dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, the tools can be complex to use; they are command-line tools and may require computational resources specially for the metagenomics datasets. In this context, selecting the best tools, configuring them to use the correct parameters and appropriate computational resources and combining them together in an analysis chain is a complex and error-prone process. These issues and the involved complexity are blocking scientist from participating in the analysis of their own data. Furthermore, bioinformatics tools are often manually executed and/or patched together with custom scripts. These practices raise doubts about a science gold standard: reproducibility  [3, 4]. Web services and automated pipelines such as MG-RAST  [5]  and EBI metagenomics  [6]  offer solutions to the accessibility issue.
A framework built on the shoulders of giants The ASaiM framework is built on existing tools and infrastructures and combine all their forces to build an easily accessible and reproducible analysis platform. ASaiM is implemented as portable virtualized container based on Galaxy framework  [8]. Galaxy provides researchers with means to reproduce their own workflows analyses, rerun entire pipelines, or publish and share them with others. Based on Galaxy, ASaiM is scalable from single CPU installations to large multi-node high performance computing environments. Deployments can be archived by using a pre-built ASaiM Docker image, which is based on the Galaxy Docker project ( http://bgruening. github. io/docker-galaxy-stable ) or by installing all needed components into an already existing Galaxy instance.
This ASaiM Docker instance is customized with a variety of selected tools, workflows, Interactive tours and data that have been added as additional layers on top of the generic Galaxy Docker instance. The containerization keeps the deployment task to a minimum. The selected Galaxy tools are automatically installed from the Galaxy ToolShed  [10]  ( https://toolshed. g2. bx. psu. edu/ ) using the Galaxy API BioBlend  [11]  and the installation of the tools and their dependencies are automatically resolved using packages available through Bioconda ( https://bioconda. github. io ). We migrated then 10 tools/suites of tools and their dependencies to Bioconda ( e. g. HUMAnN2) and integrated 14 suites into Galaxy ( e. g.
Analysis of raw metagenomic or metatranscriptomic shotgun data A workflow quickly produces, from raw metagenomic or metatranscriptomic shotgun data, accurate and precise taxonomic assignations, wide extended functional results and taxonomically related metabolism information (Figure  1 ). This workflow consists of i) processing with quality control/trimming (FastQC and Trim Galore!) and dereplication (VSearch  [14]  ; ii) taxonomic analyses with assignation (MetaPhlAn2  [12]  ) and visualization (KRONA , GraPhlAn); iii) functional analyses with metabolic assignation and pathway reconstruction (HUMAnN2  [13]  ); iv) functional and taxonomic combination with developed tools combining HUMAnN2 and MetaPhlAn2 outputs. This workflow has been tested on two mock metagenomic datasets with controlled communities (Supplementary material).
We have compared the extracted taxonomic and functional information to such information extracted with the EBI metagenomics' pipeline and to the expectations from the mock datasets. With ASaiM, we generate more accurate and precise data for taxonomic analyses (Figure  2 ): we can access information at the level of the species. More informative data for metabolic description (gene families, gene ontologies, pathways, etc) are also extracted with ASaiM compared to the ones available on EBI metagenomics. With this workflow, we can investigate which taxons are involved in a specific pathway or a gene family ( e. g. involved species and their relative involvement in different step of fatty acid biosynthesis pathways, Figure  3 ). For the tests, ASaiM was deployed on a computer with Debian GNU/Linux System, 8 cores Intel(R) Xeon(R) at 2.
Documentation and training A tool or software is easier to use if it is well documented. Hence extensive documentation helps the users to be familiar with the tool and also prevents mis-usage. For ASaiM, we developed an extensive online documentation ( http://asaim. readthedocs. io/ ), mainly to explain how to use it, how to deploy it, which tools are integrated with small documentation about these tools, which workflows are integrated and how to use them. In addition to this online documentation, Galaxy Interactive Tours are included inside the Galaxy instance. Such tours guide users through an entire analysis in an interactive (step-by-step) way. Some tours, included in every Galaxy instance, explains how to use Galaxy. We also developed such tours dedicated specifically to the ASaiM workflows.
