3D CT Radiomic Analysis Improves Detection of Axillary Lymph Node Metastases Compared to Conventional Features in Patients With Locally Advanced Breast Cancer.

OBJECTIVE: Preoperative detection of axillary lymph node metastases (ALNMs) from breast cancer is suboptimal; however, recent work suggests radiomics may improve detection of ALNMs. This study aims to develop a 3D CT radiomics model to improve detection of ALNMs compared to conventional imaging features in patients with locally advanced breast cancer. METHODS: Retrospective chart review was performed on patients referred to a specialty breast cancer center between 2015 and 2020 with US-guided biopsy-proven ALNMs and pretreatment chest CT. One hundred and twelve patients (224 lymph nodes) met inclusion and exclusion criteria and were assigned to discovery (n = 150 nodes) and testing (n = 74 nodes) cohorts. US-biopsy images were referenced in identifying ALNMs on CT, with contralateral nodes taken as negative controls. Positive and negative nodes were assessed for conventional features of lymphadenopathy as well as for 107 radiomic features extracted following 3D segmentation. Diagnostic performance of individual and combined radiomic features was evaluated. RESULTS: The strongest conventional imaging feature of ALNMs was short axis diameter ≥10 mm with a sensitivity of 64%, specificity of 95%, and area under the curve (AUC) of 0.89 (95% CI, 0.84-0.94). Several radiomic features outperformed conventional features, most notably energy, a measure of voxel density magnitude. This feature demonstrated a sensitivity, specificity, and AUC of 91%, 79%, and 0.94 (95% CI, 0.91-0.98) for the discovery cohort. On the testing cohort, energy scored 92%, 81%, and 0.94 (95% CI, 0.89-0.99) for sensitivity, specificity, and AUC, respectively. Combining radiomic features did not improve AUC compared to energy alone (P = .08). CONCLUSION: 3D radiomic analysis represents a promising approach for noninvasive and accurate detection of ALNMs.

Novel indices reveal that pollinator exposure to pesticides varies across biological compartments and crop surroundings.

Declines in insect pollinators have been linked to a range of causative factors such as disease, loss of habitats, the quality and availability of food, and exposure to pesticides. Here, we analysed an extensive dataset generated from pesticide screening of foraging insects, pollen-nectar stores/beebread, pollen and ingested nectar across three species of bees collected at 128 European sites set in two types of crop. In this paper, we aimed to (i) derive a new index to summarise key aspects of complex pesticide exposure data and (ii) understand the links between pesticide exposures depicted by the different matrices, bee species and apple orchards versus oilseed rape crops. We found that summary indices were highly correlated with the number of pesticides detected in the related matrix but not with which pesticides were present. Matrices collected from apple orchards generally contained a higher number of pesticides (7.6 pesticides per site) than matrices from sites collected from oilseed rape crops (3.5 pesticides), with fungicides being highly represented in apple crops. A greater number of pesticides were found in pollen-nectar stores/beebread and pollen matrices compared with nectar and bee body matrices. Our results show that for a complete assessment of pollinator pesticide exposure, it is necessary to consider several different exposure routes and multiple species of bees across different agricultural systems.

Solution Structures of Two Different FRP-OCP Complexes as Revealed via SEC-SANS.

Photosynthetic organisms have established photoprotective mechanisms in order to dissipate excess light energy into heat, which is commonly known as non-photochemical quenching. Cyanobacteria utilize the orange carotenoid protein (OCP) as a high-light sensor and quencher to regulate the energy flow in the photosynthetic apparatus. Triggered by strong light, OCP undergoes conformational changes to form the active red state (OCPR). In many cyanobacteria, the back conversion of OCP to the dark-adapted state is assisted by the fluorescence recovery protein (FRP). However, the exact molecular events involving OCP and its interaction with FRP remain largely unraveled so far due to their metastability. Here, we use small-angle neutron scattering combined with size exclusion chromatography (SEC-SANS) to unravel the solution structures of FRP-OCP complexes using a compact mutant of OCP lacking the N-terminal extension (∆NTEOCPO) and wild-type FRP. The results are consistent with the simultaneous presence of stable 2:2 and 2:1 FRP-∆NTEOCPO complexes in solution, where the former complex type is observed for the first time. For both complex types, we provide ab initio low-resolution shape reconstructions and compare them to homology models based on available crystal structures. It is likely that both complexes represent intermediate states of the back conversion of OCP to its dark-adapted state in the presence of FRP, which are of transient nature in the photocycle of wild-type OCP. This study demonstrates the large potential of SEC-SANS in revealing the solution structures of protein complexes in polydisperse solutions that would otherwise be averaged, leading to unspecific results.

