dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, Neumann boundary condition is applied: ∂η ∂ n where n denotes the exterior unit normal vector. The linear method (hereafter, LM) allows to obtain a quicker estimation than a full tsunami code since second-order terms are neglected but keeping the same main features. Also, this approach does not require to compute the velocity field, making the computation programs even faster. Each simulation is compared with a fully linear water shallow water equation propagation. We select the JAGURS code  (Baba et al. , 2014)  which runs in Fortran90 in a parallel array with OpenMP and OpenMP + MPI. This code is based in the classical finite difference method of  Satake (2002). For each scenario, the tsunami is propagated during two hours of tsunamitime, to obtain the main features of the runup distributions, despite the fact that later amplification of edge waves and resonances can occur.
1 ). To compute the tsunami initial condition, the Okada's equations were implemented in C-language using threading, as well as the finite difference scheme for the LM. The C-code uses pthread library. With this tool, a data struct containing a pthread_t was defined, and then a routine that send every grid's subdomain to each different core's thread for computation. This method is such reliable as any other linear scheme method, because it solves the same equations. The only significant difference is the threads distribution for time optimization. When a thread finishes, it computes for a certain time step and it joins with the others in order to avoid miscomputations. For instance, with the used machine, in a regular grid of 4 million points with a FFM of 300 subfaults, the vertical and horizontal seafloor displacements can be calculated almost instantly (less than 5 seconds) and two hours of tsunami propagation for 2011 Tohoku, Japan earthquake can be solved in 60 seconds.
Examples All the earthquakes presented here, have produced tsunamis. The range of magnitude varies from 7. 7 to 9. 1. For each event we apply the methodology described before. By using the W-phase centroid moment tensor, a scaling law, and a elliptic slip distribution the first source is defined. Then, the linear and non-linear tsunami simulations are performed. The resulting runup distributions are decomposed along latitude and longitude, in order to compare both models. The same procedure is repeated but instead considering a FFM solution. Table  1  shows the correlation of the runup distributions between the code JAGURS and the presented here (the linear method). Table  2  summaries the CPU times for each stage of the process for each simulation. There is a high degree of agreement within a short time.
Combining and multiplexing microscopy approaches is crucial to understand cellular events, but requires elaborate workflows. Here, we present a robust, open-source approach for treating, labelling and imaging live or fixed cells in automated sequences. NanoJ-Fluidics is based on low-cost Lego hardware controlled by ImageJ-based software, making high-content, multimodal imaging easy to implement on any microscope with high reproducibility. We demonstrate its capacity on event-driven, super-resolved live-to-fixed and multiplexed STORM/DNA-PAINT experiments.
2 ). We designed specific workflows depending on the desired protocol and the volumes of reagents accessible to the researcher (Supplementary Note 2 and Supplementary Fig. 4a ). The software is provided as an ImageJ/μManager plugin  7  or as a stand-alone package for independent fluidics control (Supplementary Software 1) for precise control of each steps in the protocol (Supplementary Fig. 3 ). In order to challenge the capabilities of our approach and guide in the choice of workflows, we have characterised the accuracy and precision of the volumes provided by NanoJ-Fluidics in a variety of conditions, e. g. across different Lego syringe pumps, syringes and injected volumes (Supplementary Note 3 and Supplementary Fig. 4 ). In all the performed characterisations using calibrated pumps, both the precision (standard deviation of the error) and accuracy (mean of the error) were below 5% of the nominal injected volume.
Discussion We introduce NanoJ-Fluidics (Fig. 1 ) and demonstrate its applicability in multiple experimental con-texts: event-driven sample treatment (Figs. 2  and 3 ), in-situ correlative live-to-fixed super-resolution imaging (Fig. 4 ) and multimodal multilabel super-resolution imaging (Fig. 5 ). The software modules of NanoJ-Fluidics are part of our NanoJ microscopy toolbox  18  developed in ImageJ/Fiji  19, 20. The reliability provided by the computer-controlled fluidic experiments (Supplementary Note 2 and 3) overcomes the issues of repeatability and low throughput of traditional, non-automated approaches. We also show that NanoJ-Fluidics can be integrated with other automated systems by performing sample treatment triggered by a specific biological cues (mitotic cell rounding) through the integration of NanoJ-Fluidics with the image acquisition (Figs.
