dataesr/scientific-paragraphs-categorization huggingface dataset:
67  The maximum value of O3 is 13, as the first and last residues of the structure are not considered. The RMSD is calculated between the experimental structure (PDB code 1DN3  15  ) and the BH-predicted structure. unrestrained structure was 3. 93 Å, showing significant improvement for structure prediction. The restrained hairpin results were more ambiguous, as none of the residues in the experimental structure or the BH global minimum structures satisfied the STRIDE requirements for a β-bridge conformation. Previous studies have shown that NMR descriptions of a β-hairpin do not always satisfy the traditional geometric secondary structure criterion. 69, 70  Upon visualization of the resulting structures, it is clear to see that BH global optimization with the hybrid restraint potential located a hairpin-like global minimum structure, while the unrestrained lowest-energy structure (the same structure as analyzed previously in the α-helix case) does not take on a hairpin conformation (Figure  6b ).
24  Using BH global optimization, we attempted to overcome this energy barrier and search from Ub-CR back to the native Ub conformation. Two BH runs were started from the Ub-CR conformation (PDB code 5XOI,  23  with the serine at residue 67 mutated back to the native leucine using LEaP), which was initially locally minimized using only the Amber ff99SB-ILDN force field to eliminate any clashes. One BH run used only the original Amber ff99SB-ILDN force field, and the other included the NapShift hybrid restraint potential with an α value of 0. 7. Experimental reference CS values for ubiquitin were obtained from BMRB entry 5387. 77  The same BH parameters and group rotation scheme as described for the previous benchmark systems were used here, and 50,000 BH steps were considered. Calculating the NapShift hybrid potential energy and gradient involves repeated multiplication of large matrices, which is required for neural network operations in the prediction operations.
Over the course of the unrestrained simulation, the structure lost its native packing and adopted misfolded conformations with RMSD values reaching 10 Å from the experimental structure (Figure  8a, b ). In contrast, an MD simulation with CS restraints returned the β-hairpin into its native conformation (Figure  8a-c ) to within RMSD values of about 1 Å. We used the same protocol for a restrained MD simulation of ubiquitin to sample its dynamics on the nanosecond timescale. Starting from an experimental structure (PDB code 2LJ5  79  ), the restrained simulations generated a structural ensemble in excellent agreement with order parameters (S 2 ) from experimental  15  N relaxation measurements (Figure  9a, b ), as well as other NMR-derived structural parameters, such as 3 Jcouplings (Figure  9c ) and interatomic NOEs (Figure  9d ).
IV. CONCLUSIONS Implementing restraints based on experimental data has been shown to greatly improve the quality of biomolecular simulations. Here, we demonstrate that adding a hybrid energy potential based on protein NMR chemical shifts into a molecular mechanics force field can improve the efficiency of both basin-hopping global optimization  [16] [17] [18]  and molecular dynamics simulations. For each of the proteins explored in this work, identifying a native structure via BH global optimization proved difficult without any experimental restraints. Running BH using an NMR hybrid restraint potential based on the NapShift artificial neural network consistently led to the identification of native-like conformations that displayed the correct secondary structure. This observation held over a wide range of α, the mixing parameter between the original force field energy and the hybrid restraint energy.
The best mixing parameter for the systems studied here was α = 0. 7. The hybrid restraint potential also proved beneficial when applied to Two BH runs were started from the wild-type Ub-CR conformation: one using only the original Amber force field and one including the NapShift hybrid potential with an α value of 0. 7. V Total is the total potential energy, V CS is the CS restraint energy, and q is an order parameter defined as the ratio of the distances between the Cα atoms of residues 4 and 65 and residues 4 and 67. The RMSD was calculated between the BH-predicted structure and both the native ubiquitin conformation (PDB code 1UBQ  78  ) and the Ub-CR conformation. Using a neural network like NapShift to generate predictions for a hybrid potential allows us to continually improve the quality of our model.
As more experimental data become available, the NapShift neural network can be retrained to generate increasingly accurate predictions, which in turn will enhance the accuracy of the hybrid restraint potential. We also plan to develop ANN-based hybrid restraint potentials of other experimental NMR quantities, such as residual dipolar couplings, allowing us to further utilize the information provided by NMR experiments. Further studies on larger viral membrane proteins with experimental NMR data have shown promising results and will be the subject of a future report.