Structural characterisation of α-synuclein-membrane interactions and the resulting aggregation using small angle scattering.

The presence of amyloid fibrils is a hallmark of several neurodegenerative diseases. Some amyloidogenic proteins, such as α-synuclein and amyloid ß, interact with lipids, and this interaction can strongly favour the formation of amyloid fibrils. In particular the primary nucleation step, i.e. the de novo formation of amyloid fibrils, has been shown to be accelerated by lipids. However, the exact mechanism of this acceleration is still mostly unclear. Here we use a range of scattering methods, such as dynamic light scattering (DLS) and small angle X-ray and neutron scattering (SAXS and SANS) to obtain structural information on the binding of α-synuclein to model membranes formed from negatively charged lipids and their co-assembly into amyloid fibrils. We find that the model membranes take an active role in the reaction. The binding of α synuclein to the model membranes immediately induces a major structural change in the lipid assembly, which leads to a break-up into small and mostly disc- or rod-like lipid-protein particles. This transition can be reversed by temperature changes or proteolytic protein removal. Incubation of the small lipid-α-synuclein particles for several hours, however, leads to amyloid fibril formation, whereby the lipids are incorporated into the amyloid fibrils.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging.

Convolutional neural networks (CNNs), initially developed for image processing applications, have recently received significant attention within the field of medical ultrasound imaging. In this study, passive cavitation imaging/mapping (PCI/PAM), which is used to map cavitation sources based on the correlation of signals across an array of receivers, is evaluated. Traditional reconstruction techniques in PCI, such as delay-and-sum, yield high spatial resolution at the cost of a substantial computational time. This results from the resource-intensive process of determining sensor weights for individual pixels in these methodologies. Consequently, the use of conventional algorithms for image reconstruction does not meet the speed requirements that are essential for real-time monitoring. Here, we show that a three-dimensional (3D) convolutional network can learn the image reconstruction algorithm for a 16×16 element matrix probe with a receive frequency ranging from 256 kHz up to 1.0 MHz. The network was trained and evaluated using simulated data representing point sources, resulting in the successful reconstruction of volumetric images with high sensitivity, especially for single isolated sources (100% in the test set). As the number of simultaneous sources increased, the network's ability to detect weaker intensity sources diminished, although it always correctly identified the main lobe. Notably, however, network inference was remarkably fast, completing the task in approximately 178 s for a dataset comprising 650 frames of 413 volume images with signal duration of 20µs. This processing speed is roughly thirty times faster than a parallelized implementation of the traditional time exposure acoustics algorithm on the same GPU device. This would open a new door for PCI application in the real-time monitoring of ultrasound ablation.

Human cystatin C induces the disaggregation process of selected amyloid beta peptides: a structural and kinetic view.

Neurodegenerative diseases, such as Alzheimer's disease (AD) and various types of amyloidosis, are incurable; therefore, understanding the mechanisms of amyloid decomposition is crucial to develop an effective drug against them for future therapies. It has been reported that one out of three people over the age of 85 are suffering from dementia as a comorbidity to AD. Amyloid beta (Aß), the hallmark of AD, transforms structurally from monomers into ß-stranded aggregates (fibrils) via multiple oligomeric states. Astrocytes in the central nervous system secrete the human cystatin C protein (HCC) in response to various proteases and cytokines. The codeposition of Aß and HCC in the brains of patients with AD led to the hypothesis that cystatin C is implicated in the disease process. In this study, we investigate the intermolecular interactions between different atomic structures of fibrils formed by Aß peptides and HCC to understand the pathological aggregation of these polypeptides into neurotoxic oligomers and then amyloid plaques. To characterize the interactions between Aß and HCC, we used a complementary approach based on the combination of small-angle neutron scattering analysis, atomic force microscopy and computational modelling, allowing the exploration of the structures of multicomponent protein complexes. We report here an optimized protocol to study that interaction. The results show a dependency of the sequence length of the Aß peptide on the ability of the associated HCC to disaggregate it.

