dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods The earth system model of intermediate complexity used in this study is the so-called iLOVECLIM model (version 1. 0), which is a code fork of the LOVECLIM climate model version 1. 2  (Goosse et al. , 2010). The physical climate components (atmosphere (ECBilt), ocean (CLIO) and vegetation (VECODE)) are the same, yet iLOVECLIM differs in the iceberg and the ice-sheet (GRISLI) module included.
Discussion In the presented study the coupling between the ice-sheet model GRISLI and the earth system model of intermediate complexity iLOVECLIM and the dynamical iceberg module was further developed. This set-up was used to investigate the impact of icebergs on climate and the ice sheet itself in a fully coupled low-resolution model. Modelling iceberg calving is a complex task as small-scale processes are involved which we cannot expect to represent with the 40 × 40 km resolution of GRISLI. Still, the calculated calving sites fit reasonably to observations, as do the modelled iceberg trajectories. Moreover, as we are particularly interested in the mechanisms behind the impact of icebergs on both the climate and the ice sheet, we would argue that these are relatively insensitive to the ice-sheet model resolution.
Acknowledgements. M. Bügelmayer is supported by  NWO  through the  VIDI / AC2ME  project no. 864. 09. 013. D. M. Roche  is supported by  NWO  through the  VIDI / AC2ME  project no. 864. 09. 013  and by  CNRS-INSU. The authors wish to thank  Christophe Dumas  for his advice and help in the coupling of the ice-sheet model to the iceberg module and  Catherine Ritz  for the use of the GRISLI ice-sheet model. Further, the authors wish to thank the reviewers for providing constructive comments on how to improve the quality of this paper. The authors also gratefully acknowledge the  Institute Pierre Simon Laplace  for hosting the iLOVECLIM model code under the LUDUS framework project (https://forge. ipsl. jussieu. fr/ludus). This is NWO/AC2ME contribution number  06. Edited by:  E. Larour.
Background: Quality control is a necessary step of any Next Generation Sequencing analysis. Although customary, this step still requires manual interventions to empirically choose tuning parameters according to various quality statistics. Moreover, current quality control procedures that provide a "good quality" data set, are not optimal and discard many informative nucleotides. To address these drawbacks, we present a new quality control method, implemented in UrQt software, for Unsupervised Quality trimming of Next Generation Sequencing reads. Results: Our trimming procedure relies on a well-defined probabilistic framework to detect the best segmentation between two segments of unreliable nucleotides, framing a segment of informative nucleotides. Our software only requires one user-friendly parameter to define the minimal quality threshold (phred score) to consider a nucleotide to be informative, which is independent of both the experiment and the quality of the data.
This procedure is implemented in C++ in an efficient and parallelized software with a low memory footprint. We tested the performances of UrQt compared to the best-known trimming programs, on seven RNA and DNA sequencing experiments and demonstrated its optimality in the resulting tradeoff between the number of trimmed nucleotides and the quality objective. Conclusions: By finding the best segmentation to delimit a segment of good quality nucleotides, UrQt greatly increases the number of reads and of nucleotides that can be retained for a given quality objective. UrQt source files, binary executables for different operating systems and documentation.
Other approaches trim unreliable nucleotides at the head and tail of each read. With these approaches, one wants to find the best trade-off between removing unreliable nucleotides and keeping the longest reliable or informative subsequence for the entire read. Current trimming approaches rely on two types of algorithms: the running sum algorithm and the window-based algorithm (for a review see  [4] ). However, these algorithms only return good local cutting points for each read when it is necessary to find a good global cutting point to get the best trade-off between removing unreliable nucleotides and losing too much information. Moreover, most of these QC strategies rely heavily on manually chosen parameters that are difficult to interpret and cannot be easily automatized. In the present work, we have developed the program UrQt to trim unreliable nucleotides at the heads and tails of NGS reads based on their phred scores.
