dataesr/scientific-paragraphs-categorization huggingface dataset:
1 of the supporting information for more details). We also used the more familiar pixel-wise MSE. FID and KID are singular values, while the MSE provided in Table  1  represents the average of all individual sample comparisons. For all three metrics, two identical distributions would return a value of zero, and, as an upper limit, we provide a baseline comparison between a dataset of uniform noise and the GT. In all the metrics, the scores for I SRO (Q) and I FF (Q) are at least an order of magnitude smaller than the respective noise comparison. In terms of the general magnitude of the FID and KID scores, both I SRO (Q) and I FF (Q) are closely aligned with those from established benchmarks used to assess GANs in the computer-vision field  (Betzalel et al. , 2022). The average MSE shows the same trend.
Looking in more detail at the distribution of validation MSEs (see Fig. 6  of the supporting information), 90% of the DSFU-Net generated I SRO (Q) planes have an MSE of less than 0. 032 compared with the GT. The I FF (Q) component performs even better, with 90% having an MSE of less than 0. 014. These low scores, compared with noise, indicate that DSFU-Net has successfully learnt the underlying features of the data distribution and can map an input scattering plane to the desired factorized components. The I FF (Q) scores are noticeably larger than I SRO (Q). This can be attributed to the greater diversity of intensity topology in these images and the fact that I SRO (Q) tends to have lower intensities. In the latter case, Fig. 6  of the supporting information demonstrates that the I SRO (Q) noise-GT comparison is much flatter, in some cases less than the noise-noise comparison, which could lead to an artificial decrease in the scores.
Application to an experimental example The next step was to benchmark DSFU-Net against a solved experimental example. One such case is the molecular crystal tris-tert-butyl-1,3,5-benzene tricarboxamide. The structure, solved from single-crystal data  (Kristiansen et al. , 2009)  (illustrations and coordinates are provided in Section 3. FID, KID and MSE between the DSFU-Net-generated and GT scattering planes for the validation (12 607 total samples) and training datasets (random selection of 50 000 samples) compared with a dataset of uniform noise versus the validation GT as a baseline. network of hydrogen bonds, but the columns have a negative nearest-neighbour correlation in the ab plane. This leads to a diffuse scattering pattern consisting of a hexagon surrounding each Bragg peak, modulated by the form factor difference between the two molecule orientations.
The I SRO (Q) component was extracted analytically by  Schmidt & Neder (2017). Since DSFU-Net requires no prior knowledge of the average structure, we used their equivalent method (dividing by the average form factor squared and projecting into a single Brillouin zone) as the benchmark. The I FF (Q) component can be calculated directly from the known disordered structure. Experimental data for the hk1 scattering plane were obtained from  Simonov et al. (2014a)  with kind permission, having had the Bragg peaks and background already removed (see note on the importance of this in Section 3. 3 of the supporting information). The data were reconstructed on a square 256 Â 256 pixel grid using the torchvision. resize method in Python, and input into DSFU-Net using the available pipeline, taking seconds to produce the outputs shown in Figs.
Relative to this, the output I FF (Q) accurately reproduces the positions of the main features very well; however, there are some areas where the intensities are not correctly predicted. Firstly, the circular regions of low intensity that appear as small holes in the inner part of the pattern. This is probably another manifestation of the inherent limitations in regions of very low scattering intensity. The other noticeable difference is that the overall intensities of the DSFU-Net output appear to decay faster with Q. On simulated scattering from tris-tert-butyl-1,3,5-benzene tricarboxamide, calculated using equation (  5 ), DSFU-Net was able to reproduce the two components almost exactly (see Section 3. 4 of the supporting information, revealing that this additional Q dependence is a feature of the experimental data and not a limitation with the network.
Limitations DSFU-Net will produce two components from any input, providing that the size is correct. Since it was trained to separate periodic sharp patterns from continuous ones, it will, for example, separate Bragg peaks from a smooth background. However, the output components will only be meaningful if the assumptions and mathematics underpinning the training data can be applied to input scattering data. To be able to interpret the output as the scattering due to chemical SRO and molecular form factors, the system in question must have binary substitutional disorder with one disordered site per unit cell and, for the best quantitative results, disorder from displacement and phonons should be negligible. Tris-tertbutyl-1,3,5-benzene tricarboxamide is known to have a small size-effect relaxation  (Simonov et al.
