dataesr/scientific-paragraphs-categorization huggingface dataset:
25 cm -1. It provides a quasi global coverage of the Earth twice daily (at 09:30 and 21:30 local time) with a relatively small elliptical footprint on the ground varying from 12 km × 12 km (at nadir) up to 20 km × 39 km (off nadir). These characteristics, combined with the good stability of the instrument over more than 15 years  (Saunders et al. , 2021)  and the consistency in the measurements between the three instruments  (Bouillon et al. , 2020) , make the IASI radiance dataset an excellent fundamental climate data record. For the detection and the characterization of the clouds in the field of observation of IASI, several products already exist: (i) the IASI-CIRS (Clouds from Infrared Sounders) cloud product  (Feofilov and Stubenrauch, 2017; Stubenrauch et al. , 2017) ; (ii) the integrated cloud fraction from the AVHRR (Advanced Very High Resolution Radiometer) imager, flying with IASI (product identifier GEUMAvhrr1BCldFrac;  Guidard et al.
, 2017). So far, the algorithm has been applied to the High-resolution Infrared Radiation Sounder (HIRS), the Atmospheric Infrared Sounder (AIRS) and the IASI dataset  (Feofilov and Stubenrauch, 2017; Stubenrauch et al. , 2017). The dataset is consistent over time, in particular because of the consideration of the increasing CO 2 atmospheric concentrations in the calculation to avoid long-term biases. However, the product is not strict enough to be used in the cloud removal preprocessing phase in satellite data retrieval schemes of geophysical variables. It was also never designed for this purpose but rather for establishing global climatologies of cloud properties  (Stubenrauch et al. , 2017). The AVHRR instrument on board the Metop satellites measures the radiation in five spectral channels in both visible and infrared bands with a spatial resolution of 1.
It includes a cloud flag and also provides, for each cloud scene, the cloud top pressure, the cloud fraction and the cloud phase  (August et al. , 2012; IASI Level 2, 2017). Since it was first deployed in 2007, the L2 meteorological data have undergone a series of updates (summarized up to version 6. 4 in Table  2  in  Bouillon et al. , 2020) , improving their quality to progressively reach excellent performances today. Until version 6. 4, the cloud detection was based on three independent tests: an AVHRR collocated cloud mask, a classical window channel test (observed minus simulated spectrum) based on numerical weather prediction (NWP), and a neural network applied to the IASI and AVHRR measurements. A scene was flagged as clear if all tests yielded an absence of clouds. For all IASI pixels declared as cloudy, the characterization was then performed using a CO 2 slicing and a χ 2 method  (August et al.
An entire reprocessed cloud series with the latest version of the operational L2 cloud product (version 6. 5) has, however, not been released yet, leading to discontinuities in the current data record, which makes it less suitable for use in long-term studies. Moreover, a positive trend is also observed in the time series of the cloud amount related to the CO 2 increase in the atmosphere, as is shown in Sect. 4. 2. Lastly, another issue that was reported, at least for the earlier versions of the product, is the presence of false cloud detections, in particular in the centre of large dust plumes  (Clarisse et al. , 2019). The limitations of the existing products have triggered the development of a sensitive and coherent IASI cloud detection dataset to be used for climate research and for studying trends of atmospheric compounds.
The algorithm is based on the use of a neural network (NN) and relies on IASI radiance information only, as was also successfully achieved recently to retrieve surface skin temperatures  (Safieddine et al. , 2020). In the next section, we detail the neural network retrieval algorithm (set-up, parametrization, training). IASI-NN-derived average cloud distributions and cloud seasonality are presented in Sect. 3. Section 4 gives a first assessment of the cloud product and an inter-comparison with other existing cloud products and cloud climate data records. We show that the IASI cloud mask presented here is both accurate and consistent over time and between the different IASI instruments. Conclusions are given in Sect. 5.
The neural network: set-up, training, retrieval and postfiltering The goal of the proposed algorithm is to produce a sensitive and consistent (unbiased) cloud mask over the entire IASI life span using as a reference dataset version 6. 5 (v6. 5) of the operational IASI L2 cloud product  (August et al. , 2012). The latter shows a clear improvement over the previous version (v6. 4) when compared to the cloud products from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO;  Winker, 2007) , as reported in the CALIOP-CALIPSO IASI Level 2 geophysical products' monitoring reports available at https://www. eumetsat. int/ iasi-level-2-geophysical-products-monitoring-reports (last access: 10 August 2022).
