dataesr/scientific-paragraphs-categorization huggingface dataset:
) and individual FORMAT (GT:AD:DP:GQ) annotations allowing users to further filter the dataset according to their needs. This VCF file has also been deposited at the European Variation Archive 76 (Project Accession PRJEB59912  77  ). Sample metadata is also available from the AmelHap community  78  , where revisions will be uploaded as the resource grows. The genetic maps used for imputation and example code to process sequence data, filter variants, and generate plots is available at https://bitbucket. org/gibberwocky/amelhap.
technical Validation Genotype imputation. One of the key values of a HapMap is its utility for imputing missing genotypes. To evaluate AmelHap in this regard, we tested its imputation performance on a subset of drones and an independent diploid dataset. For the imputation of a subset of haploid drones, we first subset and filtered AmelHap to retain bi-allelic SNPs which left more than 11 M SNPs positioned along the 16 linkage groups. Imputation analyses were conducted with SHAPEIT 79 v4. 2 (-sequencing) using genetic maps generated by Wragg et al. 8. We generated a "truth" dataset by self-imputing missing genotypes in AmelHap. Then, we randomly selected for imputation 100 unrelated samples (see metadata  57  ). Each sample was extracted from AmelHap and processed independently with bcftools + prune to randomly remove N SNPs in 1 Mb windows (-w 1000000 bp -n ${N} -N rand).
We repeated this for N = 12. 5 K, 25 K, 37. 5 K, 50 K, 62. 5 K, 75 K, 87. 5 K, 100 K, 250 K, 500 K, and 750 K SNPs. For each N, the pruned samples replaced their equivalent selves in a copy of the AmelHap truth dataset and self-imputation was performed. We then calculated genotype concordance by comparing the imputed versus truth genotypes with Fig. 2  Identity by state (IBS) clustering and admixture estimates validate reported sample origins and relationships. Based on a dataset of 660 K variants after filtering on minor allele frequency (MAF) >1% and linkage disequilibrium (LD) r 2 < 0. 1 for variants up to 10 kb apart. We observe a median IBS of 0. 87 ± 0. 02 across all drones. Hierarchical clustering indicates three dominant clusters, corresponding to samples with reported ancestry from the A, C and M lineages.
We next sought to evaluate its effectiveness for genotype imputation in diploids. A dataset of 139 diploid worker honey bees 7 comprising samples from 13 subspecies representing 4 evolutionary lineages (A, C, M and O) was sourced from the CNGB Sequence Archive (CNSA) of the China National GeneBank DataBase (CNGBdb accession number CNP0001986  80  ). The sequence data was processed to generate gVCF files  81, 82  as per the drone data with a single exception -variant calling was performed with-sample-ploidy 2. Joint calling of this dataset included AmelHap as the target intervals (GATK GenotypeGVCFs-intervals). Each sample within the diploid dataset was processed independently with bcftools + prune to randomly remove N SNPs in 1 Mb windows (-w 1000000 bp -n ${N} -N rand). We repeated this for N = 10 K to 100 K SNPs, in 10 K increments.
For each N, the pruned samples were merged with the AmelHap truth dataset and self-imputation was performed. We then calculated genotype concordance in the diploids by comparing their imputed versus original genotypes with bcftools stats. While not as effective at imputation in the diploid dataset, >95% genotype concordance is achieved when up to 10% of data is missing (Fig. 4a ). To establish if the reduced performance was a consequence of the data structure (i. e. homozygotised haploid genotypes) or the software, we repeated imputation on the same pruned data with Beagle 83 v5. 4, using the pruned data as the target panel and AmelHap as the reference panel, and evaluated the results in the same manner. Imputation with Beagle returned >95% genotype concordance when up to 12% of data was missing and performed substantially better than SHAPEIT at higher levels of missingness (Fig.
mellifera which is well represented within AmelHap. 3  Identity by state (IBS) distributions delineate sample relatedness and evolutionary divergence between unrelated drone pairs. Based on a dataset of 660 K variants after filtering on linkage disequilibrium (LD). Drones were considered siblings if they were reported to have been sampled from the same colony. Dashed red line indicates lower whisker of drone sibling IBS values (IBS >0. 914). A number of outliers of unknown relatedness fall within the sibling IBS range (blue circles). These outliers predominantly derive from colonies within a selection programme or were sampled from small, isolated populations which would explain their high genetic identity. Two dominant peaks within the distribution of samples of unknown relatedness are evident, which broadly correspond with evolutionary divergence: (1) the majority of drone pairs (54.