NanoJ-Fluidics is easy (and fun) to set up and use (Supplementary Note 1 and Wiki: https://github. com/HenriquesLab/ NanoJ-Fluidics/wiki). It is also low-cost and composed by parts easy to acquire (Supplementary Table  1 ). Its modular design is highly adaptable: for example, pump modules can be detached to be kept at different temperatures (Supplementary Fig. 1 ). NanoJ-Fluidics design has several advantages compared to microfluidics chips: glass-bottom chambers are easier to prepare: air bubbles formed in the tubing are easily dissipated before damaging the sample; ambient pressure allows for a higher tolerance to flowrate fluctuations. Notwithstanding, microfluidics devices have the general advantage of requiring smaller volumes of reagents. Finally, NanoJ-Fluidics can be used with other third-party, commercially available pumps, as the open-source software provides an Application Programming Interface (API) and plugin framework for control within the same software interface (Supplementary Fig.
3 ). In this work, we have chosen to focus on NanoJ-Fluidics applications to live-cell imaging and SMLM combinations. However, our fluidic exchange system can be used in any experiment that would benefit from an automated exchange of the sample media. As highlighted by our unsupervised detection of mitotic cell rounding and sample treatment (Fig. 3  and Supplementary Movie 2), our μManager plugin therefore allows for NanoJ-Fluidics to be fully integrated into microscopy acquisition software, enabling a seamless combination between the imaging and fluid exchange protocol. In conclusion, NanoJ-Fluidics makes high-content multimodal microscopy experiments tractable and available to a large audience of researchers, while improving reliability, optimisation and repeatability of imaging protocols.
The dispensed volume was weighed and an ordinary one-way ANOVA was used (GraphPad Prism version 7. 00 for Mac, GraphPad Software, La Jolla California USA) to estimate the statistical difference between the different conditions used in Supplementary Fig. 4  (using a confidence interval of 99%). For the Overflow loading mode, a volume of 100 μL was measured with a Gilson P100 micropipette (by weighing it in an analytical scale, Sartorius Cubis®, 0. 1 mg ± 0. 0003 mg). The weighed volume was inserted into the tubing and then dispensed using the pump by infusing a 500 μL volume. By flushing out a larger volume than injected, this method ensures that the entire volume is dispensed. Live-to-fixed super-resolution imaging. The NanoJ-Fluidics syringe pump array was installed on a Nikon N-STORM microscope equipped with 405, 488, 561 and 647 nm lasers (20, 50, 50 and 125 mW at the optical fibre output).
For Fig. 4  images were reconstructed using NanoJ-SRRF  12  (magnification: 4, temporal analysis settings: TRPPM for livecell data and TRM for fixed-cell data). Drift was estimated using the inbuilt function in NanoJ-SRRF and correction applied during SRRF analysis. For Fig. 5  localisations were detected using the N-STORM software (Nikon), and exported as a text file before being filtered (number of photons between 700 and 50,000; number of detections (after linking across frames) <50 frames) and rendered using ThunderSTORM  33. Additionally, a 32 nm radius Gaussian blur (ImageJ/Gaussian blur) and a Gamma correction (ImageJ/Gamma, varying between 0. 6 and 1. 5) were applied. Chromatic aberration between the red (561 nm) and far-red (647 nm) channels were corrected within the N-STORM software using polynomial warping and remaining translational drift between acquisition passes were aligned manually on high-resolution reconstructions.
