dataesr/scientific-paragraphs-categorization huggingface dataset:
As invasive species continue to increase their ranges and associated impacts across the planet  (Bellard et al. 2013 (Bellard et al. , 2016;; Seebens et al. 2017; Seebens et al. 2021) , we can reasonably surmise that Australia will also suffer many additional, negative economic repercussions from invasive species over the coming decades. Developing better methods of estimating environmental impacts of invasive alien species will also contribute to this. We recognise that such types of economically intangible costs arising from invasive species  (Bradshaw et al. 2016)  are not captured by the database -for example, ecological damage, erosion of ecosystem services and loss of cultural values are inherently challenging to measure in this regard. by the AXA Research Fund Chair of Invasion Biology and is part of the AlienScenario project funded by BiodivERsA that funded C.
Assessment How the game and its objectives will be evaluated is part of the overall design. This may include game assessment by participants, learning assessment within the game itself, or assessments outside the game. If a research project is considered, the research questions should be clearly defined, and research protocols should be established and registered. This will help determine which data and indicators will ultimately be needed and how data processing and visualization should be carried out. Care must also be taken to ensure compliance with personal data protection regulation. Consent mechanisms and the need for ethics approval must also be considered.
Data Hydrodynamic and meteorological information comprise wave and wind variables, provided by the VAG prediction model of the sea state  [16] , using retrospective 3-hourly simulations between 2009 and 2018. Tidal water levels were obtained from the SHOM-REFMAR tide gauge station located in the vicinity of the study zone, with hourly survey frequency  [52]. Bathymetric measurements were available from Single-Beam Echo Sounding on 39 cross-sectional profiles of intake measured at 25 m intervals, collected fortnightly between 2005 and 2018. Mean profiles were 100 m long with 0. 5 m spatial resolution of bathymetric data. Additional information such as the daily coolant flow rates, and channel dredging volumes and frequency, were provided by the plant operator. The available measurements of the forcings did not have the same frequencies.
These statistical indicators were calculated for each sedimentation interval, and may be characterized with correlations. For example, a positive correlation was noted between mean low tide T Lmean and wave parameters W vper and W vH. Mean wave periods W vper and mean wave heights W vH were also positively correlated. For the learning part, the data overlapped only over a limited period. A maximum of 60 measurements could therefore be used, with up to 15 forcing variables. Obviously, this "small data" configuration is a considerable handicap for the dimension of the problem, especially given that the variable of interest is a two-dimensional bathymetric field. However, permanent intake monitoring ensures that the data set will always grow and can be used to update the learning. This limitation shall not prevent testing the accuracy of the methodology on small sets such as are often encountered in physical applications, as attempted below, where learning and prediction using POD-PCE is applied to the described data.
However, the data-set used in this study was small: the bathymetry and forcings measurements shown in Subsection 4. 1 overlap for the 2012-2017 period only, leaving 64 sedimentation periods to study. Therefore, only a "Train-Predict" split was performed, where the prediction set played the role of both the test set and validation set. Hence, the numerical choices were calibrated on the training set, and validated on the prediction set for both statistical accuracy and physical prediction. The learning was then performed with an arbitrary choice of training-set size at 50, which left a prediction set of 14. The training data were chosen in chronological order (first 50 records), to mimic the learning process in an industrial context. This arbitrary training-set choice had consequences for learning ; the sensitivity of learning to training set choice was investigated (Step 2 ).
The phase representation exhibits more non-linearities than the amplitude. The contribution of D is followed by an interaction between A, D and the wave number in the estuary κ ei. As a reminder, the latter did not appear as an influencing parameter for the amplitude distribution. Globally, the phase problem involves higher polynomial degrees, and higher orders of interaction. 20. Also shown are the GWs calculated as in Equation  7 Appendix B. Confronting POD-PCE to NN As an additional proof for the POD-PCE Machine Learning capacity, multiple NN configurations are tested on the measurement-based problem for confrontation. The latter is a small-data problem, considered as the most challenging case in the presented work. The python library Scikit-learn  [51]  (https://scikit-learn. org) was used for fitting.