[4]. One singleend RNA sample from the species Homo sapiens of poor overall quality and one single-end RNA sample of good overall quality from the species Arabidopsis thaliana. For the DNA sample, we used one paired-end sample from the species Prunus persica of excellent overall quality and one paired-end DNA sample from the species Saccharomyces cerevisiae of average quality. Finally, we also included one paired-end RNA sample from the species Homo sapiens of overall good quality. The seven data sets (Table  1 ) were downloaded from the NCBI website. Rather than using the complete data set, we uniformly sampled 500,000 reads from each experiment using the software fastq_sampler. py (available at https://github. com/ l-modolo/fastq_sampler), to speed-up the computation and work with comparable reads number for each sample.
For the paired-end data, each end was mapped independently. We examined the number of mapped reads on the corresponding reference genomes (Table  1 ) for different quality thresholds (Figure  4  and Additional file 4 for the seven data sets). The same mapping procedure was also performed using BWA  [14]  (version 0. 7. 10) (with default parameters) (Additional file 3). We observed that UrQt was the only software that consistently increased the number of mapped reads for all data sets. The other programs provided the desired effect only for data sets of overall poor quality, such as for the single-end H. sapiens data (SRR002073), and produced worse results than those obtained by mapping the raw data for data sets of better quality (Figure  4 ). For the single-end H. sapiens data, we observed that UrQt better respected the chosen threshold, thus producing worst results than the other programs for the low quality threshold.
For example, with this dataset and a threshold of 5, we expect a large number of reads with an average quality slightly above 5 which are difficult to map. This respect of the threshold can also be seen for the paired-end H. sapiens data (SRR521463) or the D. Figure  4  Remaining information in the trimmed data for each software. Mapping performances for different quality threshold. The choice of t corresponds to the parameter -t for UrQt, -q for Cutadapt and Sickle and SLIDINGWINDOW:4:t for Trimmomatic. The black line corresponds to raw (untrimmed) data, and R1 and R2 correspond to the two ends of paired-end data. melanogaster RNA data (SRR919326) and a low threshold of 5 where UrQt is the only program that produces results comparable to the raw data (see Additional file 4). For data sets of excellent quality, such as P.
persica (see Additional file 4), all the trimming programs except for UrQt deteriorated the mapping performances compared with the ones obtained by mapping the raw data. This result provides additional evidence of better trimming cut-points identified by UrQt compared with the ones found by other procedures that remove too many nucleotides for data sets of excellent quality. When considering the output of a mapping software, we can discriminate between reads, which map to a unique position and reads, which map to multiple positions. The number of reads mapping at multiple positions depends on three factors: the number of reads associated with repetition, the sensitivity of the mapping procedure (we can expect more reads mapping at multiple positions when allowing for more missmatches and gaps), and the information contained in the reads.
Thus with trimming procedures, the information loss of over-trimming could lead to an increase of the number of reads mapping at multiple positions. This over-trimming effect can be seen with Cutadapt, Trimmomatic and Sickle for high threshold values (superior to 20) (see Additional file 3 for the results with Bowtie2 and BWA). However, with UrQt, the number of reads mapping to unique position increase with the choice of the threshold which is also consistent with better cut-point. These results hold for every dataset with the exception of the H. sapiens RNA sample of poor overall quality (SRR002073) for which removing a large number of uninformative nucleotides also correspond to removing a large number of reads. Overall, the results obtained with UrQt correspond to the expected results for a trimming procedure and a given quality threshold in opposition to the other programs in our test panel (see Additional file 3 and 5).
Conclusions UrQt is a new tool for the key QC step of any NGS data analysis to trim low-quality nucleotides and polyA tails from reads in fastq or fastq. gz format with an efficient C++ implementation. By finding the best segmentation to delimit a segment of informative nucleotides, UrQt greatly increases the number of reads and of nucleotides that can be retained for a given quality objective. Using this software should provide a significant gain for many NGS applications. Moreover, the consistency of our trimming procedure with the quality of the trimmed data set for a given quality threshold, will allow for better automation of the trimming step in a pipeline. We also provide a galaxy wrapper for UrQt to facilitate its integration in existing pipelines developed on this platform  [19] [20] [21].
