dataesr/scientific-paragraphs-categorization huggingface dataset:
For the latter, genetically predicted higher apolipoprotein B was associated with a lower risk of hypercholesterolemia, inconsistent with the biological role of apolipoprotein B and opposite to the mechanism of action of Mipomersen (an mRNA antisense inhibitor). However, the mismatched apolipoprotein B genetic association was only present in aptamer-based proteomic datasets and not observed in other lipoprotein GWAS where apolipoprotein B was measured using, for example, immunoturbidimetric methods as in the UK Biobank  27. Furthermore, the APOB signal was also a cis-eQTL in adipose tissues (https://genetics. opentargets. org/variant/2_21036690_T_C) and when examining the direction of the cis-eQTL, it was also opposite to what was predicted by aptamer-based cis-pQTL. The directional mismatch may be due to the cis-pQTL signal (rs1469513) being in linkage disequilibrium (LD r 2 = 0.
Trans-colocalizing target-trait pairs are informative of actionable therapeutic biology Separately, although there was limited evidence of enrichment of trans-associations in either of the datasets, when we examined the individual trans-associations, we uncovered several biologically plausible and informative target-trait associations. For example, there was evidence of genetic colocalization of the trans-association between CCL21 protein levels (encoded by CCL21 on chromosome 9) and total cholesterol level, with the signal located near the ACKR4 gene region on chromosome 3 (Figure  4 ). The ACKR4-CCL21 trans-association is biologically plausible given that the atypical chemokine receptor ACKR4 acts as a decoy receptor that sequesters and degrades chemokines like CCL21 controlling immune cell chemotaxis  28  and implicates the ACKR4-CCL21 chemotactic inflammation pathway in influencing cholesterol levels.
Discussion We investigated the relative contributions of proteomic MR and genetic colocalization, including their cis-trans status, to enrichment of target-trait pairs in gold-standard datasets. We provide empirical evidence demonstrating the importance of genetic colocalization with MR association to identify therapeutic targets. Although enrichment was limited to cis-target-trait pairs, when informed by biological mechanism and additional lines of evidence, the trans-colocalizing signals were biologically informative and we showcased three examples to illustrate their potential translational relevance. Two of the trans-colocalizing signals (nearest trans-gene: CFHR3, protein: fibulin-5, disease: AMD and FUT2, intestinal alkaline phosphatase, IBD) had no direct GWAS evidence and were not identified in previous proteomic MR-coloc studies.
First, by separately analyzing enrichments of MR-selected and coloc-selected target-trait pairs including by their cis-trans status, we were able to assess the relative contributions of proteomic MR and genetic colocalization to appraising therapeutic targets. Second, the use of larger gold-standard datasets enabled us to demonstrate stronger enrichment at progressively more stringent MR and colocalization thresholds. Third, we triangulated evidence supporting colocalizing target-trait pairs from other non-GWAS sources, identified colocalizing target-trait pairs that replicate in different protein or outcome datasets, and used protein-protein interactions to identify protein partners that are drug targets for the indication matching the colocalizing trait, all of which provides additional confidence to our results.
Fourth, we selected a larger number of outcome datasets agnostic to any specific therapeutic areas which enabled us to build a bigger list of colocalizing high confidence target-trait pairs than, for example Zheng et al (489 vs 270, using their stringent MR p-value cut-off 3. 5 x 10 -7 ) with only 13% target overlap with our dataset (Supplementary Table  13 ). Finally, we used expert assigned EFO codes to match colocalizing target-trait pairs with drug target-indication pairs as opposed to using a similarity matrix derived from MeSH headings in Zheng et al which can be sensitive to different cut-offs  2. However, we should also note a limitation from our end that the use of EFO to count unique EFO IDs will likely contain duplicate traits and miss related quantitative traits. Our study has several limitations.
Genetic associations of proteomic data We used seven publicly accessible proteogenomic datasets  9, [18] [19] [20] [21] [22] [23]  for the pan-/cis-MR (cis = +1Mbp from transcription start site) analyses and for performing genetic colocalization tests (described below). The pan-/cis-MR effects were expressed per standard deviation (SD) higher genetically predicted plasma protein concentrations. The genotyping protocols and QC of these proteomic studies have been described previously in the respective studies' publications. Annotation of protein-altering variants or variants in linkage disequilibrium (LD) at an r2 = 0. 5 was done using the TOP-LD tool  46.
