dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene expression data To evaluate the gene expression patterns, we used expression data for human and mouse across multiple organs and developmental stages  (Cardoso-Moreira et al. , 2019). We downloaded gene-level RPKM (reads per kilobase of exon per million mapped reads) values. For evolutionary comparisons, we analyzed protein coding genes predicted as 1-to-1 orthologs for human and mouse in the Ensembl database release 94, and to organ/developmental stage combinations that were directly comparable between the two species  (Cardoso-Moreira et al. , 2019). We re-normalized the data across samples and species using a median-scaling procedure based on the genes that vary the least in terms of ranks across samples  (Brawand et al. , 2011). For the expression conservation analyses, we required genes to be expressed above a minimum threshold (RPKM=1) in at least three We also aimed to compare gene expression levels between human and mouse, in cell types that are comparable between the two species and for which PCHi-C data is also available.
We analyzed embryonic stem cells or epiblast-derived stem cells (ESC or epiSC), adipocytes and B cells. For these cell types, we downloaded RNA-seq data from the ENCODE consortium and from the Sequence Read Archive (SRA) database. We computed gene expression levels using kallisto  (Bray et al. , 2016) , on cDNA sequences extracted from Ensembl 94. For each gene, we computed mean and median TPM (transcript per million) expression levels, across all replicates for each cell type. We applied the same normalization procedure as above  (Brawand et al. , 2011). These values were used to compute cell type-specific gene expression conservation values between human and mouse. The resulting expression data and sample details are provided in Supplemental Dataset S5 and Supplemental Table  S8.
S15 ), we. CC-BY-NC 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 13, 2021. ; https://doi. org /10. 1101 /10. /2021. 02. 26. 432473 doi: bioRxiv preprint. 02. 26. 432473 doi: bioRxiv preprint  built linear regressions that model the relationship between expression conservation, expression specificity and expression levels (averaged across all samples and across species, for each gene), and extracted the residual values. We referred to these residual values as "corrected expression conservation" in the figures. The gene expression conservation data is provided in Supplemental Dataset S5.
Evolutionary conservation of gene expression levels We measured quantitative gene expression differences between species for the three cell types that were present in the PCHi-C datasets of both human and mouse: B lymphocytes, pre-adipocytes and embryonic stem cells. We also performed a comparison between human embryonic stem cells and mouse epiblast-derived stem cells. To do this, we computed average TPM levels across all replicates available for a given cell type, for each species. We then estimated expression divergence as the absolute value of the difference between human and mouse, divided by the maximum of the two values, for each gene. We note that this expression divergence measure is strongly correlated with gene expression levels. To correct for this effect, we constructed a linear regression that models the relationship between the expression divergence and the average expression levels, across both species, and extracted the residual values.
Constraints on gene sequence We analyzed the probability of intolerance to loss-of-function mutations for a gene (pLI score), inferred from variation in human exome-sequencing data leading to truncating proteins  (Lek et al. 2016). We downloaded pre-computed pLI scores for transcripts from the website indicated in the original article (ftp://ftp. broadinstitute. org/pub/ExAC_release/release0. 3/functional_gene_constraint). We then associated pLI scores to genes using Ensembl94 annotations. We retained the 17,558 genes associated with a unique transcript. The pLI scores are provided in Supplemental Dataset S6.
Gene Ontology enrichment We used GOrilla  (Eden et al. , 2009)  to perform Gene Ontology enrichment analyses on single-ranked lists of genes. We considered only protein coding genes present in the human PCHi-C data and with an 1-to-1 orthologous gene in the mouse. Genes were ranked by the following factors: the number of contacted ENCODE enhancers, the number of neighbor enhancers, the median distance between enhancers and the gene TSS, the mean alignment score of contacted enhancers, the proportion of enhancers that are conserved in synteny and in contact, the gene expression profile conservation. The ontology enrichment results are provided in Supplemental Dataset S13 and Supplemental Tables  S3-S7.
