dataesr/scientific-paragraphs-categorization huggingface dataset:
The padma approach integrates multi-omic data by mapping omics measures to genes in a given pathway. Although this assignment of values to genes is straightforward for RNA-seq, CNA, and methylation data, a definitive mapping of miRNA-to-gene relationships does not exist, as miRNAs can each potentially target multiple genes. Many methods and databases based on textmining or bioinformatics-driven approaches exist to predict miRNA-target pairs (Riffo-Campos and others, 2016). Here, we make use of the curated miR-target interaction (MTI) predictions in miRTarBase version 7. 0 (Chou and others, 2018), using only exact matches for miRNA IDs and target gene symbols and predictions with the "Functional MTI" support type. Although the TCGA data used here have been filtered to include only those genes for which expression measurements are available, there are cases where missing values are recorded in other omics datasets (e.
g. , when no methylation probe was available in the promoter region of a gene, or when no predicted MTIs were identified) or where a given feature has little or no variance across individuals. In this analysis, features for a given omics dataset were removed from the analysis only if missing values are recorded for all individuals or if the feature has minimal variance across all individuals (defined here as < 10 -5 before scaling); any remaining missing values are mean-imputed, although more sophisticated imputation strategies, such as those proposed in the missMDA package, could be used instead  (Josse and Husson, 2016). After running padma, we remark that the first ten MFA dimensions represent a modest proportion of the total multiomic variance across pathways for both cancers (Supplementary Figure  5 ; BRCA median = 46.
, sample size, percentage of aberrant individuals in the population, and number of driver genes and omics) under which padma pathway deviation scores can correctly identify aberrant individuals. We compared the AUC values of padma with those of pathway deviation scores calculated using two alternatives: a PCA on all concatenated data tables (i. e. , where MFA per-table weights were not applied) and a padma single-omics approach. Full details of the simulation study may be found in the Supplementary Methods, and results are shown in Supplementary Figures  12 13 14 and Supplementary Table  6. Overall, we confirm the solid performance of padma across a wide range of settings, as well as an advantage in identifying aberrant individuals for padma compared to a PCA-based alternative, particularly in cases with smaller sample sizes (i.
85, respectively), and CASP8 (29. 72 and 22. 61, respectively). While a subset of five individuals from the top decile were all characterized by high deviation scores for JUN (TCGA-64-5775, TCGA-55-6972, TCGA-50-5051, TCGA-44-6779, TCGA-49-4488), several other genes appear to have relatively small deviation scores for all individuals plotted here (e. g. , PRF1, PARP1). In addition, we remark the presence of highly individualized gene-specific aberrations (e. g. , APAF1 in individual TCGA-55-7725). To provide an intuitive link between these gene-specific deviation scores with the original batch-corrected multi-omics data that were input into padma, we further focus on the three genes (CASP1, CASP3, and CASP8) for which large deviation scores were observed for the two highly aberrant individuals (TCGA-78-7536 and TCGA-78-7155) in the D4-GDI signaling pathway.
Our MFA-based approach allows investigators to (a) identify overall sources of variation (such as batch effects); (b) prioritize high variance pathways defined by variability across subjects; (c) identify aberrant observations (i. e. , individuals) within a given pathway; and (d) identify the genes and omics sources that drive these aberrant observations. As with any analysis of omic data we generally recommend that standard quality control analyses be performed (e. g. , genomewide PCA of each omic individually as in Supplementary Figures  3 4 , boxplots of normalized read count distributions for each sample) to identify any potential technical outliers before running padma. Here, we chose to remove known batch effects prior to the padma analysis, but in principle the method could be run on uncorrected data and MFA components correlated with undesired batch effects could be identified and removed prior to computing the pathway deviation score.
