dataesr/scientific-paragraphs-categorization huggingface dataset:
39  The ENS resulted in fewer false predictions and higher predictive accuracy than a single model. Consistency of Cu pred was evaluated for each grid cell on a SD of 1,000 predictions for each machine learning technique. Model performance was evaluated using a 10-fold cross-validation (CV). Model spatial transferability was evaluated using a spatially constrained 10-fold CV (details in SI). For each model, the averaged relative importance (ARI) of each variable in the model was calculated with an input permutation technique and onefactor-at-a-time (OFAT) sensitivity analysis (details in SI). The complete modelling framework to select variables, calibrate the model and predict Cu t is available for download at https://github. com/Boris-Droz/ML_soil_predi_conc.
50  As a result, Cu(I) may partly re-oxidize into mobile Cu(II) in oxic soil. However, an opposite trend is observed as AI may indirectly increase the Cu-binding affinity of soil organic matter (ARI of 13. 1 ± 0. 1%). Indeed, the oxidation of organic matter under oxic conditions increases the contribution of carboxylic and hydroxylic groups as preferential binding partners for Cu in organic matter. 51  In addition, pH (ARI of 7. 5 ± 1. 3%) directly controls the deprotonation of sorption sites on soil constituent. Hence, higher pH increases available sorption sites for Cu binding. 19  Distribution of the Total Copper in Topsoil Across European Vineyards. Cu pred in European vineyards were successfully made as the datasets covered the range of predictive variables within the domain of prediction.
ACKNOWLEDGEMENTS Boris Droz  was supported by a fellowship of the  Region Grand Est  and the  Rhine-Meuse Water Agency (AERM). Pasquale Borrelli  was funded by the  EcoSSSoil Project, Korea Environmental Industry & Technology Institute (KEITI), Korea  (Grant No. 2019002820004 ). We acknowledge the copper dataset owners for sharing their data:  Holger Tülp  ( Mosel, Germany ),  Denis Rusjan  ( Slovenia ),  Maria Concepcion Ramos  ( Spain ),  Nikolai Dinev  ( Bulgaria ), several Swiss state environmental agencies ( Schaffhausen, Neuchâtel, Genève ,  Tecino, Aargau, Valais) ,  Central Institute for  Supervising and  Testing in Agriculture (ÚKZÚZ, Czech Republic) ,  Bundesanstalt für Geowissenschaften und Rohstoffe (Germany) ,  le Réseau de Mesures de la Qualité des Sols (RMQS) de l'Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE, France).
We acknowledge  Koens Oorts  to sharing the toxicological Model. The LUCAS topsoil dataset, CORINE land cover and Copernic data were funded by the  European Environment Agency (EEA). MODIS data was supported by the  NASA   EOSDIS Land Processes Distributed Active Archive Center (LP DAAC ) ,  USGS/Earth Resources Observation and Science (EROS). We acknowledge  Dimitri Rambourg ,  Raphaël di Chiara ,  Marwan Fahs ,  Gerhard Schäfer  and  Benoit Masson-Bedeau  for enable computing time. We gratefully acknowledge  Margaret Johnson  for.
Average uncertainty of the Cu pred falls within ± 11 mg Cu kg -1 for 75% of the data (RMSE of 0. 63 ± 0. 06). The scattering observed in the predictions (SI, Figure S4 ) mainly concerns data above 130 mg Cu kg -1 , corresponding to 6. 3% of the dataset only. ENS predictions failed to reproduce observed Cu t in the corresponding vineyard areas, which potentially received larger historical Cu f applications than other areas under similar climatic and soil conditions. Additional model performances and limits are provided in SI.
We then genotyped SNPs exceeding 5% minor allele frequency in the rest of the population (N = 181). SNPs with minor allele frequencies of < 10% in the whole population were excluded from analyses. SNPs for which there were < 4 individuals with the minor allele (minor allele homozygotes or heterozygotes) in behavioral datasets were also excluded from analyses due to model convergence problems. SNPs that deviated from Hardy-Weinberg equilibrium (assessed via Chi-square tests with a Holm's correction, Table  1 ) were additionally excluded. Linkage disequilibrium was assessed with the web-based application SNPStats, which uses matrices of genotype data to calculate haplotypes, linkage disequilibrium statistics (D, D', Pearson's r) and P values based on Chi-square tests  69. For exonic SNPs, we also determined whether SNPs were synonymous (not changing the amino acid sequence) or nonsynonymous (changing the sequence).