The NN consists of a supervised two-layer pattern recognition network  (Kothari and Oh, 1993)  with one sigmoid hidden layer of 20 neurons and one output layer of one linear neuron. The training algorithm is a Levenberg-Marquardt Table  1. List of IASI wavenumbers selected for the training of the cloud detection neural network. IASI wavenumber (cm -1 ) 826. 00, 827. 50, 861. 50, 865. 50, 866. 25, 869. 25, 871. 25, 874. 75, 877. 00, 878. 50, 880. 00, 883. 75, 885. 75, 887. 25, 891. 25, 894. 25, 897. 75, 899. 75, 901. 50, 902. 50, 905. 00, 994. 50, 996. 25, 999. 50, 1001. 50, 1004. 75, 1006. 50, 1009. 75, 1011. 50, 1014. 50, 2145. 50, 2147. 25, 2150. 00, 2152. 50, 2153. 50, 2157. 25, 2158. 25, 2161. 75, 2164. 75, 2166. 75, 2169. 25, 2172. 75, 2174. 25, 2176. 25, 2177. 75  backpropagation. For the training, the dataset was divided randomly into three parts: (1) the training set (95 %), (2) a validation set (4 %) and a test set (1 %).
In addition to the 45 channels, we also included the surface elevation as an input parameter for the NN taken from the "National Geophysical Data Center TerrainBase Global DTM Version 1. 0" (ftp:// ftp. ngdc. noaa. gov/Solid_Earth/cdroms/TerrainBase_94/, last access: 5 September 2022). In total, we performed 10 different training sessions, and we selected the least affected by dust (see Sect. 4. 3). The corresponding MATLAB function containing all the training variables is provided in the Supplement along with an example code showing how to run the network. The training of the network takes, depending on the run, about 100-150 iterations and is completed in about 30 min on a typical personal computer. The performance of the selected training reaches 87. 3 % with an equivalent number of misdetections in the clear and cloud group.
Average distributions The entire IASI time series has been processed for Metop-A, Metop-B and Metop-C. The computational time is fast, taking about 2. 5 min in CPU time per day processed (on a typical personal computer). For Metop-A before 2017, a reprocessed radiance dataset with the latest version of the L1C has been released by  EUMETSAT in 2018 (EUMETSAT, 2018). This allows us to produce a consistent time series of the cloud mask for the 15 years of IASI observations. In this section, we give a short overview of the large-scale cloud regimes and seasonality as captured by the IASI-NN cloud product. The mean cloud amount (%; also referred to synonymously as cloud cover or cloud fraction from here onwards) derived from Metop-A observations separately from the morning and the evening overpasses between 2008 and 2020 in a 0.
Climatological mean To give a first assessment of the IASI-NN cloud product, we compare here the NN cloud mask with other existing cloud products. We start with average cloud distributions. In total, seven different products are selected for the comparison: the operational IASI-L2, the CIRS-LMD also directly based on the IASI measurements, the L1C-AVHRR and three cloud climate data records (CDRs) built from the measurements of the AVHRR sounder on board the Metop-A satellite. Those are the CLARA-A2. 1 produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM-SAF)  (Karlsson et al. , 2013 (Karlsson et al. , , 2017)) , the Level-3U ESA Cloud_cci  (Stengel et al. , 2017)  and the PATMOS-x (Pathfinder Atmosphere Extended)  (Heidinger and Pavolonis, 2009; Heidinger et al. , 2014)  datasets.
Besides that, the same cloud patterns of low and high cloud coverage appear, as reflected by the very good correlation with the NN cloud product (r = 0. 88). The correspondence in the cloud fraction with the NN becomes much better when the threshold considered for separating the clear sky from the cloud scenes over oceans is doubled (Fig. 5b ). With this parametrization, the mean cloud coverage reaches 69 % globally (76 % above oceans only). Note also for both the L2 and the CIRS-LMD product the similar features that were reported for the IASI-NN product (see Sect. 3) in the regions of tall mountain ranges associated with a weaker sensitivity to cloud detection. The four AVHRR-derived cloud products, for their part, show very different mean cloud amounts. The dataset with the lowest number of scenes flagged as clear is the L1C-AVHRR product (Fig.
