dataesr/scientific-paragraphs-categorization huggingface dataset:
PDB entry 3l8w was chosen for its superior resolution (1. 0 A ˚) compared with PDB entry 1r51 (1. 75 A ˚), which does have the 8-azaxanthine inhibitor, but comparison of the two structures in PyMOL gives r. m. s. d. values of 0. 26 A ˚over one chain and 0. 326 A ˚over all four chains and indistinguishable predicted scattering patterns were obtained using CRYSOL. Both PDB entries 3l8w and 1r51 have six amino acids missing from the C-terminus (SLKSKL). A PDB file was thus prepared starting with PDB entry 3l8w and completing the C-terminus with the six missing residues using ModLoop  (Fiser & Sali, 2003). For all proteins, additional ions or ligands that were present in the coordinate files but not present in the solution conditions were removed.
This interpretation is consistent with the observation that the batch data for urate oxidase give somewhat larger structural parameters on average (by $0. 7 A ˚in R g and $15 A in d max ) compared with SEC-SAXS measurements. Due to the timing of sample availability and SAS instrument availability some measurements for this protein were delayed by up to 6-7 months from shipment. Xylanase shows the largest mean shift in R g and d max values between SEC-SAXS and batch SAXS measurements, with all of the batch and half of the SEC-SAXS measurements yielding P(r) profiles with prominent, albeit relatively small, positive values at r values of >50 A ˚. Further, multi-angle laser light scattering (MALLS) data measured at the European Molecular Biology Laboratory (EMBL) Hamburg and BioCAT at the Advanced Photon Source (APS) (data not shown) indicated the presence of dimers.
Similarly, some merged data sets were constructed using all batch data by combining lower concentration data with higher concentration data. The merging protocol used primusQt from the ATSAS suite (versions 3. 0. 0, 3. 0. 1 or 3. 1. 0) and was performed centrally (by JT and PV) to ensure a consistency of approach. Starting with an overlap region (typically $50-100 data points) in the mid-q regime, I(q) profiles were placed on a common scale to prepare for merging the lower q region of a SEC-SAXS or lower concentration batch measurement with the higher q region of a higher concentration batch measurement. An iterative process was used to test for any influence of potential aggregation or interparticle interference from the higher concentration batch data on the merged data by systematically increasing the minimum q value accepted from the batch data and repeating the P(r) calculation for the resulting merged I(q) profile and comparing with that obtained from the SEC-SAXS or lower concentration batch data as applicable.
Comparisons with prediction A preliminary assessment of the agreement between experiment and prediction based on the crystal structures described above (in Section 3. 4) used R g values from Guinier fits of the WAXSiS predictions, as well as the R g and d max values output by CRYSOL  (Svergun et al. , 1995)  and CRYSON  (Svergun et al. , 1998)  (implemented in ATSAS online 3. 1 with default parameters and without any fitting to experimental data) (Supplementary Table  S8 ). When compared with experiment, the experimental R g values for each protein show a decrease from SAXS to SANS in H 2 O to SANS in D 2 O measurements that is generally consistent with the predictions, with SANS measurement of xylanase in H 2 O and xylose isomerase in D 2 O falling just outside the predicted values (Fig. 6 ).
Shaperestoration calculations using DAMMIN  (Svergun, 1999;  as implemented in ATSAS online 3. 0) yielded total excluded volumes of 18 380, 15 973, 26 800, 236 005 and 268 299 A ˚À3 for RNaseA, lysozyme, xylanase, urate oxidase and xylose isomerase, respectively. Taking the calculated volume/mass ratios from Supplementary Table  S1 , these volumes give estimates of the molecular masses that are 91%, 77%, 90%, 113% and 102%, respectively, of the values expected from the chemical composition. For urate oxidase, the 13% excess volume may be associated with the existence of a large central water-filled quasi-cylindrical channel in this protein (see Fig. 1 ). The somewhat lower excluded volume value for lysozyme (23% lower than expectation compared with 2-13% for all the other proteins) suggests the influence of a small amount of interparticle interference in the consensus profile.
Where they are not evident, error bars for R g from the consensus curve are smaller than the symbols. spacing of domains, or potentially some sort of oligomerization. The latter was judged to be unlikely after re-examination of selected SEC-SAXS data sets, which did not show evidence for either oligomers in the main elution peak or a monomerdimer equilibrium. As an NMR solution structure is available for RNaseA, some preliminary calculations were performed using the conformers in the NMR structure (PDB entry 2aas;  Santoro et al. , 1993)  in the RECOORD database. All 32 scattering curves from the ensemble were calculated using WAXSiS as implemented on the web server  (Knight & Hub, 2015). A linear combination of those curves that best fit the experimental curve was found using an NNLS tool in US-SOMO  (Brookes et al.