Quantitative systems pharmacology (QSP) models are progressively entering the arena of contemporary pharmacology. The efficient implementation and evaluation of complex QSP models necessitates the development of flexible computational tools that are built into QSP mainstream software. To this end, we present MatVPC, a versatile MATLAB-based tool that accommodates QSP models of any complexity level. MatVPC executes Monte Carlo simulations as well as automatic construction of visual predictive checks (VPCs) and quantified VPCs (QVPCs). CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 547-557; doi:10. 1002/psp4. 12011; published online on 22 August 2015. VPC is a model diagnostic tool that facilitates the evaluation of both the structural and the stochastic part of a model. 4] [5]  Its superiority over comparable diagnostic tools has been established  6  and reflected by the fact that regulatory agencies recommend it as one of the central model diagnostics.
7 ] [10]  These features aid in better visualizing the data and include but are not limited to summary statistics and binning of the simulated and observed data. Extensions to the VPC have also been developed aiming for a highly objective model evaluation. A VPC extension example includes QVPC, wherein the distribution of the observations around the predicted median trend is rigorously realized. 11 o ease the VPC construction process, several practical software products have been developed that automatically generate comprehensive VPC plots, which are challenging to generate otherwise. 6] [17]  Monolix carries out the simulations and uses the outcome to generate the VPC plots, whereas PsN uses NONMEM  18  as the simulation platform and performs the percentile calculations. Subsequently, the latter are plotted using Xpose.
The immense practicality of these tools, however, comes with its set of shortcomings: (i) they inevitably carry out parameter estimation of a model before constructing its VPC; (ii) they do not offer extensions to the VPC; and (iii) generating VPCs of high complexity datasets may be devious. These drawbacks render these tools unattractive for constructing VPCs of high complexity models or of models that have already been built in different software and require no further parameter estimation. 9] [30] [31] [32] [33] [34] [35] [36] [37]  VPC plots that are generated with these tools are generally not as comprehensive  31, 38  lacking important features, such as confidence intervals of simulation percentiles or data binning. As it was previously discussed, VPCs that lack these features may not be sufficiently informative.
1, 9, 11 he progressive incorporation of QSP models in the regulatory arena  39  necessitates the development of flexible computational tools that are built into software commonly used for such models; these tools can be then routinely utilized in evaluating and simulating QSP models. To this end, we developed MatVPC: a flexible, userfriendly MATLAB-based tool that carries out Monte Carlo simulation as well as VPC construction of QSP models of any complexity level. MatVPC accommodates any model, independently of the software used for model development, while offering the majority of options that comparable tools list. Provided that certain features of a model may not be readily visualized in a VPC,  11  MatVPC grants the additional option of constructing QVPC plots of the available data.
MATVPC TUTORIAL In this tool, the user simply inputs: (i) the model equations; (ii) the model parameters; and (iii) the NONMEM-like dataset with observations, and MatVPC outputs VPCs, QVPCs, or Monte Carlo simulation plots at will. The inputted model parameters here include macrokinetic or microkinetic constants, initial conditions of ordinary differential equations (ODEs) and variability terms; MatVPC allows for implementing both interindividual variability and RUV, which can be in the form of proportional, additive, or combined error. The dataset should be provided in a comma-separated values format including, at a minimum, the following headers in no particular order and in a case-insensitive manner: Once the user provides the aforementioned information, they must insert in the command line: (i) the name of the dataset; and (ii) the stratification variables, if any, as follows: MatVPC ('dataset.