Conclusions With already more than 15 years of continuous and consistent measurements and at least another 25 years to come with the recent launch of IASI on Metop-C and the future launch of IASI-NG (New Generation, with a spectral sampling of 0. 125 cm -1 ) on the Metop-SG suite of satellites  (Crevoisier et al. , 2014) , the IASI dataset is becoming a fundamental reference climate data record. To be exploited to its full potential, it requires an accurate and unbiased cloud filter. While several cloud products of high quality already exist, they are generally either not homogeneous on the whole IASI time series or not strict enough in the cloud detection for use in satellite data retrieval schemes of geophysical variables. In this paper, we present a new algorithm for the identification of cloud-free scenes in the IASI field of view that is well suited to study trends for IASI-derived trace gases and other climatological variables.
Acknowledgements. IASI has been developed and built under the responsibility of the  Centre National d'Études spatiales (CNES, France). It is flown on board the Metop satellites as part of the  EU- METSAT Polar System. The IASI L1C data are received through the EUMETCast near-real-time data distribution service. Simon Whitburn  is grateful to the  ERC  for funding his research work. Lieven Clarisse  is a research associate (Chercheur Qualifié) supported by the  Belgian F. R. S. -FNRS. Cathy Clerbaux  is grateful to  CNES  for scientific collaboration and financial support. We acknowledge the use of imagery from the  NASA  Worldview application (https://worldview. earthdata. nasa. gov), part of the  NASA   Earth Observing System Data and Information System   (EOSDIS). Financial support. This research has been supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no.
Locomotor activity Locomotor activity was measured using an open-field apparatus (open arena of 30 × 30 × 40 cm) and video-tracked using the ANYmaze software (Version 6. 18, Stoelting Co. , USA). A separate group of mice was used to access locomotor activity. Mice were habituated for three consecutive days, from 9 a. m. to 5 p. m. , before locomotor testing on the fourth day. On the first day mice were placed in the arena for 30 min, scruff handled then placed back into the arena for 30 min. On the second day, after 30 min in the box, mice were scruff handled then abdominally pricked with a syringe needle and placed back into the arena for 1 h. On the third day, mice were given an intraperitoneal saline  injection after 30 min of habituation in the arena, then placed back into it for 1 h. On the testing day, mice received an i.
Data analysis Data are expressed as the mean ± SEM. Sucrose consumption was analysed with a one-way ANOVA for repeated measures (summarised in Supplementary Table  1 ), followed, if appropriate, by a Bonferroni multiple comparisons post-hoc test (summarised in Supplementary Tables 2 and 3). For the locomotor activity, Area Under the Curve (AUC) values were analysed with an unpaired student's t-test to compare the saline-treated. All statistical analyses were done with GraphPad Prism 8 (GraphPad Software Co. , CA, USA). P values < 0. 05 were considered significant.
Microscopy A Nikon Eclipse Ti2 inverted microscope was used with a Photometrics CoolSNAP HQ2 CCD camera to capture images and a Lumencor SOLA SE LED Light Engine for fluorescent excitation. The stage holding the microfluidic device was enclosed in a plexiglass incubation chamber, maintaining a constant 37 • C environment. The acquisition software was NIS-Elements. High-resolution data was acquired at × 30 magnification, with exposure times of 20 ms for phase contrast, 100 ms for GFP (25% LED intensity) and 500 ms for mCherry (20% LED intensity). With one image for each trap size and medium composition (16 images), each containing two traps, an imaging interval of 2 min was possible.
Microscopy Data Analysis GFP and mCherry fluorescence was extracted from microscopy images using custom-written MATLAB scripts. Drug concentration at each time point was calculated by extracting the fluorescence of the medium flowing through the medium channel above the traps, as media containing tetracycline also contained a fluorescent dye. Phase contrast images were stabilized using the Image Stabilizer plugin in ImageJ. The velocity of cells was estimated from the stabilized images using the PIVlab tool in MATLAB  (Thielicke and Sonntag, 2021). All images were preprocessed, regions of interest (ROIs) encompassing the full traps were selected manually, and three interrogation areas were used to calculate velocity vectors. Estimates of velocity vectors were calibrated using reference distances in the microfluidic device, and the estimated vectors were validated in post-processing by restricting velocity limits.