Here, our second goal is to present a DDWP+DA framework in which the DDWP is the forward model that efficiently provides a large, O(1000), ensemble of forecasts for a sigma-point ensemble Kalman filter (SPEnKF) algorithm. To provide proof-of-concepts for the DDWP model with equivariance-preserving encoder-decoder U-NET and the combined DDWP+DA framework, we use sub-daily 500 hPa geopotential height (Z500) from the ECMWF Reanalysis 5 (ERA5) dataset  (Hersbach et al. , 2020). The DDWP model is trained on hourly, 6 h, or 12 h Z500 samples. The spatio-temporal evolution of Z500 is then forecasted from precise initial conditions using the DDWP model or from noisy initial conditions using the DDWP+SPEnKF framework. Our main contributions in this paper are three-fold, namely: -Introducing the equivariance-preserving encoder-decoder U-NET with a deep spatial transformer architecture for DDWP modeling and showing the advantages of this architecture over a conventional encoder-decoder U-NET.
Data We use the ERA5 dataset from the WeatherBench repository  (Rasp et al. , 2020) , where each global sample of Z500 at every hour is downsampled to a rectangular longitude-latitude (x, y) grid of 32 × 64. This coarse-resolution Z500 dataset from the WeatherBench repository has been used in a number of recent studies to perform data-driven weather forecasting  (Rasp et al. , 2020; Rasp and Thuerey, 2021). Here, we use Z500 data from 1979 to 2015 for training, 2016-2017 for validation, and 2018 for testing.
In both encoding and decoding blocks, the ReLU activation functions are used. The number of convolutional kernels (32 in each layer), size of each kernel (5 × 5), and the learning rate (α = 3 × 10 -4 ) have been chosen after extensive search. All codes for these networks (as well as DA) have been made publicly available on GitHub (see the Code Availability statement). Note that without the transformer module, Z = Z, and the network becomes a U-NET. Also, we highlight that here, we are focusing on preserving the SO(3) equivariance group that includes translation, rotation, and scaling, because those are the ones that matter the most for the synoptic patterns on a 2D plane. Other transformations and equivariance groups could be similarly included  (Wang et al. , 2020). Furthermore, here we focus on an architecture with a transformer that acts only on the latent space.
Results 4. 1 Performance of physically consistent DDWP: Noise-free initial conditions (no DA) First, we show the gain from preservation of the equivariances by comparing the performance of a U-STN and a conventional U-NET, whose only difference is in the use of the spatial transformer module in the former. Using U-STN12 and U-NET12 as representatives of these architectures, Fig. 3  shows the anomaly correlation coefficients (ACCs) between the predictions from U-STN12 or U-NET12 and the truth (ERA5) for 30 noise-free, random initial conditions. ACC is computed every 12 h as the correlation coefficient between the predicted Z500 anomaly and the Z500 anomaly of ERA5, where anomalies are derived by removing the 1979-2015 time mean of Z500 of the ERA5 dataset. U-STN12 clearly outperforms U-NET12, most notably after To further see the source of this improvement, Fig.
were supported by ONR grant N00014-20-1-2722 and NASA grant 80NSSC17K0266. A. C. also thanks the Rice University Ken Kennedy Institute for a BP HPC Graduate Fellowship. E. B. was supported by the University of Maryland Flagship Fellowship and Ann G. Wylie Fellowship, and by Monsoon Mission II funding (Grant IITMMMIIUNIVMARYLANDUSA2018INT1) provided by the Ministry of Earth Science, Government of India.
Acknowledgements. We thank  Jaideep Pathak ,  Rambod Mojgani , and  Ebrahim Nabizadeh  for helpful discussions. This work was started at  National Energy Research Scientific Computing Center (NERSC)  as a part of A. C. 's internship in the summer of 2020 under the mentorship of M. M. and K. K. , and continued as a part of his PhD work at  Rice University  under the supervision of P. H. This research used resources of NERSC, a  U. S. Department of Energy Office of Science User Facility  operated under Contract No. DE-AC02-05CH11231. A. C. and P. H.
Sequence similarity analyses and annotation of these transcripts were performed against the Arabidopsis thaliana nr protein database, Gene Ontology (GO), and KEGG databases. As a result, 13,046 GO terms and 138 KEGG maps were created. Under Pb stress, 577 and 270 genes were differentially expressed in roots and aboveground parts, respectively. Detailed elucidation of regulation of metal transporters, transcription factors (TFs), and plant hormone genes described the role of actors that allow the plant to fine-tune Pb stress responses. Our study revealed that several genes related to jasmonic acid biosynthesis and alpha-linoleic acid were upregulated, suggesting these components' implication in Hirschfeldia incana L responses to Pb stress. This study provides data for further genomic analyses of the biological and molecular mechanisms leading to Pb tolerance and accumulation in Hirschfeldia incana L.
