dataesr/scientific-paragraphs-categorization huggingface dataset:
Noticeably less editing was detected in the 1st replica of ADAR1p150 because of the considerable rRNA contamination that led to overall low coverage and shallow editing detection (Figure  2  -figure supplement 3B). Still, when combining all samples and by considering all sites that were also covered by more than 5 reads in the negative control, we could identify 9. 400 of more than 100. 000 editing sites known in mice and stored in REDIportal  (Licht et al. , 2019; Picardi et al. , 2017). Those sites were subjected to further filtering to acquire reliable sets of editing sites for each ADAR1isoform. First, we excluded all sites also detected in the RFP transfection control. Next, we removed sites with low coverage (less than 5 reads per replicate) and editing rates <1%. Finally, only sites edited in 2 out of 3 replicas and sufficiently covered in both isoform-datasets (p150 and p110, average ≥ 10 reads) were subjected to further analysis (Figure  2  -figure  supplement 3C ).
Next, we assessed editing in repetitive versus non-repetitive regions. ADAR1p150 edits more nonrepetitive parts (≈ 23%) in comparison to ADAR1p110 (≈ 16%) (Figure  2E ). 3'UTRs that are preferentially edited by ADAR1p150 carry in general higher conservation than introns that are mostly edited by ADAR1p110. The vast majority of editing in repetitive regions by both ADAR1 isoforms occurs in short interspersed nuclear (SINE) elements (Figure  2F. Closer examination of editing events in repeats revealed also editing in LTR and long interspersed nuclear (LINE) elements (Figure  2F ). Moreover, we studied ADAR1-editing in 'hyperedited' regions. Those regions contain many potential editing sites and frequently bear repetitive regions, in particular inverted SINEs. In our dataset, ADAR1p150 was a considerably greater editor of those hyperedited regions, which was becoming more evident when only sites with a clear preference for ADAR1p150 or ADAR1p110-editing (sites that are edited 2,5 × better by one isoform than another) were considered (Figure  2G ).
A fourth replicate was included where 10 µg of plasmid DNA was electroporated. However, as this replicate did not contain the ADAR1P193A sample, it was not included in the final analysis. After STAR alignment, background signals occurring in the negative control were subtracted, and the remaining sites were subjected to further filtering. Sites edited to 0. 5% by any mutant protein were collected into the final dataset. Overall editing in cells electroporated with 5 µg DNA was low. However, editing patterns remained similar as for cells transfected with 10 µg of DNA (Figure  3   2 ). Using these normalized values, we could validate the majority of ADAR1p150-selected sites. However, individual sites in Tra2a, Rpa1 and Rad23 were preferentially edited by ADAR2 and cytoplasmic ADAR2. Our initial experiments did not include ADAR2, and hence, the occurrence of sites ADAR2-editing sites next to ADAR1p150 sites cannot be excluded.
Overall, the data show that ADAR1 p150 RIP-seq peaks have a larger overlap with sites edited by p150 while there seems less overlap with sites precipitated by ADAR1 p110 and those that are edited by ADAR1p150 and ADAR1p110. However, it should also be noted that RIP-seq identified significantly fewer peaks for ADAR1p110. This observation, together with preferential intronic binding of ADARp110 versus ADAR1p150 binding to exonic, 3'UTR regions, indicates that the identified peaks provide a valuable dataset of high specificity. Still, we cannot exclude that overexpression of ADAR1 leads to promiscuous binding and, hence ADAR1p110 could bind substrates that are otherwise exclusive for ADAR1p150. However, apart from the N-terminus of ADAR1p150, ADAR1isoforms are identical  (George and Samuel, 1999; Patterson and Samuel, 1995).
More than 100,000 known editing sites identified mostly in mouse brain are currently available on REDIportal (http://srv00. recas. ba. infn. it/atlas/search. html) (Picardi et al. , 2017). Of these, we detected 9,400 sites by restoring ADAR1-mediated editing in ADAR-deficient MEFs. Using additional stringent filtering criteria, we obtained a comprehensive, high-quality dataset of more than 3000 editing sites. We found ~2000 editing sites efficiently edited by one or another ADAR1-isoform. While ADAR1p110 edits almost exclusively in introns, ADAR1p150 seems a powerful editor of 3'UTRs. As expected, the majority of editing sites is located in SINE elements. Nevertheless, ADAR1p150 showed a bigger portion of editing in non-repetitive regions, which suggests that ADAR1p150-mediated editing might be more conserved among species.
