dataesr/scientific-paragraphs-categorization huggingface dataset:
The greatest risk to a young investigator is to be seduced by a new approach which turns out to have hidden pitfalls that preclude success. CTEMPs first provides deep training so that potential users can correctly identify if the technology they read about is truly appropriate for their goals. Finally, collaborating as a community is essential to remove the financial and logistical barriers to adoption of new tools. In the case of CTEMPs, they provide users with hundreds of thousands of dollars worth of equipment at low (for professors) or no (for students) cost. But cost is just one of the barriers. People think of 3D printing and microprocessor-based data systems as "cheap", but they forget the knowledge base required to be successful in using these technologies. After all, how else can we explain the rather low rate of penetration of these technologies into hydrological science given they have been readily available for a decade? In this vein, forming a community approach will be transformative, and the OPEnS lab is an example of how to remove these critical barriers to the adoption of new technology.
ACKNOWLEDGMENTS The authors thank  A. Barendregt  for help with mass spectrometry measurements. The  ESRF  is thanked for providing beamtime (Proposal  CH4593 ). This work was financially supported by the  Debye Graduate Programme  ( NWO Project 022. 004. 016 ), and  ESRF   Graduate Programme. The mass spectrometry research was performed within the framework of  NWO  and supported by the large scale proteomics facility Proteins@Work (Project  184. 032. 201 )embedded in The  Netherlands Proteomics Centre.
With the obtained stress-strain curves in uniaxial and cyclic shear tests, the material parameters suited to aluminum alloy sheet AA7055 can be determined through the use of an appropriate inverse hybrid numerical-experimental methodology  [25]. The relevant numerical and experimental result are the displacement-load responses. The inverse method here is to search the minimum error value between simulation and experiment responses. The Trust Region reflective method is used, which suits itself to the nonlinear least squares optimization problem. The optimization process involves the approximate solution of a large linear system based on the method of preconditioned conjugate gradients. The optimization algorithm is coded within MATLAB and Python script in conjunction with the FE commercial software ABAQUS.
The Trust Region reflective method is used, which suits itself to the nonlinear least squares optimization problem. The optimization process involves the approximate solution of a large linear system based on the method of preconditioned conjugate gradients. The optimization algorithm is coded within MATLAB and Python script in conjunction with the FE commercial software ABAQUS. Note that the use of only these two tests cannot allow the accurate determination of all model parameters such as the determination of micro-cracks closure parameter h and distortional parameters ( l1 c X , l2 c X , l1 p X ). In this study, the micro-cracks closure parameter is assumed to be h = 0. 2. The distortional effect in this study will be ignored by considering l1 l2 l1 p c c X X X   . The set of material parameters for the AA7055 is summarized and given in Table  1.
The more precise and extensive is the available calibration dataset, the less is the model uncertainty. For reservoir sedimentation surveys, needs regarding field data are significant and must focus on the river geometry, flow hydraulics but also on sediment features. A frequent issue in projects is that sediment sample collection and lab experiments are often challenging, costly and time consuming. Another issue is that the characterization of transported and deposited sediments concerns several parameters for which standardized methods may not exist yet. Rather than providing a detailed view of one specific project, this paper presents several illustrating cases that demonstrate the robustness and replicability of a generic approach dedicated to reservoir sedimentation management. In particular, in and ex-situ experimentations implemented to provide sediment calibration data are described, as well as essential validation stages required for numerical modelling surveys.
Two and three-dimensional modelling: TELEMAC-2D & 3D -SISYPHE SISYPHE is the sediment module coupled with TELEMAC-2D and TELEMAC-3D which solve the 2D shallow water equations or Navier Stokes equations respectively. Finite elements or finite volume numerical schemes can be used. Similarly to the 1D code, a 2D or 3D advection-dispersion equation deals with the suspended sediments. For cohesive sediments Krone and Partheniades are also used to treat the source terms of this equation. For noncohesive sediment, the net sediment flux is determined by comparing a reference suspended concentration estimated near the bed to an equilibrium near-bed concentration (Zyserman-Fredsoe, van Rijn or Bijker Formulae). The main parameters describing sediments in TELEMAC-MASCARET models are concentrations and grain sizes in the bed, settling velocities, critical shear stress for erosion and deposition and Partheniades coefficient for erosion.