Conclusions We have designed a deep-learning method, DSFU-Net, based on the Pix2PixGAN  (Isola et al. , 2018) , which takes as input a plane of diffuse scattering and separates the contributions from the molecular form factor and the chemical short-range order, facilitating local structure modelling. DSFU-Net was trained on 198 421 samples of simulated scattering data and performed extremely well on 12 607 simulated validation datasets, with >90% of outputs having a mean square error of <0. 04, relative to the ground truth, even when the inputs included dead zones from detector gaps. We have demonstrated that DSFU-Net also generalizes to real experimental data, and that the output I FF (Q) and I SRO (Q) are both of sufficient quality that refinement of disorder parameters or structural elements is possible.
(2017). This opens up possibilities of structure solution using only diffuse scattering, an attractive option considering continuous scattering does not suffer from the phase problem  (Ayyer et al. , 2018) , and it could be particularly beneficial if crystallinity or crystal size is an issue. DSFU-Net is available on GitHub (https://github. com/ dclw29/DSFU-Net) with documentation and scripts allowing users to apply the trained neural networks to their own problems. If required, the applicability of DSFU-Net could be easily extended to other systems with one disordered site per unit cell. For example, those containing pure displacement disorder or potentially a mixture of displacement and substitutional disorder, given an appropriate training dataset. Our GitHub provides the means to generate new training data and retrain the neural network.
Study Design and Clinical Protocols This is a single-center retrospective study of 354 consecutive adults (age 18 years) who underwent HCT with PTCy-based GVHD prophylaxis between January 1, 2015, and February 1, 2022. For patients who underwent more than 1 PTCy-based HCT during the time period, the most recent transplantation event was evaluated, and the earlier HCT was recorded as a prior transplantation. Patients were identified through the Dana-Farber Cancer Institute HCT data repository. Patients received peripheral blood or bone marrow (BM) hematopoietic cell grafts from haploidentical, 10/10 HLA-matched unrelated (MUD), or 8-9/10 mismatched unrelated (MMUD) donors with either an MAC or RIC regimen. The majority of patients received an RIC regimen of fludarabine, cyclophosphamide and low-dose total body irradiation, whereas the remaining patients received a variety of other conditioning regimens, as outlined in Supplementary Table  S1.
Data Collection and Statistical Analysis Clinical information including baseline characteristics, disease history and history of prior HCT, transplantation characteristics, transplantation outcomes, and infection history, was collected from the electronic medical record. The primary outcome of interest was time to first OI from day 0 to day +365 post-HCT. Baseline and transplantation characteristics and outcomes were compared using the Fisher exact test for categorical data and the Wilcoxon rank-sum test for nonparametric continuous variables. OIs were identified from day 0 through 1 year post-HCT. The 1-year cumulative incidence function of OIs was calculated based on time to the first OI using the dates of infection-free death, disease relapse, and repeat HCT as competing risks. The 1-year NRM was calculated using disease relapse as a competing risk.
Additional OIs, including BK virus, herpesviruses such as human herpesvirus 6, and toxoplasmosis were not assessed here and would be important areas in which to expand the analysis. Here the incidence of OIs also may be impacted by institutional prophylaxis practices. We also acknowledge that because our study spanned a period before the approval of letermovir for CMV prophylaxis, the differential impact of CS-CMVi after haploidentical HCT is likely to be mitigated in the letermovir era. Nonetheless, this study represents one of the largest cohorts to date with granular data focused on OIs after haploidentical HCT and MUD/MMUD HCT with PTCy and demonstrating a higher risk of OIs after haploidentical HCT. Our findings have implications for infection monitoring and prophylaxis in the haploidentical setting, as well as potential impacts on donor selection.
ACKNOWLEDGMENTS Financial disclosure statement: R. D. reports research funding from  Ligue Contre le Cancer ,  Arthur Sachs ,  Monahan Foundation ,  Servier Foundation ,  Philippe Foundation , and  DCP AP-HP , honoraria from  Novartis  and  Takeda , and nonfinancial support from  Kite Pharma/Gilead. R. M. S. reports a paid lecture for Miltenyi Biotech. S. E. P. reports support for clinical trials from  Atara Biotherapeutics ,  Jasper , and  AlloVir  and consulting for  Smar -tImmune and Cellevolve and is an inventor of IP licensed to Atara, with all rights assigned to  Memorial Sloan Kettering Cancer Center. J. K. reports serving on scientific advisory boards for Cugene and Therakos (Mallinckrodt); consulting for Equillium, Gentibio, Cue Biopharma,  Biolojic Design , and TR1x; research support from  Amgen ,  Clinigen ,  BMS ,  Miltenyi Biotec ,  Regeneron ,  Equillium ; and legal for AbbVie.