Statistics and graphical representations We used R 3. 5. 2 for all statistical analyses and graphical representations  (R Core Team, 2014). Given that the variables we analyze are often not normally-distributed, we used non-parametric statistical tests as a general rule. We computed 95% confidence intervals for mean values using the BCa non-parametric bias-corrected and accelerated bootstrap method  (DiCiccio and Efron, 1996) , as implemented in the coxed_0. 3. 3 package in R  (Kropko and Harden, 2019). We performed 100 bootstrap replicates. We computed 95% confidence intervals for median values using the formula +/-1. 58 IQR/sqrt(n), where IQR is the inter-quartile range of the distribution and n the total number of values. This formula is implemented in the boxplot. stats function in the grDevices package in R  (Chambers et al.
Acknowledgements We thank  C. Berthelot ,  Y. Ghavi-Helm ,  F. Picard  and  D. Mouchiroud  for discussions and advice on the project. We also thank  T. Latrille  and  T. Tricou  for their valuable advice and for assistance with bioinformatics and  P. Veber  for advice on statistics. Computational analyses were performed using the computing facilities of the  CC LBBE/PRABI  and the  Core Cluster of the Institut Français de Bioinformatique (IFB). This work was funded by the  French National Research Agency  ( ANR-17-CE12-0019-01  «LncEvoSys»).
CC-BY-NC 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in bioRxiv preprint doi: ; https://doi. org/10. 1101/2021. 02. 26. 432473 this version posted December 13, 2021. The copyright holder for this samples. The resulting expression data and sample details are provided in Supplemental Dataset S5.
of membrane protein structures in the worldwide Protein Data Bank (wwPDB)  (Berman, 2000). Structure-based ligand discovery efforts targeting membrane proteins, such as fragment screening, remain inadequate due to technical barriers in experimental setups. In meso crystallization trials are typically performed with sandwich plates, where the sample mesophase is squeezed between two glass plates  (Caffrey and Cherezov, 2009; Cherezov et al. , 2004). Membrane protein crystals grown in meso are often of micrometer-sized dimensions and decay rapidly because of radiation damage. Thus, analysis by X-ray diffraction is usually performed through the so-called serial synchrotron crystallography (SSX) approach, applied at both ambient and cryogenic temperatures  (Gati et al. , 2014; Huang et al. , 2015 Huang et al.
A laser-cut area containing multiple ADI-POR2 microcrystals was defined in CRIMS as single pin and harvested by the CrystalDirect harvester such that a single pin contained 10-100 crystals (Figure  S2 ). Diffraction data were collected at the Swiss Light Source (SLS) on PXI (X06SA) from each microcrystal as 10 wedges of minisets as previously described  (Basu et al. , 2019; Wojdyla et al. , 2016 ) (STAR Methods). A complete ADIPOR2 dataset was obtained from a single harvested pin, requiring 300 ng of protein. The structure of ADIPOR2 harvested automatically from a CrystalDirect plate was resolved at 2. 4 A ˚(Figures  1  and S6C ; STAR Methods); this resolution is equivalent to that previously reported from manual harvesting from glass plates  (Vasiliauskaite-Brooks et al. , 2017) , indicating that our automated approach does not lead to any reduction in data quality.
We also crystallized an intramembrane sphingolipid hydrolase, ACER3, using this method (STAR Methods). In glass sandwich plates, ACER3 crystals tend to grow in the sponge phase and are particularly challenging to harvest manually. Using the CrystalDirect harvester, all attempted ACER3 crystal harvests were successful. Nine harvested pins were required to collect a complete dataset for ACER3 employing the same SSX methodology as for ADIPOR2. The data collection required 36 min of synchrotron beam time at the Swiss Light Source (STAR Methods). A high-quality structure of ACER3 at 2. 6 A ˚resolution.
In situ membrane protein serial crystallography The CrystalDirect plates can also be directly used for in situ X-ray experiments (Figure  1 ). We used this approach for room-temperature analysis of ADIPOR2 through in situ diffraction (STAR Methods). We collected in situ datasets from ADIPOR2 crystals by mounting the plate directly on an SBS-plate goniometer at the P14 beamline of the Petra III synchrotron (Hamburg, Germany). The crystals were exposed to the X-ray beam and diffraction was recorded at room temperature with a continuously oscillating raster-scan across each LCP bolus  (Gati et al. , 2014)  (Video S2 and Figure  S6A ). A collection strategy that optimized beam transmission and exposure time while minimizing radiation damage was applied (STAR Methods). A complete dataset was acquired from five boluses in different wells in approximately 20 min of beam time, representing a total of 1.