For large, multi-omic data such as TCGA, padma allows investigators to summarize overall variation and assist in generating hypotheses for more targeted analyses and follow-up studies. As a case in point, we identified two lung cancer patients with aberrant multi-omic profiles at three CASP genes. With access to the tumor samples and more fine-grained clinical data, future molecular experiments could help to clarify the role (if any) that these genes play in contributing to lung cancer mortality. Although the multi-omic TCGA data considered here were quite large (n = 144 and n = 504 matched samples for lung and breast cancer, respectively), Thioulouse (2011) recently suggested that descriptive methods like PCA and MFA can be used without limitation on the ratio between the number of samples and number of variables; as such we anticipate that padma could be useful even for more modestly sized multi-omic datasets.
g. , the most "diseased" as compared to a healthy reference); this strategy would be similar in spirit to the individualized pathway aberrance score (iPAS) approach, which proposed using accumulated (unmatched) normal samples as a reference set  (Ahn and others, 2014). There is also no reason to limit this approach to pathways, as the A. Rau and others analysis could be performed just once, genome-wide (accordingly, inferences would no longer be applicable to specific pathways). Here, we have structured the data with genes representing data tables and omics representing columns within each table. Alternatively, the data could be reweighted by having omics represented as data tables and genes as columns within each, similar to de Tayrac and others (2009). Extensions to our work could include incorporating the hierarchical structure of genes within pathways, or relatedness structure among samples.
Experimental Section CD4+ T Cells Cultivation and Editing: PBMCs were isolated from the leukocyte reduction system chambers obtained from the Blood Donation Center after informed donor consent. CD4+ T cells were isolated using CD4 MicroBeads (Miltenyi, Germany) and cryopreserved. After thawing, cells were cultivated in X-Vivo15 (Lonza, Switzerland) supplemented with 20 U mL -1 IL-2 (Miltenyi) and activated with T Cell Activation/Expansion Kit (Miltenyi). After 3 days, beads were removed and electroporation was performed by combining 5 × 10 6 CD4+ T cells with 10 µg of each TALEN mRNA or 5 µg of GFP mRNA. [41] Cells were electroporated in 100 µL electroporation buffer using P3 Primary Cell 4D-Nucleofector kit (Lonza) and program EO-115 or in 200 µL of BTXpress Cytoporation medium T (BTX, Harvard Bioscience, USA) (1 pulse 1200 V, 0.
Cytokine concentrations were determined using the Cytometric Bead Array for IFN-, IL-2, or TNF- (BD Biosciences). Cells were stimulated with CD3/CD28/CD2 beads (Miltenyi), 1 µg mL -1 ionomycin (Merck-Millipore, Germany), or 10 ng mL -1 PMA (Sigma-Aldrich). Off-Target Analyses: Potential OTs were predicted with PROGNOS (http://bao. rice. edu/cgi-bin/prognos/prognos. cgi) using the TALEN v2. 0 algorithm. [31] Five mismatches per half-sites were allowed. Loci were PCR amplified using primers listed in Table  S1 , Supporting Information. Libraries were prepared with NEBNext Ultra II DNA Library Prep Kit (NEB), quantified with ddPCR Library Quantification Kit for Illumina TruSeq (Biorad, Germany), sequenced on Illumina MiSeq platform using MiSeq Reagent Kit v2, 500-cycles (Illumina, USA), and data analyzed with CRISPResso2.
[49] p values were adjusted with the Benjamini & Hochberg  [50]  method. For OCA, 1 × 10 6 PBMCs (ALLCELLS, USA)/mL in X-Vivo15 medium (Lonza), supplemented with 5% human AB serum (Gemini, USA) and 20 ng mL -1 of IL-2 (Miltenyi), were activated using human T activator CD3/CD28 (ThermoFisher). 4 days later, 5 × 10 6 T cells in cytoporation medium T were electroporated with 20 µg of TALEN mRNA (10 µg per subunit) and 10 µL of pre-annealed oligodeoxynucleotides (dsODN, 100 µm) using AgilePulse MAX system (Harvard Apparatus, USA) and a 0. 4 cm cuvette. Genomic DNA was extracted 3 days later, randomly sheared to 300 bp fragments by sonication (Covaris LE220plus), fragments end-repaired/A-tailed (NEBNext Ultra End Repair/dA-Tailing Module), and NGS Y-adapters (TruSeq Annealed Adapter) added. Two rounds of anchored PCR using dsODN-specific and adapter-specific primers were performed.