Locus We assessed the repeatability of putative personality traits (neophobia, boldness, exploratory behavior) in the current dataset using R package rptR. Package rptR estimates repeatability based on variance components extracted from mixed models  76. Models used to estimate repeatability were linear mixed models (LMMs)  75  for boldness and neophobia, and a Poisson GLMM  74  for exploratory behavior. Repeatability estimates were adjusted for effects of year, test date and test number for exploratory behavior, and year and test number for boldness and neophobia. For neophobia, we assessed repeatability both when adjusting for baseline return latencies (see above), and when not making this adjustment. To model effects of SNPs on behaviors, problem-solving and breeding parameters, we constructed general generic models, wherein genotypes were entered as three level factors.
For males, a low number of problem-solving individuals in our dataset made testing associations with problem-solving success difficult for some SNPs (18 genotyped females, but only 9 genotyped males were successful). We also constructed models predicting two fitness-related parameters: female laying date (logtransformed) and number of fledglings in both sexes. We had repeated measures for many individuals, and thus adopted a mixed modeling approach  74. Return latencies (log-transformed) during novel object tests, latency to contact during obstacle removal tests, and laying date (log-transformed) were analyzed using linear mixed effects models (LMMs) with Satterthwaite approximations for degrees of freedom (R package lmerTest)  75. Success on obstacle removal tests was analyzed using a generalized linear mixed effects model (GLMM) with a binomial distribution.
Genetic polymorphisms. Of 26 SNPs identified at a minor allele frequency of > 5% in our initial sample, 5 deviated from Hardy-Weinberg equilibrium, and 9 displayed a minor allele frequency of < 10% in the entire sample or too few individuals (< 4) with the minor allele in behavioral datasets, resulting in 12 SNPs retained for analysis (Table  1 ). Four retained SNPs were in the promoter region. Of the 8 exonic SNPs retained for analysis, only SNP226 in exon 1 was nonsynonymous, causing the amino acid change E26D (glutamic acid replaced by aspartic acid). Two other nonsynonymous SNPs located in exon 3 (SNP100, A231V and SNP187, L260Q) were present at < 10% minor allele frequency. None of the SNPs had been previously reported in great tits, although Timm et al. (2018, 2019)  40, 41  identified a different nucleotide change at one of the positions (SNP187 in exon 1; A/T in previous study, C/T in current study).
Thus, one might expect that SNP36 could mediate an association between incorrect attempts and neophobia behavior. However, we did not find a statistically significant association between incorrect attempts and neophobia behavior. The relationship between SNP187 in exon 1 and incorrect attempts was associated with a smaller, but non-negligible, effect size (R 2 = 0. 108). A different nucleotide change at position 187 in exon 1 (A/T instead of C/T) was previously related to hissing behavior during nest defense in female great tits  41. However, recent work in our study population showed no relationship between SNP187 and either hissing behavior or female-female aggression  58. Thus, whether SNPs at this locus have significant behavioral effects across multiple traits remains equivocal. In our dataset, linkage between SNP226 and SNP187 in exon 1 made the effects of the two difficult to disentangle.
Acknowledgements We thank  Geert Eens  and  Peter Scheys  for providing support during fieldwork,  Jasmijn Daans  for help with genetic work in the laboratory, and members of the  Behavioural Ecology and Ecophysiology group  at the  University of Antwerp  for intellectual support. This study received financial support from the  FWO Flanders  (to ASG, Grant ID:  1. 2I35. 17N )and  European Commission  ( Marie Skłodowska-Curie  fellowship  799667  to MLG). The authors declare that they have no conflicts of interest.
Effects of latitude and bioclimatic variables on fly cold tolerance vary even between closely-related species The principal component analysis (PCA), which we performed on macroclimatic variables on fly collection sites, showed PC1 to separate the low-altitude coastal sites from the high-altitude mountainous ones based on differences in winter temperatures, temperature variability and precipitation. PC2 further arranged the clinal populations according to their summer and annual mean temperatures. Thus, in our dataset latitude represents photoperiodic differences (day length) between the sites, while PC2 corresponds to latitudinally varying mean temperatures and PC1 to macroclimatic variation between the coast and mountains. Overall, genetic variation was lower in chill coma resistance (CT min ) than in chill coma recovery time (CCRT), and CT min showed significant variation only in D.