Discussion The SAXS and SANS data presented here were measured with sources that varied in brightness by orders of magnitude, from a rotating-anode X-ray source to synchrotrons of different generations and neutron instruments with distinct resolutions at three reactors that produce different neutron fluxes. Some instruments were not equipped with SEC-SAS. Nevertheless, the SAS profiles for each protein have proven to be reproducible, with the caveat that an additive constant adjustment was generally required to account for the difficulties in ensuring perfect solvent subtraction. The demonstrated reproducibility included the SASderived structural parameters and the overall SAS profile shape, including characteristic oscillations, for all five proteins. Further, most participating facilities contributed data for each protein.
Xylose isomerase proved to be the most robust of the proteins for SAS measurements, with no evident issues with aggregation in any of the SAS measurements. This protein is easily stabilized as an ammonium salt microcrystalline precipitate and this study has demonstrated that it can be stored for years without degrading. Further, it is soluble to very high concentrations and is unaffected by D 2 O in the solvent. However, critical to successful measurement of xylose isomerase was the avoidance of the interparticle interference that was consistently observed in the lowest q SAS regime if the concentration of this highly charged protein exceeded 1 mg ml À1 , despite the charge-screening effects of the 100 mM NaCl present in the solvent. To overcome this problem, it was necessary to only use data acquired at <1 mg ml À1 in the low-q regime and to carefully merge with higher concentration data to achieve the optimal statistical quality of the measured q-range.
Hampton Research no longer supplies the xylose isomerase used for this study and, given its favourable properties as a standard, there is an argument for pursuing options for supplying this protein to SAS users, perhaps via one of the national or international facilities that already support sample preparation or the supply of standards. For urate oxidase, the necessity to ship solutions on ice and the upper limit on protein concentration of 5 mg ml À1 resulted in generally poorer statistics and a larger spread in Guinier R g values compared with xylose isomerase that is attributable to a small degree of sample heterogeneity. Also, a few SAXS profiles for RNaseA were discarded due to evident severe aggregation, which was possibly radiation-induced. Xylanase proved to be a challenge for most facilities due to its unanticipated tendency to form persistent dimers.
For now, the consensus result for lysozyme obtained in this study is probably the most accurate currently available lysozyme SAS data set, but one would clearly like to improve on the reproducibility for this protein for it to be a useful SAS standard. Inline SEC substantially reduced the probability of the presence of aggregates in the measured sample, and in the case of xylanase was essential for successful characterization of the monomeric form. Distributions of R g [both Guinier-and P(r)derived] and d max values generally showed narrower distributions in SEC-SAXS compared with batch SAXS measurements. For the xylanase example, only SEC-SAXS and SEC-SANS measurements were characteristic of the monomeric Data for (top to bottom traces) RNaseA, lysozyme, xylanase, urate oxidase and xylose isomerase from SEC-WAXS (black symbols, measured on the P12 BioSAXS beamline at EMBL, no lysozyme data) and batch WAXS (red symbols, measured on the 12-ID-B beamline at the APS, no urate oxidase data).
The plot is log-log, with every second data point skipped for the sake of clarity and starting at q = 0. 1 A ˚À1 to better highlight the data beyond q = 1 A ˚À1. Full log-log and log linear plots are given in Supplementary Fig. S12. Error bars are standard errors based on propagated counting statistics. form. For RNaseA and lysozyme in D 2 O, merging SEC-SANS with batch data was essential to remove the influence of aggregates. Thus, if inline SEC is available it is the preferred approach to obtaining a monodisperse sample for SAS measurements, which is the fundamental requirement for interpretation in terms of a single structure. That said, the potential for the dissociation of complexes during SEC and the increased exposure to radiation damage for more dilute solutions must be considered in planning experiments.
Further, the xylanase example is a cautionary tale given that in four of the eight SEC-SAXS measurements small amounts of dimer were not removed. It is therefore highly desirable to use SAS-independent measures to evaluate samples for their tendency towards oligomerization or aggregation before SAS measurements, noting here that the MALLS data for xylanase did indicate that dimer formation was a potential issue, and that better separation was achieved compared with inline SEC by using a larger column and a different tubing setup that decreased the band broadening. Of course, inline SEC comes at the cost of lower sample concentrations due to the unavoidable dilution during elution from the column and hence lower signal to noise, especially in the medium-to-high q-range. This effect is nicely demonstrated by comparison of the WAXS data collected to q > 2.
