dataesr/scientific-paragraphs-categorization huggingface dataset:
This study represents a step forward for increasing confidence in LULUCF fluxes reported by countries while building an 665 upscaling framework that ensures greater consistency between country-level efforts and reporting, and the global carbon budget estimated by models. This consistency is crucial to building the necessary confidence in our monitoring and reporting systems to support investment in land-use mitigation and assess countries' collective progress under the Global Stocktake process towards the goals of the Paris Agreement. This study uses the maps of intact and non-intact forests as a proxy for unmanaged and managed forests, respectively, except for Brazil and Canada where the country maps of unmanaged and managed forests were 885 available. etc). A larger number of country-level data are included in Supplementary Table  1.
emissions due cultivation) to shifting cultivation) Organic soils Fluxes from organic Fluxes on Fluxes on Fluxes on soils in various land peatland from peatland from peatland from categories (forest land, external dataset external dataset external dataset cropland, grassland) (same as BLUE) Other managed Fluxes from lands not Fluxes due to Fluxes due to Fluxes due to land included in the above changes in non- changes in non- harvest in non- categories (e. g. forest land forest land forested land, all cropland, grassland, (conversions (conversions other wetland, between between conversions settlements,and croplands, croplands and (between non- conversion non pastures and other pastures, forested natural involving forests) lands) clearing and lands, croplands, regrowth of pastures, urban non-forest lands) ecosystems, changes in croplands/pastu res/harvesting) LULUCF net Sum of fluxes of the Sum of fluxes of Sum of fluxes Sum of fluxes of categories above, as the categories of the the categories reported in the above ('ELUC categories above ('ELUC Common Reporting net') above ('ELUC net') Format tables net').
Biallelic variants in ABCA4 cause Stargardt disease (STGD1), the most frequent heritable macular disease. Determination of the pathogenicity of variants in ABCA4 proves to be difficult due to 1) the high number of benign and pathogenic variants in the gene; 2) the presence of complex alleles; 3) the extensive variable expressivity of this disease and 4) reduced penetrance of hypomorphic variants. Therefore, the classification of many variants in ABCA4 is currently of uncertain significance. Here we complemented the ABCA4 Leiden Open Variation Database (LOVD) with data from ~11,000 probands with ABCA4-associated inherited retinal diseases from literature up to the end of 2020. We carefully adapted the ACMG/AMP classifications to ABCA4 and assigned these classifications to all 2,247 unique variants from the ABCA4 LOVD to increase the knowledge of pathogenicity.
For these individuals, it is important to know whether the variants they have are pathogenic. Currently, the pathogenicity of ABCA4 variants is critical as many gene therapy trials are under investigation, for which only individuals with biallelic pathogenic alleles are eligible. Moreover, as the carrier rate of pathogenic ABCA4 variants in the general population is relatively high  [6, 27] , carrier analysis is performed frequently to determine the risk for future offspring and in these cases, it is important to know whether identified variants are pathogenic. Databases such as the ABCA4-Leiden Open (source) Variation Database (LOVD) (https://databases. lovd. nl/shared/genes/ABCA4) and ClinVar (http://www. clinvar. com/), provide a wealth of information for ABCA4 variants including pathogenicity classifications.
However, there is discordance between databases. The ABCA4-LOVD reports that up to 85% of the variants could be pathogenic, whereas in ClinVar, ~40% of the variants are reported to be likely pathogenic, which might be because many novel variants of uncertain significance are reported in ClinVar. Both the ABCA4-LOVD and ClinVar also allow for variable classifications as they are submitter-reported. Therefore, due to the variety of classification methods, it is difficult to truly assess and compare the pathogenicity of different variants. A clear and broadly used pathogenicity classification system is the ACMG/AMP classification as described by  Richards et al. in 2015 [28]. This classification method incorporates information such as type of variant, cis/trans criteria, variant frequency, phenotype, functional studies, segregation and in silico predictions.