9%) are between-lineage comparisons; (2) the majority of drone pairs (83. 6%) are within-lineage comparisons. 2 ) diploids (orange) imputed using SHAPEIT (orange), and (3) diploids imputed using Beagle (red). Haploid results are based on imputing 100 unrelated drones within AmelHap. For each of the 100 drones, N variants were randomly pruned in 1 Mb windows, with N ranging from 12. 5 K to 750 K SNPs. Following self-imputation with the AmelHap data, genotypes were compared against the truth dataset. Diploid results are based on imputing 139 unrelated workers. For each worker N variants were randomly pruned in 1 Mb windows, with N ranging from 10 K to 100 K SNPs. Following self-imputation with AmelHap, imputed genotypes were compared against the original genotypes. The dotted horizontal line indicates a 95% concordance threshold.
Each point represents concordance data for a single sample and chromosome from one of the pruned genotype sets. A localised regression (loess) trend line is presented across each imputed dataset, with intercepts at y = 0. 95 of x = 0. 61 for haploids, x = 0. 10 for diploids imputed with SHAPEIT and x = 0. 12 for diploids imputed with Beagle. (B) Genotype average depth versus call rate (see metadata 57 filter 3 variants) indicates that sequencing to >3X genotype depth of coverage typically results in >90% call rate. For clarity the y axis has been limited to a maximum depth of 5, resulting in data for 1063 samples being excluded from the plot -of which 56 had a call rate <90%.
Usage Notes We have demonstrated that by using AmelHap to impute high levels of missing data (61%), very high genotype concordance ( > 95%) can be achieved in drones. We also demonstrate the resource to be effective at imputing moderate levels of missing data (12%) in an independent diploid dataset. We have not extensively investigated the parameter space for imputation, or the full range of tools available, and so further improvements on imputation performance are likely achievable. Moreover, restricting the reference panel to lineages relevant to the target panel may further increase accuracy. Based on the AmelHap filter 3 metrics (see metadata  57  ), sequencing to at least 3X coverage should achieve a 90% call rate (Fig. 4b ), demonstrating that AmelHap will be a useful resource for genotype imputation in low pass whole-genome sequencing.
Beyond supporting phasing and genotype imputation, AmelHap can serve as a valuable reference to support studies of population genetics, including hypothesis testing on population differentiation and the evolutionary history of the species. It will also be an important resource for assay development in relation to the identification of ancestry-specific markers. There remains an ongoing need to improve on the limited genomic resources for the species. As has been demonstrated in other species, increasing the sample size and diversity of AmelHap will yield improvements in genotype imputation  84, 85. As such, we aim to continue to develop the resource and have provided details on a Zenodo community for AmelHap (https://zenodo. org/communities/amelhap/about/) to enable researchers to easily replicate the sample processing steps, to generate a haploid gVCF file from raw fastq data, using the Nextflow sarek workflow (https://nf-co.
code availability Code underpinning the pipeline (BAGPIPE) used for aligning sequence data and calling variants BAGPIPE is available at https://bitbucket. org/renzo_tale/bagpipe/. Detailed code outlining the various analyses and parameters is available without restriction at https://bitbucket. org/gibberwocky/amelhap. All analyses have been performed with freely available software. These include: BWA-MEM v0. 7. 17; SAMtools v1. 9; GATK v4. 0. 11. 0; vcftools v0. 1. 13; bcftools v1. 13; Plink v1. 90p; R 87 v4. 1. 3; R packages (vcfR  88  , alstructure, ggplot2, ggpubr, ggdist, tidyverse, readxl, reshape2, ComplexHeatmap, circlize, paletteer); and SHAPEIT v4. 2. With the exception of plotting data, all analyses were conducted on the Edinburgh Compute and Data Facility (ECDF), a high performance computing cluster (HPC) running a Linux operating system.