2 A ˚À1 with and without inline SEC. The successful merging of SEC-SAS data with batch data overcame this limitation. The issues noted above bring to the fore the fact that even for well characterized proteins it takes significant attention to the details of sample preparation, data acquisition, reduction and analysis to obtain a reliable SAS result. The issues that were encountered in this study will only be amplified for less well known systems. Even for these very well known and relatively stable proteins, it was essential to use all of the measures specified in the 2017 publication guidelines for biomolecular SAS  (Trewhella et al. , 2017) , including utilizing non-SAS methods for initial sample characterization, to have confidence in the final analysis. The datcombine tool was developed to optimally combine data from different instruments, with optional filters to remove outlier data points or data that only served to increase the errors.
Such an undertaking would be extremely complex and is beyond the scope of this study. Furthermore, the reproducibility of the individual measured SANS profiles with their nominal q scales and the general good agreement between R g values and predicted SANS profiles for the consensus curves demonstrate that the smearing effects are not so large so as to undermine the basic conclusions of the study. In using the consensus profiles, it is important to keep in mind that in merging or combining data from different sources and using filtering options one can obtain a distribution of errors in I(q) versus q that is distinct from the generally steady change in uncertainty with q obtained for the typical SAS profile of a protein from measurement on a single instrument in a fixed configuration. When using an error-weighted leastsquares fit of SAS data, such as that implemented in GNOM, to calculate P(r), the result can be sensitive to the distribution of experimental errors.
Such a resource is under development (co-authors EB and CJ) as a web-based tool to conveniently compare the results of different SAXS profile calculators, built using the GenApp framework  (Savelyev & Brookes, 2019)  that was originally developed under the Collaborative Computational Project for SAS  (Perkins et al. , 2016). Community support for such a resource would be of broad benefit, especially for attracting new SAS users.
Conclusions With the growing uptake of guidelines for the publication of modelling results from SAS data along with tools for model validation, it was timely to use a round-robin approach to obtain high-quality SAS data over an extended q-range from a set of proteins that would be good candidates to use in benchmarking different approaches to the prediction of SAS profiles from atomic coordinates and in the process test the limits of experimental reproducibility. The experimental data on the five proteins studied here demonstrate a high level of reproducibility for this set of relatively well characterized proteins, as well as the limits, for example, in the accuracy of solvent subtraction. The value of adherence to the 2017 guidelines for publication of biomolecular SAS and 3D modelling is well demonstrated.
The five consensus SAS profiles obtained provide a core set of consensus SAS data for evaluating, comparing and potentially improving any one approach to theoretical SAS profile prediction. It is desirable to extend the methods employed here to improve the consensus scattering profiles, especially in the cases of lysozyme and xylanase. All of the original data used to generate the consensus profiles, including the raw SANS data with resolution information calculated based on the geometry and optics of the instrument configuration, are publicly available, so that if new methods for combining data are developed an improved set of consensus data sets may be produced. Furthermore, there will be continuous improvement in beamlines, instruments, data reduction, analysis and sample preparation. Newly collected data from new instrumentation with new procedures on high-quality samples would be expected to increase the reproducibility and ultimately the quality achievable for a consensus profile.
The approach used here can be extended to any suitable protein studied using SAS by any group in the world thus motivated. An insightful reviewer of this manuscript pointed out that because active sites commonly require structural flexibility, their exposure to solvent can render a protein more vulnerable to aggregation. In this study, the most robust protein for SAS measurement, with no samples showing signs of aggregation, was xylose isomerase, which has internally oriented active sites. In contrast, urate oxidase has externally oriented active sites, while each of the three monomeric proteins have surfaceexposed active sites. Consideration of this property could be added to those outlined in Section 2 for selecting future candidate proteins. Provided that the protein can be made available for measurement on a reasonable number of instruments, data could be collected in a similar way to the measurements reported here and, after comparative analysis, the consensus scattering profile could be added to this core set, thus steadily improving upon and enlarging the benchmark SAS pattern set for prediction.
For the SANS data submissions, the associated zip files include the unsubtracted six-column format batch data with resolution information for all sample and solvent measurements, plus solvent-subtracted six-column format SEC-SANS ILL/D22 data. In addition, raw SANS and SEC-SANS data recorded on the ILL D22 instrument are available at https://doi. ill. fr/10. 5291/ILL-DATA. INTER-465. Raw SANS data from ANSTO/Quokka are available from the Zenodo digital archive at https://doi. org/10. 5281/ zenodo. 6789723. The simulation systems used for WAXSiS calculations also are available via the Zenodo digital archive at https://doi. org/10. 5281/zenodo. 7057567. Integrated Diffraction Analysis Technologies (IDAT) program. We thank Sanofi-Aventis for the gift of urate oxidase and Professor H. van Tilbeurgh (I2BC, Gif-sur-Yvette, France) for shipping costs.