(2011)  for S. frugiperda. Whorl leaves from the bmr and non-bmr sorghum were collected from the plants when there were between six and eight fully developed leaves (stages V6-V8)  (Magalhães and Durães, 2003)  and taken to the laboratory, where they were cleaned and cut into pieces of approximately 50 cm 2. The leaves in the bioassay containers were replaced every 48 hours. The survival and biomass of D. saccharalis larvae were evaluated 10 days after the beginning of the experiment. The evaluation was performed for 10 days, which is the duration of the behavior of D. saccharalis in seeking the plant stem; after that, the larvae no longer fed on the leaves. Data on these biological parameters were subjected to the Shapiro-Wilk and Bartlett tests to check the assumptions of normality of residuals and homoscedasticity, respectively.
Injury caused by bored larvae was evaluated every 60 days after infestation. For injury evaluation, plants were cut close to the ground and opened longitudinally to detect the presence of galleries bored in the stem of plants. The parameters evaluated were plant height (cm), bored internodes (%), gallery size (cm), number of galleries per plant, survival (%) and biomass (mg) of larvae and pupae recovered from the plants. Data recorded for these parameters were subjected to the Shapiro-Wilk and Bartlett tests to check the assumptions of normality of residuals and homoscedasticity, respectively, and then analyzed by ANOVA. The means of treatments were compared by Tukey's test (α=0. 05). The analyses were performed using the statistical R software version 3. 5. 3 (R Development Core Team, 2019).
Correlation coefficients were also estimated to correlate the biological variables with the AI of S. frugiperda. For the survival analysis, a curve was generated in SigmaPlot software 10. 0 ® (Systat Software Inc. , 2006) from the output of the chi-squared test. For the larval and pupal biomass data, the Shapiro-Wilk test was performed to check normality and the Bartlett test was used to check for homogeneity of variances. As data did not follow a normal distribution or exhibited heterogeneity of variances, was performed using generalized linear model (GLM) and negative binomial distribution, and the means of treatments were compared by Tukey's (α=0. 05). The analyses were performed using the statistical R software version 3. 5. 3 (R Development Core Team, 2019). The experiment to evaluate plant injury in the greenhouse followed the same procedure described for the sugarcane borer.
The calibration equations for the analysis of ADF, NDF, lignin, and calorific value were based on values obtained and stored in the Embrapa Corn and Sorghum database, which covers a total of 400 samples. Data obtained from the chemical analyses were subjected to the Shapiro-Wilk and Bartlett tests to check the assumptions of normality of residuals and homoscedasticity, respectively, and were analyzed by ANOVA. The means of treatments were compared by Tukey's test (α=0. 05). The analyses were performed using the statistical R software version 3. 5. 3 (R Development Core Team, 2019).
Acknowledgements The authors thank the  Graduate Program in Entomology of the Universidade Federal de Lavras (UFLA)  for the scientific support to the students;  Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)  for granting a PhD scholarship to the first author;  Embrapa Milho e Sorgo  for allowing this research to be carried out; and the  BNDES  project for the financial support.
The column was then washed with 99%B for 5 min before returning to 1% B. Column was equilibrated at 1%B for 10 min before the next injection. Column was maintained at room temperature. Mass spectrometry data was recorded between 8-145 min using data dependent acquisition with a cycle time of 1 sec. Full scan MS1 was acquired in the orbitrap with resolution 240,000 (m/z 200). HCD MS/MS spectra was acquired in the linear ion trap at unit mass resolution with isolation window 1. 2 m/z using turbo scan. HCD energy was 30%. AGC was 250% for MS1 and 150% for MS2. Precursors with charges between 2-7 was selected for MS2, Dynamic exclusion was set a ± 10 ppm for 60 sec with isotopes excluded. Initial protein identification was carried out using Proteome Discoverer (V2. 5) software (Thermo Scientific) against the human protein database downloaded from Uniprot.
org (v2022. 06. 14. ) along with a customer protein database containing UBA1 variants and a common contaminant database. Search parameter include: full trypsin digestion with up to 2 missed cleavages; precursor mass tolerance 20 ppm; fragment mass tolerance 0. 5 Da. Fixed modification: methylthio of cysteine (+45. 988); variable modifications: oxidation of M, ubiquitylation (GG) of K, S, T and protein N-terminal; and protein N-terminal acetylationPercolator FDR control was set at 1% for both protein and peptide ID with concatenated target/decoy database. Proteins with 1 peptide ID were included in the report. Protein and peptide quantification were based on precursor intensity. MS/MS spectra of oxyesters at ubiquitylated S and T residues were manually validated.