Acknowledgements This work is funded by the  French National Association of Research and Technology (ANRT)  through the  Industrial Conventions for Training through REsearch (CIFRE)  in agreement with EDF R&D. The authors acknowledge their support, and are grateful for data collection and feedback from EDF operators. In particular, we would like to thank  D. Rougé  for providing the data-set used in this study and for his continuous availability. We also would like to thank  Pr. L. Terray  ( CERFACS ) and  Dr. M. Rochoux  ( CERFACS ) for constructive discussions on POD and PCE respectively, and  Pr. B. Sudret  ( ETH Zurich ) for providing key literature elements on the treatment of ultrahigh dimensional problems and functional inputs using PCE. The authors also gratefully acknowledge the  OpenTURNS open source community  ( An Open  source initiative for the  Treatment of Uncertainties, Risks'N Statistics ).
X-Ray Diffraction XRD characterizations were performed under an anoxic atmosphere thanks to an airtight chamber mounted on the PANalytical X'Pert Pro MPD diffractometer operating at IMPMC equipped with a cobalt anode and an X'celerator® detector positioned in Bragg-Brentano configuration. Diffractograms were acquired using a Co K  E 1,2 radiation (40 kV, 40 mA) from 5 to 95° 2  angle with an increment step of 0. 033°. XRD data were processed with xrayutilities Python library  (Kriegner et al. , 2013). Peaks were indexed by comparison with reference patterns available from the Crystallography Open Database (COD)  (Gražulis et al. , 2012).
Data Availability Statement All original data supporting the conclusions of this article are available in this manuscript. Crystallographic Information Files used to generate XRD reference patterns were retrieved from https://www. crystallography. net/cod/. They were processed by xrayutilities Python library  (Kriegner et al. , 2013)  available for download at https://github. com/dkriegner/xrayutilities. Energy-dispersive X-ray spectra were processed from EDS datacube by Hyperpsy Python library  (Peña et al. , 2020)  available for download at https://github. com/hyperspy/hyperspy.
Results A potentially novel bioinformatics pipeline identifies TFs regulating the DOT1L gene. To identify upstream signals regulating DOT1L gene expression, we conducted a bioinformatics analysis of the DOT1L promoter, using a pipeline we designed (Figure  1A ). First, the DNA sequence of the human DOT1L proximal promoter was obtained from the eukaryotic promoter database (EPD)  (12). We analyzed this sequence with different bioinformatic tools, namely BindDB, PROMO, CONSITE, and TFsitescan  (13) (14) (15) (16). We found 276 TFs predicted to interact with the DOT1L promoter in silico. As these bioinformatic tools use different algorithms and TF databases, we compared the outputs. This resulted in 31 TFs simultaneously predicted by at least 2 different tools, which were selected for further analysis (Figure  1, A  and B ).
Combining both approaches resulted in 18 TFs that may selectively regulate DOT1L (Figure  1, A  and C ). These TFs include thyroid hormone receptor β (THRB), previously reported to increase Dot1l expression in tadpole intestine  (17) , validating our in-house analysis pipeline. Bioinformatics analysis unravels an undiscovered link between hypoxia and DOT1L. Next, we explored interactions and regulatory networks of the obtained TFs. To this end, we used STRINGdb  (18) , a database of known and predicted protein-protein interactions (Figure  2A ). We also used HumanBase (GIANT) to build a cartilage-specific network (Figure  2B )  (19). In the regulatory networks obtained, there was a prominent node around hypoxia-inducible factor-1 α (HIF1A) (Figure  2 , A and B), and the hypoxia pathway was enriched (Figure  2C ).
Collectively, these data demonstrate that hypoxia directly regulates DOT1L expression via HIF1A. DOT1L contributes to the protective effects of hypoxia in hACs. To translationally validate these findings, we assessed effects of hypoxia on primary hACs. IOX2 treatment induced DOT1L expression in these cells (R 2 = 0. 27, P = 0. 040), whereas culture in hypoxic conditions suggested a similar trend (1. 80-fold increase [95% CI, 0. 83-3. 88] P = 0. 081) (Figure  5, A  and B ). Expression of positive control VEGF was also induced (R 2 = 0. 95, P < 0. 0001; 7. 27-fold increase [95% CI, 3. 17-16. 68], P = 0. 0093; respectively] (Figure  5, A  and B ). Then, we verified that a hypoxic environment is beneficial for the molecular phenotype of the articular chondrocyte. Hypoxia mimetic IOX2 increased expression of COL2A1 and ACAN (R 2 = 0.