Acknowledgements The authors thank  Boris Fehse  and the  NIH AIDS Research and Reference Reagent Program  ( Division of AIDS, NIAID ) for providing plasmids and PM1 cells,  Kay Ole Chmielewski  for help with OT analysis,  Ilona Skatulla  and  Petra Scheliga-Marschner  for technical support, the  Lighthouse Core Facility  and the  Blood Donation Center  (both  Medical Center-University of Freiburg ) for flow cytometry support and for providing PBMCs. This work was supported by a grant of the  German Federal Ministry of Education and Research  ( BMBF-01EO1303  to T. I. C. and T. C. ) and funded research cooperation with  Cellectis  ( ZVS20170614  to T. I. C. and T. C. ). Open access funding enabled and organized by Projekt DEAL.
e. beneficiaries of an internal promotion: representing 6 or 7 people every year, they follow the same lessons as trainee curators, but for the first six months only. They do an internship of one month in a institution and participate in an important pedagogical module on project management. They are not evaluated: at the end of the training, Enssib issues them a certificate of follow-up. As soon as they enter Enssib, they know their future institution and position, the institution most often being the one in which they were already employed in the lower administrative body, that of "librarians". The results of curator contests, since there are several ways to access this body, are mostly known from the first half of July, but the training of the laureates starts only on January 1 of the following year.
The policy of evaluating training by learners, either internally or through national surveys, is also reinforced as part of this reform. All trainings are subject to a double evaluation by the people who follow it: an oral evaluation, in group, carried out by the person in charge of the training at the end of it; an individual assessment online, at the end of each teaching module. The training evaluation policy is based on another system: as a member of the French Public Service Schools Network (RESP), Enssib is subject to a biennial survey, conducted by an external service provider, which, one year after their exit from Enssib, measures the satisfaction of the librarians and curators former trainees and that of the institutions which employ them. While the results obtained by the school are always difficult to interpret, they provide important trends in the evolution of positions and in the correspondence between the training provided and the expectations of trained staff and libraries.
These results are now presented to the Council of professionals, to contribute to further reflection. Enssib is also engaged in other steps: • a process of certification and label of trainings modules that the school does not provide itself, but to which it can add value; • to be included in national databases as a training provider; • an involvement in local or national networks that allow to compare and share best practices: we can notably mention that of continuing training structures under the University of Lyon, with which the Enssib has signed, in 2017, a quality commitment charter on "Lifelong learning". These mechanisms, while not directly benefiting the training of probationary officials, have strengthened the school's pedagogical expertise. Finally, to carry out its reform, Enssib has made its internal organization evolve: the Department of studies and internships, which includes 34 people including 14 teachers-researchers and 20 functional staff, was reorganized into large poles, around training but also transversal ones: a bureau for internships and professional integration, a second for academic procedures and students activities, another one for distance learning.
MFIT is a new open-source, Windows-based computer package for the analytical modeling of tracer BTCs. This software integrates four transport models that are all capable of simulating single-or multiple-peak and/or heavy-tailed BTCs. The four transport models are encapsulated in a general multiflow modeling framework, which assumes that the spatial heterogeneity of an aquifer can be approximated by a combination of independent one-dimensional channels. Two of the MFIT transport models are believed to be new, as they combine the multiflow approach and the double-porosity concept, which is applied at the scale of the individual channels. Another salient feature of MFIT is its compatibility and interface with the advanced optimization tools of the PEST suite of programs. Hence, MFIT is the first BTC fitting tool that allows regularized inversion and nonlinear analysis of the postcalibration uncertainty of model parameters.
A generic multiflow modeling approach is therefore intuitively appealing for the interpretation of tracer tests in fractured and karst aquifers. Of course, the actual (and generally unknown) geometry of the discrete flow network experienced by the tracer is likely more complex than that depicted in Fig. 1. The channels are therefore not assumed to represent individual fractures or karst conduits but are lumped submodels of the main flow routes used by the tracer through the fractures/karst conduit network. The four transport models integrated in the MFIT software are based on the multiflow approach. The first model is a reimplementation of the MDM. The second model is a variant of the MDM that assumes an exponentially decaying injection of the tracer concentration at the inlet of the flow system.