Fly body colour and body weight We analysed variation in the body colour and body weight (as a proxy of body size) of the same females that had been phenotyped in CT min or CCRT tests. Immediately after the cold tolerance tests, the females were put individually into tightly sealed PCR plates and kept in -20 °C freezer until measuring their body colour and weight. For body colour measurements, the females were photographed under Leica L2 microscope with × 5 magnification, using Leica DFC320 R2 camera together with the Leica Application Suite software v4. 3. 0. Exposure time, zoom factors and illumination level were kept constant, and the photographing arena was surrounded by a plastic cylinder to eliminate glares on the chitinous surface of the fly. All photographs were taken within 3 months after the cold tolerance tests.
Other datasets showed deviation from the normality (Additional file 1: Fig. S4 ), and they were analysed using generalized linear mixed model (GLMM) with gamma distribution, using glmmTMB function from glmmTMB package  [106]. Technical replicates and isofemale strains were handled as crossed random effects. In our dataset latitude and altitude were negatively correlated (Pearson correlation coefficient = -0. 82) and to prevent this multicollinearity from affecting the analysis, we used only latitude as an explanatory factor. However, we considered the effect of altitude and climatic variables through a principal component analysis (PCA). We downloaded climatic information from WorldClim database v2. 1 (2. 5 min spatial resolution; current data 1970-2000;  [107] ; http:// www. world clim. org) and extracted 19 bioclimatic variables for each site using their latitudinal and longitudinal coordinates (Additional file 1: Table  S3 ; Fig.
1 ) with raster package v. 2. 8-19  [108]. Moreover, we obtained growing season length (days) for each site from article by  [92]  and www. weath erbase. com. Growing season is defined as the average number of days per year when the average daily temperature is at least 5 °C. We performed the PCA on the 19 bioclimatic variables, altitude and growing season length (Additional file 1: Table  S3 ) to summarize climatic differences on temperature and precipitation in each site using "FactoMineR" package  [109]. We included the first two PCs in the model comparison of cold tolerance, body colour and body weight (see 'Results'). The simplest model included latitude and PC2, which enabled us to distinguish between the latitudinally varying photoperiods and temperatures. Moreover, the effect of macroclimatic conditions varying between the western coast and the Rocky Mountains (PC1), interaction terms between latitude/PC2 and PC1, and body colour (divided by 100 to scale the variables) and body weight were included in the model selection (Additional file 1: Table  S6 ).
The best-fit model for CT min , CCRT, body colour and body weight of both species was chosen for further analysis based on Akaike information criterion (AIC) value and Akaike weight (Additional file 1: Table  S6 ) using aictab function from AICcmodavg package  [110]. All the analyses were conducted in R (v1. 2. 1335-1) and R studio (v3. 6. 1).
Association between the number of nucleotide or amino acid differences and differences in cold tolerance traits in D. montana and D. flavomontana populations To evaluate the role of vrille in cold adaptation, we investigated the association between vrille sequence variation and variation in cold tolerance traits (CT min and CCRT) in D. montana and D. flavomontana populations. We obtained published D. montana genomic sequences from NCBI under accession number LUVX00000000 and the respective genome annotation from Dryad: https:// doi. org/ 10. 5061/ dryad. s813p 55  [111]. Based on D. virilis and D. melanogaster orthologs in the annotation file, vrille sequence is found in scaffold2327-size32305 at position 13,933-17,558. We used Illumina 150 bp paired-end data from single wild-caught females or their F 1 female progeny, i.
3. 1dirty [116] , and the variant calling (VCF) file was filtered for minimum depth and quality of 20 using bcftools view v1. 8 (SAMtools. 2018. https:// github. com/ samto ols/ bcfto ols/ relea ses/ tag/1. 8). Finally, vrille FASTA sequences for each sample were obtained using bcftools consensus. The vrille sequences together with information on exons from the annotation file (see Additional file 1: Figs. S5, S6) were imported to Geneious v9. 1. 8. (Biomatters ltd). For exons, D. montana and D. flavomontana nucleotide sequences and translated amino acid sequences were aligned using muscle algorithm. The number of pairwise nucleotide and amino acid differences were then calculated for D. montana and D. flavomontana populations (Additional file 1: Tables  S12, S13 ). Similarly, pairwise differences in mean CT min (in Celsius degrees) and CCRT (in minutes) were calculated for D.
montana and D. flavomontana populations (Additional file 1: Tables  S14, S15 ). The correlations between distance matrices were statistically tested with a Mantel test with 1000 permutations using mantel. rtest function from ade4 package  [117]. The correlations were tested between (i) the number of nucleotide differences and differences in mean CT min (ii) the number of amino acid differences and differences in mean CT min , (iii) the number of nucleotide differences and differences in mean CCRT, and (iv) the number of amino acid differences and differences in mean CCRT among D. montana and D. flavomontana populations (only within-species correlations).