All information can be collected per variant and can easily be combined to a final pathogenicity classification with five tiers: Benign, Likely benign, Variant of Uncertain Significance (VUS), Likely Pathogenic or Pathogenic. Since it is consistently used worldwide, it allows easy interpretation and comparison of pathogenicity levels. In order to increase the knowledge on the pathogenicity of genetic variants in ABCA4, we collected all the published data on ABCA4-retinopathy cases that have been published up to 31 December 2020 and uploaded them into the ABCA4-LOVD. Here, we adapted ACMG/AMP classifications specifically for ABCA4 and applied them to all 2,247 variants present in the ABCA4-LOVD.
Methods We collected all papers published until 31 December 2020, which contain likely pathogenic ABCA4 variants in individuals with autosomal recessive retinopathy by searching the following search terms in PubMed: Reported variants were collected per patient as well as additional available data such as gender, type of vision impairment, ethnicity, geographical origin, age at onset, phenotype at onset, segregation data, consanguinity status and other remarks. The data were supplemented with data from 412 persons with ABCA4-retinopathy from PreventionGenetics (a division of Exact Sciences). All data have been uploaded into the ABCA4-LOVD (www. lovd. nl/ABCA4).
Published ABCA4-retinopathy cohort After the removal of likely duplicates, the collection of data contained variants from 10,391 likely ABCA4-retinopathy individuals, of which 3,411 were already reported in our 2017 study  [31]. The cohort contained 6,240 likely biallelic cases and 4,151 mono-allelic cases. Among these were 943 nonconsanguineous homozygous cases and 127 consanguineous homozygous cases. A total of 2,095 unique variants were identified in the cohort. All data were uploaded to the ABCA4-LOVD database www. lovd. nl/ABCA4. ACMG/AMP classification ACMG/AMP classifications were given to all variants from the published cohort as well as other ABCA4 variants from the ABCA4 LOVD (Table  S1 ) and were annotated with the predicted pathogenicity severity according to Cornelis et al. [27]  when available.
Null variants In total, 752 null variants were reported. Interestingly, 24 of these variants are still classified as VUS after the application of the ACMG/AMP classification system (Figure  2 ). Compared to other null alleles, the splice predictions for these 24 variants indicated that alternative in-frame splicing could occur or in-frame deletions were predicted. Furthermore, their occurrence in the dataset was too low to reach significance in the frequency analysis.
Frequency analysis In the frequency analysis, 856 variants reached significant enrichment in the likely biallelic dataset compared to the GAM gnomAD control dataset after correction for multiple testing with the Benjamini Hochberg method  [32]. Interestingly, two variants, c. 5603A>T and c. 4253+43G>A, were significantly enriched, but had an odds ratio close to 1 (1. 10 and 1. 49 respectively) without having 1 in the confidence interval. This is a smaller odds ratio than 3, which is generally considered by Richards et al. [28] as the minimal odds ratio for variants with a modest Mendelian effect size. This effect is likely due to the reduced penetrance of these variants. Furthermore, 1,125 variants were not enriched in the likely biallelic dataset but had an allele frequency below 0. 0001 in all gnomAD populations and therefore got 'PM2_Supporting' evidence.
It should be noted that in the collection of variant data, it was clear that c. 5603A>T was underreported in the data from before 2017, since it has recently been found to co-occur in cis with c. 2588G>C in individuals with ABCA4-retinopathy. Most c. 2588G>C occurrences in literature, however, do not mention a cooccurrence with c. 5603A>T. This means that the pathogenicity of c. 2588G>C may be overclassified here, while that of c. 5603A>T may be underclassified, especially since the pathogenicity of c. 2588G>C as the only variant in an allele is questionable based on allele frequency calculations in a large cohort  [26]. Similarly, the mild reduced penetrant variants c. 4253+43G>A and c. 769-784C>T, which show a splice defect in vitro  [16] , are classified as VUS and as likely benign, respectively.