Genetic associations of complex trait data We selected complex trait GWAS as outcomes if they had at least one genome-wide significant (p < 5 x 10 -8 ) locus, ensuring a systematic disease-agnostic trait selection strategy. This resulted in 3,766 traits, including traits from FinnGen (release 5), UK Biobank, and GWAS Catalog. A list of these studies are provided in Supplementary Table  1.
Harmonization of protein and outcome summary statistics We used genomic coordinates based on the GRCh38 genome assembly; where required, we lifted over genomic coordinates from the GRCh37 assembly to GRCh38. We also ensured that the effect allele in a GWAS locus is the alternative allele in the forward strand of the reference genome and used a strand consensus approach to infer strand for palindromic variants (variants with A/T or G/C alleles or variants with the same pair of letters on the forward strand as on the reverse strand). Details of the harmonization workflow and the strand consensus approach are described in our previous publications  6, 13  and documented with code in our GitHub pages (https://github. com/EBISPOT/gwas-sumstats-harmoniser; https://github. com/opentargets/genetics-sumstat-harmoniser).
Mendelian randomization To construct genetic instruments for MR analysis, we used genome-wide ('pan-MR') significant near-independent (r-squared < 0. 05) genetic instruments with no evidence of statistical heterogeneity and accounted for residual linkage disequilibrium (LD). The process of genetic instrument selection and MR analysis was automated using the generalized summary data-based Mendelian randomization (GSMR  47  ) approach with the heterogeneity-independent instrument (HEIDI)-outlier flag turned on). The GSMR software, using the HEIDI-outlier method, removes potentially pleiotropic instruments and accounts for the residual correlation between instruments (important as we are using near-independent genetic instruments). To select near-independent genetic instruments and account for linkage disequilibrium (LD) in the MR analyses, we used genotype data from 10,000 randomly sampled UK Biobank participants to create a reference LD matrix.
Genetic colocalization analysis The full analysis methods of genetic colocalization of cis-pQTLs have been described in our previous publications  6, 8. In brief, the full GWAS summary statistics of complex traits and cis-genetic regions of plasma proteins was used to identify conditionally-independent signals with genome-wide significance (p < 5e-8) using GCTA-COJO  48  ; pairwise genetic colocalization was carried out using the coloc R package  49  on the conditionally-independent summary statistics when at least one variant overlapped between credible sets for the two traits. For trans-MR associations, we used the full marginal summary statistics of each locus (not conditionally independent). The default priors in coloc were used, that is, the prior of a SNP (single nucleotide polymorphism)-trait association is 1 × 10 -4 , and the prior of a SNP associating with both traits is 1 × 10 -5.
Enrichment analyses Ensembl IDs for genes and experimental factor ontology (EFO) IDs for traits were used to merge data from the approved drugs dataset (https://platform. opentargets. org/downloads, v22. 11) and the GWAS gold standards (https://github. com/opentargets/genetics-gold-standards) with the target-trait dataset from MR and genetic colocalization analysis. For comparisons of MR and genetic colocalization with approved drug targets at different MR and colocalization thresholds, the 2 x 2 tables compared target-trait pairs that were phase 4 approved drug targets with any overlapping target-trait pair irrespective of their drug target status (Supplementary Table  11 ). For comparisons with the larger gold standard positive target-trait pairs, the 2 x 2 tables compared target-trait pairs that were gold standard positive target-trait pairs with any overlapping target-trait pair irrespective of their gold standard positive status (Supplementary Table  12 ).
The cis-colocalization only analysis was limited to cis-colocalizing target-trait pairs irrespective of MR significance. The cis-MR only analysis was limited to cis-MR associations without evidence of genetic colocalization (H4 < 0. 8). The Parent EFO IDs were used to map the traits in the case of approved drugs dataset and trait-specific EFO IDs were to map traits in the gold-standard positive dataset. Fisher's exact test was used to compute p-values.
Protein-protein interactions analyses Direct EFO terms were used for drug indications. To search for drugs targeting proteins that physically interact (henceforth 'partner') with each target under study, IntAct database was queried using a stringent threshold (molecular interaction score > 0. 42), which means robust support for physical interactions as reported by the database and used by others  3, 50. Self-interactions were not considered further for this analysis. When the partner drug had the same indication as the trait associated with the target under study, the name of the partner, the name of the drug and phase of clinical trial for the trait were annotated. To know if the target-trait pairs were supported by OT platform evidence, we annotated each with the direct associations scores from the OT platform and collated the data sources supporting the associations  24.
