dataesr/scientific-paragraphs-categorization huggingface dataset:
Farm sizes were sampled from a truncated Poisson distribution with means equal to 1, 2, 5, and 20 (average farm size). Farm effects followed a normal distribution with zero mean and a farm variance equal to 3 times the additive genetic variance. We set σ σ f g 2 2 3 = based on the estimates obtained by Al  Kalaldeh et al. (2021)  and other studies in smallholder contexts where the farm or environmental variance is much larger than the additive genetic variance. We used 3 values for the heritabilities (h 2 = 10%, 20%, and 50%) for a total of 12 scenarios. For each scenario, we generated 50 simulated datasets, giving a total of 600 simulated datasets. Breed effects were not incorporated in the simulations. Model estimation for the simulated data used Markov chain Monte Carlo (MCMC) chains of 5,000 iterations, 2,000 burn-in, and 5 thinning.
Real Data Application For the real data application, we fitted a BayesR model with random farm effects (model 1, Equation  1 ), plus another model that added production environment (low, medium, high) as fixed effect (model 2). Model parameters for the real data application were estimated using 3 MCMC chains each with 25,000 iterations, 5,000 burn-in, and 5 thinning. This means that (25,000 -5,000)/5 = 4,000 iterations per MCMC chain were used for inference. The MCMC convergence was assessed using the multivariate version of the Gelman-Rubin diagnostic  (Gelman and Rubin, 1992). Chains were combined and visualized using the R package coda  (Plummer et al. , 2006). Functional annotation of markers with the highest posterior inclusion probability (PIP) in the BayesR model was carried using the Variant Effect Predictor tool from Ensembl, release 107  (Ensembl, 2022).
Note that it can also accommodate fixed effects other than the overall mean m. The model was fitted using the open-source software GCTB  (Zeng et al. , 2018) , available at https: / / cnsgenomics. com/ software/ gctb. To compare the performance of the proposed Bayesian model, we also estimated GBLUP and BayesC models in Julia v1. 6. 2  (Bezanson et al. , 2017)  using the package JWAS  (Cheng et al. , 2018). All models were run in a Linux server (Intel Xeon 6130, 2. 10 GHz, and 314 GB of RAM).
DISCUSSION We demonstrated by simulations that it is possible to separate genetic effects in smallholder farms using a flexible Bayesian model that simultaneously accounts for nongenetic random effects for farms and varying SNP variance. The model also provides a good way to produce interim GEBV for candidates or newly geno-typed animals directly using estimated SNP effects, which provide a prediction equation. The proposed BayesR model is implemented in GCTB, a very efficient, freely available software. The application to monthly TD milk records of 4,655 crossbred cows in India, with 106,109 records, 689,750 autosomal SNPs, and an average farm size of 1. 7 animals per farm, yields an estimated heritability of 0. 16 (0. 04) in a model that also includes production environment. Using cross-validation, we estimated a GEBV prediction accuracy of 0.
Additionally, SSGB-LUP models have provided similar results to GBLUP in other smallholder contexts  (Mrode et al. , 2021). Therefore, no perfect solution exists, and we believe in continuing the research efforts to develop genetic models that, although imperfect, could provide the best performance for a given context. Typically, Bayesian models also require longer running times than the conventional GBLUP. For the Indian data set at hand (4,655 animals and 689,750 SNPs), although the proposed BayesR model was 1. 2 times faster than BayesC (11 vs. 13 h), it was also 26. 4 times slower than the conventional GBLUP (11 h vs. 25 min). In terms of computer memory, the proposed BayesR model only needed about 20% of the peak RAM memory (13 GB) required by the BayesC and GBLUP models (64 GB). However, this lower RAM consumption might reflect an efficient software implementation of BayesR in GCTB, compared with a general-purpose package such as JWAS in Julia, rather than intrinsic differences between these genetic models.
Materials And Methods Patients affected by pathologic myopia and showing lacquer cracks in at least one eye were retrospectively selected from databases of two specialized retina centers (Department of Ophthalmology of the University Paris Est Créteil and Department of Ophthalmology of IRCSS San Raffaele Milano) among records from July 2007 to August 2022. Inclusion criteria were availability of high quality (Q > 15) OCT acquisitions documenting the rst appearance of LC (acquisitions before and after their appearance) and age older than 18 years. A minimum follow up of 2 years was required for inclusion in n-CNV group. LC presence was assessed by three experienced graders (E. C. , M. B. and S. F) based on the examination of high resolution (1536 x 1536 pixels) 30°x30° fovea-centered enface infrared (IR) and late phase ICGA images.