, 2018). In our TRIBE experiment, these hyperedited regions were preferentially edited by ADAR1p150. This indeed raises the question of whether sufficient editing of these hyperedited 3'UTRs is a key and essential feature of ADAR1p150-mediated editing. A recent study from Kim et al. showed only limited editing sites that are exclusively edited by ADAR1p150 to prevent MDA5-activation  (Kim et al. , 2021). In this study, mouse knockout models including an ADARp110 specific knockout in the presence or absence of an additional ADAR2 deficiency were used to characterize the remaining ADAR1p150-mediated editing in brain and thymus. We intersected the exclusive-ADAR1p150 editing pattern identified in that study with the ADAR1 p150 editome identified here in MEFs. We found a big overlap between the datasets.
This might explain the lack of phenotype in ADAR1 P195A/ P195A mice and the lack of AGS patients carrying homozygous P193A mutations  (Maurano et al. , 2021b; Rice et al. , 2017). In our study, we determined the characteristic of ADAR1-isoform-specific editing and binding. We obtained a comprehensive, high-quality dataset that identifies specific and overlapping substrates between ADAR1p150 and p110. Our data indicate that editing specificity of ADAR1p150 is at least partly driven by its cytoplasmic localization. A proper genetic mouse model with a physiological expression of either cytoplasmic ADAR1p110 or cytoplasmic ADAR2 in an otherwise editing-free background would be required to fully estimate the role of the cytoplasmic localization and ZBDa in ADAR1p150-specificity. Additionally, we identified sites with no obvious sensitivity to unspecific cytoplasmic editing; putative exclusive ADAR1p150 sites.
ADAR-RIP NGS data analysis Sequenced reads were aligned to the human reference genome (H. sapiens/GRCh38) using STAR aligner version 2. 5. 2a (Dobin et al. , 2013). RIP-seq peak calling was performed with Genrich version 0. 6 (https://github. com/jsh58/Genrich, parameters: -q 0. 05 -a 200). Genrich was originally designed for genomic enrichment assay. The peaks were called on all replicates collectively for each ADAR1p150, ADAR1p110 and mock transfection. Respective input RNA samples were used as control samples. To allow strand-specific peak calling by Genrich, forward and reverse strands of a chromosome were treated separately. Analysis was performed on peaks found in two or more replicas where p-values were below 0. 1. Peaks appearing in empty-transfection control (PEI) were extracted from the final peakset using bedtools (version 2.
Data deposition NGS data generated in this study are available at GEO with the following accession numbers: ADAR1 RIP-seq data: GSE188937 Editing data of transfected MEFs: GSE188842      Mouse embryonic fibroblasts (MEFs) generated from ADAR1 -/-, ADAR2 -/-knockout mouse embryos, were electroporated with ADAR1p150, ADAR1p110 or RFP mammalian expression vector. 24 hours later cells were harvested and RNA was extracted. rRNA-depleted RNA-seq libraries were prepared and subjected to NGS. Editing was examined at know-editing sites. (Kim et al. , 2021). The coverage of overlapping sites is almost 2-fold lower in the thymus sample. Kim, J. I. ,  Nakahama, T. , Yamasaki, R. , Costa Cruz, P. H. , Vongpipatana, T. , Inoue, M. , Kanou, N. , Xing, Y. , Todo, H. , Shibuya, T. , et al. (2021). RNA editing at a limited number of sites is sufficient to prevent MDA5 activation in the mouse brain.
Acknowledgement: The authors would like to thank  Katarina Milanovic  for her help with amplicon-seq. Tanja Rohr  and  Alwine Hildebrandt  for excellent technical support to. We also thank  Cornelia Vesely ,  Mamta Jain ,  Andrea Tanzer  and  Konstantin Licht  for helpful discussions and valuable input. The ADAR1 and ADAR2 vectors were a kind gift of  Mary Anne O'Connel (Ceitec, Brno, Czech Republic ) and  Ron Emeson  ( Vanderbilt University, Nashville, TE ). Illumina sequencing was performed at the Core Facilities of the  Medical University of Vienna , a member of VLSI. The Amplicon-seq experiment was performed by the  Next Generation Sequencing Facility at Vienna BioCenter Core Facilities (VBCF) , member of the  Vienna BioCenter (VBC) ,  Austria. Funding:.