56, P = 0. 001; R 2 = 0. 66, P = 0. 0006; respectively) (Figure  5C ). In addition, culturing primary hACs in a hypoxia chamber induced healthy chondrocyte markers (11. 36-fold increase for COL2A1 [95% CI, 5. 53-23. 36], P = 0. 0047; 4. 14-fold for ACAN [95% CI, 2. 88-5. 95] P = 0. 0035) (Figure  5D ). Our data demonstrate that hypoxia induces DOT1L expression and has protective effects in primary hACs. A key question is whether DOT1L contributes to hypoxia's protective effects. To answer this, we evaluated effects of IOX2 or a hypoxia chamber in the absence of DOT1L in primary hACs. Whereas IOX2 treatment in control conditions resulted in similar changes in COL2A1 and ACAN expression, as seen above (Figure  5 , C and E), and silencing of DOT1L using siRNA had a global effect on the expression of ACAN in the ANOVA analysis model (P = 0.
Next, we used the EPD Search Motif tool, which uses the JASPAR database, to confirm predicted binding to the DOT1L proximal promoter. Potential specificity for DOT1L was in silico interrogated by assessing the binding of these TFs to the promoters of aggrecan, collagen 2a1, and actin using two different approaches. The first approach consisted of using the EPD Search Motif tool to individually interrogate whether the selected TFs predicted to bind to the DOT1L gene also bind to the promoter of the control genes mentioned above. The second approach consisted of analyzing the promoter sequences of the control genes using CONSITE, TFsitescan, BindDB, and PROMO and determining whether any of the selected TFs appears in the output. Finally, STRINGdb (https://string-db. org) (18)  and HumanBase (https://hb.
From these results, the individual ChIP-Seq experiments that showed binding to DOT1L were selected for further analysis. The peak-caller Model-based Analysis of ChIP-Seq (MACS2) algorithm captures the influence of genome complexity to evaluate the significance of enriched ChIP regions. These MACS2-binding significance scores were evaluated for DOT1L and VEGF in each individual ChIP-Seq experiment. Finally, BigWig data of HIF1A ChIP-Seq preformed in several cell types were visualized around the DOT1L TSS. All the data were mapped to the reference human genome (hg19) using the Integrative Genomics Viewer. Our analyses included the following publicly available ChIP-Atlas data sets: SRX2584127, SRX4741788, SRX4741791, SRX4802362, SRX4802347, SRX4108918, SRX3583255, SRX3583256, SRX3342251, SRX3342252, SRX4096728, SRX4096729, SRX666556, SRX1034772, SRX1995010, SRX3051209, SRX4802363, SRX4802349 and SRX4096727.
Images were taken using an Olympus IX83 microscope. Fluorescence quantification was performed with ImageJ Software (NIH Image) using 20 images per condition for each independent experiment. DMM mouse model of OA. Wild-type male C57BL/6 mice were purchased from Janvier. At 8 weeks of age, posttraumatic OA was induced by DMM. To this end, a mild instability of the knee was obtained by surgical transection of the medial menisco-tibial ligament of the right knee  (32). Sham surgery served as control. The knees were histologically analyzed 12 weeks after surgery. Intra-articular IOX2 injections. One week after DMM surgery, mice were intra-articularly injected with IOX2 (0. 5 mg/kg) or vehicle (30% PEG400 in PBS) every 10 days for a total of 7 injections. Twelve weeks after surgery, the knees were harvested and analyzed.
2) for 10 minutes at 95°C. Sections were treated with 3% H 2 O 2 /methanol for 10 minutes to inactivate endogenous peroxidase, blocked in goat serum for 30 minutes, and incubated overnight at 4°C with primary antibodies against DOT1L (Abcam, ab64077, 6 μg/ml) or HIF1A (Abcam, ab82832, 10 μg/ml) or for 90 minutes with primary antibody against H3K79me2 (Abcam, ab3594, 1 μg/ml). Rabbit IgG (Santa Cruz, sc-2027) was used as negative control. Avidin-biotin complex amplification (Vectastain ABC kit, Vector Laboratories) was used, except for the immunohistochemical detection of H3K79me2. Peroxidase goat anti-rabbit IgG (Jackson Immunoresearch) was applied for 30 minutes, and peroxidase activity was determined using DAB. Images were taken using an Olympus IX83 microscope. Quantification of the DAB staining was performed with a color deconvolution plugin (Jacqui Ross, Auckland University) in ImageJ Software (NIH Image).