For LC area calculation, IR images were rst cropped using an open-source image processing software (ImageJ, NIH, Bethesda, MD)(Fig. 1A ). LC segmentation was performed with MatLab software (Mathworks, Natick, MA) using a manual threshold of 185 and an adaptive threshold of 35% sensitivity (Fig. 1B ). Erosion for disc shapes with radius = 2 was then performed to re ne the segmentation. Validation of the segmentation from a human grader was necessary to ensure that segmented images matched the real shape and dimensions of the lacquer cracks as seen by human eye. Spurious and patch atrophy areas were excluded (Fig. 1C ). Areas of the segmented regions was than calculated and converted to mm 2 ( Fig. 1 D ). The difference between LC area at baseline and LC area at nal follow up acquisition was calculated and de ned as D LC area (mm 2 ).
Statistical analysis Statistical analysis was conducted using SPSS v. 26 (IBM SPSS statistics). Normal distribution of the quantitative variables was assessed with Shapiro-Wilk test and variables were described using mean and standard deviation. Qualitative variables were described with number of cases over total and percentage. Comparison of quantitative variables was performed using C 2 test while qualitative variables were compared using two-tail T test for independent samples. Linear regression was performed using CNV development during follow up as a dependent variable and all quantitative variables resulting as statistically signi cant at univariate analysis as independent variables. Linear correlation between LC area and time to onset of CNV and between D LC area and time to onset of CNV was assessed using Spearman correlation.
Data analysis Basecalling was performed using the super-accurate model of Guppy v5. 0. 11. Blastn was used to subset the reads by comparing them with the EdPV-1 and -2 reference sequences (GenBank: AY684126 and MH376689), after which each subset was de novo assembled using Canu v2. 0  [24]. Medaka v1. 4. 3 was used to polish the resulting contigs, which had a minimum coverage of 1,000X at each position. The final contigs were joined with the previously obtained partial L1 sequences using SeqMan v7. 0. 0, yielding two complete, circular genome sequences. The finished genomes were manually inspected for any residual errors using CLC Genomics Workbench v20. 0. 2. The complete genome sequences were submitted to GenBank under the accession numbers MZ647948 and MZ647949. Open reading frame and feature annotation was done using PuMA  [25].
To further characterize these two viruses, the obtained amplicon sequences were used as templates for the design of two long-template PCRs that spanned the remainder of the circular genomes, with estimated fragment sizes of 7. 2 and 8. 7 kb. Nanopore sequencing of these two fragments and subsequent read assembly, contig polishing and joining with the original amplicons resulted in the generation of two complete virus genome sequences. The EdPV-1 genome is nearly identical to the previously published EdPV-1 genome sequence (GenBank: AY684126), having the exact same length (7,428 nucleotides) and sharing 99. 89% sequence identity (Fig. 2 ). The two genomes differ in only eight positions, three of which are located in the upstream regulatory region (URR). Of the remaining mutations, three are synonymous, resulting in a single amino acid change in the E2 (H71D), L2 (V2A) and L1 (K419R) proteins.
The EdPV-2 genome is markedly more dissimilar from its published counterpart (GenBank: MH376689), sharing only 93. 33% nucleotide identity. While the genome presented here is also slightly longer, having a length of 8,815 nucleotides, the two genomes are nonetheless sufficiently similar to consider them two variants of the same papillomavirus type. As shown in Fig. 3 , the differences between the two sequences are spread roughly evenly throughout the genome. Intriguingly, the secondary non-coding region between the early and late regions is as equally conserved as the rest of the genome. In the case of EdPV-2, this region is exceptionally large, although its precise function remains to be elucidated. We have previously published a detailed overview of the known regulatory motives in the genome of EdPV-2  [9].