An Euler method with adaptive time stepping was used for the evolution of the physical equations of motion of the cells and the biochemical reactions. The size of the time step was capped at a maximum of 10 -4 min, and the maximum displacement of the cells was kept below 0. 02 µm by decreasing the time step as necessary. The diffusion equation for the continuous nutrient field was solved using a forward-time centered-space finite-difference scheme on a grid with a spatial discretization step size of 1 µm in both directions and the same time step as for the equations of motion of the cells, using a standard 5-point stencil in two dimensions. The model was implemented in Julia  (Bezanson et al. , 2017). Note that, to allow for a larger time step when solving the diffusion equation numerically, we use a diffusion constant D n in the simulation that is smaller than the real diffusion constant for small molecules in this system (which is on the order of 500 m 2 /s, see timescale discussion above and  Bittihn et al.
All 6 MUTYH positives were correctly identified using the 51% SBS18/36 threshold, with no false negatives (Fig. 3a ). Of the 2424 MUTYH negative CRCs, 45 were incorrectly classified as MUTYH positive and thus considered false positives. Therefore, the baseline classifier achieved 98. 1% accuracy (95% confidence interval 97. 5-98. 6%), with 100% sensitivity (54. 1-100%) and 98. 1% specificity (97. 5-98. 6%) when applied to the validation set. The number of somatic mutations and degree of TMS reconstruction error are associated with false positive SBS18/36 TMS. We confirmed the absence of pathogenic variants in the 45 false positives by examining the sequencing data for any pathogenic variants that may have been overlooked by the variant calling pipeline. To determine features that could improve classification accuracy, we assessed each tumor's somatic mutation count and TMS reconstruction error.
1225C>T p. R409W). Somatic mutation landscape of CRCs from biallelic MUTYH pathogenic variant carriers. To evaluate the impact of biallelic inactivation of MUTYH on the somatic mutational landscape, we combined all 19 MUTYH positive tumors across the three datasets. We previously observed that SBS18 and SBS36 are associated with specific pathogenic variants in MUTYH  7. Specifically, homozygous pathogenic variants at the 5′ end of the gene (exons 1-10) tend to give rise to SBS36, while SBS18 is more prevalent in homozygous pathogenic variants at the 3′ end of the gene. Comparing homozygous p. Y179C tumors to p. G396D homozygous tumors, SBS18 and SBS36 were both significantly different between these two groups of tumors (p = 0. 015 and 0. 024, respectively, t-test; Supplementary Fig. 6 ). Three additional carriers with homozygotes near p.
Discussion We previously demonstrated that combining MUTYH-related base excision repair deficiency mutational signatures SBS18 and SBS36 was more effective than each signature alone for identifying germline biallelic MUTYH carriers using whole exome sequencing of CRC tumors  7. In this study, we trained, validated, and then tested the effectiveness of our MUTYH SBS18/36 TMS classifier for identifying CRCs from biallelic MUTYH pathogenic variant carriers in a large cohort of 5649 tumors that underwent targeted multi-gene panel sequencing from formalin-fixed paraffin-embedded (FFPE) tissue DNA. The addition of somatic mutation count and TMS reconstruction error to the SBS18/36 threshold enabled the determination and validation of classifier parameters, namely SBS18/36 TMS proportion >51%, TMS reconstruction error <39%, and somatic mutation count ≥9, that yielded 100% accuracy for distinguishing MUTYH positives from MUTYH negatives when applied to an independent dataset.
This suggests that the affected domain alters tumor etiology, which could help us better understand the biology of tumors that arise and potentially inform clinical decision making. For example, both significantly enriched somatic mutations in KRAS c. 34G>T p. G12C (CCA>A) and PIK3CA c. 1636C>A p. Q546K (GCA>A) found in the MUTYH positives (Table  2 ) are in variant contexts that differ significantly between signatures, suggesting domain-specific hotspots that may inform treatment decision making. The finding of commonly occurring specific somatic mutations and mutated genes has treatment implications. Cross-referencing the significant biomarkers found in this study with existing clinical actionability databases  29  identified relevant drug associations, including FDA guidelines suggesting likely resistance to Cetuximab and Panitumumab (KRAS p.