These interactive tours are used to complement tutorials and trainings. Some tutorials about the integrated workflows have been developed to explain step-by-step the workflows with small example datasets. Hosted in the Galaxy Training Network (GTN) GitHub repository ( https://github. com/galaxyproject/training-material ), the tutorials are available online at http://training. galaxyproject. org. They have been used during several workshops on metagenomics data analysis with ASaiM as training support. These tutorials are also accessible directly from ASaiM and its documentation for self-training. Installation and running ASaiMRunning the containerized ASaiM simply requires to install Docker and to start the ASaiM image with: $ docker run -d -p 8080:80 quay. io/bebatut/asaim Thanks to Docker, ASaiM can be installed under every operating systems, even with a graphical tool (Kitematic: https://kitematic.
Built on a Galaxy instance wrapped in a Docker image, ASaiM can be easily deployed with a comprehensive set of tools and their dependencies. These tools are complemented with a set of predefined and tested workflows to address the main microbiota questions (community structure and functions). All these tools and workflows are extensively documented online ( http://asaim. readthedocs. io ) and supported by Galaxy Interactive Tours and tutorials. With this complete infrastructure, ASaiM offers a good environment for sophisticated microbiota analyses to scientists without computational knowledge, while promoting transparency, sharing and reproducibility. MethodsFor the tests, ASaiM was deployed on a computer with Debian GNU/Linux System, 8 cores Intel(R) Xeon(R) at 2. 40 GHz and 32 Go of RAM.
An effort in development was made to integrate these tools into Conda and the Galaxy environment, with the help and support of the Galaxy community. We also developed two new tools to search and get data from EBI Metagenomics and ENA databases using the API of the databases (EBISearch and ENASearch) and a tool to group HUMAnN2 outputs into visualization are included such Phyloviz for phylogenetic trees or the Genome browser Trackster for visualizing SAM/BAM, BED, GFF/GTF, WIG, bigWig, bigBed, bedGraph, and VCF datasets.
Availability of supporting source code and requirements • Project name: ASaiM • Project home page: https://github. com/ASaiM/framework • Operating system(s): Platform independent • Other requirements: Docker • License: Apache 2 All tools described herein are available in the Galaxy Toolshed ( https://toolshed. g2. bx. psu. edu ). The Dockerfile to automatically install deploy ASaiM is provided in the GitHub repository and a pre-built Docker image is available at https://quay. io/repository/bebatut/asaim-framework.
An effort in development was made to integrate these tools into Conda and the Galaxy environment, with the help and support of the Galaxy community. We also developed two new tools to search and get data from EBI Metagenomics and ENA databases using the API of the databases (EBISearch and ENASearch) and a tool to group HUMAnN2 outputs into visualization are included such Phyloviz for phylogenetic trees or the Genome browser Trackster for visualizing SAM/BAM, BED, GFF/GTF, WIG, bigWig, bigBed, bedGraph, and VCF datasets. InterProScan Combination of functional and Combine MetaPhlAn2 and taxonomic results HUMAnN2 outputs Visualization Export2graphlan, GraPhlAn , Krona This table presents the tools, organized in section and subsections to help users. A more detailed table of the available Workflows tools and some documentation can be found in the online documentation ( http://asaim.
Diverse source of dataAny easy way to upload user-data into ASaiM is provided by an web-interface or more sophisticated via FTP or SFTP. Moreover, we added specialised tools that can interact with external databases like NCBI, ENA or EBI Metagenomics to query them and download data into the framework. integrated two other tools to explore and represent the community structure from outputs of MetaPhlAn: KRONA (Ondov et al. 2011) and GraPhlAn. Moreover, as in any Galaxy instance, other customizable workflows are proposed in ASaiM. All the available workflows with tool and parameter choices are documented ( http://asaim. readthedocs. org/ ).
Another line of work is to design an optimal dedicated training program for the EyeCane. It is worthwhile to mention that when first introduced, the white cane requires hours of dedicated training with a special mobility trainer. It will be interesting to test whether perceptual learning mechanisms enable Eye-Cane users to perform even better or even avoid upper body obstacles completely. Finally, future work should test the use of the EyeCane in the real world over longer and more significant periods. Specifically, this should include collecting users' testimonies, investigating the Eye-Canes' efficiency in detection and avoidance of various real-life obstructions during everyday life, and exploring its acceptance by blind and visually impaired individuals.