Nearly all of these motives are identical to the ones found in the genome presented here, with only minor differences in the E2 DNA-binding motif Fig. 2  Genome organization of the detected EdPV-1 strain. The virus strain described here differs only in eight positions from the reference sequence (GenBank: AY684126). All differences are highlighted, with circles indicating non-synonymous mutations. Open reading frame and feature annotation was done using PuMA  [25]. URR = untranslated regulatory region, E1BS = E1 binding site, E2BS = E2 binding site Fig. 3  Genome organization of the detected EdPV-2 strain. A comparison of the EdPV-2 genome described here with the reference sequence (GenBank: MH376689) shows a roughly even distribution of dissimilarities throughout the genome. Comparison made using SimPlot V3.
Study population AtezoTRIBE  16  (NCT03721653) was a phase II randomized, open-label, multicenter trial where 218 patients with initially unresectable mCRC (18-70 years old with Eastern Cooperative Oncology Group performance status (ECOG-PS) of 2 or less or 71-75 years old with an ECOG-PS of 0) were randomized in a 1:2 ratio to receive FOLFOXIRI/bev or the same regimen plus the anti-PD-L1 atezolizumab. All treatments were administered up to eight cycles, followed by 5-fluorouracil plus bev with or without atezolizumab, according to the randomization group, until disease progression, unacceptable adverse events, or consent withdrawal in both arms. Patients with availability of an archival tumor sample collected before starting the study treatment and adequate for tumor immune cell infiltrate assessment were included in the present analysis.
Immune-markers associated with clinical outcome in the control arm of the AtezoTRIBE study, would have been validated in sample from patients previously enrolled in the TRIBE2 trial  26  (NCT02339116), a phase III randomized, open-label, multicenter trials where 679 patients with initially unresectable mCRC (aged 18-70 years old with ECOG-PS of 2 or less or 71-75 years old with an ECOG-PS of 0) were randomized in a 1:1 ratio to receive FOLFOX/bev followed by FOLFIRI/bev after disease progression or FOLFOXIRI/bev followed by the reintroduction of the same agents after disease progression. All treatments were administered up to eight cycles, followed by 5-fluorouracil plus bev until disease progression, unacceptable adverse events, or consent withdrawal in both arms.
Indeed, in the present study, Immunoscore data was available only for 77 patients, as compared with 157 in which Immunoscore-IC was successfully assessed. Moreover, the Immunoscore results could be potentially biased by the enrichment in the assessable population of patients with resected primary tumors that could have better prognosis than unresected patients  42  and not be suitably representative of the overall study population. More recently, a biopsy-adapted Immunoscore, a digital immunohistochemistry quantification of CD3+ and CD8+  T cells in the tumor region of biopsy samples, was developed for locally advanced rectal cancer. In this setting, biopsy-adapted Immunoscore positively correlates with histopathological response to neoadjuvant chemoradiotherapy and disease relapse thus supporting this biomarker, in addition to post-neoadjuvant treatment images, as a promising tool for selecting patients candidate to watch-and-wait strategy.
Ecology and forestry sciences are using an increasing amount of data to address a wide variety of technical and research questions at the local, continental and global scales. However, one type of data remains rare: fine-grain descriptions of large landscapes. Yet, this type of data could help address the scaling issues in ecology and could prove useful for testing forest management strategies and accurately predicting the dynamics of ecosystem services. Here we present three datasets describing three large European landscapes in France, Poland and Slovenia down to the tree level. Tree diameter, height and species data were generated combining field data, vegetation maps and airborne laser scanning (ALS) data. Together, these landscapes cover more than 100~000~ha and consist of more than 42 million trees of 51 different species.
Such data could also prove useful for testing forest management strategies and accurately predicting the evolution of ecosystem services. Airborne Laser Scanning (ALS) surveys are a promising way forward to address this challenge, as they can provide high-resolution data over wide areas. However, retrieving individual tree attributes from ALS point clouds remains a challenge in particular in closed-canopy forests. At present, one solution is to combine ALS data with tree-level field data  [Lamb et al. , 2018; Silva et al. , 2016 ]. Here we present three datasets describing three large European landscapes in France (Bauges Geopark ≈ 89,000 ha), Poland (Milicz forest district ≈ 21,000 ha) and Slovenia (Snežnik forest ≈ 4700 ha) down to the tree level. Individual trees were generated combining inventory plot data, vegetation maps and ALS data.