De Novo Assembly of Transcriptome Quality control of the sequences generated by Illumina was assayed using the Fastqc v0. 11. 9 tool. Reads were used for de novo assembly using Trinity tool v2. 14. 0, with the default parameters such as K-mers equal to 25 and a minimum length equal to 200 bp  [23]. After that, a second assembly was carried out using CD-HIT (http://weizhong-cluster. ucsd. edu/cd-hit/with 95% identity) (accessed on 10 June 2022)  [24]. The quality of the secondary assembly was then evaluated using BUSCO tool version 5. 4. 3  [25]. In addition, the reads were mapped again to the transcripts generated by CD-Hit using the Bowtie2 tool v2. 4. 5 (http://bowtie-bio. sourceforge. net/bowtie2/index. shtml) (accessed on 10 June 2022) (Figure  1 ). The transcripts generated in the study are available online: http://genoweb.
Annotation and Function Profiling Annotation of assembled transcripts was performed with BLASTX (E-Value 1e -10 ) on a local server using protein sequences of Arabidopsis thaliana downloaded from the ensemblgenomes. org database (http://ftp. ensemblgenomes. org/pub/plants/release-52/fasta/arabidopsis_thaliana/ pep/Arabidopsis_thaliana. TAIR10. pep. all. fa. gz) (accessed on 10 June 2022). The BLASTX results (file represents each transcript ID with their TAIR ID) were combined with the Gene Ontology (GO) annotation file of Arabidopsis thaliana (https://www. arabidopsis. org/download_files/ GO_and_PO_Annotations/Gene_Ontology_Annotations/gene_association. tair. gz) (accessed on 10 June 2022) using Excel software 2021 for retrieving GO terms for each transcript (Figure  1 ). With these GO terms, all transcripts identified are classified into biological process (BP), molecular function (MF), and cellular component (CC) domains.
Transcript Estimation and DEG Analysis For the quantification of gene expression, we mapped the reads of each replicate against the transcriptome generated by CD-HIT using the galaxy server (https://usegalaxy. org) (accessed on 10 June 2022). In this analysis, the RSEM v1. 3. 3 algorithm  [23]  was used for transcripts estimation, then bowtie2 v2. 4. 5 (http://bowtie-bio. sourceforge. net/bowtie2 /index. shtml) (accessed on 10 June 2022) was used as a tool for mapping the reads onto the transcriptome. DEGs were inferred based on the normalized counts with R software by using the DESeq2 v3. 15 package (https://bioconductor. org/packages/release/bioc/html/DESeq2. html) (accessed on 10 June 2022), and results were extracted using the restrictive method "Bonferroni" with a p-adj value <0. 05  [28].
After that, the Venn-Diagram v1. 7. 3 algorithm was used to determine the numbers of DEGs shared between control and Pb-treated plants in roots and aboveground parts  [29]. Then, the Gene Ontology and Enrichment Analysis tool available online (http://bioinformatics. sdstate. edu/go) (accessed on 10 June 2022) and REVIGO tool  [27]  were used for functional annotation of expressed genes identified in roots and aboveground parts and to determine overrepresented GO terms across BP, MF, and MF domains. A p-value < 0. 05 was used. The homologous genes found using BLASTX were used for pathway annotation through the KEGG database v103. 1 (Arabidopsis thaliana used as a reference) (Figure  1 ). Roots and aboveground parts were harvested from sixweek-old plants cultivated on Pb at 100 µM and the control.
This percentage was slightly lower than GC content reported in B. juncea (44. 38%)  [30] , and higher than GC content reported in Brassica nigra (37%)  [32]. The reads were mapped again to the secondary assembly using the bowtie2 aligner tool to evaluate the consistency of the transcriptomes generated by CD-Hit. All reads were successfully mapped to the secondary assembly with a percentage higher than 93. 5 percent (Table  S1 ). The quality of the transcriptome assembly was verified using the BUSCO score algorithm. As shown in Table  1 , a high percentage of BUSCO score was obtained; about 94. 2% were complete, 2. 8% were fragmented, and 3. 0% were missing (Table  S1 ). The percentage of the BUSCO reported in the present study was slightly lower than that reported in Brassica nigra  [32]. The annotation of 77,491 secondary transcripts was performed using BLASTX on the protein database of Arabidopsis thaliana.