Please note that the former option requires the user to simply provide a desired name for the model, unlike the MATLAB file ODEs, wherein the user provides the model equations. Option 4 is expected to be useful in cases where simulations of a model have already been carried out and the user wants to simply reconstruct VPCs with modified characteristics, such as different color lines or altered binning edges. Parenthetically, when this option is exploited, the simulation-related parameters should be set to the same values as in the original VPC construction. Upon providing all the above, a user-friendly interface commences allowing the user to modify the default values in a series of optional inputs. This input includes options related to the methods (e. g. , ODE solver to be used or number of datasets to be simulated) as well as options that determine the graphical settings (e.
plotting curves). Table 1 details the accepted optional input along with the default values. It is important to stress that, unlike analogous tools that are traditionally used in pharmacometrics, MatVPC constitutes an all-in-one package that integrates the following: 1. It is publicly open (at https://sourceforge. net/projects/ matvpc/); 2. Constructs VPC plots of complex QSP models; 3. Offers automatic binning of the observed and simulated data using a rigorous approach 9,40 ; 4. Constructs QVPC plots of complex QSP models; 5. Performs Monte Carlo simulations of the model and plots the results with any requested summary statistics; 6. Does not require prior implementation (e. g. , parameter estimation) of the model within MATLAB; 7. Endows the user with the option to perform postprocessing of the VPC, QVPC, or simulation plots.
In this interface, the user can modify the default values of 40 characteristics of the VPC and QVPC plots (for a detailed description of these features see Table  1 ). The VPC and QVPC plots shown in Figure  3  were constructed using the input values shown in this figure. Upon completion of the above steps, the following command must be typed in the command window: MatVPC('obs. csv',{'study'}). This command requests a VPC that is based on the dataset called obs. csv and is stratified by study. Once the user inserts this command and presses "enter," they are asked to provide the name of the model and whether they want to simply create a VPC from previously saved simulation results or to simulate new data and plot the VPCs. Following their response (y/n), MatVPC opens a user-friendly interface with optional input that bears default values.
Modifying these characteristics can also be done using the command line, but it is not recommended as it is usually rather laborious. More details about this can be found on MATLAB's website (http://www. mathworks. com/ help/symbolic/edit-graphs. html?searchHighlight5edit). As the sampling times in this case were asymmetric, we exploited the automatic binning option of MatVPC to bin the data of model one (i. e. , "yes" was selected in the "automatic binning" option). The VPC and QVPC plots of model one as generated by MatVPC are shown in Figure  3. VPC plots of model one were also constructed with Xpose and PsN (running the simulations in NONMEM and using the bin edges calculated by MatVPC) and are also provided in Figure  3. For simplicity, we only show the results from compartment two of each study but the agreement between MatVPC plots and Xpose plots was consistent for all compartments.
In this example, the term "params(5)" was added to Eq. 5 as the IV infusion is given in compartment one (see file "parameters" and "ODEs" in Figure  4 ). Similarly to model one, in order to generate the VPC and QVPC plots of model two using MatVPC, the user must open the MATLAB file "parameters" and insert the model parameters. Once the parameters have been provided, the user must open the MATLAB file "ODEs" and type the ODEs shown in Eqs. 5 and 6, along with the additional term that accounts for IV infusion dosing. A snapshot of the completed MATLAB files "parameters" and "ODEs" file is depicted in Figures  4a, b , respectively. Please note that in file "parameters" an additional parameter, "params(5)50," has been listed. As shown in file "ODEs," this parameter has then been incorporated in the first ODE of the model (see last term in first ODE).
Upon inserting this information in the two files, the user should type in the command window the following: "MatVPC('obs. csv')". MatVPC then asks the user to provide the model name and to define whether they want to construct VPC plots from scratch or to capitalize on previously generated simulation data. Upon completing these steps, a MATLAB interface with the optional input pops up. A snapshot of this interface with the optional input used for model two is shown in Supplementary Figure  S3. Please note that automatic data binning has been requested through this interface as the sampling timepoints were not consistent and manual binning of data would be challenging. After pressing "OK" at the bottom of this interface, MatVPC undergoes all the necessary steps and constructs the VPC and QVPC plots of model two.