Methods Study participants. All participants provided written informed consent, and each study was approved by the relevant research ethics committee or institutional review board. The University of Melbourne Human Research Ethics Committee approved this research (study IDs 1750748, 1954921). Three independent sets of CRC-affected individuals (Fig. 1 ) were included in the study: (1)    2 3 4 5. Tumor sequencing analysis. The mean coverage of MUTYH across the capture regions for the training, validation, and test tumor datasets was 581. 2 ± 156. 9, 753. 9 ± 578. 0, and 1542. 5 ± 1176. 8, respectively (mean ± SD) (Supplementary Fig. 8 ). For the training data, somatic variant calls were generated from the intersection of Strelka v2. 9. 2  35  and Mutect2 36 , with minimum tumor sequencing depth of 25 reads and variant allele fraction of 10%.
TMB was calculated as the combined number of SNVs, insertions, and deletions (indels) per megabase of capture sequence. MSI status was determined using the method described by MSIseq  39. Reported transcript and protein changes in MUTYH refer to NM_001128425. 1 and NP_001121897. 1 respectively. Germline MUTYH variant calling. The mean coverage of MUTYH across the capture regions for the training, test, and validation germline datasets was 372. 0 ± 118. 1, 280. 4 ± 352. 6, and 425. 7 ± 321. 5 respectively (mean ± SD) (Supplementary Fig. 8 ). Germline variants in the test and validation datasets were called using Strelka  35  and limited to PASS calls with a minimum depth of 50 reads and a minimum variant allele fraction of 10%. Variant Classifications. Variants classified by ClinVar 40 as likely pathogenic or pathogenic were grouped and considered "pathogenic" for the purposes of this study (n = 18 unique variants).
1  43  was reduced to a set of 14 signatures previously observed in 59 whole-genome sequenced CRCs as determined in PCAWG  1  , including the known base excision repair signatures SBS18 and SBS36 associated with defective MUTYH  16  and SBS30 associated with defective NTHL1  6, 44. The TMS reconstruction error measures how accurately a reported signature profile reflects the observed mutations and was calculated as the cosine distance between the observed mutational context counts and the predicted mutational context counts computed from the mutational signatures  45. We used the Python (v3. 7. 4) SciPy (v1. 4. 1) 46 implementation of simulated annealing ("basinhopping") to calculate the linear combination of TMSs that minimized reconstruction error. Determining SBS18/36 TMS thresholds for identifying MUTYH positive CRCs.
The test set was then utilized as an independent dataset to assess the accuracy of the optimized classifier. The test set somatic mutation count was compared to the classifier threshold after adjusting by the proportional difference in panel sizes (1. 34/1. 96). To further assess the classifier's utility for MUTYH variant classification, we applied it to CRCs defined as MUTYH monoallelics, potential MUTYH biallelics, and MUTYH uncertain (Fig. 1 ). Statistical analyses. All statistical analyses were performed using Python 3. 7. 4. NumPy 1. 17. 3  47  was used for numerical calculations. Statistical calculations were performed using SciPy 1. 4. 1  46. All t-tests were performed as two-sided and assuming equal variance with all p-values reported unadjusted unless otherwise specified.
Acknowledgements Funding from a  National Health and Medical Research Council of Australia (NHMRC)  project grant  GNT1125269  ( PI-Daniel Buchanan ), and from  NIH/NCI  grants ( U01CA137088 ,  R01CA223498 , and  X01-HG009781 ;  PI-Ulrike Peters ) supported the design, analysis, and interpretation of data. Additional acknowledgements relating to coauthors and contributing studies are described in the Supplementary Methods as Supplementary Note 1.
Data availability All data generated in this study are included in this published article (and its supplementary information files/Source Data file). The original panel-sequenced data used in this study are available at the database of Genotypes and Phenotypes (dbGaP). The Ontario Institute of Cancer Research (OICR) data is available under accession code phs002050. v1. p1. The Center for Inherited Disease Research (CIDR) data is available under accession code phs001905. v1. p1. The whole exome sequencing data used in this study has been previously published  7. This data is available from the Colon Cancer Family Registry via a "request to collaborate with the CCFR" application process (www. coloncfr. org/collaboration). Colorectal Adenocarcinoma TCGA copy number data was downloaded from cBioPortal (https://www.
cbioportal. org/) using the data sequenced in the Colorectal Adenocarcinoma (TCGA, PanCancer Atlas) study. Copy number loss was assessed in the PCAWG with the consensus copy number data downloaded from https:// dcc. icgc. org/releases/PCAWG/consensus_cnv. Mutational signature definitions were downloaded from the COSMIC website at https://cancer. sanger. ac. uk/signatures/ downloads/. Source data are provided with this paper.