For MF, a total of 4535 terms were found (Figure  2 ; Table  S3 ). The most overrepresented activities were binding "GO:0005488" followed by catalytic activity "GO:0003824" (Figure  2 ; Table  S3 ). The same activities were shown most represented in the MF category of Brassica napus  [31]. However, 1463 GOs were found in the CC, most of them located in organelle "GO:0043226", cytoplasm "GO:0005737", and membrane "GO:0016020" (Figure  2 ; Table  S3 ). Similar results were reported in Brassica juncea  [30]. In addition, pathway analyses were performed using the KEGG database (Arabidopsis thaliana as the reference) to identify the pathways and understand the possible gene interactions. In total, 13,046 transcripts were assigned to 138 pathways. Among these pathways, metabolic pathways, biosynthesis of secondary metabolites, and plant hormone signal transduction were the top nine pathways represented by transcripts (Table  S3 ).
In aboveground parts, the majority of genes significantly upregulated are involved in lipid metabolic process, defense response, and response to jasmonic acid. In addition, several genes implicated in jasmonates biosynthesis were significantly upregulated under Pb exposure, suggesting that H. incana uses the JAs pathway as a strategy to alleviate Pb stress. The dataset generated in this study also contributes to the molecular resources of Hirschfeldia incana.
Acknowledgments: The authors would like to thank " IRD: JEAI-PhytoMet" 2018-2020; IRD Joint Laboratory "LMI AMIR" 2020-2025 ";  Programme Prioritaire de recherche "PPR2-BIOMIVER"-Ministère de l'Enseignement Supérieur, de la Recherche Scientifique et de la Formation des Cadres-Morocco  and the  COST Action  " RoxyCOST  ( CA18210 )"  2019-2023  for their support. We thank reviewers and editors for their time and fruitful comments.
CRyogenic InfraRed Echelle Spectrograph Data The goal of our observations is to detect the 3 He I * λ1. 0833 μm absorption line to obtain the first direct measure of the helium isotope ratio beyond the Local Interstellar Cloud. Detecting 3 He I * absorption is challenging, as we expect the 3 He I * absorption line to be very weak, and only detectable in spectra of both high resolution (R  40,000) and very high signal-to-noise ratio (S/ N > 1000). To minimize the effect of pixel-to-pixel sensitivity variations affecting the final combined S/N, we designed the observing program to ensure that the target spectrum was projected onto different detector pixels with each exposure. A total of 10 exposures were acquired at two nodding positions (20 exposures total); each nod was separated by 7 5, and the target spectrum was shifted by 0 5 on the detector with each exposure.
The pattern noise is periodic along the spectral dimension, and appears to be constant for all exposures (see top panel of Figure  1  for an example). By subtracting science frames at different nod positions, we were able to fully remove the detector pattern and hot pixels in the difference frames (see bottom panel of Figure  1 ). 15 We used the LINETOOLS package XSPECTRUM1D to ensure that flux is conserved. LINETOOLS is available from https://linetools. readthedocs. io/en/ latest/. 16 The achieved S/N is almost a factor of ∼2 lower than that expected for the requested integration time (S/N  2200). The reduced S/N is likely a combination of residual data-reduction systematics (e. g. , unaccounted for pixel-to-pixel sensitivity variations) and the poor centering of the target in the slit due to an acquisition error (private communication, ESO User Support Department).
Atomic Data The helium absorption lines analyzed in this paper originate from the metastable 2 3 S state of He I, which has a lifetime of ∼130 min. This state is populated by recombinations from the He II state, when the recombining electron has the same spin as the electron in the He II ground state. The excited state 18  of the absorption lines (the 3 P o state) exhibits fine structure, with total angular momentum quantum numbers J′ = 0, 1, 2, while the ground state of metastable helium (the 3 S state) has J = 1. We use the energy levels compiled by  Morton et al. (2006)  to determine the vacuum wavelengths of the helium transitions used in this paper. The oscillator strengths ( f ) for the 4 He transitions were retrieved from the National Institutes of Standards and Technology (NIST) Atomic Spectral Database (ASD;  Kramida et al.
Profile Fitting To model the He I * absorption lines, we use the Absorption LIne Software (ALIS) package. 20  ALIS uses a Levenberg-Marquardt algorithm to determine the model parameters that best fit the data, by minimizing the chi-squared statistic. We perform a simultaneous fit to the stellar continuum, the interstellar absorption, the wavelength calibration, the zero-level, telluric absorption, and the instrument resolution. This fitting approach allows us to propagate the uncertainties of these quantities to the model parameters. Before describing the implementation of this fitting procedure in detail, we first draw attention to a faint emission feature that is detected in the red wing (∼+23 km s -1 ) of the He I * λ1. 0833 μm profile (see Figure  1 ). While this emission is largely removed as part of the differencing process (see Section 2), we note that the absorbing medium may be relatively thin owing to the conditions required for He I * absorption.