Removing non-nuclei information from histopathological images: A preprocessing step towards improving nuclei segmentation methods.

Disease interpretation by computer-aided diagnosis systems in digital pathology depends on reliable detection and segmentation of nuclei in hematoxylin and eosin (HE) images. These 2 tasks are challenging since appearance of both cell nuclei and background structures are very variable. This paper presents a method to improve nuclei detection and segmentation in HE images by removing tiles that only contain background information. The method divides each image into smaller patches and uses their projection to the noiselet space to capture different spatial features from non-nuclei background and nuclei structures. The noiselet features are clustered by a K-means algorithm and the resultant partition, defined by the cluster centroids, is herein named the noiselet code-book. A part of an image, a tile, is divided into patches and represented by the histogram of occurrences of the projected patches in the noiselet code-book. Finally, with these histograms, a classifier learns to differentiate between nuclei and non-nuclei tiles. By applying a conventional watershed-marked method to detect and segment nuclei, evaluation consisted in comparing pure watershed method against denoising-plus-watershed in an open database with 8 different types of tissues. The averaged F-score of nuclei detection improved from 0.830 to 0.86 and the dice score after segmentation increased from 0.701 to 0.723.

Segmentation of Brain Metastases Using Background Layer Statistics (BLAST).

BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases is important for treatment planning and evaluating response. The aim of this study was to assess the performance of a semiautomated algorithm for brain metastases segmentation using Background Layer Statistics (BLAST). MATERIALS AND METHODS: Nineteen patients with 48 parenchymal and dural brain metastases were included. Segmentation was performed by 4 neuroradiologists and 1 radiation oncologist. K-means clustering was used to identify normal gray and white matter (background layer) in a 2D parameter space of signal intensities from postcontrast T2 FLAIR and T1 MPRAGE sequences. The background layer was subtracted and operator-defined thresholds were applied in parameter space to segment brain metastases. The remaining voxels were back-projected to visualize segmentations in image space and evaluated by the operators. Segmentation performance was measured by calculating the Dice-Sørensen coefficient and Hausdorff distance using ground truth segmentations made by the investigators. Contours derived from the segmentations were evaluated for clinical acceptance using a 5-point Likert scale. RESULTS: The median Dice-Sørensen coefficient was 0.82 for all brain metastases and 0.9 for brain metastases of ≥10 mm. The median Hausdorff distance was 1.4 mm. Excellent interreader agreement for brain metastases volumes was found with an intraclass correlation coefficient = 0.9978. The median segmentation time was 2.8 minutes/metastasis. Forty-five contours (94%) had a Likert score of 4 or 5, indicating that the contours were acceptable for treatment, requiring no changes or minor edits. CONCLUSIONS: We show accurate and reproducible segmentation of brain metastases using BLAST and demonstrate its potential as a tool for radiation planning and evaluating treatment response.

Upgraded D22 SEC-SANS setup dedicated to the biology community.

Described here is the current status of the upgraded in situ size-exclusion chromatography (SEC) system implemented with the D22 small-angle neutron scattering (SANS) instrument at the Institut Laue-Langevin. Since its initial proof of principle in 2016, this SEC-SANS arrangement has been continuously requested by the user community, leading to the design of an upgraded version. A detailed description of the setup and its control is provided, and a few examples of protein structural investigations are presented, which will highlight the various possibilities and limitations of the setup to optimize experimental success.

ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI.

Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysis is the potential for shifts in signal-to-noise ratio, contrast, resolution, and presence of artifacts from site to site due to variances in scanners and acquisition protocols. DNNs are famously susceptible to these distribution shifts in computer vision. Currently, there are no benchmarking platforms or frameworks to assess the robustness of new and existing models to specific distribution shifts in MRI, and accessible multi-site benchmarking datasets are still scarce or task-specific. To address these limitations, we propose ROOD-MRI: a novel platform for benchmarking the Robustness of DNNs to Out-Of-Distribution (OOD) data, corruptions, and artifacts in MRI. This flexible platform provides modules for generating benchmarking datasets using transforms that model distribution shifts in MRI, implementations of newly derived benchmarking metrics for image segmentation, and examples for using the methodology with new models and tasks. We apply our methodology to hippocampus, ventricle, and white matter hyperintensity segmentation in several large studies, providing the hippocampus dataset as a publicly available benchmark. By evaluating modern DNNs on these datasets, we demonstrate that they are highly susceptible to distribution shifts and corruptions in MRI. We show that while data augmentation strategies can substantially improve robustness to OOD data for anatomical segmentation tasks, modern DNNs using augmentation still lack robustness in more challenging lesion-based segmentation tasks. We finally benchmark U-Nets and vision transformers, finding robustness susceptibility to particular classes of transforms across architectures. The presented open-source platform enables generating new benchmarking datasets and comparing across models to study model design that results in improved robustness to OOD data and corruptions in MRI.

Developing advanced models of biological membranes with hydrogenous and deuterated natural glycerophospholipid mixtures.

Cellular membranes are complex systems that consist of hundreds of different lipid species. Their investigation often relies on simple bilayer models including few synthetic lipid species. Glycerophospholipids (GPLs) extracted from cells are a valuable resource to produce advanced models of biological membranes. Here, we present the optimisation of a method previously reported by our team for the extraction and purification of various GPL mixtures from Pichia pastoris. The implementation of an additional purification step by High Performance Liquid Chromatography-Evaporative Light Scattering Detector (HPLC-ELSD) enabled for a better separation of the GPL mixtures from the neutral lipid fraction that includes sterols, and also allowed for the GPLs to be purified according to their different polar headgroups. Pure GPL mixtures at significantly high yields were produced through this approach. For this study, we utilised phoshatidylcholine (PC), phosphatidylserine (PS) and phosphatidylglycerol (PG) mixtures. These exhibit a single composition of the polar head, i.e., PC, PS or PG, but contain several molecular species consisting of acyl chains of varying length and unsaturation, which were determined by Gas Chromatography (GC). The lipid mixtures were produced both in their hydrogenous (H) and deuterated (D) versions and were used to form lipid bilayers both on solid substrates and as vesicles in solution. The supported lipid bilayers were characterised by quartz crystal microbalance with dissipation monitoring (QCM-D) and neutron reflectometry (NR), whereas the vesicles by small angle X-ray (SAXS) and neutron scattering (SANS). Our results show that despite differences in the acyl chain composition, the hydrogenous and deuterated extracts produced bilayers with very comparable structures, which makes them valuable to design experiments involving selective deuteration with techniques such as NMR, neutron scattering or infrared spectroscopy.

Targeting tumour-associated macrophages in hodgkin lymphoma using engineered extracellular matrix-mimicking cryogels.

Tumour-associated macrophages are linked with poor prognosis and resistance to therapy in Hodgkin lymphoma; however, there are no suitable preclinical models to identify macrophage-targeting therapeutics. We used primary human tumours to guide the development of a mimetic cryogel, wherein Hodgkin (but not Non-Hodgkin) lymphoma cells promoted primary human macrophage invasion. In an invasion inhibitor screen, we identified five drug hits that significantly reduced tumour-associated macrophage invasion: marimastat, batimastat, AS1517499, ruxolitinib, and PD-169316. Importantly, ruxolitinib has demonstrated recent success in Hodgkin lymphoma clinical trials. Both ruxolitinib and PD-169316 (a p38 mitogen-activated protein kinase (p38 MAPK) inhibitor) decreased the percent of M2-like macrophages; however, only PD-169316 enhanced the percentage of M1-like macrophages. We validated p38 MAPK as an anti-invasion drug target with five additional drugs using a high-content imaging platform. With our biomimetic cryogel, we modeled macrophage invasion in Hodgkin lymphoma and then used it for target discovery and drug screening, ultimately identifying potential future therapeutics.

A radiomic biomarker for prognosis of resected colorectal cancer liver metastases generalizes across MRI contrast agents.