The image resolution was estimated by calculating the Fourier Ring Correlation (FRC)  34  with the typical 1/7 threshold using NanoJ-SQUIRREL plugin and reconstructing the original dataset separated into two different stacks composed of odd or even images  15. NanoJ-SRRF, NanoJ-SQUIRREL and ThunderSTORM are available in Fiji  20. Unsupervised NanoJ-Fluidics and integration with μManager. HeLa HRTG were plated on 35 mm iBidi dishes and incubated with Cdk1 inhibitor (RO-3306, 5 nM) for 15-20 h before imaging. Immediately prior to imaging, cells were washed with fresh medium using NanoJ-Fluidics and live-cell imaging was started. For each experiment, 30-50 fields-of-view were selected and imaged every 5 min in the GFP and mCherry channels. Imaging was performed at 37 °C and 5% CO 2 on a Nikon Eclipse Ti with a CFI Apo TIRF 60X Oil (1.
49 NA), LED illumination (CoolLED P-4000) and Andor Zyla 4. 2 sCMOS camera (100 ms exposure time and 20% illumination for each channel). A BeanShell script (available on our GitHub page) was designed to run the acquisition and image analysis in parallel. For each field-of-view, cells were segmented by Otsu thresholding followed by morphological opening. The circularity of each cell was calculated and if the average circularity of a field-of-view exceeded a user-defined circularity threshold (here set to 0. 82) the field-of-view was declared as triggered. The circularity was estimated via the common shape factors measurement of ImageJ, C ¼ 4πA P 2 ð1Þ where C is the circularity estimator, A is the area and P is the perimeter. Once a user-set fraction of fields-of-view triggered (here 25%), the 'cell rounding event' was activated.
Acknowledgements We thank  Prof. Ralf Jungmann  at  Max Planck Institute of Biochemistry Munich  for reagents and advice. This work was funded by grants from the  UK Biotechnology and Biological Sciences Research Council  ( BB/M022374/1 ;  BB/P027431/1 ;  BB/ R000697/1 ;  BB/R021805/1 ) (R. H. , P. M. P. and R. F. L. ), the  UK Medical Research Council  ( MR/K015826/1 ) (R. H. ), the  Wellcome Trust  ( 203276/Z/16/Z ) (S. C. , B. B and R. H. ) and the  Centre National de la Recherche Scientifique  ( CNRS ATIP-AVENIR program   AO2016 ) (C. L. ). P. A. was supported by a PhD fellowship from the  UK's Biotechnology and Biological Sciences Research Council. C. L. D. was supported by PhD funding from the  Medical Research Council, UK  ( 1214605 ). Research by B. B. was supported by  UCL ,  Cancer Research UK  ( C1529/A17343 ), and  MRC  ( MC_CF12266 ).
Point charges were computed as density derived atomic point charges (DDAPC)  68  obtained by a single point calculation on the [NaDy(DOTA)(H 2 O)]$4H 2 O unit cell using the PBE0 functional  69  with periodic boundary conditions included. The soware package CP2K 70 based on a mixed Gaussian and plane wave  71  (GPW) formalism was used. Since basis sets for dysprosium were not available at the time in the package, Dy(III) ions were substituted by La(III) ions. Double-z polarized basis sets (mid-PBE for La, DZVP-MOLOPT-SR47 for other atoms) with Goedecker-Teter-Hutter norm conserving pseudopotentials were employed. The PW cutoff was set to 400 Ry. Calculations to estimate the excited state energies and magnetic anisotropy were performed with the package of programs MOLCAS 8. 1. 72 The active space consisted of nine electrons in the seven 4f orbitals of the lanthanide ion, i.
Acknowledgements We acknowledge the nancial contribution of the  ERC  through the  AdG MolNanoMas  ( 267746 ). B. L. G. thanks the French GENCI/IDRIS-CINES center for high-performance computing resources. We are thankful for the nancial support of the  Brazilian agency Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ).