After rinsing with PBS, the micromasses were stained with Alcian Blue (0. 1% AB 8GX, MilliporeSigma) for 2. 5 hours, washed with water, and air dried. Quantification of the staining was performed by dissolving the micromasses with 6 M guanidine (MilliporeSigma) for 6 hours and measuring the absorbance at 595 nm with a spectrophotometer (BioTek Synergy). Statistics. analysis and graphical presentation were performed with GraphPad Prism version 8 and R-Studio Version 1. 1. 463 (packages car, coin, emmeans, ggpubr, lme4, lmerTest, MuMIn, nlme, piecewiseSEM, readr, rstatix, and tidyverse). Data are presented as mean ± SEM and as individual data points, representing the mean of technical replicates, as indicated in the figure legends. Gene expression data and ratio data were log-transformed for statistical analysis.
Data are reported by F values and t values, with degrees of freedom and exact P values (if P > 0. 0001) in Supplemental Table  4  where applicable. P values of pair-wise comparisons are indicated in the graphs. Effect sizes (R 2 or pseudo-R 2 for regression models or differences between means in 2 group comparisons) are reported in Results section. Distribution of the dependent variables was assessed by histogram inspection. Model assumptions were further checked by QQ plots and homoscedasticity plots. Homogeneity of variance was evaluated by standardized residuals versus fit plot. For some generalized least square models, the regression was better fitted using the constant plus power variance function structure (Supplemental Table  4 ). For analysis of the animal experiment, the nonparametric Kruskal-Wallis rank-sum test was used followed by pairwise Holm-Bonferroni-corrected Wilcoxon tests.
05 by 1-sided t test). (D) MACS2-binding scores around DOT1L and VEGF transcription start site (TSS) of publicly available HIF1A and HIF2A ChIP-Seq data (ChIP-Atlas database). (E) Visualization of HIF1A ChIP-Seq in various cells around the DOT1L TSS. Box indicates overlapping HREs. ChIP-Atlas data mapped to reference human genome (hg19) using Integrative Genomics Viewer (IGV). Data are shown as the mean ± SEM.
Acknowledgments We are grateful to  L. Storms  for her technical support of this study and for, together with  A. Hens , taking care of the animal facility management. We thank  I. Jonkers  and  J. Peeters  for making the hypoxia incubator available. We are indebted to the  UZ Leuven traumatology and orthopaedics nursing  staff for their efforts to provide cartilage samples for ex vivo and in vitro work. This work was supported by grants from the  Flanders Research.
this, for moderate ellipticity contrast and for a precision of a few percentage points, our algorithm runs on a laptop computer and outputs a satisfactory approximation of the homogenized matrix within a few seconds in two dimensions, and within a few minutes in three dimensions. Our code is written in the Julia language and is freely available online, see the link in  (6. 1). The method we explore here was introduced in  [52]  in the context of discrete finite-difference equations. The main idea is to decompose the homogenized matrix into a series of terms, each of which accounting for a different length scale. The terms associated with short length scales naturally enjoy very small boundary layers and low computational effort. Those associated with larger length scales are a priori more demanding, but only appear as small correction terms in the decomposition, and can thus be computed on much smaller sample domains.
Numerical tests In this section, we report on numerical results for the method presented in this paper. The code was written in the Julia language, and is available at this address: https://github. com/haampie/Homogenization. jl. (6. 1) In all the examples we consider, the coefficient field is Z  -stationary, where  ∈ {2, 3} is the dimension. For convenience, we replace averages against the mask    in (1. 6) and (1. 7) by averages over the cube (-  ,   ). While strictly speaking, this situation is not covered by Theorem 1. 1, it is not difficult to show that the statement is still correct in this case (in fact, the argument is then somewhat simpler). For simplicity, we also fix  = 0 in (1. 5). As discussed below (1. 8), it is not difficult to modify the proof and cover this case as well, at the cost of an arbitrarily small loss of exponent in (1.