Model three: PD model describing the time-course of body weight The third case example is a formerly presented highly nonlinear PD model that describes the time-course of body weight. 42 he simulated dataset includes observations from a total of 500 subjects that are involved in two different studies, 250 subjects belonging to study one and 250 subjects belonging to study two. The difference between the two studies here is the sampling times. The PD model is provided in Eq. 7: dy 1 dt 5k in 2k out Á y 1 Á 11 DSTIM Á k de k de 2k rel Á ðe 2k rel ÁTime 2e 2k de ÁTime Þ Á kde (7) whereby k in is the rate of weight gain, k out is the rate of weight loss, k de and k rel are associated with the onset and loss of the lifestyle intervention effect and DSTIM is the maximum fractional increase in k out caused by the lifestyle intervention.
The total of the black and grey bar combined presents the percentage of available data (here 100%). three are shown in Figures  6a, b , respectively. As the data originate from two distinct studies, the user would need to stratify on the study number. To do so, the following should be typed in the command window: "MatVPC ('obs. csv',{'study'}). " This command requests VPC and QVPC plots of the PD model shown in Eq. 7, using the dataset called "obs. csv" while stratifying on the variable "study. " Once the user defines the name of the model, and whether they want to use previous simulation results, the MatVPC interface with the optional input appears. The optional input used in the construction of VPCs for model three is provided in Supplementary Figure  S6. Upon pressing "OK" in this interface, the VPCs and QVPCs of the model are generated and saved.
DISCUSSION We have presented a MATLAB-based tool, dubbed MatVPC, that simulates and generates VPC and QVPC plots of QSP models. The plots generated by MatVPC were compared with plots constructed by the gold standard tools in the pharmacometrics community, PsN (with NONMEM) and Monolix,  43  and the results were interchangeable across all case studies. Despite its flexibility, MatVPC does have its drawbacks. At present, MatVPC generates QVPC plots using only the available data and does not account for missing data. 11 In addition, it can currently run only on a single computer and is not designed for running in parallel processors. However, MatVPC will be continuously advanced and its future versions will address these imperfections. MatVPC is publicly available and can be utilized by users with little or no prior MATLAB experience.
This technique has been successfully applied for studying interactions between the matrix and human breast cells  [33] , and mapping scar formation in histological tissue sections  [34, 35]. Compared to edge detection algorithms that have been developed for rapidly detecting fiber orientation in micrographs, such as algorithms based on Sobel or Canny operators  [36] [37] [38] , this technique has improved accuracy. In this study, we develop a 3D weighted orientation vector summation technique in MATLAB for the rapid and accurate quantification of fiber orientation at each voxel within an image stack. We describe our methodology, including an overview of the analysis algorithm and evaluation of the technique performance. We assess the accuracy of this detection algorithm by simulating images with known orientations, monitor computational time with varying image and window sizes, and make a comparison between 3D and 2D techniques.
Evaluation of the vector summation technique performance To evaluate the accuracy of this vector summation technique to detect fiber orientation, images with known orientations were generated and assessed. First, to determine the relationship between measurement error and fiber thickness, 7 image stacks with a size of 200 200 200 × × voxels with 28 cylinders were produced with different diameters of 3, 5, 7, 9, 11, 13 and 15 pixels. To generate these cylinders, the center of each cylinder could be a random voxel within the stack, and then the cylinder was formed by stretching across the space based on randomly defined θ and ϕ orientations (using the rand MATLAB function), which covered the full range of (0°, 180°) and known during the image generation. The only difference among these 7 stacks was the cylinder diameter.
5 and 3 arbitrary unit (a. u. ). Finally, to evaluate the performance of the algorithm for the analysis of curvy collagen fibers, we created a 312 312 500 × × voxel stack with spirals. The thickness of the spirals was 5 pixels in diameter. Then we made a comparison between 3D and 2D techniques. We created 8 fiber stacks with a size of 150 150 150 × × voxels. The diameter was 5 pixels, and each stack had approximately 35 fibers. To mimic the breast tissue model where parallel sheets of collagen fibers dominated the alignment, the ϕ orientation of approximately 72% of fibers in each stack was between 80° and 100°, and that of the rest of fibers was randomly generated within the range of (0°, 80°) and (100°,180°). For all the fibers, the θ orientation was randomly defined (the rand MATLAB function).