Acknowledgments The authors wish to thank  E. Harow ,  T. Behor ,  Z. Jaron ,  U. Shemesh ,  A. Doron  and  R. Habusha  for their help in running the experiment, and  Itay Blumenzweig  for his help in building the obstacles. This work was supported by The  European Research Council  Grant ( 310809 ), a  James S. McDonnel Foundation scholar award  (no. 220020284 ), haubenstock stiftung, The  Edmond and Lily Safra Center for Brain Sciences (ELSC) ,  The Martier  family foundation Grant.
As part of an effort to validate the edge plasma model in the SOLPS-ITER code suite under ITER-relevant divertor plasma and neutral conditions, we report on progress in the modeling of the Alcator C-Mod divertor plasma with the new code. We perform simulations with a complete drifts model and kinetic neutrals, including effects of neutral viscosity, ion-molecule collisions and Ly α -opaque conditions, but assuming a pure deuterium plasma. Through a series of simulations with varying divertor geometries, we show the importance of including neutal leakage paths through the divertor substructure on the divertor plasma solution. Moreover, the impact of drifts on inner-outer target asymmetries is assessed. Including both effects, we achieve excellent agreement between simulations and upstream and outer target Langmuir Probe data.
Introduction The ITER divertor has been designed on the basis of extensive edge plasma simulations using the SOLPS4 code suite  [1]. Recently, a new version of the code, named SOLPS-ITER, has been adopted at the ITER Organization  [2, 3]. The SOLPS-ITER code is a coupling between the B2. 5 code version from SOLPS5. 2  [4]  , containing the most advanced numerical treatment of fluid drifts, and an up-todate version of the kinetic neutral transport solver EIRENE  [5]. As part of a continuous effort to benchmark and validate the new code, we have started a numerical study of the Alcator C-Mod divertor  [6]. C-Mod is of particular interest to ITER due to its high magnetic field, high density, and target shaping representative for the ITER divertor. Divertor densities and fluxes to the targets are only a factor of 3 lower compared to the ITER baseline conditions  [7].
Moreover, drifts are known to be strong on C-Mod  [8]  , and hence pose a strong challenge for the model. After an initial study of the Alcator C-Mod divertor using fluid neutrals and excluding drifts in Ref. [6]  , we report here on further progress in the modeling of the C-Mod divertor plasma using the SOLPS-ITER code suite. We present the first results of fully coupled SOLPS-ITER simulations for C-Mod with drifts fully activated and using an up-to-date kinetic neutral model from EIRENE, but without impurities. In particular, we investigate the effect of neutral leakage through the sub-divertor structure and drifts on the in-out target asymmetries typically observed experimentally in forward field. The simulations are compared to data from a discharge of the divertor bypass experiments  [9].
As is typical for C-Mod discharges, the inner target LP data shows clear detachment near the strike point, while the outer target is in a high recycling state. This characteristic in-out asymmetry will provide a strong test for the model, in particular for the effect of drifts. To study the effect of field reversal, we also use data from discharge 1070725014 at 700 ms, which has similar main discharge parameters as our forward field shot. We note, however, that the radiated power in the core region for this shot is estimated at P rad ∼ 200 kW, hence lower than the forward field shot. Moreover, the new C-Mod divertor was already installed, where the inner bridge and nose indicated in Fig. 1  are removed, and the inner target is now parallel to the major axis, see e. g. Ref. [8].
Plasma and neutral models We use the SOLPS-ITER code suite  [2, 3]  to model the SOL and divertor plasmas. The code has the coupled B2. 5 and EIRENE codes as main transport solvers. B2. 5 solves a set of continuity and parallel momentum equations for the density n a and parallel velocity u ||, a of each ion species a , and internal energy equations for the electron and (common) ion temperatures T e and T i. The plasma potential is solved from the current continuity equation. As a first important improvement compared to our previous modeling efforts  [6]  , the full drifts model from SOLPS5. 2 is activated  [4]. For now, we still assume a pure deuterium plasma. A second large step forward in our modeling is the use of a kinetic description of atoms and molecules through EIRENE  [5]  , instead of the fluid neutral model available in B2.