This work was funded by the Austrian Science Fund (FWF) grant numbers I2893, P30505, F8007 to MFJ. RK was supported by the Austrian Science Fund (FWF) doctoral program W1207. AFL was supported by the Austrian Science Fund (FWF) doctoral program DOC 32_B28 doc. fund. AT was supported by the Austrian Science Fund (FWF) Elise Richter program V-762.
We used Arabidopsis genomic DNA extracted from seedlings that was either PCR amplified (ampDAP, DNA cleared of methylation) or not amplified (DAP, DNA retaining methylation). Both experiments were performed in triplicates with high reproducibility (Supplemental Figure  1 ; Supplemental Table  1 ). As controls, we used two TFs described as methylation sensitive based on available ampDAP and DAP datasets  (O'Malley et al. , 2016)  (Supplemental Figure  2 ). For each genomic region bound by a given TF, we plotted the DAP/ampDAP signal ratio as a function of i) the methylation density in the whole bound region (based on Arabidopsis seedling methylation maps  (Zhang et al. , 2016)  (Figure  1A-C ). ), and ii) the number of methylated cytosines within the best TF binding site (TFBS), identified using position weight matrices in each bound region (Figure  1D-F ).
In particular, the hydrophobic contacts between LFY and DNA are likely to be enhanced by the presence of a methyl group in positions 4 and 5 of the LFY binding site (LFYBS), consistent with the DAP versus ampDAP analysis (Figure  1G ). How much this weak sensitivity to DNA methylation might help LFY to perform its master function during flowering remains to be determined. Such computational analysis has the potential to be generalized to all TFs for which DAP and ampDAP data are available. It represents a powerful complement to methylation-sensitive SELEX (systematic evolution of ligands by exponential enrichment) analysis which was used to detect the effect of methylation to TF-DNA binding using randomized DNA sequences  (Yin et al. , 2017).
Bioinformatic analyses Regions bound by LFY in vitro, to methylated and non-methylated genomic DNA, were detected from DAP-seq data generated in this study as described previously  (Lai et al. , 2020). In vivo LFY bound regions were obtained from ChIP-seq data from two weeks-old seedling 35S::LFY tissue  (Sayou et al. , 2016)  and inflorescence tissue of 35S:LFY-GR ap1 cal  (Goslin et al. , 2017). DNA accessibility, nucleosomes position and probability of methylcytosines were obtained from processed DNaseI-seq, MNase-seq and bisulfite sequencing data  (Zhang et al. , 2012 , Zhang et al. , 2015 , Zhang et al. , 2016) , respectively. Microarray analyses were performed on published data of LFY overexpression lines (GEO: GSE911)  (William et al. , 2004) , and (GEO: GSE28062)  (Winter et al. , 2011)  and data from lfy lines (GEO: SE576)  (Schmid et al.
Acknowledgements We thank  K. Kaufmann  for discussions. This project was supported by the  ANR-DFG  project  Flopinet  ( ANR-16-CE92-0023-01 ) to C. Z. and F. P. , and GRAL, a program from the  Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes  ( ANR-17-EURE-0003 ). to C. Z. , F. P. and A. S.
We also collected 50 projects made using digital fabrication, which employees of AdaptSchool designed or co-designed between 2013 and 2020. We refer to these as projects as each might have been produced several times, iterated on but only one version is used, and might contain several objects (e. g. , construction kit). We identified the projects through our observations, interviews, and the documentation produced by employees. All data excerpts were translated into English by the field researcher. We did not collect quantitative data such as time spent using ideation software or digital fabrication tools. Participants found quantitative data collection as invasive or unrepresentative of their work. We believe that pushing against quantitative research approaches reflects a broader conflict around managerial approaches of health and education relying on data they find unrepresentative of their practices or harmful.
Data Analysis We had initially organised the data by projects made by participants and coded it inductively, focusing on themes such as design characteristics of the projects, tools use for design (in ideation, modelling tools, fabrication), uses in teaching, the role of the making and professional community and of personal interests. We used a Qualitative Data Analysis (QDA) software (Nvivo). However, analysing data this way proved difficult: projects are negotiations between educational and making practices, which overlap and impact each other. We could not use QDA software to represent and explore these dynamics. We also noticed during coding that the codes and themes developed were similar to the components of Engeström's  [14]  Activity System Model (described in section 3. 4. 1). Moreover, we wanted to write an account that could be useful to practitioners.