In the third and fourth models, the double-porosity concept (2RNE model and SFDM) is applied at the scale of the individual channels. It is unclear whether this idea of combining multiflow and double-porosity systems is new. In the TRACI software, it is technically possible to fit a series of SFDM curves to a multimodal tracer BTC and then calculate the mean combined model curve, but to the best of the author's knowledge, this method has never been discussed or applied in the literature. A possible reason is the increasing number of fitting parameters, which makes the inverse problem more complicated. The use of modern inversion tools such as PEST enables overcoming this problem, as discussed in section 3. In this article, the combination of multiflow and double-porosity systems is referred to as the multi-double porosity (MDP) approach.
(  15 ) simplifies to Eq. (3). Table  1  summarizes the parameters of the four MFIT transport models. Table  1. Parameters of the transport models integrated in the MFIT software. The subscript j denotes a parameter that must be defined for each flow channel. The parameters without this subscript are common to all channels. 11 ) and (  15 ) is performed using the QAG adaptive integration routine from the GNU Scientific Library with a 61-point Gauss-Kronrod rule and a relative error convergence criterion of 10 -2. These four programs can be run as console applications to solve a direct (forward) problem, i. e. , computing a series of time-concentration values for a given set of model parameters. Both the input and output files are in ASCII format and can be edited with any text editor program for pre-/postprocessing.
A convenient alternative is to use the MFIT software as a GUI for these applications. The MFIT software has been developed using the C++ Builder environment (Embarcadero RAD Studio 10. 1 Berlin) and provides a GUI for (i) importation and graphic visualization of user-provided BTC data; (ii) parameterization, direct running, and graphical output of the analytical transport models; (iii) inversion (automatic calibration) of model parameters for optimal curve fitting; and (iv) assessment of the uncertainty of calibrated parameter values.
Model Both the optimization and uncertainty analysis of model parameters are carried out using PEST routines  (Doherty, 2019a (Doherty, , 2019b)). PEST is a public domain model-independent program suite that has been widely used over the past two decades, notably in the field of surface and subsurface hydrology (e. g. ,  Long, 2015; Woodward et al. , 2016; Gaudard et al. , 2017; Wang et al. , 2019). The theoretical framework and full range of capabilities of the PEST software are well documented  (Doherty et al. , 2010; Doherty, 2015 Doherty, , 2019a Doherty, , 2019b) )  and are not repeated here. Only the concepts and methods that were deemed to be the most relevant to the multiflow modeling approach and that have been made accessible through the MFIT GUI software are briefly reviewed below.
PEST is based on a gradient optimization method and, as such, requires the derivatives of model outputs with respect to the adjustable model parameters to be calculated in each iteration for implementing the Jacobian (sensitivity) matrix. As pointed out by  Doherty (2015) , the accuracy of these derivative calculations is critical to the performance of the PEST optimization algorithm. In the MFIT program suite, most of the model partial derivatives are calculated analytically and externally provided to PEST. This approach ensures both the accuracy and speed of this part of the optimization process. Less straightforward partial derivative expressions were derived using MAPLE and exported as C code using the MAPLE code generation routine. The partial derivative functions were implemented in the MDMi, MDMed, MDP_SFDM, and MDP_2RNE programs and are processed during the PEST system calls to these programs by providing an optional "/d" command line argument to the program name.
, 2019)  comparison of various algorithms. The PEST program suite includes two such global optimizers based on the SCE-UA method  (Duan et al. , 1992)  and the CMA-ES method  (Hansen and Ostermeier, 2001). The corresponding programs are named SCEUA_P and CMAES_P, respectively. It must be noted, however, that global optimization methods suffer from their own drawbacks, including sensitivity to tuning parameters and low computational efficiency. An alternative strategy to improve the chances of convergence toward the global optimum with gradient-based methods is the "multistart" approach, which consists of repeating the optimization process starting from different initial parameter value sets  (Skahill and Doherty, 2006; Piotrowski and Napiorkowski, 2011). Such a strategy has been implemented in the MFIT software.