Introduction: Contrast-enhanced MRI is routinely performed as part of preoperative work-up for patients with Colorectal Cancer Liver Metastases (CRLM). Radiomic biomarkers depicting the characteristics of CRLMs in MRI have been associated with overall survival (OS) of patients, but the reproducibility and clinical applicability of these biomarkers are limited due to the variations in MRI protocols between hospitals. Methods: In this work, we propose a generalizable radiomic model for predicting OS of CRLM patients who received preoperative chemotherapy and delayed-phase contrast enhanced (DPCE) MRIs prior to hepatic resection. This retrospective two-center study included three DPCE MRI cohorts (n=221) collected between January 2006 and December 2012. A 10-minute delayed Gd-DO3A-butrol enhanced MRI discovery cohort was used to select features based on robustness across contrast agents, correlation with OS and pairwise Pearson correlation, and to train a logistic regression model that predicts 3-year OS. Results: The model was evaluated on a 10-minute delayed Gd-DO3A-butrol enhanced MRI validation cohort (n=121), a 20-minute delayed Gd-EOB-DTPA (n=72) cohort from the same institute, and a 5-minute delayed Gd-DTPA cohort (n=28) from an independent institute. Two features were selected: minor axis length and dependence variance. The radiomic signature model stratified high-risk and low-risk CRLM groups in the Gd-DO3Abutrol (HR = 6.29, p = .007), Gd-EOB-DTPA (HR = 3.54, p = .003) and Gd-DTPA (HR = 3.16, p = .04) validation cohorts. Discussion: While most existing MRI findings focus on a specific contrast agent, our study shows the potential of MRI features to be generalizable across main-stream contrast agents at delayed phase.

Stages of OCP-FRP Interactions in the Regulation of Photoprotection in Cyanobacteria, Part 2: Small-Angle Neutron Scattering with Partial Deuteration.

We used small-angle neutron scattering partially coupled with size-exclusion chromatography to unravel the solution structures of two variants of the Orange Carotenoid Protein (OCP) lacking the N-terminal extension (OCP-ΔNTE) and its complex formation with the Fluorescence Recovery Protein (FRP). The dark-adapted, orange form OCP-ΔNTEO is fully photoswitchable and preferentially binds the pigment echinenone. Its complex with FRP consists of a monomeric OCP component, which closely resembles the compact structure expected for the OCP ground state, OCPO. In contrast, the pink form OCP-ΔNTEP, preferentially binding the pigment canthaxanthin, is mostly nonswitchable. The pink OCP form appears to occur as a dimer and is characterized by a separation of the N- and C-terminal domains, with the canthaxanthin embedded only into the N-terminal domain. Therefore, OCP-ΔNTEP can be viewed as a prototypical model system for the active, spectrally red-shifted state of OCP, OCPR. The dimeric structure of OCP-ΔNTEP is retained in its complex with FRP. Small-angle neutron scattering using partially deuterated OCP-FRP complexes reveals that FRP undergoes significant structural changes upon complex formation with OCP. The observed structures are assigned to individual intermediates of the OCP photocycle in the presence of FRP.

Effect of amphiphilic environment on the solution structure of mouse TSPO translocator protein.

The translocator protein (TSPO) is a ubiquitous transmembrane protein of great pharmacological interest thanks to its high affinity to many drug ligands. The only high-resolution 3D-structure known for mammalian TSPO was obtained by NMR for the mouse mTSPO in DPC detergent only in presence of the high-affinity PK 11195 ligand. An atomic structure of free-ligand mTSPO is still missing to better understand the interaction of ligands with mTSPO and their effects on the protein conformation. Here, we decipher the solution structures of the recombinant mTSPO without ligand both in (i) SDS, the detergent used to extract and purify the protein from E. coli inclusion bodies, and (ii) DPC, the detergent used to solve the PK 11195-binding mTSPO NMR structure. We report partially refolded and less flexible mTSPO helices in DPC compared to SDS. Besides, DPC stabilizes the tertiary structure of mTSPO, as shown by a higher intrinsic Trp fluorescence and changes in indole environment. We evaluate by SEC-MALLS that ∼135 SDS and ∼100 DPC molecules are bound to mTSPO. SEC-small-angle X-ray (SAXS) and neutron (SANS) scattering confirm a larger mTSPO-detergent complex in SDS than in DPC. Using the contrast-matching technique in SEC-SANS, we demonstrate that mTSPO conformation is more compact and less flexible in DPC than in SDS. Combining ab initio modeling with SANS, we confirm that mTSPO conformation is less elongated in DPC than in SDS. However, the free-ligand mTSPO envelope in DPC is not as compact as the PK 11195-binding protein NMR structure, the ligand stiffening the protein.