Kapichira reservoir (Malawi) Kapichira reservoir, on the Shire River in Malawi, has experienced significant deposition since its completion in 2001. Field data have been collected at site, including bathymetry survey data, suspended sediment samples and bed sediment samples. The grain-size analysis of bed materials shows that the deposits consist in both sand and fine sediment. This field dataset has enabled the set up and calibration of a numerical model (Telemac-3D) which is able to reproduce the sediment deposits in the reservoir since the building of the dam. The model takes into account at the same time the transport of fine cohesive sediment (in suspension, with Partheniades erosion law) and non-cohesive sediment (using a total load transport formula). The result of this calibration is presented in Figure  2.
Single-cell RNA-sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.
ZINB-WaVE leads to novel biological insights. To demonstrate the ability of ZINB-WaVE to lead to novel biological insights, we focused on two inferential questions typical of scRNA-seq studies: (i) the identification of developmental lineages and (ii) the characterization of rare cell types. First, we re-analyzed a set of cells from the mouse olfactory epithelium (OE) that were collected to identify the developmental trajectories that generate olfactory neurons (mOSN), sustentacular cells (mSUS), and microvillous cells (MV)  29. In the original publication, the data were normalized by full-quantile normalization, followed by a regression-based adjustment for sample quality. Clustering on the first 50 principal components identified cellular states that were then ordered into developmental lineages by Slingshot  30  using the first five PCs.
and implemented μ ij ¼ E Y ij jZ ij ¼ 0; X; V; W ½ and π ij ¼ Pr Z ij ¼ 1jX; V; W ð Þ. We model ln(μ) and logit(π) with the regression specified in the figure. Note that the model allows for different covariates to be specified in the two regressions; we have omitted the μ and π indices for clarity (see Methods for details) in the R package Seurat  34. We modified the clustering procedure to use ZINB-WaVE (with K = 10) instead of PCA as the dimensionality reduction step. To visualize the clustering results, we applied t-SNE to the inferred W matrix (Fig. 3d ). Our approach recapitulates the major cell populations found in Zheng et al. 32  (Fig. 3d, e ). In particular, 80% of the cells are Tcells (Clusters 0-1 CD4+ T-cells; Clusters 2-6 CD8+ T-cells). As in Zheng et al. 32  , we are able to identify populations of activated cytotoxic T-cells (Cluster 5; 9%), natural killer cells (Cluster 7; 6%), and B-cells (Cluster 8; 5%) (Fig.
ZINB-WaVE leads to a low-dimensional representation that is less influenced by technical variation and to tighter, biologically meaningful clusters. The ZINB-WaVE projection was robust to the number of genes selected (Supplementary Fig. 4 ) to as size factor). Other more complex, non-linear effects, such as those collectively known as batch effects, require more sophisticated normalization  28. Accordingly, a typical scRNA-seq pipeline will include a normalization step, i. e. , a linear or non-linear transformation of read counts, to make the distributions of expression measures comparable between samples. The normalization step is usually carried out prior to any inferential procedure (e. g. , clustering, differential expression analysis, or pseudotime ordering). In this work, we considered three popular between-sample normalization methods: total count (TC), trimmed mean of M values (TMM), and full-quantile (FQ) normalization (see Methods); we also compared the results to unnormalized data (RAW).
4b ) and glioblastoma (Fig. 4c ) data sets, while it was slightly worse than PCA (after FQ normalization) on the mESC data set (Fig. 4d ) and than ZIFA (after FQ normalization) on the V1 data set (Fig. 4a ). Interestingly, the overall average silhouette width was lower for the S1/CA1 and glioblastoma data sets than it was for the V1 and mESC data sets, suggesting that ZINB-WaVE leads to better clustering in more complex situations (e. g. , lower sequencing depth). ). e Heatmap of expression measures for marker genes for the 18 clusters found by our procedure: columns correspond to clusters and rows to genes; the value in each cell is the average log expression measure per cluster, centered and scaled so that each row has mean zero and standard deviation one Although the overall average silhouette width is a useful metric to rank the different methods in terms of how well they represent known global biological structure, looking only at the average across many different cell types may be misleading.