Slx5/Slx8 and Cdc48 control seven ubiquitin hotspots across the yeast genome To investigate Slx5/Slx8-catalyzed ubiquitylation of chromatinassociated proteins, we developed chromatin immunoprecipitation (ChIP) protocols for ubiquitylated proteins  (Appendix Fig S1A)  and Slx5/Slx8 in Saccharomyces cerevisiae. We used the FK2 ubiquitin antibody with broad specificity toward mono-ubiquitylated proteins and K29-, K48-, and K63-linked chain types combined with genome-wide tiling arrays  Fig 1A). We detected ubiquitin signals at open reading frames (ORFs). These signals appear to represent histone H2B mono-ubiquitylation that has been described to be particularly abundant on highly transcribed genes  (Braun & Madhani, 2012) , as they were either lost in cells that harbor a mutation of the main H2B ubiquitylation site (h2b-K123R) or in cells that lack Rad6, the E2 enzyme for H2B ubiquitylation  (Jentsch et al, 1987; Robzyk et al, 2000)   (Fig 1A and Appendix Fig S1B, Dataset EV1).
However, some ubiquitin signals were H2B ubiquitylation-independent and notably enhanced in cdc48 mutants (cdc48-6, Fig 1A, Dataset EV1). In similar experiments, Slx8 bound specifically to only few sites in the genome  Fig 1B, Dataset EV2). Comparison of our genome-wide profiles of regions enriched for both ubiquitin-and Slx8-binding revealed a striking correlation for seven sites, which we term "ubiquitin hotspots"  Figs 1B and C, and EV1A and B,. Besides these seven "ubiquitin hotspots", we detected only two sites of major ubiquitin accumulation in cdc48 mutants without Slx8 enrichment (ub-only-sites), and two distinct sites of major Slx8 enrichment without ubiquitin accumulation (Datasets EV1 and EV2, see also  Appendix Fig S2E). Next, we determined the accumulation of specific ubiquitin chain types at ub-hotspots.
Using a ubiquitin K48 chain-specific antibody (clone Apu2, "ub-K48"), we detected the same ub-hotspots as with the FK2 antibody  (Fig EV1A, Dataset EV3). Moreover, strains harboring different mutant alleles of the CDC48 gene showed an increase of ubiquitin-ChIP signals from around 5-10-fold (WT) to 15-50-fold (cdc48-6, cdc48-3) enrichment over background, with comparable results for both antibodies  (Fig EV1C and D). Since the ub-K48 antibody was more specific for the Slx8-bound and Cdc48controlled ub-hotspots, we used this antibody for the rest of our study. Consistent with recruitment of the Slx5/Slx8 heterodimer to chromatin, we found that both subunits were enriched to similar levels at selected ub-hotspots, and observed a moderate increase in Slx5/ Slx8 binding in cdc48-3 mutant cells  (Fig 1D).
While the ubiquitin signals did not correlate with Slx5/Slx8 enrichment levels at the tested sites  (compare Fig 1D and E) , they dropped to background levels in slx5D and slx8D cells  (Fig 1F) , indicating that Slx5/Slx8 is the relevant ubiquitin E3 ligase at these sites. Cdc48 targeting is usually facilitated by cofactors that mediate substrate specificity  (Buchberger et al, 2015). Consistent with previous results for other chromatin-bound substrates  (Verma et al, 2011) , we found that specifically the Cdc48 Ufd1-Npl4 complex removes ubiquitylated proteins from ub-hotspots  (Figs 1G and EV1E) , assisted by additional Ubx4 and Ubx5 cofactors  (Fig EV1E and Appendix Fig S1C, Dataset EV3). In contrast, impairment of Cdc48 substrate delivery to the proteasome (rad23Δ dsk2Δ;  Richly et al, 2005)  or proteasome assembly (ump1Δ;  Ramos et al, 1998)  did not cause accumulation of ubiquitin conjugates at ub-hotspots  (Appendix Fig S1D).
A sequence motif within ub-hotspots is bound by Ymr111c/Euc1 Interestingly, all ub-hotspots lie within intergenic regions, do not seem to be associated with any annotated features within the yeast genome, and appear to be distributed among the sixteen yeast chromosomes  (Fig 1C). We could also not identify any shared pathway or function of the adjacent genes (Dataset EV4). To investigate whether any sequence features define ub-hotspots, we cloned a 1,038-bp region of ub-HS4 on chromosome XIII (ChrXIII) and inserted it into the LEU2 locus on ChrIII (ectopic ub-HS4, Fig  2A ). This fragment was sufficient to drive formation of an We also used the MEME suite to predict sequence motifs within the ub-hotspots  (Bailey et al, 2009). Consistent with our experimental mapping, a sequence motif of 36 bp was identified, which largely overlapped with the experimentally mapped 39-bp fragment  (Fig 2C).