It is an essential step given the quantity of data involved (2000 pages of notes and transcripts along with collected documents). We used the activity system model to tag data across sources (interviews, observations, documents) as they pertained to the projects collected. We then wrote-up projects as a timeline of linked activity systems (Figure  2 , Step 1. An excerpt of the timeline is also available in Supplementary Material). Once we could compare projects, we focused on identifying factors shaping practices, which we confirmed with the participants. In other words, we looked at how several projects contributed to a larger activity system the organisation, through which our participants defined what digital fabrication could or should be used for (Figure  2 , Step 2). We take the introduction of each digital fabrication tool as the initial questioning of practices (first step of the expansive learning cycle).
ACKNOWLEDGMENTS We thank our research participants for sharing their expertise, in particular  Nathalie Bedouin ,  Laurence Boulade  and  Grégoire Denis , as well as all the children involved in our fieldwork. Many thanks to  Bess Williamson  and  Nolwenn Maudet  for their feedback. This work was supported by  ANR  ( Accessimap  - ANR-14-CE17-0018 ;  GESTURE -ANR-16-CE33-0023 ).
A numerical simulation of a traveling magnetic field using COMSOL software was carried out under identical geometry and conditions of AFRODITE configuration set-up. Fig. 6  gives the profile of the Lorentz force contribution in the horizontal direction (x) and the vertical component of the magnetic field (Bz), calculated at mid-thickness of the cavity (y = 5 mm) for various vertical levels (z = 0, 5, 10, 15 and 20 mm). Fig. 6  also shows that the corresponding effective skin depth of the magnetic field is approximately δ ≈ 5 mm and the corresponding magnetic field amplitude is around 45 mT for an applied current I = 8 A and frequency f = 50 Hz.
It also allows the use of two different mesh refinements. Enthalpy methods basically account for latent heat in energy equations by allocating a nodal latent heat value to each numerical cell, based on the temperature of the cell. However, the distinction between solid and liquid poses a numerical difficulty, as pure material properties change immediately at the melting temperature Tm. In order to circumvent convergence problems, the enthalpy-porosity formulation implemented in the COMSOL software proposes the introduction of a mushy zone where the thermophysical properties of the solid and liquid phase are spread over a user-defined melting temperature range ΔT (in our study ΔT = 1. 2 K). It is well known that the phase transition occurs in a very narrow zone, although the model uses a transition half-width, ΔT.
A reasonable trade-off between precision and computational effort is obtained in the test cases of the present analysis with the chosen value of 1. 2 K. The work is carried out with the commercially-available COMSOL Multiphysics TM software, which offers a suitable user-input environment for implementing the enthalpy-porosity method. The principal advantage of the porosity formulation is the use of only a single mesh to solve the equations governing fluid flow and heat transfer. However, stable coupling to CFD for buoyant and Lorentz forces requires additional user treatment by defining the set of complimentary algebraic equations for material proprieties. Latent heat during a phase change is incorporated in the energy equation using the following definition of enthalpy for each phase: ℎ = Q@0 + ∆S (4) where ΔH = gl (T).
Indeed, to avoid this problem, the implicit time dependent BDF solver (free backward differentiation formula) chosen in our numerical model is computing the solution to a possibly nonlinear system of equations at each time-step via a set of iterative techniques based upon Newton's method. If these iterative techniques fail at any time-step, the predefined algorithms in the free-BDF solver increment the resolution column called NLFAIL. These Newton's method techniques for solving a nonlinear system of equations evaluate a function, as well as its derivative, at every time-step. This derivative is also known as the Jacobian and is relatively expensive to compute. Therefore, the COMSOL software will try to minimize reevaluating the Jacobian, by default. If the nonlinear solver has difficulty converging, it will reduce the requested time-step size and try to compute the solution.
Conclusion: A 3D solidification model, implemented in COMSOL software, is used to simulate the solidification of the pure material (tin) in a horizontal differentially heated rectangular cavity. The configuration is related to the AFRODITE solidification benchmark experimental setup. The experimental results are also available as temperature data recorded during the solidification phase, allowing comparison between numerical and experimental results. The solidification model has been derived on the basis of an enthalpy formulation based on fixedgrid techniques. With this model, very good agreements are obtained with the measured thermal fields and front progress during the solidification process. This implies that the buoyancy convection is correctly taken into account in the 3D simulation.