Milk Analyses and Database The monitoring of FP using reference method ISO 5764|IDF 108:2009 was achieved with Cryostar© (automatic I Funke Gerber, Berlin, Germany). All milk samples (bulk daily and individual weekly) were analyzed via the Express method (EM) using the automatic milk analyzer Milkoscan© (FOSS-FT1TM, Hilleroed, Denmark) calibrated for camel milk. The analyzed parameters were fat, protein, lactose, density, total solids, total non-fat, citric acid, acidity, free fatty acids and freezing point. In addition, pH and titratable acidity were determined by reference methods  [17, 18]. The monitoring of FP was achieved with the milk being processed into pasteurized milk, leben and cheese; the analyses also involved the milk after pasteurization, the leben and the whey. At the same time, the samples were analyzed for microbiological value according to the protocol described in Al-Rasheedi et al.
(2015)  [14] : milk samples (0. 01 mL) were streaked on Plate Count Agar (IDF 100B, 1991) for total flora and VRBL Agar; (IDF 73B, 1998) (Merck KGaA, Darmstadt, Germany) for coliforms. The plates were then examined for colony counting. All the data were recorded regularly on a monthly sheet, then input into the farm database written under Access© described in Al-Samghan et al. (2015)  [19].
Variance analysis with co-variable "parity" and "month" (ANCOVA procedure) was used on this partial table to test the breed and month effect. For assessing the microbiological effect, the values in coliforms and total flora were classified in groups as "very low values" (<10 2 ), "low values" (10 2 to <10 3 ), "medium values" (10 3 to <10 4 ), "high values" (10 4 to <10 5 ) and "very high values" (<10 5 ). The relationships between RM and EM data were assessed by using Pearson's correlation. Before submitting data to ANOVA, the normality was tested by using the Shapiro-Wilk and Anderson-Darling tests. All distributions being normal, no transformation of the data occurred when applying ANOVA. The software XLstat (Addinsoft ©, 2022, 5. 1) was used for all statistical analyses.
Correlation/Standardization of FPD Determination by RM and EM Based on 680 milk samples for which RM was determined, the mean value was -0. 332 ± 0. 030 • C (range -0. 260-0. 478). Calculated from the 736 milk samples (individual, mixed and pasteurized milk), the mean value of EM was -0. 523 ± 0. 064 • C (range -0. 331-0. 695). However, in the farm database, 219 samples of raw mixed fresh camel milk only were simultaneously analyzed by the 2 methods. The mean value of RM in this sub-sampling was -0. 321 ± 0. 04, while the mean value for the same samples analyzed using EM was -0. 544 ± 0. 034. Thus, on average, FPD values measured with RM were higher than with EM. The correlation between RM and EM achieved on the 219 raw mixed fresh camel milk samples was positive and significant (r = 0. 341; p < 0.
0001). The equation of regression was Y = 0. 0439X -0. 2974, where X was EM data (Figure  1 ). Regarding pasteurized milk, 115 samples were simultaneously analyzed. The mean RM value was -0. 321 ± 0. 006 while EM value was -0. 571 ± 0. 019. The correlation was significant (r = 0. 318; p < 0. 001). The equation of regression was Y = 0. 0094X -0. 3161 with the same parameters as the above (Figure  2 ). By taking in account all the valuable samples (raw, pasteurized, for cheese making) analyzed simultaneously using the two methods (r = 0. 172; p < 0. 001), the equation of regression was Y = 0. 6913X -0. 3476.
Correlations with Milk Composition Relationships between FPD values and milk composition were explored for individual data based on EM, and for mixed data based on both RM and EM methods. Regarding individual raw camel milk, EM was positively and significantly correlated (p < 0. 001) with all the physico-chemical parameters measured by FOSS-FT1 milk analyzer (Table  1 ) except for free fatty acids (FFA). The highest correlations were observed with lactose content (r = 0. 684, p < 0. 0001) and non-fat solids (r = 0. 864, p < 0. 0001) (Figure  5 ). Regarding bulk camel milk, FPD by EM was also positively and significantly correlated (p < 0. 001) to all the components of the milk (Table  2 ). The highest correlation was observed with lactose as for individual samples (r = 0. 683; p < 0. 0001).
320 ± 0. 007 vs. -0. 321 ± 0. 007). The correlation was highly significant (r = 0. 544; p < 0. 0001). Our data base included the composition of different types of milk (individual or mixed raw milk, pasteurized milk, mixed raw milk for cheese making, whey and fermented milk-leben). FPD was determined in most of the cases, particularly showing a slightly lower value for whey and overall for leben (Table  6 ). As for individual data, a slight negative but significant correlation (r = -0. 095; p < 0. 05) occurred between FDP by EM of milk mixed at each milking and the milk production. Freezing point increased slightly when the total production increased (Figure  9 ). A reverse relationship was observed between milk production and FPD by RM, with a slight tendency to observe a decrease of FPD value in bulk milk with the milk production (Figure  10 ).