Data availability Summary data for both proteins and outcomes used for genetic analyses are publicly available from the GWAS catalog https://www. ebi. ac. uk/gwas/downloads/summary-statistics. Full results can be downloaded here: https://ftp. ebi. ac. uk/pub/databases/opentargets/publishing/mendelian_randomisation_results/ Filtered results (bxy_pval < 0. 0005 or coloc_h4_h3 > 1) can be browsed here: https://mk31. shinyapps. io/mr_app2/ Code availability https://github. com/opentargets/mendelian-randomisation.
The CM is based on the reconstruction of the ν e spectra from the main reactors' fuel isotopes ( 235,238 U and 239,241 Pu) cumulative β spectra. This method was first introduced by Schreckenbach et al. [3, 4]. Huber and Mueller (H. M. )  [5, 6]  have recently revisited this method, and they have used information from modern databases to improve the procedure of the CM. In particular, the approach of Mueller et al. [6]  is a combined method, it starts from the SM and corrects the remaining discrepancies with the CM. On the other hand, the reconstruction of ν e spectra with the SM is made by building such spectra from the contributions of the individual β decays of all of the fission products produced in reactors, then the ν e spectra can be obtained from these β decay spectra via the relation of conservation of energy  [7].
Acknowledgments We acknowledge the help and support of the IGISOL group in the measurements. This work has been supported by the  Ministry of Science and Innovation of Spain  grant No. PID2019-104714GB-C21 , the  Generalitat Valenciana Santiago Grisolía  program grants, the  SANDA  project funded under  H2020-Euratom-1. 1. grant No. 847552 , the  CNRS  challenge  NEEDS  and the associated  NACRE  project, as well as the  CHANDA FP7/EURATOM  project (Contract No. 605203 ), and the  CNRS/in2p3 Master projects Jyväskylä  and  OPALE.
Essential vegetation variables including leaf area index (LAI), fraction of absorbed photosynthetic active radiation (FAPAR) and fraction of green vegetation cover (FCover) are produced and distributed in the Copernicus Global Land Service. We describe here the algorithmic principles, consistency and improvements of GEOV2, Version 2 of LAI, FAPAR and FCover products derived from SPOT/VGT (1999-2013) and PROBA-V data (2014-2020) at 1 km resolution, as compared to the earlier version GEOV1. GEOV2 is based on neural networks first trained with CYCLOPES and MODIS products to estimate LAI, FAPAR and FCover from daily top of canopy reflectance. Temporal techniques are then applied to filter, smooth, fill gaps and get a composited value every 10 days. Results show that GEOV2 products keep a high consistency with GEOV1 (90% of residuals within ± max (0.
Introduction Vegetation state and dynamics play a key role in the global climate and biochemical cycles. The leaf area index (LAI) and the fraction of absorbed photosynthetic active radiation (FAPAR) are essential climate variables  (GCOS, 2022)  intervening within key processes, including photosynthesis, respiration and transpiration. These vegetation variables control the exchanges between the biosphere and the atmosphere, and they are crucial in several applications from climate and land surface modelling at the global scale to agricultural and forest management at the landscape scale  (Fang et al. , 2019a). The fraction of green vegetation cover (FCover) as seen from nadir is also crucial for partitioning contributions between soil and vegetation in land surface models. Over the last two decades, several LAI, FAPAR and FCover global datasets have been routinely produced from moderate resolution satellite data including MODIS  (Myneni et al.
S2 ). EBF identification corresponds to the EBF/non-EBF classification of the GEOCLIM dataset, based on the magnitude and seasonality of GEOV1 LAI time series  (Verger et al. , 2015). Since the neural networks were trained on VGT reflectance data, we applied a bandpass adjustment to PROBA-V (2014-2020) to get VGT like TOC reflectance and rescaled the PROBA-V neural network estimates to VGT ones.