2018; Frankel et al. 2020) ; in our analysis, we use the semiempirical result of  Minchev et al. (2018) , R gal,e = 7. 3 ± 0. 6 kpc. 28  We generated a suite of VICE models covering a grid of primordial 3 He/ 4 He values. For each 3 He/ 4 He value, we take an average of 16 VICE simulations to minimize the post-BBN scatter of 3 He/ 4 He values due to radial migration (this scatter is of order 1%-2%). We include this scatter as part of the model uncertainty, even though this is subdominant compared to the measurement errors. To determine the most likely value of the primordial 3 He/ 4 He ratio, we linearly interpolated over our averaged grid of VICE models, and then conducted a Markov Chain Monte Carlo (MCMC) analysis using the EMCEE software  (Foreman-Mackey et al. 2013). We assume a uniform prior on the 3 He/ 4 He ratio, and a Gaussian prior on the birth galactocentric radius of the Sun, as described above.
, 2016 (Marques et al. , , 2018)). We thus integrated these datasets using Seurat  (Stuart et al. , 2019)  and selected 5,516 progenitor and oligodendroglial cells. Unsupervised clustering and visualization of cells in two dimensions with uniform manifold approximation and projection (UMAP), identified nine different clusters following a differentiation trajectory. Based on known cell-subtype specific markers (Fig. S2E  and supplementary Table3), we could identify these clusters as (Fig. S2C, E):  (1) two types of neural stem/progenitor cells, that we named NSCs and NPCs according to their expression of stem cell (Vim, Hes1, Id1) and neural progenitor (Sox11, Sox4, Dcx) markers; (2) OPCs expressing their known markers (Pdgfra, Cspg4, Ascl1) and cycling OPCs also enriched in cell cycle markers (Mki67, Pcna, Top2); (3) two stages of iOLs, both expressing the recently proposed markers Itpr2 and Enpp6  (Marques et al.
Finally, to assess whether TNS3 is potentially required during human development, we explore for the presence of TNS3 gene variants in the human population using the gnomAD database containing 125,748 exomes and 15,708 whole-genome sequences from unrelated individuals  (Karczewski et al. , 2020; Lek et al. , 2016). Homozygous predicted loss-of-function (pLoF) alleles of TNS3 were not found, and heterozygous pLoF were greatly below the expected frequency (0. 1 observed/expected ratio, with 90% confidence interval of 0. 05-0. 19; and LOEUF of 0. 19; Fig. S9A ; https://gnomad. broadinstitute. org), meaning that heterozygous loss-of-function variants of TNS3 causes ~80% developmental mortality, a rate similarly high to key neurodevelopmental genes such as SOX10 (LOEUF=0. 21; Fig. S9B ), CHD7 (LOEUF=0.
Development of CRISPR/Cas9 tools CRISPOR software (http://crispor. tefor. net/) was used to design gRNAs with predicted cutting efficiency and minimal off-target and PCR amplification primers. The validation of Tns3targeting CRISPR/Cas9 system was performed in 3T3 cells by transfection with Lipofectamine 3000 of PX459 plasmids containing 4 different sgRNA sequences. After 2 days incubation, puromycin was added to medium for 4 days allowing survival of cells containing the PX459 plasmid. Three days after proliferation in fresh medium without puromycin, DNA was extracted using DNeasy blood & tissue kit (Qiagen). The target DNA for 5' Tns3 region was amplified by PCR using primers with the following sequences: Forward: 5'-AGG TGG CCT TCA GCT CAGT-3', Reverse: 5'-GCT ATC ATC CCC ACT CAC CA-3'; annealing temperature of 64°C, with the PCR product expected to be 326bp.
A final washing was done before mounting the coverslips on slides to be visualized under the microscope. Immunofluorescence was visualized with Zeiss® Axio Imager. M2 microscope with Zeiss® Apotome system. Pictures were taken as stacks of 5-10µm with 0. 5µm between sections. Image acquisition and processing are achieved by ZEN Microscopy and imaging Software, Z-projections and orthogonal projections were done in ImageJ and processed with Adobe Photoshop. Figures were made using Adobe Illustrator.