This "ub-HS-motif" was found in all the ub-hotspots in at least one copy  (Appendix Fig S2B) , suggesting that a sequencespecific DNA-binding protein might localize to ub-hotspots. Supporting this notion, a single point mutation within one of the central, ). Therefore, we here name YMR111C as "EUC1" (Enriches Ubiquitin on Chromatin 1). Consistent with a key role of Euc1 in the formation of ubhotspots, we found that activation of the HIS3 reporter by AD-SUMO was Euc1-dependent  (Appendix Fig S2C) , suggesting that Euc1 binding occurs before SUMO binding or a SUMOylation event. In line with this, Euc1 could not bind the mutated ub-HS4 sequence  (Appendix Fig S2D). We raised an antibody against Euc1 to test its association with the endogenous ub-hotspot sites in  Dataset EV5). As expected from the Y1H assays, Euc1 strongly accumulated at ub-hotspots in WT but not in euc1Δ cells (Fig  2H ), nor at the mutated ectopic ub-HS4 sequence  (Fig EV1H).
DNA from non-specific IgG ChIP experiments served as background control. Significantly enriched regions are marked by bars above the respective ChIP-chip tracks and are summarized in Dataset EV1. Data represent means from two independent replicates, except for ubiquitin (FK2) in rad6Δ and IgG-ChIP in WT (n = 1). All experiments, including those using cdc48-6 and other temperature-sensitive (ts) alleles, were performed at 30°C (semi-permissive temperature for ts-alleles) unless stated otherwise. B Seven ub-HSs show strong correlation between ubiquitin binding and Slx8 enrichment (Slx8-9myc ChIP). 16-kb windows of the indicated regions centered around the ub-HSs are depicted for ubiquitin and Slx8 binding profiles. Ubiquitin (FK2) data for WT and cdc48-6 are from the same experiment as depicted in (A).
Data represent means from two independent replicates. C Schematic representation of the 16 yeast chromosomes (I-XVI) with positions of the ub-HSs marked by red triangles. Vertical bars indicate positions of centromeres. D Slx8 and Slx5 are recruited to ub-HSs. DNA from Slx8-9myc (top) or 3HA-Slx5 (bottom) ChIP experiments of the indicated strains was analyzed by quantitative realtime PCR (ChIP-qPCR) at selected ub-HSs. Data represent mean AE standard deviation (SD, n = 2). Consistent with a previous study, we did not observe any Slx8 enrichment at centromeres (van de Pasch et al, 2013); however, we note that we could not confirm the reported centromere binding of Slx5 (see also Appendix Fig  S2E ). E K48-linked ubiquitin chains accumulate in a cdc48 mutant (cdc48-3). Ub-K48 ChIP followed by qPCR for the same loci as in (D) is depicted.
The Slx5/Slx8-dependent ub-hotspot pathway controls Euc1 function The sophisticated STUbL recruitment mechanism suggested that Slx5/Slx8 could regulate Euc1 and ub-hotspot function. To determine whether Euc1 might be active as a transcriptional activator at ub-hotspots, we performed transcriptome analysis in euc1Δ, euc1-KR cells and in cells overexpressing EUC1 (pGAL-EUC1). In euc1Δ cells, only 2 of 15 genes in direct proximity of ub-hotspots were significantly up-or downregulated in expression, with opposing trends (RCO1 up, SSF2 down, Fig  7A, Appendix Fig S8A). More than 150 genes showed a significant change in euc1Δ (P < 0. 01), suggesting that Euc1 may play a widespread function in the regulation of gene expression or reflecting a cellular adaptation to loss of EUC1 (Dataset EV6). Gene ontology (GO) term enrichments suggest an upregulation of small molecule metabolic processes (e.