Small-angle x-ray and neutron scattering of MexR and its complex with DNA supports a conformational selection binding model.

In this work, we used small-angle x-ray and neutron scattering to reveal the shape of the protein-DNA complex of the Pseudomonas aeruginosa transcriptional regulator MexR, a member of the multiple antibiotics resistance regulator (MarR) family, when bound to one of its native DNA binding sites. Several MarR-like proteins, including MexR, repress the expression of efflux pump proteins by binding to DNA on regulatory sites overlapping with promoter regions. When expressed, efflux proteins self-assemble to form multiprotein complexes and actively expel highly toxic compounds out of the host organism. The mutational pressure on efflux-regulating MarR family proteins is high since deficient DNA binding leads to constitutive expression of efflux pumps and thereby supports acquired multidrug resistance. Understanding the functional outcome of such mutations and their effects on DNA binding has been hampered by the scarcity of structural and dynamic characterization of both free and DNA-bound MarR proteins. Here, we show how combined neutron and x-ray small-angle scattering of both states in solution support a conformational selection model that enhances MexR asymmetry in binding to one of its promoter-overlapping DNA binding sites.

Biophysical characterization of calcium-binding and modulatory-domain dynamics in a pentameric ligand-gated ion channel.

Pentameric ligand-gated ion channels (pLGICs) perform electrochemical signal transduction in organisms ranging from bacteria to humans. Among the prokaryotic pLGICs, there is architectural diversity involving N-terminal domains (NTDs) not found in eukaryotic relatives, exemplified by the calcium-sensitive channel (DeCLIC) from a Desulfofustis deltaproteobacterium, which has an NTD in addition to the canonical pLGIC structure. Here, we have characterized the structure and dynamics of DeCLIC through cryoelectron microscopy (cryo-EM), small-angle neutron scattering (SANS), and molecular dynamics (MD) simulations. In the presence and absence of calcium, cryo-EM yielded structures with alternative conformations of the calcium-binding site. SANS profiles further revealed conformational diversity at room temperature beyond that observed in static structures, shown through MD to be largely attributable to rigid-body motions of the NTD relative to the protein core, with expanded and asymmetric conformations improving the fit of the SANS data. This work reveals the range of motion available to the DeCLIC NTD and calcium-binding site, expanding the conformational landscape of the pLGIC family. Further, these findings demonstrate the power of combining low-resolution scattering, high-resolution structural, and MD simulation data to elucidate interfacial interactions that are highly conserved in the pLGIC family.

A round-robin approach provides a detailed assessment of biomolecular small-angle scattering data reproducibility and yields consensus curves for benchmarking.

Through an expansive international effort that involved data collection on 12 small-angle X-ray scattering (SAXS) and four small-angle neutron scattering (SANS) instruments, 171 SAXS and 76 SANS measurements for five proteins (ribonuclease A, lysozyme, xylanase, urate oxidase and xylose isomerase) were acquired. From these data, the solvent-subtracted protein scattering profiles were shown to be reproducible, with the caveat that an additive constant adjustment was required to account for small errors in solvent subtraction. Further, the major features of the obtained consensus SAXS data over the q measurement range 0-1 Å-1 are consistent with theoretical prediction. The inherently lower statistical precision for SANS limited the reliably measured q-range to <0.5 Å-1, but within the limits of experimental uncertainties the major features of the consensus SANS data were also consistent with prediction for all five proteins measured in H2O and in D2O. Thus, a foundation set of consensus SAS profiles has been obtained for benchmarking scattering-profile prediction from atomic coordinates. Additionally, two sets of SAXS data measured at different facilities to q > 2.2 Å-1 showed good mutual agreement, affirming that this region has interpretable features for structural modelling. SAS measurements with inline size-exclusion chromatography (SEC) proved to be generally superior for eliminating sample heterogeneity, but with unavoidable sample dilution during column elution, while batch SAS data collected at higher concentrations and for longer times provided superior statistical precision. Careful merging of data measured using inline SEC and batch modes, or low- and high-concentration data from batch measurements, was successful in eliminating small amounts of aggregate or interparticle interference from the scattering while providing improved statistical precision overall for the benchmarking data set.