMETSAT  member states through CM SAF.
Gulf of Guinea is a very active area regarding maritime traffic as well as oil and gas exploitation (platforms). As a result of some actors of both sectors that fail to comply with environmental standards, the region is subject to a large number of oil pollutions. This study aims to detect oil slicks spilled in the Gulf of Guinea and analyse their spatial distribution using Synthetic Aperture Radar (SAR) images. If previous works have already locally mapped oil slicks in this area, this study is the first one to achieve a global statistical analysis based on a very high number of radar images covering 17 Exclusive Economic Zones of the Gulf of Guinea. To carry out the present study, a database of 3,644 SAR images, collected between 2002 and 2012 by the Advanced SAR (ASAR) sensor onboard the European Spatial Agency (ESA) Envisat mission has been used.
Image preprocessing The database of 3,644 images has been georeferenced in the geographic coordinate reference system over the WGS84 ellipsoid, datum WGS84. A land mask has been applied and the images have been radiometrically corrected. The radiometric correction consists in correcting the brightness variations due to SAR peculiarities. Indeed, the radar backscattering on the offshore area is dominated by non-Lambertian reflections (the surface does not reflect the radiation uniformly in all directions). This non-Lambertian reflection leads to heterogeneity of the brightness in the radar image. The input images have a 16-bits Digital Number (DN) dynamic which requires reduction to 8-bits to be displayable on an usual screen. The applied preprocessing consists in applying a local stretching with an average of 140 and a standard deviation of 60 on a sliding window of 301 pixels in order to optimize the detectability of the oil slicks (fig.
Data availability All the Envisat ASAR images  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  used in this study are available at ESA website https://eocat. esa. int/sec/#data-services-area. The spatial distribution of the oil slicks in the Gulf of Guinea between 2002 and 2012 is available at ZENODO: https://doi. org/10. 5281/zenodo. 6470470 (Najoui, 2022).
Conclusion and perspectives An unprecedented database of oil spills has been generated over the EEZ of the Gulf of Guinea using the 11 years of acquisitions of SAR images at C-band by ASAR in wide-swat mode (150 m of spatial resolution) contained in the archive of the Envisat mission. This database has been achieved using a manual approach. The present study shows that all of the countries EEZ are sites of natural oil seepages due to the extensive geological context of the Gulf of Guinea. It shows also that oil spills from ships are well correlated to the shipping routes along the coasts of the 17 EEZ of the Gulf of Guinea while oil spills coming from oil platforms are concentrated along the coasts of oil-producing countries like Nigeria, Republic of Congo, Angola, and Ghana. The temporal analysis during 10 years  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  shows a decrease in the mean area covered by oil between 2008 and 2009.
In the following simulations, the default KOMEN package is used with the Wu  [32]  default formulation (Equation (  5 )) for the drag coefficient C D. The 2nd order polynomial formula proposed by Zijlema et al. [33]  (Equation (  6 )) and the  Hwang [34]  formulation (Equation (  7 )), default for the ST6 packages) are also tested for comparison, being choices typically used by designers. C D_Wu = 1. 2875 × 10 -3 for U 10 < 7. 5 m/s 10 -3 (0. 065U 10 + 0. 8) for U 10 ≥ 7. 5 m/s (  5 ) C D_Zijlema = 10 -3 - C D_Hwang = 10 -4 -0. 016U 10 2 + 0. 967U 10 + 8. 058  (7)  The flux of energy due to the wind source is implemented into STWAVE by the equation proposed by Resio  [35] : F in = 0. 85λ ρ a ρ w C m u * 2 g ( 8 ) The friction velocity u*, defined as before u* 2 = C D U 10 2 , is computed through the drag coefficient C D = 0.
[40]. The equivalent roughness length scale of the bottom kn is set as default (0. 05 m), and following the Bretschneider et al. [41]  relationship, it corresponds to a Manning coefficient of 0. 04 s/m 1/3 in shallow water. To consider the Zijlema et al. [33]  formulation, the DRAG FIT command is used. To activate the ST6 package the GEN3 ST6 command is employed. Since there is no default ST6 parameter set available, one of the example parameter sets given in  [42]  was used. The selected ST6 parametrization considers a1sds, a2sds, p1sds, p2sds, and wscaling parameters equal to 2. 8 × 10 -6 , 3. 5 × 10 -5 , 4, 4, and 32 respectively. Aydo gan & Ayat  [17]  found that this latter parameter is the most significant in the ST6 package at adjusting wave heights. The ST6 package requires also the setting of the type of swell dissipation and, as suggested by the example, the non-breaking dissipation of Ardhuin et al.
The winds in Torbole make it an attractive destination for sailing and windsurfing. A statistical analysis was carried out on available wind datasets from several meteorological stations located in the western and northern sides of the lake. For both the locations a wind of 17 m/s coming from the Ora direction has a return period T R of 10 years. Only these winds coming from the South generate waves in front of Torbole. For Brenzone, a wind of 15 m/s coming from the Peler direction has T R = 10 years. Direct wind measurements are more reliable than simulated data since, where the surface winds are affected by the land's presence, the skill of wind models diminishes (de León and Soares,  [56] ).