Acknowledgements. AH was partially supported by the  Stenbäck foundation and Academy of Finland  project  312340. JCM was partially supported by the  ANR  grants  LSD  ( ANR-15-CE40-0020-03 ) and  Malin  ( ANR-16-CE93-0003 ) and by a grant from the  NYU-PSL Global Alliance. HS was partially supported by  Academy of Finland  project  305759.
Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8. 5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits.
We use this resource to develop a comprehensive catalog of cis-and trans-mQTLs, which enables us to examine the genetic architecture of DNAm. By constructing networks of multiple cis-and trans-mQTLs, we learn about their collective impact on pathways and complex traits. Finally, we interrogate the potential role of DNAm in disease mechanisms by mapping the causal relationships between variable DNAm and 116 complex traits and diseases in a bidirectional manner. A database of our results is available as a resource to the community at http://mqtldb. godmc. org. uk.
Results Genetic variants influence 45% of tested DNAm sites. To map genetic influences on DNAm, we established an analysis workflow that enabled standardized meta-analysis and data integration across 36 population-based and disease datasets. Using a two-phase discovery study design, we analyzed ~10 million genotypes imputed to the 1000 Genomes reference panel  11  and quantified DNAm at 420,509 sites using Illumina HumanMethylation BeadChips in whole blood derived from 27,750 European participants (Fig. 1a , Supplementary Figs. 1 2 3 4 , Extended Data Fig. 1 , Supplementary Tables  1  and 2 , and Supplementary Note). The microarray technology used in most cohorts limited us to the analysis of only 1. 5% of the ~28 million DNAm sites across the genome  12  , including 96% of CpG islands and CpG shores and 99% of RefSeq genes  13  , and all inferences relate only to these sites.
Each DNAm site, for which a mQTL in cis had been detected, had a median of two independent variants (IQR = 4 variants; Supplementary Fig. 5 ). For all subsequent analyses, we used index SNPs from clumping procedures to be conservative and unbiased due to the nonindependence of genetic variants. We sought to replicate the mQTLs in the Generation Scotland cohort (n = 5,101) using an independent analysis pipeline. Replication data were available for 188,017 of our discovery mQTLs (137,709 sites). We found a strong correlation of effect sizes for both cis and trans effects (Pearson's r = 0. 97, n = 155,191 and 0. 96, n = 14,465, at P < 1 × 10 -3 , respectively; Fig. 1c ); 99. 6% of the associations had a consistent direction of effect (Supplementary Note). At Bonferroni's corrected threshold of 0.
We found that these inferences were often shared across other tissues. DNAm sites with low or intermediate DNAm levels have similar DNAm distributions in 12 tissues (Supplementary Figs. 18-20) with stronger enrichments in blood datasets for the enhancer states, indicating some level of tissue specificity for mQTLs in these regions (Supplementary Figs. 12, 14 and 21). To investigate whether mQTLs are tissue specific, we compared the correlation of effect estimates of cis-and trans-mQTLs in blood against adipose tissue (n = 603)  23  and brain (n = 170)  9  (Supplementary Note and Extended Data Fig. 5 ). We found a larger extent of QTL sharing of blood and adipose tissue compared with blood and brain, which might be explained by shared cell types, in line with cis-expression (e)QTL findings  24.
the significance was categorized as: *FDr < 0. 001; **FDr <1 × 10 with a mean of 473 SNP-DNAm site pairs in 1,000 permuted datasets (OR = 1. 36, Fisher's P = 6. 5 × 10 -7 and empirical P < 1 × 10 -3 ; Supplementary Fig. 25 ). To summarize, our results show that trans-mQTLs are in part driven by long-range cooperative TF interactions and that, for a small proportion of interchromosomal trans-mQTLs, the spatial distance in vivo is likely to be small. Trans-mQTL effects form DNAm communities. Genetic variation can perturb chromatin activity  32, 35, 37  , DNAm 8 or gene expression  38  across multiple sites in cis and trans, revealing coordinated activity between regulatory elements and genes. We observed that there were 1,728,873 instances where a SNP acting in trans also associated with a cis-DNAm site (before LD pruning).