320 ± 0. 007 vs. -0. 321 ± 0. 007). The correlation was highly significant (r = 0. 544; p < 0. 0001). Our data base included the composition of different types of milk (individual or mixed raw milk, pasteurized milk, mixed raw milk for cheese making, whey and fermented milk-leben). FPD was determined in most of the cases, particularly showing a slightly lower value for whey and overall for leben (Table  6 ).
Study area and cropland distribution China has 177 million hectares of cropland area, and the area is distributed throughout nine agricultural districts, including Northeast China Plain (NCP), Yunnan-Guizhou Plateau (YGP), Northern arid and semiarid region (NASR), Southern China (SC), Sichuan Basin and surrounding regions (SB), Middle-lower Yangtze Plain (MYP), Qinghai Tibet Plateau (QTP), Loess Plateau (LP) and Huang-Huai-Hai Plain (HHP). The spatial distribution of the agricultural districts can be found in Fig. S1. Cropland distribution data are downloaded from the Resource and Environment Science and Data Center (https://www. resdc. cn/data. as px?DATAID=99). The dataset provides the spatial distribution of croplands at 1 km resolution for 2010. Cropland is further subdivided into PF and NPF.
Applications and implications This study investigated the spatial-temporal variations of annual and seasonal ET, GPP and WUE in PF and NPF of China during 2000-2021, some suggestions for future work need to be identified. The first is that the uncertainty of data quality is a noteworthy problem. The MODIS products are greatly affected by the algorithm performance, which depends on the accuracy of vegetation cover, meteorological data, etc. ,  (Mu et al. , 2007; Turner et al. , 2006). Most of the researchers confirmed that the ET values based on MODIS performed well based on EC flux data and grid evaluation  (Velpuri et al. , 2013). However, for MODIS GPP, although some researchers reported the data was sufficiently accurate by comparison with the site measurement and other datasets (NASA Bigfoot) in cropland, the underestimation results were found compared with EC in cropland due to different photosynthetic capacity between C 3 and C 4 species.
Therefore, more accurate species-distribution products are urgent to estimate regional and global GPP  (Yuan et al. , 2015). In addition, ε max in the algorithm of MODIS GPP remains constant for each vegetation species, it is highly variable between the different species, especially between C 3 and C 4 species  (Turner et al. , 2006). Thus, further improvement in the GPP algorithm and its parameters is necessary for accurate GPP estimates. We used a static cropland map for the year 2010 to quantify cropland extent. The static map cannot describe the cropland dynamic during 2001-2021, which may partially affect the conclusions drawn. We further assessed the cropland dynamic from 1985 to 2015 using the same land cover map from the Resource and Environment Science and Data Center. We found the cropland area shows little change from 1985 to 2015, ranging from 177 to 179 Mha (Fig.
Acknowledgments We are grateful to the  Distributed Archive Center of Oak Ridge National Laboratory , and the Earth Observing System Data for making MODIS data available. This work was also financially funded by the  National Key Research and Development Program of China  ( 2021YFD1600803-1 ),  Fundamental Research Funds for the Central Universities  ( YJ202259 ), and the  Sichuan Province Science and Technology Support Program  ( 23ZDYF3117 ,  2022YFN0021 ).
Data and conceptual scheme Net erosion, also known as sediment yield, is the sum of sediment produced by all sources of soil erosion, including from splash erosion, overland flow and runoff, and stream channel areas, minus the amount of sediment deposited in these areas and in valley floodplains, which cross the outlet of a river basin (e. g. Toy et al. 2002). For this study, we have referred to the rainfall and net erosion data provided by the Regional Agency for Environmental Protection of Basilicata (ARPAB, Centro Funzionale Protezione Civile, http:// centr ofunz ional ebasi licata. it). For the BRB, net erosion data are available for the years  1950-1958 and 1961  (Annals Project of the former Hydrographic and Mareographic National Service, 1957-1972, https:// www. ispra mbien te. gov.