Conclusion In this contribution, we have reported on progress in the modeling of the Alcator C-Mod divertor plasma using the SOLPS-ITER code suite. Using simulations with a complete kinetic neutral model but no drifts, we show the importance of including neutral leakage paths through the divertor plenum in the model. The model predicts that only if the divertor closure is nearly perfect, very high neutral pressures and correspondingly low divertor temperatures can be achieved in the entire divertor volume. As soon as a small to moderate leakage path is available, which in C-Mod will always be the case due to relatively large intrinsic leakage paths, the neutral pressure drops significantly. The divertor settles into a regime where the strength of the leakage through the plenum is not determined by the plenum conductance, but rather by the conditions at the (outer) strike zone, and hence the divertor and main plasma conditions become rather insensitive to the actual size of this conductance.
The R package lcmm provides a series of functions to estimate statistical models based on linear mixed model theory. It includes the estimation of mixed models and latent class mixed models for Gaussian longitudinal outcomes (hlme), curvilinear and ordinal univariate longitudinal outcomes (lcmm) and curvilinear multivariate outcomes (multlcmm), as well as joint latent class mixed models (Jointlcmm) for a (Gaussian or curvilinear) longitudinal outcome and a time-to-event that can be possibly left-truncated right-censored and defined in a competing setting. Maximum likelihood esimators are obtained using a modified Marquardt algorithm with strict convergence criteria based on the parameters and likelihood stability, and on the negativity of the second derivatives. The package also provides various post-fit functions including goodness-of-fit analyses, classification, plots, predicted trajectories, individual dynamic prediction of the event and predictive accuracy assessment.
Introduction The linear mixed model  (Laird and Ware 1982; Verbeke and Molenberghs 2000; Hedeker and Gibbons 2006; Fitzmaurice, Davidian, Verbeke, and Molenberghs 2009)  has become a standard statistical method to analyze change over time of a longitudinal Gaussian outcome and assess the effect of covariates on it. Yet longitudinal data collected in cohort studies may be too complex to enter the framework of the linear mixed model: -the longitudinal outcomes are not necessarily Gaussian but possibly binary, ordinal or continuous but asymmetric (e. g. , absence/presence of symptoms, psychological scale); -not only one but several longitudinal outcomes may be collected, especially when the interest is in a biological or psychological process that cannot be measured directly (e. g. , quality of life, cognition, immune response); -the longitudinal process may be altered by the occurrence of one or multiple times-toevent (e.
Post-fit functions are also provided in order to assess the goodness-of-fit of the models, their predictive accuracy, as well as to compute outputs and average or individual predictions from the models. In its current version (V1. 7. 4), lcmm includes four main estimation functions: hlme, lcmm, multlcmm and Jointlcmm. The paper is organized as follows. Section 2 defines the statistical models implemented in the package and Section 3 details the common estimation process. Section 4 describes the implementation of the four estimation functions and gives details on the management of initial values. Section 5 details post-fit analyses and computations. Sections 2, 3 and 5 can be skipped if the reader is already familiar with the statistical methodology. Finally, Section 6 provides a series of examples based on the paquid dataset available in the package and Section 7 concludes.
Implementation The package currently includes 4 estimation functions: -Univariate latent process mixed models possibly including latent classes are estimated with lcmm; -Multivariate latent process mixed models possibly including latent classes are estimated with multlcmm or mlcmm; -Joint latent class mixed models are estimated with Jointlcmm or jlcmm. The four estimation functions rely on estimation programs (log-likelihood computation, optimization algorithm) written in Fortran 90. This section describes the calls of these functions and details the initialization of the iterative algorithm. The package also includes other functions  (generic, post-fit, etc). Table  1  gives the list of the main functions. The exhaustive list can be obtained with code ls("package:lcmm"). The post-fit functions are detailed in section 5.