5, 20%) for LAI and max(0. 05, 10%) for FAPAR and FCover. These levels correspond to the CGLS (2015) and GCOS (2011) accuracy requirements for LAI and FAPAR. • Statistical analysis between products: We compared GEOV2 products derived from SPOT/VGT with those from PROBA-V, GEOV1 and MODIS C6 products of the closest date over 3 km × 3 km support area over the LANDVAL sites (https://calvalportal. ceos. org/sampling)  (Fuster et al. , 2020). • Accuracy assessment: We validated GEOV2, GEOV1 and MODIS products against the ground measurements available in the DIRECT2. 0 (http://calvalportal. ceos. org/web/olive/site-descriptio n) database during the 2000-2017 period. The comparison was performed at 3 km × 3 km. The closest satellite product date to the field campaign within a maximum period of ± 15 days was used.
Statistical analysis The scatterplots between PROBA-V and SPOT/VGT GEOV2 over the LANDVAL sites during the overlapping period (2013/10/16---2014/ 03/31) show that the two products agree well (RMSD < 0. 2 for LAI and < 0. 03 for FAPAR and FCover). Additionally, the relationship is unbiased (Fig. 8a-c ). GEOV2 and GEOV1 LAI products show very good consistency for LAI lower than 4 (Fig. 8d ). For larger values of LAI, there is higher scattering between GEOV2 and GEOV1 and a systematic bias partially due to the noise in the data and to the different processing applied to EBF pixels for the two versions. GEOV1 is lower than GEOV2 for EBF pixels and produced wider distributions of LAI (Fig. S10 ). Indeed, it is more affected by noise and shows a negative bias due to cloud misdetections as compared to GEOV2 (Fig.
, 2021b). This may indicate some residual inconsistencies in the input TOC reflectance between PROBA-V and SPOT/VGT which transfer to the GEOV2 time series. The new collection of PROBA-V TOC reflectance  (Toté et al. , 2021) , soon to be released, may contribute to improve GEOV2 times series. Improving their long-term consistency is crucial for a better understanding of the trend of global LAI and the spatio-temporal patterns of carbon sink and source of ecosystems. GEOV2 highly improves GEOV1 in terms of product completeness (Fig. 4 ): <1% of missing data for GEOV2 compared to more than 70% and 80% of gaps for GEOV1 in the tropical forests and high latitudes in winter, respectively. If GEOCLIM climatology is available for a given pixel, GEOV2 products have no missing data because the CACAO method allows filling all the gaps in the time series.
, 2023). Further the relationship between FAPAR and LAI is asymptotic (Fig. S14 ), and, for LAI greater than 3, FAPAR saturates at around 0. 8-0. 9 and is relatively insensitive to changes in LAI. Fang et al. (2021a)  explored the relationship between vegetation biophysical variables and showed consistency between GEOV2 FCOVER product and MODIS C6 LAI except in few situations at northern latitudes higher than 50 • where the products have higher uncertainties. Further confrontation with continuous ground measurements are required to assess the realism of these global datasets. Yu et al. (2021)  validated several kilometric LAI global products using continuous field measurements of LAINet and showed that GEOV2 well reproduced the reference LAI in terms of magnitude and phenology whilst a TIP based product systematically underestimated high LAI values and showed a temporal mismatch with ground measurements.
This is a legacy effect since the CYCLOPES V3. 1 and MODIS C5 products used for the calibration of GEOV2 algorithm were developed from physical radiative transfer models which are not applicable over heterogeneous surface partially covered with water  (Xu et al. , 2020). Further algorithm improvements and/or semiempirical corrections are necessary to improve the global LAI, FAPAR and FCover products over the wetlands and flooded areas. Further validation and comparison with ground data are required in particular for FCover. The confrontation with DIRECT2 measurements showed that both GEOV1 and GEOV2 FCover have a systematic positive bias for intermediate values despite it is partially reduced for GEOV2 (Fig. 9 c, f ). Note however that the accuracy of ground based FCover estimates is partially compromised by the very limited footprint, low spatial resolution and sensitivity to exposure setting of traditional fisheye cameras which result in an underestimation of intermediate FCover ground-based estimates  (Yin et al.
Conclusions This paper presented the GEOV2 algorithm implemented operationally in the Copernicus Global Land Service (CGLS) for the generation of LAI, FAPAR and FCover Collection 1 km V2 products derived from SPOT/VGT and PROBA-V data at global scale from January 1999 to June 2020 at 1/112 • spatial resolution and 10-day frequency. The GEOV2 products are generated in two steps. The first step is based on neural networks trained on a combination of the existing CYCLOPES and MODIS products, that generate daily LAI, FAPAR and FCover estimates. The second step uses dedicated temporal smoothing and gap filling techniques to provide the final 10-day products and ensure consistency and continuity in the time series. We assessed the quality of GEOV2 products with due attention to the consistency and improvements with GEOV1.