RNA purity was analysed with NanoDrop ® ND-1000 spectrophotometer (Thermo Fisher Scientific) and concentration with Qubit ® 2. 0 Fluorometer and RNA BR kit (Thermo Fisher Scientific). cDNA was synthesized using equal quantities of RNA (200 ng) with iScript Reverse Transcription kit (Bio-Rad Laboratories ® ). We measured the expression levels of vrille with quantitative real time PCR (qPCR). qPCR primers for vrille and reference genes were designed based on D. montana genomic sequences under NCBI accession number LUVX00000000  [111] , together with information from D. virilis exons using Primer3 (primer3. ut. ee) and NetPrimer (www. premi erbio soft. com/ netpr imer) programs (gene accession numbers and primer sequences in Additional file 1: Table  S16 ). qPCR mix contained 10 µl of 2 × Power SYBR Green PCR Master Mix (Bio-Rad Laboratories), 0.
All data showed deviation from normality (Additional file 1: Fig. S7 ) and were analysed with generalized linear mixed model using gamma distribution (GLMM; glmmTMB function from glmmTMB package  [106]. In the models, response variables were either CT min (Celsius degrees + 10 to prevent negative values from affecting the results) or CCRT (minutes) data, and the test replicates were used as a random effect. All the analyses were conducted in R (v1. 2. 1335-1) and R studio (v3. 6. 1). and 19 bioclimatic variables on the Principal Component (PC). Table  S6. The best-fit model for CCRT, CT min , body colour and size of D. montana and D. flavomontana was defined based on Akaike Information Criterion (AIC). Table  S7. Summary of the best-fit model results on the effects of latitude and/or climatic factors (PC1) on cold tolerance and body colour of D.
Availability of data and materials The climatic data were obtained from WorldClim database v2. 1, http:// www. world clim. org. The phenotypic data generated and analysed during the current study are available in the Dryad repository, https:// doi. org/ 10. 5061/ dryad. 98sf7 m0fv. D. montana genome sequences were obtained from NCBI repository, under accession number LUVX00000000, and the respective genome annotation was obtained from the Dryad repository, https:// doi. org/ 10. 5061/ dryad. s813p 55. vrille sequence data (BAM files) analysed during the current study are available in the EMBL Nucleotide Sequence Database (ENA) repository, under accession number PRJEB45370.
Rain can also affect toad movements  19  , so we noted whether or not it had rained in the 24 h preceding each capture event. Toad positions in the absence and presence of recent rain were not available at Gosselin, as it did not rain at Gosselin during the tracking period, but it did rain at all other sites (Montjoly rain: Day 4; Kaw rain: Days 4, 5; Regina Wash rain: Days 3, 4, 5, Nights 4, 5). Behaviour can also be state-dependent; for example, body condition can influence foraging decisions  43. To calculate the body condition of the tracked toads we obtained body mass (g) and SVL (mm) data from 142 male and 98 female toads from across northern French Guiana  40. Using these data we calculated sex-specific relationships between mass and SVL by plotting ln(mass) against ln(SVL). For males this relationship was y = 2.
Foraging movements. We used data on the nocturnal position of each toad to calculate: (i) Distance from diurnal shelter (continuous, m). The distance between the toad's diurnal refugium and its nocturnal location; where the location of the refugia used before and after that night were both known, we took the mean of these two distances (3 values/toad). (ii) Probability of foraging (binomial, foraged yes/no). Whether or not the toad had emerged from its diurnal shelter to forage (i. e. , was found > 5 m from its daytime refugium; 3 values/toad). (iii) Foraging distance (continuous, m). If a toad had emerged from its diurnal shelter, the distance between the diurnal shelter and where the animal was found foraging (this measure is the same as distance from diurnal shelter, but excludes occasions when toads did not emerge to forage; ≤ 3 values/toad).