Example of call The functionalites of hlme are detailed in section 6. 2 using the paquid dataset. We give here two simple examples of calls with data_hlme, a simulated dataset available in the package: R> hlme1 <-hlme(Y ~Time * X1, random =~Time, subject = ID , ng = 1, + data = data_hlme) R> hlme2 <-hlme(Y ~Time * X1, random =~Time, subject = ID , ng = 2, + data = data_hlme, mixture =~Time, classmb =~X2 + X3, B = hlme1) From dataset data_hlme, the first call (hlme1) fits a standard linear mixed model in which the dependent variable Y is explained according to Time, X1 and the interaction betwen Time and X1. Two correlated random effects are assumed for the intercept and Time. These random effects are grouped by ID, the identification variable for the subjects. The second call fits a 2-class linear mixed model (hlme2) in which the dependent variable Y is explained again according to Time, X1 and the interaction betwen Time and X1 but the intercept and the effect of Time are different in class 1 and 2.
Example of call The functionalites of lcmm are detailed in section 6. 3 using the paquid dataset. We give here two simple examples of calls with data_lcmm, a simulated dataset available in the package: R> lcmm1 <-lcmm(Ydep2 ~poly(Time, degree = 2, raw = TRUE), random =~Time, + subject = ID , data = data_lcmm) R> lcmm2 <-lcmm(Ydep2 ~poly(Time, degree = 2, raw = TRUE), random =~Time, + subject = ID , data = data_lcmm, link = "5-quant-splines") From dataset data_lcmm, the first call (lcmm1) fits a standard linear mixed model in which the dependent variable Ydep2 is explained according to a quadratic function of Time at the population level (fixed effects), and a linear function of Time at the individual level with 2 correlated random effects on the intercept and Time (the random effect on the quadratic function of Time is not relevant on these data).
Example of call The functionalites of multlcmm are detailed in section 6. 4 using the paquid dataset. We give here an example of call with data_lcmm, a simulated dataset available in the package: R> mlcmm1 <-multlcmm(Ydep1 + Ydep2 + Ydep3 ~X1 * poly(Time, degree = 2, + raw = TRUE), random =~Time, subject = ID , data = data_lcmm, + link=c("linear","3-quant-splines","3-quant-splines")) From dataset data_lcmm, the call fits a latent process mixed model for three dependent variables Ydep1, Ydep2, Ydep3 in which the latent process underlying the three dependent variables is explained at the population level (fixed effects) according to a quadratic function of Time for each level of the binary covariate X1, and at the individual level according to a linear function of Time with 2 correlated random effects on the intercept and Time.
Jointlcmm call The call of Jointlcmm (or of its shortcut jlcmm) is Jointlcmm(fixed, mixture, random, subject, classmb, ng = 1, idiag = FALSE, nwg = FALSE, survival, hazard = "Weibull", hazardtype = "Specific", hazardnodes = NULL, TimeDepVar = NULL, link = NULL, intnodes = NULL, epsY = 0. 5, range = NULL, cor = NULL, data, B, convB = 1e-4, convL = 1e-4, convG = 1e-4, maxiter = 100, nsim = 100, prior, logscale = FALSE, subset = NULL, na. action = 1, posfix = NULL, partialH = FALSE) Most arguments of Jointlcmm call are the same as those of hlme or lcmm calls. Arguments defining the class-specific survival model are added. Argument survival is a two-sided formula that defines the structure of the survival model. The left-hand side includes a Surv object as defined in survival package  (Therneau 2013).
1. 5. Finally, with a unique cause of event, the class-specific cumulative incidence is: P (T i ∈ (s, s + t], δ i = 1|X Si , c i = g; θ G ) = P (T i ∈ (s, s + t]|X Si , c i = g; θ G ) = S i (s|X Si , c i = g; θ G ) -S i (s + t|X Si , c i = g; θ G ). ( 21 ) With multiple causes of event (P > 1), the class-specific cause-specific cumulative incidence is: P (T i ∈ (s, s + t], δ i = p|X Si , c i = g; θ G ) = s+t s λ p (u | c i = g; θ G ) exp - P l=1 A l (u | c i = g; θ G ) du (22) with λ p (t | c i = g; θ G ) the cause-p-specific instantaneous hazard defined in (10) and A p (t | c i = g; θ G ) the corresponding cumulative hazard. When P > 1, the cause-specific cumulative incidence requires the numerical computation of the integral. This is achieved by a 50-point Gauss-Legendre quadrature. Individual dynamic predictions can be computed for any subject (included or not in the dataset used for estimating the model).