Structural modeling, molecular dynamics simulations and pKa calculations To generate a model structure of UBA1 A478S bound to Ub-AMP, the residue A444 in the crystal structure of S. cerevisiae UBA1 bound to Ub-AMP (PDB: 4NNJ) was mutated to serine and subsequently energy minimized by 500 iterations in Schrödinger (release 2022-2) with Maestro (Schrödinger Release 2017-4: Maestro. Schrödinger, LLC, New York, NY, 2017). Molecular dynamics simulations were performed in Schrödinger (release 2022-2) with Desmond. Structures of UBA1 from yeast (PDB: 6NYA and 4NNJ) and human (PDB: 6DC6) were prepared identically. Briefly, sidechains and missing loops were modeled with Prime  (Jacobson et al, 2004)  and ionization states of sidechains were determined with PROPKA3  (Olsson et al, 2011). The system was solvated with the TIP3P water model applying a rhombic dodecahedron periodic boundary condition and neutralized with counterions to a final ionic strength of 150 mM.
Equilibration was performed in the isothermal-isochoric (NVT) and isothermal-isobaric (NPT) ensemble at 300 K. Production simulations were performed in the NPT ensemble for 100 ns with a 2 fs timestep. The per residue root mean square fluctuations (r. m. s. f. ) were calculated in Desmond across the entire trajectory and mapped onto the indicated crystal structures. From a 100 ns trajectory, distances between serine and the putative catalytic base were measured between the sidechain oxygens and plotted as a function of time with a moving window average of 100 ps. Structures were analyzed and figures generated by using PyMol (PyMOL Molecular Graphics System, http://www. pymol. org). When calculating the pKa of serine, cysteine, and aspartic acid in the S621C variant, the yeast UBA1 structure (PDB: 4NNJ) was used.
* = UBA1 ubiquitin oxyesters as demonstrated in Figure  3 F   was modeled into the structure using an S. pombe UBA1-Ub/ATP•Mg structure (PDB:4II3). Residues substituted by non-canonical VEXAS mutations are highlighted in red (spheres (left) or stick view (right)), revealing that they cluster around the adenylation site and participate in orienting ATP and Mg +2 (green spheres) in the catalytic pocket. To clearly visualize the ATP binding pocket in the expanded view, the cross over loop containing the S621 is not shown. All stick representations: carbon (grey), oxygen (red), nitrogen (blue), and phosphorus (orange). B) Coomassie gel of WT UBA1b His and indicated non-canonical VEXAS mutants recombinantly expressed and purified from E. coli, revealing high purity. C) Schematic overview of the in vitro pyrophosphatase assay used to determine the efficiency of the adenylation reaction of UBA1.
D) Only UBA1 p. G477A, but no other non-canonical VEXAS variant significantly affects adenylation activity in vitro at saturating ATP and ubiquitin conditions. n = 2-3 biological replicates with 3 technical replicates each, error bars = s. d. , **** = p < 0. 0001, one-way ANOVA. E) Michaelis-Menten kinetics for ATP usage showed ~2-fold deficiency in forming the Ub-AMP in UBA1 p. S56F, p. G477A, and p. S621C. The rate of Ub-AMP formation (kcat) was calculated for n = 3 -5 biological as indicated. Indicated recombinant UBA1b proteins (500 nM) were incubated with 10 µM ubiquitin and 5 mM ATP for 10 min at 37 o C followed by anti-UBA1 immunoblot analysis. D) Crystal structure of S. cerevisiae UBA1 (grey) bound to Ub-AMP (Ub A , PDB: 4NNJ) was modeled with the p. A478S mutation (S. cerevisiae p.
Indicated UBA1 HA variants were expressed in HEK 293T cells and lysates were either not treated (non-reducing, NR) or treated with indicated reducing agents and subjected to anti-HA immunoblotting. D) Ribbon diagram of the serine-rich region surrounding UBA1 p. S621C following 100 ns of molecular dynamics. The simulation was performed using a yeast UBA1 structure (PDB: 4NNJ) with cysteine substitution of S589 (corresponding to human S621). For consistency, we refer to the corresponding human residues and indicate yeast residues in parentheses. Interactions highlighted with dashed lines are color coded for panels E and F. Sidechain heavy atoms are displayed in grey, red, or yellow for carbon, oxygen and sulfur respectively. Double red sticks indicate carbonyl oxygen atoms. E) Distance (Å) over time (ns) between serine side chain hydroxyl hydrogen atoms and the nearest side chain oxygen of D585 or E616 as indicated and displayed in panel D.