it/ it/ proge tti/ carte lla-proge tti-incorso/ acque-inter ne-e-marino-costi ere-1/ proge tti-concl usi/ proge tto-annali), which were used for the calibration of the erosion model. The Irsina rainfall station (Fig. 2b ), located at 548 m a. m. s. l. , is the only one in the basin area that provides a continuous daily rainfall time-series for a long period, i. e. 1950-2020, which was used for model calibration and reconstruction of net erosion. Monthly data of vegetation cover are not available before 1981. Until 1980, the monthly vegetation cover percentage (VCP) was parameterized annually on a constant basis, with a monthly variation from January to December. The vegetation cover data up to 1980 were thus obtained by calibration of the model. Subsequently (until 2020), variable values were obtained over the BRB based on actual Normalized Difference Vegetation Index (NDVI) data from NOAA Climate Data Record (Vermote 2019), provided by Climate Explorer at a resolution of 0.
2002). This fraction (%) was set constant until 1980, and subsequently variable, until to 2020, accordingly with the NDVI actual data as described above. As canopy cover reduces the erosivity of raindrop for detachment, it also reduces interrill sedimenttransport capacity. The last component of the model is the sediment delivery ratio (SDR), which is the sediment yield from the catchment area divided by the gross erosion (GE) of that same area. Expressed as a fraction, it represents the efficiency of the catchment in moving soil particles from areas of erosion to the (1) REDEM (BRB) = A • R S + R Q • e -0. 07•VCP • SDR point where sediment yield is measured. Including SDR as an explicit variable in the model allows for the consideration of factors other than vegetation cover that influence runoff and its peak, such as basin shape, relieflength ratio, soil texture, the transport process and the gradient of the main channel slope (e.
The precipitation data used to generate the graph were derived from E-OBS v23. 1e gridded dataset (https:// surfo bs. clima te. coper nicus. eu// dataa ccess/ access_ eobs. php# datafi les) via Climate Explorer measures on the spatio-temporal scale of the processes in the BRB is an unstable equilibrium, where events close to moderate-extreme storms can easily determine unsustainable conditions in soil loss in a long-term decline picture. This is the case of the period 2000-2020, when extreme autumn precipitation events and winter overland flow present higher values (Fig. 7 ). However, human intervention (e. g. channelization, arable land contraction) and the increase of natural vegetation appear to produce a counteracting effect  (Romano and Cozzi 2007). This is also in agreement with the decrease in landslides and floods in the period 1951-2000 found by  Clarke and Rendell (2006)  for the Basilicata region.
Study patients and design For this retrospective single-center study, we searched our institutional database to identify patients who underwent endovenous cyanoacrylate ablation to treat great or small saphenous vein insufficiency at the Dijon University Hospital (Dijon, France) between December 2018 and March 2021. Institutional review board was not required for this study due to its retrospective nature, but our ethics committee approved the study. For this single-centre cohort study, the requirement for patient informed consent was waived by the ethics committee in compliance with French law on retrospective studies of deidentified health data. The inclusion criteria were age 18 years or older and superficial insufficiency of the great or small saphenous vein demonstrated by Doppler ultrasound with the compression-release maneuver and/or Valsalva maneuver in the standing position.
Data collection The study data were collected by review of the medical charts, including the preprocedural and postprocedural ultrasound reports and procedural reports. We used standardized forms to record the clinical and ultrasound data collected before the procedure, including the Clinical, Etiological, Anatomical, Pathophysiological (CEAP) class. The same data were collected for the follow-up visits seven days then one, three, six, and 18 months after the procedure. At each visit, bruising at the access site was sought by physical examination and vein recanalization and deep vein thrombosis by ultrasound. At baseline and at the 18-month visit, each patient completed two quality-of-life questionnaires, the 36-item Short-Form survey (SF-36, and the VEINES-QOL/Sym questionnaire with help from the physician if needed.
The limitations of our study include the retrospective design, which carries a risk of information bias. It includes a relatively small sample size at a single center. Our study was observational, and endovenous cyanoacrylate ablation was not compared to other treatment methods. The recruitment at a single university hospital may have induced selection bias. A single experienced operator performed all the procedures. No standardized assessment tools were used to measure pain intensity or bruising. Finally, 8 patients were unable to complete the 18-month questionnaire at the 18-month visit and their data were collected using less reliable methods. Moreover, a formal cost-benefit analysis has not been performed, especially since the product remains expensive and is not reimbursed by the French social security system.