The model used has also shown its effectiveness in predicting the process of tin solidification under forced convection generated by electromagnetic stirring in terms of velocity field, temperature field and morphology of the solidification front. The primary application of the EMS is to homogenize and control the temperature field during the stabilization stage (stage 2). However, EMS can be also used during solidification stages (stages 4 and 5 in Fig. 2 ). Our approach to calculating the interface temperature had shown good results to validate our model as obtained in Fig. 12. Lastly, a full experimental solidification process run in the AFRODITE setup and its 3D numerical counterpart is reported. The experimental temperature data has been processed thoroughly to define appropriate unsteady thermal boundary conditions for the numerical model.
Acknowledgments: This work is a joint cooperation between the  Materials physics Laboratory (LPM) of Amar Telidji University, Laghouat (Algeria) , the  SIMAP laboratory, Grenoble INP (France)  and the  Christian-Doppler Laboratory for Metallurgical Applications of Magnetohydrodynamics, Montanuniversitaet of Leoben ,  Franz-Josef-Str. 18, A-8700 Leoben, (Austria). The authors gratefully acknowledge financial support from The  Ministry of Higher Education and Scientific Research of Algeria. The SIMAP laboratory acknowledges the financial support provided by the  ESA-MAP   MICAST  project contract  14347/01/NL/SH. Abdellah Kharicha  acknowledges financial support from the  Austrian Federal Ministry of Economy, Family and Youth  and the  National Foundation for Research, Technology  and.
VPT consists of allowing the programmer to configure computations' precision dynamically at run-time. The previous paper presented a preliminary feasibility study to evaluate the potential of the methodology. In the current paper, we focus on generalizing VPT to all stationary iterative algorithms such as Jacobi and Gauss-Seidel. For such applications, the requirements in terms of precision vary at run-time. This means that standard FP formats are over-designed for these workloads. Moreover, it is shown that even fixed reduced precision formats are over-designed for some parts of these applications. Hence, there is a need for FPU architectures that enable fine-grained VPT. A video presentation explaining our previous work is available at  [22]. This paper first revisits the proposed hardware architecture and its lightweight software support in more detail (Section 4).
FP Variable Type Optimization (VTO) For a given triplet {application, input dataset, QoR constraint}, tools such as  [6] [7] [8]  perform coarse-grained VTO using the delta-debugging search heuristic  [23]. The authors' objective is to minimize the number of high-precision variables and maximize the number of low-precision variables. For some of these tools  [7, 8] , the objective is to optimize for speed, whereas for others, such as Promise  [6] , the goal is to maximize the number of float variables.
Non-Standard/Arbitrary Precision Support The mentioned tools support standard IEEE 754  [11]  formats only, except Precimonious  [8] , which also supports Intel's 80-bit format implemented as long double in C. The authors of fpPrecisionTuning  [9]  proposed an arbitrary precision impact simulation methodology based on an automatic source code transformation tool. Libraries such as FlexFloat and FloatX  [17, 18]  enable developers to simulate the impact of reduced precision on application-level Quality of Result (QoR). These tools support arbitrary precision only in simulation to help designers decide which precision is adequate for their application. However, they do not provide hardware-level support to leverage these decisions for power, execution time, or energy savings.
Arbitrary Reduced Precision In  [10, 19] , we proposed a simulator (AxQEMU) (source code available at  [32] ) that simulates the impact of arbitrary reduced FP precision on applications in a non-intrusive way without modifying the source code. This approach is complementary to VTO since the tools mentioned before can be used as a starting point. Furthermore, using AxQEMU allows primary memory footprint optimization and enables fine-grained accuracy/energy trade-offs.
Variable Precision in Time (VPT) VPREC  [20]  is a software back-end component that enables non-intrusive run-time variable precision simulation in the Verificarlo  [33]  software toolchain. This tool simulates run-time variable precision, specifically for iterative algorithms. However, VPREC is designed for impact simulation only in software and does not provide a hardware implementation for power reduction, whereas this work targets both software and hardware-level implementations. Moreover, VPREC evaluates the needed reduced precision for each iteration through off-line and data-dependent studies performed after the execution. In contrast, we propose a technique to automatically select the adequate precision for each iteration online at run-time in this work.