2 ). However, the clusters corresponding to media 2i and a2i are further segregated by batch (Fig. 5a ). Including indicator variables for batch in ZINB-WaVE (as columns of X) removed such batch effects, consolidating the clustering by medium (Fig. 5b ). This led to slightly larger average silhouette widths, both overall (Fig. 4d ) and at the cluster level (Fig. 5c ). Conversely, the average silhouette width for batch, a measure of how well the cells cluster by batch, was much larger for the model that did not include the batch covariate (Fig. 5d ), indicating that explicitly accounting for batch in the ZINB-WaVE model effectively removed the dependence of the inferred low-dimensional space on batch effects. Similar results were obtained by normalizing the data with the ComBat batch correction method  38  , implemented in the Bioconductor R package sva  39  (Supplementary Fig.
16 ). The advantage of ZINB-WaVE is the ability of including batch effects in the same model used for dimensionality reduction, without the need for prior data normalization. The mESC data set is an example of good experimental design, where each batch includes cells from each biological condition (this is known as a factorial design). Hence, it is relatively easy to correct for batch effects and, unsurprisingly, both ComBat and ZINB-WaVE successfully do so. The glioblastoma data set is an example of a more complex situation, in which there is confounding between batch and biology, each patient being processed separately  22. Luckily, the glioblastoma design is not completely confounded, as patient MGH26 was processed in two batches (Fig. 5e ). We used this feature of the data to test whether ZINB-WaVE was able to adjust for batch effects even in the presence of confounding.
Goodness-of-fit of ZINB-WaVE model. We compared the goodness-of-fit of our ZINB-WaVE model to that of a negative binomial (NB) model (as implemented in edgeR  41  ) and a hurdle model (as implemented in MAST  23  ). As previously noted in the literature, NB models, which are quite successful for bulk RNA-seq, are not appropriate for singlecell RNA-seq, as they cannot accommodate zero inflation. In particular, NB models poorly fit the data in terms of the overall mean count and zero probability (Supplementary Figs. 18  and 19 ) and appears to handle the excess of zeros by over-estimating the dispersion parameter (Supplementary Fig. 20 ). We also examined the goodness-of-fit of the MAST hurdle model, which is specifically designed for scRNA-seq. However, a direct comparison of MAST with NB and ZINB is cumbersome, due to differences in parameterization.
6 ). This is likely because one column of W acted as a de facto intercept, overcoming the absence of V and explaining why AIC and BIC are minimized at K = 4 (Supplementary Fig. 23 ). In addition, we observed robustness of the results to the choice of dispersion parameter Sample-level int. No sample-level int. Common disp. Genewise disp. 1 2 3 4 1 2 3 4 -2 -1 0 1 2 -2 -1 0 1 2 a Sample-level int. No sample-level int. Common disp. Genewise disp. For each gene and cell, bias and MSE were averaged over B = 10 data sets simulated from our ZINB-WaVE model, based on the S1/CA1 data set and with n = 1000 cells, J = 1000 genes, scaling of one for the ratio of within to between-cluster sums of squares (b 2 = 1), K = 2 unknown factors, zero fraction of about 80%, X = 1 n , cell-level intercept (V = 1 J ), and genewise dispersion.
Discussion Recent advances in single-cell technologies, such as droplet-based methods  2  , make it easy and inexpensive to collect hundreds of thousands of scRNA-seq profiles, allowing researchers to study very complex biological systems at high resolution. The resulting libraries are often sequenced at extremely low depth (tens of thousands of reads per cell, only), making the corresponding read count data truly sparse. Hence, there is a growing need for developing reliable statistical methods that are scalable and that can account for zero inflation. ZINB-WaVE is a general and flexible approach to extract lowdimensional signal from noisy, zero-inflated data, such as those from scRNA-seq experiments. We have shown with simulated and real data analyses that ZINB-WaVE leads to robust and unbiased estimators of the underlying biological signals.