A possible explanation for this is that deep intronic variants -other than the ones reported by  Braun et al. in 2013 [33]  -are underreported in literature, mainly because they were missed in most studies that used targeted ABCA4 sequencing or WES and additionally because their interpretation can be challenging. Therefore, they are less likely to reach significant enrichment in the dataset and may be excluded from the PM3 criterium in trans classification when they are not reported. This the difficulty of classifying frequent mild that show reduced penetrance. Therefore, relatively frequent variants occurring in patients harboring a severe variant in trans should be treated with caution and larger studies are necessary to identify additional reduced penetrance variants in ABCA4. Modifiers seem to play a role in ABCA4-retinopathies and may explain this reduced penetrance of variants.
Furthermore, several studies indicate that pathogenic variants in other genes can be responsible for ABCA4-retinopathy even when one Likely Pathogenic ABCA4 variant is present in the patient. Disease causing variants can sometimes be found in genes like PRPH2 and PROM1, but also in less common genes associated with ABCA4-like disease like BEST1, CDHR1, CERKL, CNGA3, CRX, ROM1, RPE65  [19, 21, 38]. There are some limitations to this study. The first one is associated with PS4, the allele frequency analysis. First, the GEM BAP control dataset used is based on the reported ethnicity, which may not correspond with the gnomAD population that they were matched with, since ethnicity is a social construct and gnomAD populations are for a big part based on principal component analyses  [39]  and it is unknown to what extent those overlap.
Second, for those patients without reported ethnicity, the GEM BAP gnomAD has incorporated estimated ethnicity based on population statistics. However, there may be a bias in the ethnicity of patients who are able to either afford healthcare, who have the option to take part in a study, or who feel safe to take part in a study since, for example, historic transgressions have been made against Black research participants  [40]. Finally, as labs that study ABCA4-retinopathy and report individuals with ABCA4-retinopathy are unequally distributed over the world, a bias in the genetics of subpopulations of those regions could occur. Therefore, it is likely that, to some degree, population stratification will have affected the results of the allele frequency test. both genetic and disease data of white individuals with ABCA4-retinopathy are overrepresented, creating an imbalance in understanding of the genetic cause of ABCA4-retinopathy and treatment options between these individuals and individuals of color with ABCA4-retinopathy.
In order to improve healthcare for everyone, it is therefore important that rare genetic variants in individuals with under reported genetic ancestry in literature are studied more to improve the knowledge on all genetic variants, and that variants with a high frequency difference between populations are investigated more closely to study their effect since differences in identified variants and numbers of ABCA4-retinopathy cases between different ethnicities have been reported  [41]. Moreover, based on the final classification, 298 total variants from the biallelic dataset (2. 3%) are likely benign or benign. This means that up to 298 cases from this dataset are not actually known biallelic, which might indicate that those cases are not actually ABCA4-retinopathy cases, which could create a bias in the enrichment analysis for the variants present in trans.
Another limitation of our approach is the underlying variant detection techniques used to identify variants. Especially older data may lack reporting of deep intronic variants that might either be missed as single alleles or in cis with reported variants. This underrepresentation of deep intronic variants may cause them to go unnoticed in the PS4 step. Furthermore, more comprehensive ABCA4 testing strategies are used in countries with good access to genetic testing, which means that new variants and deep intronic variants may be underreported in datasets from countries where access to these techniques is limited. Therefore, the absence of the PS4 category should not be interpreted as proof of a variant not being enriched in the dataset. Furthermore, the ACMG/AMP guidelines and recommendations warn for the use of functional studies.
We indeed encountered that a variant, c. 4539+2028C>T, which likely is a pathogenic variant based on genotype-phenotype correlations  [33] , shows a higher percentage of WT RNA in patient-derived retinal-like cells than expected  [42]. We decided to remove this data point from the dataset as an outlier in the classification based on functional studies in step BS3. In addition, other variants, e. g. , c. 4539+2001G>A and c. 1937+435C>G, show a relatively high percentage of WT RNA production, 75% and 55% respectively, in patient-derived retinal-like cells  [42]  and midigene assays respectively  [16] , while genotype-phenotype correlations show that these variants are likely pathogenic  [33, 43]. This indicates that other variants could show a similar pattern, meaning that intronic variants causing a relatively high amount of WT RNA, may nevertheless be pathogenic.