To soak ADIPOR2 crystals, the top sealing film of the plate was removed, ligand solutions were added with a mosquito robot, and the plate was resealed for the desired soaking time (Figure  3 , top row, and STAR Methods). Addition of 96 soaking solutions to an entire microplate takes <2 min in total, indicating the rapidity and high throughput of our automated soaking pipeline. After addition of the ligands, experiments were incubated inside a crystal imaging robot and images of soaked crystals were used to define harvesting plans. After soaking, crystals were harvested and prepared for synchrotron shipment as outlined for ADIPOR2 and ACER3 (Figure  3 , middle row). Processing of SSX diffraction data for a large number of samples remains a challenging computational task. Efficient detection of well-diffracting microcrystals via raster scanning and automatic triggering of multiple dataset collection with small wedges are imperative for high-throughput SSX data collection  (Basu et al.
, 2019). On-the-fly data processing and feedback together with efficient selection of useful datasets is also necessary to enable high-throughput screening of samples. In this work, cryogenic SSX measurements were carried out at the Swiss Light Source, where data collection and real-time data processing, selection, and merging are available through the sxdm package  (Basu et al. , 2019). To confirm that in meso crystals grown in CrystalDirect plates could be soaked with small molecules, we soaked ADIPOR2 crystals with heavy-metal-containing organic caged molecules, typically used as phasing agents  (Engilberge et al. , 2017; Girard et al. , 2003). After incubation with ligand, automated harvesting was performed on regions of interest containing dozens of microcrystals. For initial soaking experiments, we soaked several in meso boluses for each ligand and then collected cryogenic SSX diffraction data.
The electron-density maps show two distinct binding sites for Tb-XO 4 , in clefts on the surface of the antibody fragment portion of the ADIPOR2-scFv complex  (Figures 5D and 5I). The structure of ADIPOR2 soaked with Gd-DO 3 revealed binding of the compound to transmembrane helix 3 (Figures  5E  and 5J ). Both ADIPOR2 metal complexes were solved at 3 A ˚resolution (Table  1 ). To demonstrate that our pipeline can also be applied to ligand discovery, we soaked ADIPOR2 crystals with a library composed of 60 small-molecule fragments. We soaked each fragment into two in meso boluses containing ADIPOR2 microcrystals in order to ensure collection of a complete dataset. Soaked crystals were harvested and subjected to SSX diffraction data collection, data processing, and ligand detection (STAR Methods).
Cryogenic serial crystallography data collection Diffraction data from samples vitrified using the CrystalDirect technology were collected remotely at the X06SA beamline of the Swiss Light Source (SLS), Villigen, Switzerland. After sample transfer to the goniometer, pins containing multiple crystals were rapidly raster-scanned (Figure  S2 ) with the X-ray beam to identify positions corresponding to diffracting crystals  (Basu et al. , 2019; Wojdyla et al. , 2016). In a second step 10 wedges were collected with 0. 1 sec exposure time from each of the identified positions at a speed of 2 /sec. If dose permitted, 2-3 sweeps of 10 wedges were collected from each of those identified crystals. Each 10 wedge takes typically 5 sec. Effective data collection time required to obtain a complete dataset from each target is summarized in Table  1.
Diffraction images were collected using an EIGER X 16M detector (Dectris) with a sample-to-detector distance of 30 cm, beam energy was 12. 4 KeV for apo crystals, and 7. 2 KeV for soaked crystals in order to exploit the anomalous signals from the heavy elements. The beam size was 10x20 mm 2 and 100% transmission. Data processing and structure refinement Initial diffraction patterns, collected under cryogenic condition at the SLS, were processed using XDS  (Kabsch, 2010) , followed by real-time automated selection and merging of statistically equivalent datasets with XSCALE using the sxdm tool  (Basu et al. , 2019)  as deployed at the beamline. Datasets were selected using a criterion or a combination of multiple criteria, including ISa cut-off of 3. 0, unit-cell, pair-CC based hierarchical clustering as well as D-CC 1/2 iteratively  (Assmann et al.