CHO rescue assays were performed as described in Figure  1D , followed by anti-UBE2L3 and anti-UBE2Simmunoblotting and quantification. n = 3 biological replicates per condition. I) Schematic representation of our in vitro findings, revealing that non-canonical VEXAS mutations inactivate UBA1 by most prominently affecting ubiquitin transfer to E2 enzymes. UBA1 domains highlighting the location of mutations causing canonical and noncanonical VEXAS syndrome (red), causing spinal muscular atrophy (SMA, blue), or implicated in lung cancer in never smokers (LCINS, green). B) Structural mapping of the position of the different disease-associated UBA1 amino acid substitutions using the crystal structure of human UBA1 (grey) with ubiquitin (cyan) in the adenylation site (PDB: 6DC6) and ATP (yellow spheres) modeling as described for figure 2A.
0001, one-way ANOVA. form aberrant ubiquitin thioester species in vitro. Denoted recombinant UBA1b proteins (500 nM) were incubated with 10 µM ubiquitin and 5 mM ATP for 10 or 60 min at 37 o C. Reactions were treated with reducing agents (b-ME or NH2OH) as indicated, followed by anti-UBA1 immunoblot analysis. B) Ribbon diagram of an expanded region of S. cerevisiae UBA1 (PDB: 4NNJ) wild-type protein (upper panel) and with H643Y substitution (lower panel). Labeling for this panel and panel C follows figure  3D. The mutation disrupts hydrogen bonds (red dashed lines) from H643 to N639 and a bound water molecule (red sphere) and forces rearrangements to prevent steric clashes between Y643 and T633/I629 (cyan dashed lines). Stick representation is used to display sidechain heavy atoms of UBA1 residue I629, C632, T633, N639, A640, and H643.
In this panel and panel C, oxygen, nitrogen, and sulfur are colored red, blue and yellow, respectively. C) Ribbon diagram of an expanded region of S. cerevisiae UBA1 (grey, PDB: 4NNJ) wild-type protein (upper panel) with a thioester-linked ubiquitin (light green) and the final frame of a 100 ns molecular dynamics simulation of UBA1 Q724P mutant (lower panel). A hydrogen bond (red dashed line) between the N721 backbone oxygen atom and Q724 (orange) backbone nitrogen atom is disrupted in the Q724P mutant by kinking of the helix (noted in red). The thioester bond between UBA1 C632 and ubiquitin G76, as well as the sidechain heavy atoms of UBA1 residue F637, N721, Q724, F729, F741, W742 and L814 are displayed by stick representation. D) Model of how UBA1 p. H643Y and p. Q724P lead to the formation of aberrant UBA1 ubiquitin thioesters via intramolecular transthiolation reactions.
Acknowledgments: We thank  Dr. Alan Schwartz  from  Washington University in St. Louis  for the kind gift of the CHO cells. We thank the  NIDCR Imaging Core  ( ZIC DE000744-04 ) and the  Mass Spectrometry Facility  ( ZIA DE00075 ) for excellent technical assistance. We thank patients and their families for participation in our study. This research was supported by the  Intramural Research Program  of the  National Institutes of Dental and Craniofacial Research (NIDCR ,  ZIA DE000749 ) and the  National Cancer Institute (NCI ,  ZIA BC011490 ). D. B. B. is supported by the  Jeffrey Modell Foundation , the  Relapsing Polychondritis Foundation, AAMDSIF , the  Department of Defense  ( BMFRP-IDA HT9425-23-1-0507 ), and funding from the  NIH  ( R00AR078205 ). This work was, in part, supported by computational resources from the  NIH  HPC Biowulf cluster (http://hpc.
Data and materials availability: All data supporting the findings are available in the main text and the supplementary. Proteomics data are provided in Supplementary Figure  3  and 4  and were deposited into the Mass Spectrometry Interactive Virtual Environment (MassIVE) under the accession number MSV000092914 (Reviewer password: Achim). Requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Achim Werner (Achim. werner@nih. com).
Indicated recombinant UBA1b proteins (500 nM) were incubated with 10 µM ubiquitin and 5 mM ATP for 10 min at 37 o C followed by anti-UBA1 immunoblot analysis. D) Crystal structure of S. cerevisiae UBA1 (grey) bound to Ub-AMP (Ub A , PDB: 4NNJ) was.