Finally, the ABCA4 variant data set analyzed here mostly stems from 421 peer-reviewed publications as well as data from PreventionGenetics (a division of Exact Sciences). In the future, it would be very valuable to also include variant data collected in many academic and non-academic diagnostic centers worldwide. This will be challenging as privacy rules may prevent data sharing and different privacy rules in different countries likely create a bias in the data. Furthermore, data currently existing in different online databases may show overlap and are not all curated. With the advent of novel therapies, it is essential to have an accurate genetic diagnosis, which emphasizes the importance of the classification of variants and proper guidelines. ABCA4 variant classification is challenging due to the higher variant frequency, presence of complex alleles and hypomorphic variants with reduced penetrance.
Elfride De Baere ,  Frans P. M. Cremers , and  Miriam Bauwens  were supported by  EJPRD19-234  ( Solve- RET ). Elfride De Baere  is a  Senior Clinical Investigator  of the  Research Foundation-Flanders (FWO)  ( 1802220N ). Elfride De Baere  and  Frans P. M. Cremers  are members of the ERN-EYE consortium, which is co-funded by the  Health Program   of the European Union  under the  Framework Partnership Agreement  [grant number  739534-ERN-EYE ]. Elfride De Baere  was supported by grants from  Ghent University Special Research Fund  ( BOF20/GOA/023 );  FWO  research project  G0A9718N ,  Foundation JED ,  Foundation   John W. Mouton   Pro Retina. Robert B. Hufnagel  is funded by intramural funds at the  National Eye Institute ,  National.
The latest IPCC report describes an increase in the number and intensity of marine heatwaves (MHWs) and a decrease in marine cold-spells (MCSs) in the global ocean. However, these reported changes are not uniform on a regional to local basis and it remains unknown if coastal areas follow the open ocean trends. Ocean temperature measurements collected by satellites (from 1982-2022) and 13 coastal buoys (from 1990-2022)  are analyzed in the Northeast Atlantic and three subregions: English Channel, Bay of Brest and Bay of Biscay. The activity metric, combining the number of events, intensity, duration and spatial extent, is used to evaluate the magnitude of these extreme events. The results from in situ and satellite datasets for each of the studied regions are quite in agreement, although the satellite dataset underestimates the amplitude of activity for both MHW and MCS.
The use of in situ observation is limiting the length of the analyzed time series. However, we can observe larger activity in recent years from both datasets. For most cases, similar most active years are detected with in situ observations and satellite data. Considering coastal stations over the observed periods, we see a more pronounced increase in MHW activity from 2010. The English Channel and the Bay of Biscay in situ stations highlight the year 2022 as the most active year exceeding 140 °C. days. In the Bay of Brest, the impact of the 2022 MHWs is less pronounced, in agreement with OISST observation (Figure  2b ) due to tidally driven vertical mixing. The three most active MCS occur in winter 1987 (-24 °C. days. 10³. km²), 1986 (-18 °C. days. 10. km²) and 1994 (-13 °C. days. 10³. km²).
Coastal MCS activity Along the coasts, MCS activity as determined by local buoys remains weaker than MHW activity. As for the MHWs, MCS intensity is slightly underestimated in satellite observations but evolutions are similar. From in situ observations from coastal stations, two years can be highlighted due to their intense MCSs: 2006 and 2010 (Figure  7 ). The year 2010 is the most intense, in terms of MCSs. The mean activity is reaching -100 ºC. day in the Bay of Brest and around -60 °C. day in the Bay of Biscay and the English Channel. In 2006, the activity was also important compared with other years: around -80 °C. day in the Bay of Brest and around -50 ºC. day in the English Channel. This extreme year 2006 was also unique with a peak in MHW activity during the summer (Figure  4 ). Before the year 2000, only observed in the English Channel from coastal stations, three other years reveal intense  MCS activity: 1997 MCS activity: , 1991 MCS activity: , 1994  (from the most intense to the less active winter) from OISST data at the closest point of in-situ data.