GEOV2 products are consistent with GEOV1 at the global scale and meet CGLS and GCOS uncertainty requirements in 90% of cases for LAI, and 80% for FAPAR and FCover. GEOV2 showed a similar accuracy as GEOV1 for LAI and slight improvements for FAPAR and FCover as evaluated over the limited ground measurements available. In addition, GEOV2 highly improves GEOV1 in terms of product completeness and does not show any missing data thanks to climatological gap filling that ensures Fig. 8. of concurrent products over the LANDVAL sites between PROBA-V and VGT GEOV2 products during the overlapping 2013-2014 period (top), GEOV2 and GEOV1 (middle), and GEOV2 and MODIS C6 (MOD15A2H, bottom) during 2012-2015: LAI (left), FAPAR (middle) and FCover (right). The comparison is performed at 3 km × 3 km: i. e.
Acknowledgments This research was supported by the  European Commission  through the Framework Service contract no. 199494-JRC  and the  7th Framework Programme for Research  under grant agreement no. 218795  (geoland2 project). The GEOV2 and GEOV1 products were produced by  VITO  through the  Copernicus Global Land Service. We acknowledge  NASA  for the MODIS products, and  CEOS WGCV LPV  for providing DIRECT 2. 0 ground data. This study contributes to the  CSIC-PTI TELEDETECT.
Data availability The GEOV2 and GEOV1 products are available at the Copernicus Global Land Service data portal (https://land. copernicus. eu/global/products). The MOD15A2H products are available at https://ladsweb. modaps. eosdis. nasa. gov. The DIRECT2. 0 reference dataset is available at http://calvalportal. ceos. org/web/olive/site-description.
, 2017). This can undermine the understanding of plankton annual cycles and biogeochemistry. The conventional ocean color technology provides no information about the water quality parameters at night  (Hostetler et al. , 2018; Jamet et al. , 2019; Behrenfeld et al. , 2022). Space-borne ocean color observations using lidar have a great potential to provide complementary information to existing passive ocean color sensors  (Churnside, 2014; Hostetler et al. , 2018; Jamet et al. , 2019). Lidar is an active remote sensing technology and has been extensively used for atmospheric applications. However, it did not get a lot of attention from the ocean color community. Lidar technique can overcome some limitations of the passive ocean color observations: it can provide an enhanced temporal coverage including nighttime observations and increased spatial range including polar regions which offers a great potential expansion of the available datasets and possibility of furthering our knowledge of the polar oceans and its processes.
, 2022) , there are currently no space-borne lidars designed specifically for this purpose. Nevertheless, there have been studies that have utilized data from the CALIOP and ATLAS sensors aboard CALIPSO and ICESat-2 satellite respectively, which were not initially intended for ocean applications  (Behrenfeld et al. , 2013; Behrenfeld et al. , 2017; Lu et al. , 2014; Lu et al. , 2016; Hostetler et al. , 2018; Lu et al. , 2022). The Cloud-Aerosol lidar and Infrared Pathfinder Satellite Observations mission, abbreviated as CALIPSO, was jointly developed by NASA and CNES aiming to fill gaps in observation of aerosols and clouds globally. The measurements were collected with the intention of accurately getting information on the atmospheric extinction coefficient profiles, cloud height data, identifying non-spherical aerosol particles and their sizes, and discriminating water clouds from ice clouds  (Winker et al.
, 2006). The main sensor is the Cloud-Aerosollidar Orthogonal Polarization (CALIOP) which is near-nadir viewing lidar sensor with two wavelength polarizations at 532 nm and 1,064 nm. CALIOP data were used, for the first time, for ocean applications in 2007 and this work has provided the first global image of subsurface ocean with a lidar satellite  (Hu et al. , 2007; Behrenfeld et al. , 2013)  pioneered assessments of global ocean phytoplankton biomass (C phyto ) and particulate organic carbon (POC) using CALIOP particulate backscattering coefficient, b bp (532) estimates. Several following studies improved the data processing  (Lu et al. , 2014; Lu et al. , 2016; Lu et al. , 2021a; Lu et al. , 2021b; Behrenfeld et al. , 2022 ) and used the CALIOP b bp product for studying the polar regions  (Behrenfeld et al.