Together, these landscapes (hereafter virtual landscapes) cover more than 100,000 ha including about 64,000 ha of forest and consist of more than 42 million trees of 51 different species. In addition to the datasets, we provide here a simple method to predict the diameter, height and species of all trees in a landscape using increasingly available data: inventory and ALS data. This method also has the advantage of being fast: less than 5 hours on a six-core laptop are needed to generate the 35 million trees making up the 51,500 ha of forest in the Bauges Geopark.
In particular, mixed stands of broadleaf species are found at low elevation. The Milicz forest district is located in the province of Lower Silesia in south west Poland at a mean elevation of 126 m a. s. l. (elevation ranging from 96 to 227 m a. s. l. ). Much of the area is almost flat or slightly undulating with gentle slopes. This part of the landscape is covered by developed terraces and aeolian formations. The remaining part of the landscape is a slightly undulating moraine plateau above which irregularly shaped moraine hills are found. The average annual rainfall is 565 mm and the mean annual temperature is 8. 2°C. Monthly temperatures range from -1. 3 to 17. 8°C. The Milicz forest district covers a total area of 21,086 ha including 7713 ha of public forest. Small patches of private forest are also found in the landscape but they were not considered here as no field data were collected there.
General approach Here we outline the approach we adopted to produce the virtual landscapes corresponding to our three study areas (Figure  2 ). First, we produced raster maps of stand total basal area (BA), mean quadratic diameter (Dg) and proportion of broadleaf trees BA (BA b ) at a 25 m resolution (see ALS mapping). For that, we used ALS point clouds along with field data (tree diameter and species identity). Thereafter, we generated trees in each 25x25 m 2 cell, specifying their diameter at breast height (dbh), number (n) and species (sp; see Downscaling algorithm). For that, we first assigned to each cell a stand from the field data based on the similarity of their BA, Dg and BA b values. We then transformed the structure of the stand chosen from the field data (by changing the trees dbh, basal area and weight) to reach the BA and BA b values of the cell.
Forest areas. Reference areas for forest mapping were defined as the intersection of two layers for each site, one defining the administrative boundary and one defining the forest mask. Those extents are respectively: • Bauges: the Geopark administrative extent with the forest mask defined by the BD Forêt v2 from the National Institute of Geographic and Forest Information  [IGN, 2019] , excluding the "herbaceous", "moors" and "Populus plantations" categories; • Milicz: the public forests of Milicz with the forest mask defined by the Forest Data Bank [Bureau for Forest Management and Geodesy, 2020]; • Snežnik: the forest management units of Leskova Dolina and Snežnik with the forest mask defined by Snežnik-forest cover  [Service, 2020].
Field data. In the Bauges, a local forest inventory with 320 plots was implemented in 2018. On each plot, all living trees with a dbh larger than 17. 5 cm and within a 15 m horizontal distance from the plot centre had their dbh, position and species recorded. Trees with a dbh between 7. 5 and 17. 5 cm were counted according to simplified categories of diameter and species (coniferous / broadleaf). Plot centres were geolocated with survey-grade GNSS (Global Navigation Satellite System) receivers. Plots co-registration with the ALS data was improved when possible by comparing the positions of trees with the Canopy Height Model (CHM) derived from the point cloud. At Milicz, a local forest inventory with 901 plots of 12. 62 m radius was carried out in 2015. Species and diameter of all living trees with dbh above 7 cm were recorded.
ALS metrics. All computations were performed with R software. Terrain metrics (aspect, elevation and slope) were computed by fitting a plane surface to points classified as ground. Before the computation of vegetation metrics, ALS point clouds were normalised, i. e. height above ground was computed for each point. Two types of metrics were then computed from the points classified as vegetation with a height above 2 meters (this limit was set to remove points of shrubs and low vegetation from the analysis): • Point cloud metrics were directly computed from the point cloud or from the derived CHM, using the aba metrics function from the lidaRtRee R package. Those metrics summarise the geometry of the point cloud in a given area. • Tree metrics were computed with the std tree metrics function from the characteristics of local maxima extracted tree_segmentation function) from the CHM.