Another female toad entered private property on the final day of the tracking period, and we were only able to estimate her position within 10 m for Day 5 (M3; see Fig. 1 ). To minimize handling of the toads prior to tracking their movements, SVL measurements were taken at the conclusion of tracking; we therefore lack data on size and body condition for these three toads. We also tracked three of the other Montjoly toads for an additional night, yielding 5 days and 4 nights of data for these individuals. Finally, eight toads that used the same diurnal shelter every day (i. e. , never moved > 5 m) were excluded from measures of path straightness, leaving 24 toads for this measure (3 Gosselin, 8 Montjoly, 3 Kaw, 10 Regina Wash). All available data were used for all analyses. Ethics declaration.
Data analyses Effects of initial capture method, site, rainfall, and variation between individuals on toad movements. We first used data from the coastal toads to determine whether initially capturing toads at night (rather than during the day) biased our movement estimates. We therefore used separate models to analyse all of the movement parameters outlined above as responses to the fixed effect of initial capture method. To account for variation between sites and individuals, site and individual ID were included in these models as nested random effects. Upon finding that locating toads at night did not have pronounced effects on our movement estimates, we excluded this factor from all subsequent models. We then used data from all of the sites (and toads from both initial capture methodologies) to test for sitespecific differences in toad movement or shelter selection.
These individual-level predictors included SVL (continuous: mm), body condition (continuous: scaled mass index, g), and sex (categorical: female, male). We also included all first-order interactions. Because all of the toads tracked in the rainforest were males, we first excluded the rainforest sites from all models and looked for an effect of these individual-level predictors on toad movement within coastal sites alone. For models where a significant effect of sex was found, we retained sex as a predictor in subsequent analyses in which data from all four sites were included. However, if no effect of sex was found within coastal sites, we omitted sex as a predictor in subsequent analyses of the full dataset. This was done to avoid confounding any effects of sex with those of site. (Within coastal sites, the only movement parameter that was significantly affected by sex was the probability that a toad would emerge to forage; therefore, this is the only model that included sex as a predictor when the full dataset was analysed).
Statistical procedures. All analyses were conducted in R  46. In all models, distances were ln-transformed (ln[y + 1]) prior to analysis to meet homoscedasticity assumptions. Any continuous response variables for which multiple measures were taken per toad were analysed as mixed-effect models (package nlme:lme  47  ). Individual ID was included in these models as a random effect to account for variation between individual toads. Continuous measures for which there was only one final value per toad were instead analysed via linear regression using the lm procedure. Binomial responses were analysed via logistic regression (package lme4:glmer  48  ), with individual ID included as a random effect. The number of diurnal shelters used per toad was analysed using a quasipoisson model (glm; to account for data underdispersion).
For models that included multiple predictors (e. g. , site as well as rain or individual-level predictors), we used a stepwise removal procedure to eliminate nonsignificant predictors from the model, starting with non-significant interactions. We used p = 0. 1 as the threshold for elimination; therefore, the final models only included variables for which p < 0. 1 (though non-significant main effects were retained if they were involved in a significant interaction). All models were initially assessed using the anova function (or package car:Anova  49  , for models with a binomial or Poisson distribution). Where significant effects were identified we used Tukey post-hoc tests (package emmeans  50  ) to conduct pairwise comparisons and identify significant differences.
RNA-seq, Analysis, and Neoepitope Prediction RNA was isolated from fresh tumor samples using a CsCl cushion as described in ref. 40  and then quantified with Qubit RNA HS assay kit (Thermo Fisher Scientific). RNAseq libraries were constructed using the TruSeq Stranded mRNA Sample Preparation Kit (Illu mina) and sequenced on an Illumina HiSeq 2500 platform using 100bp pairedend sequencing. An average depth of global sequence coverage of 111 million and a median coverage of 75 million were attained. Differential junctions using alternative acceptors were identified as previously described  (20)  comparing the 8 SF3B1 mut tumors with 5 SF3B1 WT tumors. Sequences of aberrant and cor responding normal transcripts were extracted using ANNOVAR and ENSEMBL databases  (43). NetMHCpan v4, an artificial neural networkbased algorithm, was used to predict MHC class I affinity for splicing anomalies derived peptides  (23).