, 2020). Overall, pre-processed data was manually transferred from SLS and refined with BUSTER program in Global Phasing. In case of fragment screening datasets, we further processed unmerged scaled MTZs with Global-Phasing Pipedream software pipeline. For in situ datasets collected at PetraIII P14 beamline, we used an in-house script to run CrystFEL  (White et al. , 2012)  version 0. 8. 0 automatically as the data had arrived. This allowed us to quickly identify hits and reduce data volume and produce a mtz file ready for structural analysis. All the structures were solved by molecular replacement using PHASER and previously determined structures of ACER3 and ADIPOR2 as search models (PDB IDs 6G7O, 5LWY respectively). The resolution cut-offs for all structures were determined by a combined metric of CC 1/2 of 0.
0 s. (F-H) Inset views of active sites of (A), (B), and (C), respectively, showing Zn 2+ atoms as magenta spheres, coordinated by His residues shown as sticks, with the respective (2F o À F c ) maps, matching the color. (I and J) Inset views of the (I) Tb complex and (J) Gd complex, shown in magenta sticks with metal atoms shown as gray spheres, along with the (2F o À F c ) map in blue and anomalous maps in green (contoured at 7. 0 s). PDB codes are ACER3 cryo: 6YXH; ADIPOR2 cryo: 6YX9; ADIPOR2 RT: 6YXD; ADIPOR2-Tb-XO 4 : 6YXG; ADIPOR2-Gd-DO 3 : 6YXF. (K) Superposition showing distinct conformations of the oleic acid molecule at the catalytic site of ADIPOR2 in the cryogenic (green) and room-temperature structures (yellow).
ACKNOWLEDGMENTS We want to thank  Thomas Schneider  and the  EMBL Hamburg Team  for excellent support with data collection at P14 of the  PETRA III synchrotron (DESY, Hamburg, Germany) , the staff at the  Swiss Light Source (Paul Scherrer Institut, Villigen, Switzerland ), and the staff at the ID23-2 microfocus beamline at the  European Synchrotron Radiation Facility (ESRF, Grenoble, France ). In addition, we thank  Frank Felisaz  and the  EMBL Grenoble Diffraction Instrumentation Team  for support during the design of the plate adaptor. This work benefited from access to the  HTX Lab at EMBL Grenoble. This project was supported by funding from the  European Union 's  Horizon 2020 Programme  under the projects iNEXT (grant  653706 ) and iNEXT  Discovery (grant   871037 ), as well as the  Region Auvergne-Rhône-Alpes  through the  Booster program.
of frames NA NA 382,552 NA NA No. of hits (datasets 21,250 (425) 15,750 (315) 28,949 28,500 (570) 20,500 (410) collected) No. of indexed frames a 18,550 (371) 5,200 (104) 25,297 23,350 (467) 17,800 (356) No. of datasets merged 101 100 N/A 210 96 No. of CD boluses 9 1 5 16 7 No. of crystals/bolus $17 $33 $100 $27 $22 Effective collection time b 8. 4 (30. 9) 8. 3 (26. 2) 20 (76) 38. 9 (47. 5) 29. 6 (35) (min) Protein consumption (mL) 1. 6 0. 3 1. 5 4. 8 2. 1.
ChAT-IR was used to identify intrinsic cardiac ganglia. As seen in figure  1  Global cardiac innervation was further studied by developing a volumetric quantification approach using Imaris. Based on ChAT staining, the volume of each individual ganglion was estimated, resulting in a total ganglion volume of 1. 95±0. 17 10 -2 mm 3 (n=3; fig. 1b, e ) per heart. The total length of TH-IR nerve fibers innervating both ventricles was quantified using ImageJ software. The total length found was 1. 68±0. 21 10 2 mm (n=3; fig. 1c, f ).
Code availability This reconstruction technique is included in the open-source library Grid2Grid  [1, 19]. This library is developed by the LHEEA research department (Ecole Centrale Nantes and CNRS) to connect any CFD solver with HOS-Ocean  [28, 40]  and HOS-NWT  [2, 27] , two open-source HOS wave solvers. The library Figure  12 : VOF field convected by the irregular wave field interpolated from the HOS method after one peak period with the zero-padding improvement: HOS grid refined with a factor of 4. is published with the GNU General Public License v3. 0 and can be downloaded from the url: https: //github. com/LHEEA/Grid2Grid.