Then, for each of B = 10 data sets, simulate γ μ and γ π from a bivariate Gaussian distribution using the mvrnorm function from the MASS package, with the mean and covariance matrix output by Mclust. To increase/decrease the zero fraction, increase/decrease each element of the mean vector for γ π inferred from Mclust (shifts of {0,2,5} for V1 data set and {-1. 5, 0. 5, 2} for S1/CA1 data set). 3. Create the ZINB-WaVE model using the function zinbModel from the package zinbwave. 4. Simulate counts using the function zinbSim from the package zinbwave. Simulating from the Lun & Marioni 42 model: To simulate data sets from a different model than our ZINB-WaVE model, we simulated counts using the procedure described in Lun & Marioni  42  (details in Supplementary Materials of original publication and code available from the Github repository https://github.
sh, to create a simulation function constructed by fitting a ZINB distribution to these counts. The script simulations/ submitter. sh was then run to simulate counts based on the estimated parameters (negative binomial mean, zero inflation probability, and genewise dispersion parameter). We simulated C = 3 clusters with equal number of cells per cluster. The parameters which were varied in the simulations are as follows. The number of cells was set to n = 100; 1000; 10,000. The proportion of zero counts, zfrac ¼ P i;j 1ðY ij ¼ 0Þ=nJ, was set via the zero inflation probability: zfrac ≈ 0. 4, 0. 6, 0. 8. For zfrac = 0. 4, we did not modify the code in Lun & Marioni  42. However, to simulate data sets with greater zero fractions, namely zfrac = 0. 6 and zfrac = 0. 8, we added respectively 0. 3 and 0.
6 to the zero-inflation probability (p ′ i , in their notation). For each of the 3 (n)) × 3 (zfrac) = 9 scenarios, we simulated B (B = 10) data sets, resulting in 9) × 10 = 90 simulated data sets in total. Negative binomial model: The negative binomial (NB) distribution is a special case of the ZINB distribution that does not account for zero inflation, i. e. , for which π = 0. Thus, E½Y ij ¼ μ ij ; P Y ij ¼ 0 À Á ¼ 1 þ ϕ j μ ij 1 ϕ j : A NB distribution was fit gene by gene to the V1 data set, after full-quantile normalization, using the Bioconductor R package edgeR 41 (Version 3. 16. 5) with only an intercept (i. e. , default value for the design argument as a single column of ones) and genewise dispersion. Model-based analysis of single-cell transcriptomics: The model-based analysis of single-cell transcriptomics (MAST) hurdle model proposed by Finak et al.
Silhouette widths may be averaged within clusters or across all observations to assess clustering strength at the level of clusters or overall, respectively. The silhouette width s i of a sample i is defined as follows: s i ¼ b i À a i max a i ; b i f g ; where a i ¼ d i; C clðiÞ À Á , b i ¼ min l≠clðiÞ d i; C l ð Þ, C clðiÞ is the cluster to which i belongs, and dði; C l Þ is the average distance between sample i and the samples in cluster C l. Average silhouette widths were used to compare ZINB-WaVE, PCA, and ZIFA on both real and simulated data sets. For simulated data, the cluster labels correspond to the true simulated W and, for each scenario, silhouette widths were computed and averaged over B data sets. For real data, the authors' cluster labels or known cell types were used. Clustering: Precision and recall: Two different clusterings (i.
We simulated 3 clusters and set the number of clusters equal to 3 in kmeans (argument centers). Precision and recall were computed using the extCriteria function from the R package clusterCrit  59. Dimensionality reduction: Correlation with QC measures: To evaluate the dependence of the inferred low-dimensional signal on unwanted variation, we computed the absolute correlation between each dimension (e. g. , principal component) and a set of quality control (QC) measures. For the V1 data set, FastQC (http://www. bioinformatics. babraham. ac. uk/ projects/fastqc/) and Picard tools (https://broadinstitute. github. io/picard/) were used to compute a set of 16 QC measures. These measures are available as part of the Bioconductor R package scRNAseq (https://bioconductor. org/packages/ scRNAseq).