Acknowledgements ANSTO supported the provision of beamtime on the Quokka SANS instrument (Proposal No. 8038 ). Shipping costs for this project were donated by the  Australian Synchrotron, ANSTO. Sample preparation was supported under proposal NDF8018 at the  National Deuteration Facility , which is partly supported by the  National Collaborative Research Infrastructure Strategy , an initiative of the  Australian Government. The experiments on BL40B2 at SPring-8 were performed with the approval of the  Japan Synchrotron Radiation Research Institute  (JASRI; Proposal No. 2019A2059 ). ALS SIBYLS data collection was made possible by  Department of Energy.
Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology's role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS).
Open access Deep learning poses its own obstacles. These models can forgo several of the steps necessary for radiomics, including segmentation and feature extraction, and may be more robust to noise variability in image acquisition. However, deep learning requires a sufficiently large and high-quality data set for neural network training. The ImageNet challenge in 2012, which first established the breakthrough performance of CNNs, had a training data set of over 1. 2 million images. 13 Obtaining similarly large training data sets of medical images has historically been difficult due to high cost and concerns about patient confidentiality. Promising efforts towards resolving this problem have been made such as The Cancer Imaging Archive and the National Lung Cancer Screening Trial. 21 22 ther issues with the deep learning strategy include a somewhat 'black box' decision-making process, unexplainable selected features, and a higher risk for overfitting when using small training sets.
SHAP and Grad-Cam are some of the technologies used to visualize the importance of certain pixels in input images and how they are related to the output, effectively allowing users to understand what the model 'sees' by using a heat map. 24 25 TERIAL AND METHODS Literature search strategy and study selection In order to review the published literature relating to the use of radiomics in immunotherapy, we queried three databases, MEDLINE (PubMed), CENTRAL (Cochrane Central Register of Controlled Trials), and Embase, from their inception through February 26, 2022. This scoping review included the following key search terms: (immunotherapy) AND (CT OR MRI OR PET) AND (radiomics OR texture OR deep learning OR artificial intelligence). One additional relevant study that did not appear in this search but that was identified in literature references was added.
The validation set thus acted as an 'extension' of the training set, drawn from the same or a similar patient base, but to which the model had not been previously exposed. In a similar fashion, 'tuning; validation set' meant that a study's original cohort was split into three groups: a training, tuning (for adjustment of model parameters after training), and validation set. A test set was identified when the cohort used to evaluate model performance consisted of patients that were independent of the training/validation sets, that is, they were drawn from a different database/institution/clinical trial, received a different type of treatment (chemotherapy vs immunotherapy), or were collected prospectively following initial training of the radiomics model. Test sets were used by studies either directly after training, denoted by 'test set', or following training and the use of a validation set, which we labeled as 'validation set; test set'.
RQS An RQS was calculated for each eligible study. The articles were individually reviewed and data was extracted based on adherence to a 16 component scoring system originally defined by Lambin et al. 20  Higher scores indicate higher quality studies, and the highest possible score is 36 points. This system was developed to provide a measure of standardization for the assessment of radiomics studies, assigning point values for different features of the scientific protocol (eg, points for if multiple segmentation or measures against overfitting were performed, if the study was prospective, if the model was validated and how, etc). The score does not take into account sample size or how the model actually performs, but instead represents an evaluation of both how rigorous model development is and how impactful the study may be to the field.
4%), an independent test set alone (n=7, 8. 5%), or a validation set followed by an independent test (n=7, 8. 5%). The remaining studies either did not perform validation testing (n=10, 12. 2%) or relied on less rigorous validation protocols, such as cross-validation on training set data alone (n=18, 22. 0%). Of the test sets identified (n=14, 17. 0%), more than half were collected retrospectively from an external institution or database (n=9), while most of the others were derived from cohorts at the same institution as the training/validation sets but differing in the treatment regimen (n=2) or consisting of data collected prospectively after model training (n=2). Only one study (1. 2%) followed what is considered the most thorough strategy for model development, progression through training and validation sets followed by performance evaluation on a prospective, external cohort.
30 igh baseline scores on this signature (and therefore greater CD8+ T cell infiltration) were associated with a higher chance of achieving objective response at 3 and 6 months as well as improved OS. This same radiomics signature was subsequently used to assess response after stereotactic body radiation therapy and pembrolizumab in metastatic treatment-refractory adult solid tumors, once again demonstrating a significant association with progression free survival. 31 Additionally, a deep learning network incorporating multimodal data sources was trained on the multiomics of imaging, laboratory, and clinical data of patients with non-small cell lung cancer (NSCLC) who received anti-PD-1/PD-L1 agents. 32 This model was capable of distinguishing responders from non-responders and predicting survival benefit to therapy in certain patients with stable disease.