Validation and Application In this section, the two-phase SWENSE method is validated on three test cases: • High-order wave loads on a vertical cylinder: This case validates the method on the calculation of the wave force on a fixed structure with simple geometry. The horizontal wave force on the cylinder is recorded. Up to fourth harmonic components are extracted and compared with experimental data and numerical results of potential flow solvers. A systematic convergence study is also conducted. • Fixed Catenary Anchored Leg Mooring (CALM) buoy with a heave-damping skirt in regular and irregular waves: This configuration is used to assess the method with a complex geometry and includes violent free surface deformation and viscous effects representing a real ocean engineering application. The results of foamStar-SWENSE are compared with experimental data.
Comparison of the accuracy During the simulations, the wave forces and free surface elevations are recorded during 7 periods. The time history is transformed to the frequency domain. The first and second harmonic amplitudes are extracted and compared. The amplitudes of wave force are normalized with akρg∇, where a = H/2 is the amplitude of the waves, k the wave number and ∇ the displacement volume of the buoy at the designed draft. The free surface elevations are normalized by a. The results are shown in Tab. 15. The relative differences compared with the experimental data is given in percentage form. To analyze the results, let us focus first on the first harmonic amplitudes of the horizontal wave force: • x80 (total cells 2. 58M): The results of both solvers are in good agreement with the experiment.
Extensive simulations were carried out  [499] , and included a number of FEL sensitivity studies  [517] , using the production code GENESIS  [518]  and the unaveraged spectral code CHIMERA  [519]. The chromatic matching enables the gain of one order of magnitude growth on the FEL intensity compared to a strong focusing of the electron beam  [499]. Different regimes can be considered, depending on the electron beam parameters and the operating wavelength. In the seeded configuration, the chirp introduced in the chicane, induces a red shift of the FEL radiation wavelength with respect to the seed. It can lead to an interference fringe pattern, that can allow for a full temporal reconstruction of the FEL pulse temporal amplitude and phase distributions  [520]. An FEL test experiment is underway, and the main limitation so far comes from the measured electron beam parameters that do not match the baseline ones.
However, simulations are yet to be performed to validate this concept. Finally, two doublets are used to match the beam to the undulator entrance. The two magnet pairs are separated by a distance greater than 2 meters to enable the insertion of different diagnostics: a Beam Position Monitor (BPM) to measure the beam position, an X-band Transverse Deflecting Structure (TDS) to measure the time structure  [540]  and a dipole to measure the energy spectrum, when needed. One of the main features for the transport line is to retain the beam quality (and more specifically the emittance)  [538]. Towards this aim, a python script has been written to match the beam to the undulator and to minimize the emittance growth along the machine (to minimize the Montague function at the undulator entrance). The matching constraints at the entrance of the undulator for the different schemes are given in Table  10.
5 m between both doublets to insert long diagnostics like the TDS or a spectrometer). Finally, the beam transfer line is optimized with the tracking code TraceWin  [543]  to match the beam to the undulator entrance and to minimize the emittance growth. This optimization takes into account the entire beam distribution with no assumptions on the initial conditions. As an example, the evolution of the beam distribution along the transport line is shown in Fig. 29  for the case A. The evolution of the slice properties along the longitudinal beam distribution are shown in Fig. 30. Beam properties are well preserved along the transfer line. The parameters of the quadrupoles are summarized in Table  11  and the final beam characteristics are presented in Table  10. The associated results assume values for both magnetic field and K u deflection parameter that are feasible with the technologies presented in the previous Sections: namely either a cryogenic permanent magnet undulator (CPMU) or a superconducting undulator (SCU), assuming any beam stay clear gap larger than 6 mm.
Acknowledgments The authors acknowledge the support by the  European Unions   Horizon 2020 research and innovation program  under grant agreement EuPRAXIA No. 653782  led by  R. Assman , COXINEL  ERC  project, contract  340015  and  X-Five ERC  project, contract No. 339128. The authors also thank  A. Curcio  for fruitful discussions. Numerical simulations in the LPA section were performed using HPC resources from  GENCI-TGCC  ( Grand Équipement National De Calcul Intensif, France)  (Grant No. 2020-A0090510062 ) with the IRENE supercomputer. This work was granted access to the HPC resources of TGCC under the allocation 2017 -A0010510062 made by  GENCI, France. W e : Energy gained by electrons L deph : Length depletion R: Radius of the bubble.