CHM resolution was set to 0. 5 m at Milicz, and 1 m at Snežnik and the Bauges due to higher variability of point density. Local maxima with a height lower than 5 m were discarded. Those metrics summarise the characteristics of trees detected in a given area of the point cloud. One of the tree metrics is the ALS dominant height (Hdom ALS ), which is the mean height of the six highest local maxima. In case less than six maxima were present, the mean height of all maxima was used. The metrics were computed for each field plot based on the point cloud located inside their extent, in order to build the dataset for model calibration (training step). The metrics were also computed in each 25x25 m 2 cell of the raster layout covering each acquisition, in order to build the prediction dataset (mapping step).
The retained stratifications for the prediction models and the root mean square error (RMSE) of prediction estimated in leave-one-out cross validation are presented in Table  1. Prediction accuracy is better for mean diameter and lower for BA, which is common when estimated with ALS. Precision is quite low for broadleaf proportion, which could be expected as spectral data are usually better than ALS at classifying species. Prediction accuracy was higher at Milicz, intermediate at the Bauges and lower at Snežnik. Milicz was well suited for making predictions with its dense ALS data, homogeneous stands and precise co-registration. The Bauges has precise co-registration, but heterogeneous forest stands and two different ALS datasets. At Snežnik the data were much noisier, especially because of the ice storm event.
Algorithm. Our algorithm consisted in associating to each 25×25 m 2 cell a field plot based on the similarity of their dendrometrical variables, and then in modifying the trees dbh, basal area and weight of this field plot in order to reach the total BA and the proportion of broadleaf BA (BAb) of the cell (i. e. the values provided by the ALS maps). The algorithm breaks down as follows: 1. First, we calculated the total basal area (BA), mean quadratic diameter (Dg) and proportion of broadleaf BA (BA b ) of all field plots. 2. Second, we associated to each 25×25 m 2 cell a field plot based on the similarity of their BA, Dg and BA b. (a) For this, we scaled the values of BA, Dg and BA b between 0 and 1. We scaled the ALS and field data together to account for the possible differences in their range.
(b) We then calculated the Euclidean distance between each cell and each field plot in the three-dimensional space made up by the scaled values of BA, Dg and BA b. We then associated the closest field plot sharing the same forest type to each cell. 3. Third, we transformed the field plots stand structure so that it matched the BA and BA b values of the cells they were associated with. (a) For this, we first calculated α, a multiplier correction coefficient to apply to all trees dbh of the field plots. α is given by: = ALS F Dg ® Dg (7) with Dg ALS the Dg value of the cell given by the ALS mapping, and Dg F the Dg value calculated with the dbh of the trees from the field plot. (b) Thereafter, we calculated the weight per ha of each tree as follows: where dbh F is the tree dbh in the field plot, and ba tree ALS,F is the tree individual basal area derived from the ALS mapping and the field plot data using the following equation: , = × × × BC S ALS ALS ALS F F F p tree tree ba BA Prop Prop Prop (9) where BA ALS is the total BA of the cell given by the ALS mapping, Prop BC ALS is the BA proportion of broadleaf (resp.
Dataset validation Method To assess the realism of the virtual landscapes we generated, we compared the stand dominant heights estimated by ALS (Hdom ALS ) to those calculated from the trees we generated (Hdom T ). We expect Hdom ALS to be as close to reality as possible, as tree height is among the most reliable ALS measurement   Van Leeuwen & Nieuwenhuis, 2010]  and can be derived from ALS data with little processing and no field data. Hdom ALS therefore serves here as a reference to which Hdom T is compared. As shown in Figure  2  Hdom ALS is totally independent from the procedure that generates the trees. Thus, comparing Hdom ALS and Hdom T makes it possible to evaluate the overall reliability of our workflow. [ In practice, Hdom T is calculated as the mean height of the six highest trees, while Hdom ALS is calculated as the mean height of the six highest local maxima (see ALS metrics).
Data availability -results obtained from the data can be displayed or distributed provided they do not allow the estimation of growing stock in individual private properties; -data funding (Ademe grant 1703C0069) should be cited. • ALS data in the northern part (Haute-Savoie) are available to download from the Recherche Data Gouv dataverse at https://doi. org/10. 57745/ZUT1MJ, under the Etalab open license 2. • ALS data in the southern part (Savoie) can be purchased upon request to (Régie de Gestion des Données Savoie Mont Blanc) at https://www. rgd. fr/.