For the glioblastoma and mESC data sets, we used the scater Bioconductor R package 60 (Version 1. 2. 0) to compute a set of 7 QC measures. For the S1/CA1 data set, we used a set of 6 QC measures provided by the authors (http://linnarssonlab. org/cortex/). Dimensionality reduction: Correlation of pairwise distances between observations: For simulated data, we assessed different dimensionality reduction methods (ZINB-WaVE, PCA, and ZIFA) in terms of the correlation between pairwise distances between observations in the true and in the estimated reduceddimensional space. In particular, we monitored the influence of the number of unknown cell-level covariates K when the other parameters are set correctly (V is a column of ones 1 J and genewise dispersion). For each simulation scenario, the correlation between true and estimated pairwise distances was computed and averaged over B data sets.
Acknowledgements We would like to thank  Olivier Mirabeau  from the  Curie Institute  for the pre-processing of the Glioblastoma data set. We also thank  Aaron Lun  and the  Marioni Lab  for making the code needed for the simulations available online. Finally, we are grateful to  Russell Fletcher  for his help with the re-analysis of the OE data. D. R. and S. D. are supported by the  National Institutes of Health BRAIN Initiative  (Grant  U01 MH105979 , PI:  John Ngai ). S. G. and J. -P. V. are supported by the  French National Research Agency  (Grant  ABS4NGS ANR-11-BINF-0001 ). J. -P. V. is supported by the  European Research Council  (Grant  ERC-SMAC-280032 ), the  Miller Institute for Basic Research in Science , and the  Fulbright Foundation.
Sediment plates are capable of recording and highlighting details of stratification to a very fine scale and high degree of precision. This method represents a valuable tool for research, education, and exhibition purposes. A dataset of 50 m 2 of sediment plates was created from the primary sedimentary structures emplaced during 1) the August 2006 eruption of Tungurahua volcano (Ecuador), 2) the Laacher See eruption (Germany) and 3) Astroni surge deposits (Campi Flegrei, Italy). This dataset has been put to use in several contexts: 1) analysis of sedimentation processes at the boundary-layer scale, 2) data comparison with remote measurements (e. g. , ground penetrating radar), 3) archiving and conservation, 4) as teaching material and 5) for dissemination exercises during exhibition in museums.
Sediment plates Sediment plates (a type of lacquer peels) are meterscale, planiform samples of unconsolidated deposits that have been preserved in their original state (including particle locations and orientations, textures and sedimentary structures) via hardening of an impregnated epoxy in situ in the field (Fig. 1a ). Here, we refer to "sediment plates", since the term "lacquer peels" implies the idea of sampling by the "peeling off", with its concomitant flexuring of an extremely thin skin of sediment (often glued on a cloth), whereas the dataset presented here is composed of rigid "boards" of hardened cross-sections of sediment strata. These sediment plates have a thickness of several particle diameters (typically 3-50 mm-thick) and possess areas of dm 2 to m 2. The technique for producing sediment plates consists of impregnating the surface of an outcrop with an initially low-viscosity, hardening glue.
(2015). e Lapilli-type sediment plate stabilized with mounting foam and wooden boards at Laacher See before cutting and extracting the plates from outcrop. f Wooden frames on which sediment plates were mounted during the exhibition "G'Schichten von Tungurahua" Here, a series of tests have been performed on three stratified pyroclastic outcrops of differing grain size distribution and chemical composition (Laacher See/Eifel -Germany, Astroni/Campi Flegrei -Italy, Tungurahua -Ecuador). The technique has also been employed to generate an unprecedentedly large dataset on sediments from pyroclastic currents triggered by the 2006 eruption of Tungurahua  (Douillet et al. 2018 a, Douillet et al. 2018 b). Here, we report on the methods tested to produce these sediment plates, the dataset they represent and a range of examples of their uses.