g. , carboxylic acid and amino acid metabolism, Dataset EV6). Overall, these data do therefore not provide support for a role of Euc1 as direct transcriptional activator of ub-hotspot adjacent genes, at least not in the tested conditions. We noticed that mutations affecting Euc1 SUMOylation (euc1-KR, siz1Δ siz2Δ) led to stronger reporter gene activation using Euc1-N-Gal4-BD reporter constructs (Appendix Fig  S8B  and C ). Importantly, however, we failed to detect strongly deregulated genes in a up-or downregulation, but many other genes showed stronger expression changes, in particular genes involved in cellular metabolism. Interestingly, however, overexpression of EUC1 (pGPD-EUC1, pGAL-EUC1) led to lethality at elevated temperatures or upon exposure to the membrane-fluidizing drug benzyl alcohol  (Lone et al, 2015) , while deletion of EUC1 did not impair growth under   EV4C ), and this additional binding was strongly reduced in euc1-DBD* and euc1-N30Δ cells.
Importantly, binding to these additional loci did not lead to the formation of additional ub-hotspots, collectively indicating that effects of Euc1 overexpression appear unrelated to ub-hotspots (Figs  7E  and EV4D ). Widespread deregulation of genes upon EUC1 overexpression could reflect direct Euc1-dependent transcriptional regulation of these genes, but indirect effects are possible as well, given that EUC1 overexpression deregulated other transcription factors, such as CIN5 (a TF mediating pleiotropic drug resistance and salt tolerance) and TEC1 (a TF targeting filamentation genes ) (Fig 7B, Appendix Fig S8E, Dataset EV8). Importantly, overexpression of SLX5/SLX8 (pADH-SLX5+SLX8) rescued Euc1 toxicity and prevented aberrant binding at STI1-CIN5 and TEC1-us loci (Figs  7F  and EV4E ), but overexpression of the SUMOylation-and Slx5-binding-deficient euc1-KR-SBM1+2 could not be compensated by additional SLX5/SLX8 overexpression  (Figs 7F and EV4E, Appendix Fig S9D).
EUC1 shows genetic interactions with regulators of gene expression upon thermostress To explore the function of Euc1 and ub-hotspots, we investigated genetic interactors of EUC1. Previous high-throughput studies have suggested candidate genetic interactors that show GO term enrichments for chromatin organization, negative regulation of transcription, histone deacetylation, and related processes (Dataset EV9;  Zheng et al, 2010; Costanzo et al, 2010 Costanzo et al, , 2016)). Indeed, we observed a negative genetic interaction of EUC1 with HTZ1, encoding for histone H2A. Z and the functionally linked nucleosome remodeling complex SWR1-C (SWR1 and YAF9 genes), in particular upon thermostress (heat or cold),  DMSO, or HU treatment (Fig 8A, Appendix Fig S10A and B). Histone H2A. Z deposition by SWR1-C has been implicated in the regulation of inducible promoters, heterochromatin maintenance, and genome maintenance (Billon & Cote, 2013).
Interestingly, combination of mutations in Rpd3S (rco1Δ, eaf3Δ) and Rpd3L (sds3Δ) showed strongly increased sensitivity to heat stress and DMSO, suggesting functional redundancy of the Rpd3L and Rpd3S complexes  (Fig 8D). In this background, the effect of Rpd3S mutations was not further aggravated by deletion of EUC1, indicating epistasis and that Euc1 acts with Rpd3S in a pathway that mediates thermotolerance and resistance to DMSO stress (Fig  8D  and E ). In agreement with our assignment of EUC1 to an Rpd3S-related function, we found significant correlations of genes downregulated in euc1Δ with those downregulated in published rpd3Δ transcriptomes, but also with datasets for set2Δ, which acts as an upstream regulator in the Rpd3S pathway (Appendix Fig  S11A )  (McKnight et al, 2015; McDaniel et al, 2017).