In the Bay of Biscay, we see a linear warming rate in summer since the beginning of the studied period. This is in accordance with  DeCastro et al. (2009)   The results from in situ and satellite datasets for each of the studied regions are quite in agreement, albeit the satellite underestimates the amplitude of activity for both MHW and MCS. Conversely,  Izquierdo et al. (2022a)  found an overestimation of the MHW using satellites compared to in situ in the coastal upwelling region South of the Bay of Biscay, which might be related to local processes. The satellite's coarse resolution mostly (i) smoothes small-scale and short events and (ii) interpolates with offshore regions, having greater thermal inertia  (Marin et al. , 2021)  which can lead to the overestimation of the duration of events and the underestimation of the intensity.
For both MHWs and MCSs, the mean intensity shows only weak changes over the last four decades. Moreover, we found that the satellite dataset used is in good accordance with in situ data in the Northeast Atlantic, except for the fact that satellites underestimate the amplitude of both hot summer and cold winter marine extreme events in the coastal areas. The implemented in situ stations appear as a well-designed observing system to detect the long-term evolution of MHW and MCS activity and to document local features related to coastal hydrodynamics. MHWs activity is particularly high in 2018 and 2022 through two different situations. The year 2018 is characterized by a large extent of MHWs in the Bay of Biscay with long events in the South of the Bay and intense events in the Armorican Shelf.
Acknowledgements This work was partially supported by national funds through  FCT (Fundação para a Ciência e a Tecnologia, Portugal)  through project  ROADMAP  ( JPIOCEANS/0001/2019 ). It is also funded by the regional project ( Contrat Plan Etat-Region)   ObsOcean/ROEC-ILICO  and the regional  COXTCLIM  project funded by the  Loire-Brittany Water Agency , the  Brittany region , and  Ifremer. We thank  Oregan Segalen  for fruitful discussions. We thank  Tim Smyth  for providing data from Western Channel Observatory. We acknowledge the COAST-HF (http://www. coast-hf. fr) national observing network component of the  National Research Infrastructure ILICO.
| Questionnaires (Patient-Reported Outcome Measures [PROMS]) Questionnaires for Health-related Quality of life (QoL) evaluation and questionnaires assessing asthma control form an essential part of the medical evaluation to assess the impact of a given disease on a patient. PROMs can be captured by standardized/validated questionnaires which are easy to use tools to collect data and serve as endpoints in both clinical trials and daily practice. 62  Asthma Control Questionnaire (ACQ)-5, Asthma Control Test (ACT) and Asthma Quality of Life Questionnaire (AQLQ) and Paediatric Asthma Quality of Life Questionnaire (PAQLQ), evaluate disease control and functional status, as well as physical, occupational, emotional and social aspects in patients with asthma. 63, 64  The visual analogue scale (VAS) is a psychometric test widely used to measure the perception of symptom severity and disease control in patients with allergic rhinitis.
As longer treatment periods are evaluated realistic responder profiles are revealed including possible effects on comorbidities. 113  Prospective observational effectiveness studies as well as database cohort analyses provide valuable new insights often not retrievable by RCTs, provided that quality criteria are carefully predefined and followed. 114  The same methodological standards as for RCTs need to be applied as determined in the consort statement, however without randomization. Reviewed the real-world evidence (RWE) on immunotherapy effectiveness in respiratory allergies from 14 evaluable studies concluded that six studies revealed unique information not retrievable from RCTs in terms of long-term effects up to 20 years after treatment discontinuation, new sensitizations, asthma development and symptom and medication use reduction, respectively.
114, 115  None of the studies evaluated, met all of the quality criteria set as a prerequisite for guideline development with 11 out of the 14 showing lacks in results reporting including confounders. In the REACT study, a retrospective cohort analysis, data from a German Health Insurance database from 2007 to 2017 were analyzed to obtain an estimate of AIT treatment effects in patients with AR with and without asthma. 27  It has be shown that AR and asthma medication prescriptions, including both asthma controller and reliever, were reduced as compared to the matching control groups. In the AIT group, a significant reduction of asthma exacerbations next to asthma treatment reduction was demonstrated as well as improvement in associated factors like pneumonia with antibiotic prescriptions, hospitalizations and duration of inpatients stays.