Code verification The robustness of the PEST inversion program has been demonstrated in a number of studies, see, e. g. ,  Anderson et al. (2015)  and  Hunt et al. (2019) , and is not reassessed here. The purpose of this section is to assess the accuracy of MFIT direct simulations through five synthetic test cases. Tests 1 and 2 address the case of a single flow channel described as a singleporosity medium in which the transport is governed by advection-dispersion. An instantaneous injection of the tracer is assumed in test 1, whereas test 2 addresses the case of an exponentially decaying concentration at the inlet. A double-porosity medium, single flow channel is assumed in tests 3 and 4, which conform to the assumptions of the SFDM and 2RNE model, respectively. In test 5, the tracer is transported by advection-dispersion in a multiflow system composed of three channels.
The total duration of an injection is typically less than 3 min. 5. Monitoring the tracer BTC at the pumped well using a flow-through fluorometer (Albillia GGUN-FL22) connected to a branch pipe extending from the discharge line at ground level. The fluorometer is periodically calibrated in the laboratory with solutions of 10 µg l -1 and 100 µg l -1. To date, more than 70 cross-well tracer experiments have been performed at the HES. The purpose here is not to interpret each of these experiments but to pick a few examples for illustrating the application of the MFIT software. The selected data correspond to three tracer experiments that were performed in 2016 and 2017 using well M22 as the pumped well and M16, MP6, and P2 as injection wells. Fig. 4  shows the experimental BTCs and a collection of calibrated MFIT curves for different numbers of channels.
Fig. 5  displays the PHI(N) and PHI(P) curves summarizing the best-fitting results achieved with the use of the multistart PEST optimization and with the use of the SCEUA_P and CMAES_P global optimization routines. A number of observations can be made from this figure. As a first remark, the SCEUA curves for the two MDP models are missing in Fig. 5. The reason is that the SCEUA_P program has no "forgive-error" capability, i. e. , if a set of trial parameters causes the numerical evaluation of Eq. (  11 ) or Eq. (  15 ) to crash, the optimization process is stopped instead of moving to a new set of parameter values. Such a forgiveness option is available in the PEST and CMAES_P programs. The next observations that can be made from Fig. 5  are that the CMAES and SCEUA curves are (i) more irregular, (ii) always above or equal to their PEST-computed counterparts, and (iii) do not always follow the expected decreasing trend in the PHI value (meaning a better model fit) as the number of channels rises, as depicted by the PEST curves.
Acknowledgments. The  HES  field research facility is managed by  Gilles Porel. The experimental protocol used for the HES tracer experiments was developed by  Benoit Nauleau  and  Gilles Porel. The assistance of  Benoit Nauleau ,  Gilles Porel , and  Denis Paquet  in conducting the tracer experiments is gratefully acknowledged. Financial support. This research was supported by the  French National Observatory H+ , the  European Union (ERDF) , and " Région Nouvelle Aquitaine ".
Parameters Maximum number of calibration parameters for an n-channel solution MDMi (ADE, instantaneous injection) Q, mj, T0j, Pej 3n+1 MDMed (ADE, exponentially decaying C0, Qj/Q, T0j, Pej, γj 4n+1 injection) MD2P-SFDM Q, mj, T0j, Pej, βj 4n+1 MD2P-2RNE Q, mj, Lj, T0j, Pej, ψj, ωj 6n+13 Code implementation and inversionThe four analytical models described in the previous section have been implemented in C++ and compiled as executable Windows programs named MDMi. exe (for MDM, instantaneous injection), MDMed. exe (for MDM, exponentially decaying injection), MDP_SFDM. exe, and MDP_2RNE. exe. The code and executable files are freely available on the public Zenodo repository https://doi. org/10. 5281/zenodo. 3470751. In the MDP_SFDM and MDP_2RNE programs, the numerical evaluation of the integrals in Eqs.
MULTIAGENT SYSTEM The community energy network is modeled as a multiagent system where agents are nodes and the environment is everything outside the agent. The state of the environment is represented by sensory data including the charge levels of batteries, indoor and outdoor temperatures, temperature limits for comfort, the prices of energy and possible limits for energy flows. The actions of agents are the amount of energy pulled from or injected to the grid. The model represents a structure of agent inter-actions. It extends a multiagent model of a building by aggregating different buildings and district battery using AGRNet. This net can be viewed as a local energy market where district actors negotiate their energy exchange. AGRNet is also connected to an external source of power that represents a connection point between the community and the main grid.