Search

Qualitative visual assessment of MRI scans is a key mechanism by which inflammation is assessed in clinical practice. For example, in axial spondyloarthritis (axSpA), visual assessment focuses on the identification of regions with increased signal in the bone marrow, known as bone marrow oedema (BMO), on water-sensitive images. The identification of BMO has an important role in the diagnosis, quantification and monitoring of disease in axSpA. However, BMO evaluation depends heavily on the experience and expertise of the image reader, creating substantial imprecision. Deep learning-based segmentation is a natural approach to addressing this imprecision, but purely automated solutions require large training sets that are not currently available, and deep learning solutions with limited data may not be sufficiently trustworthy for use in clinical practice. To address this, we propose a workflow for inflammation segmentation incorporating both deep learning and human input. With this 'human-machine cooperation' workflow, a preliminary segmentation is generated automatically by deep learning; a human reader then 'cleans' the segmentation by removing extraneous segmented voxels. The final cleaned segmentation defines the volume of hyperintense inflammation (VHI), which is proposed as a quantitative imaging biomarker (QIB) of inflammation load in axSpA. We implemented and evaluated the proposed human-machine workflow in a cohort of 29 patients with axSpA who had undergone prospective MRI scans before and after starting biologic therapy. The performance of the workflow was compared against purely visual assessment in terms of inter-observer/inter-method segmentation overlap, inter-observer agreement and assessment of response to biologic therapy. The human-machine workflow showed superior inter-observer segmentation overlap than purely manual segmentation (Dice score 0.84 versus 0.56). VHI measurements produced by the workflow showed similar or better inter-observer agreement than visual scoring, with similar response assessments. We conclude that the proposed human-machine workflow offers a mechanism to improve the consistency of inflammation assessment, and that VHI could be a valuable QIB of inflammation load in axSpA, as well as offering an exemplar of human-machine cooperation more broadly.

Qualitative visual assessment of MRI scans is a key mechanism by which inflammation is assessed in clinical practice. For example, in axial spondyloarthritis (axSpA), visual assessment focuses on the identification of regions with increased signal in the bone marrow, known as bone marrow oedema (BMO), on water-sensitive images. The identification of BMO has an important role in the diagnosis, quantification and monitoring of disease in axSpA. However, BMO evaluation depends heavily on the experience and expertise of the image reader, creating substantial imprecision. Deep learning-based segmentation is a natural approach to addressing this imprecision, but purely automated solutions require large training sets that are not currently available, and deep learning solutions with limited data may not be sufficiently trustworthy for use in clinical practice. To address this, we propose a workflow for inflammation segmentation incorporating both deep learning and human input. With this ‘human-machine cooperation’ workflow, a preliminary segmentation is generated automatically by deep learning; a human reader then ‘cleans’ the segmentation by removing extraneous segmented voxels. The final cleaned segmentation defines the volume of hyperintense inflammation (VHI), which is proposed as a quantitative imaging biomarker (QIB) of inflammation load in axSpA. We implemented and evaluated the proposed human-machine workflow in a cohort of 29 patients with axSpA who had undergone prospective MRI scans before and after starting biologic therapy. The performance of the workflow was compared against purely visual assessment in terms of inter-observer/inter-method segmentation overlap, inter-observer agreement and assessment of response to biologic therapy. The human-machine workflow showed superior inter-observer segmentation overlap than purely manual segmentation (Dice score 0.84 versus 0.56). VHI measurements produced by the workflow showed similar or better inter-observer agreement than visual scoring, with similar response assessments. We conclude that the proposed human-machine workflow offers a mechanism to improve the consistency of inflammation assessment, and that VHI could be a valuable QIB of inflammation load in axSpA, as well as offering an exemplar of human-machine cooperation more broadly.

Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current ‘state of the art’ from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of ‘soft recommendations’ about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage ‘open science’ practices.

This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experimental design (CED) methods may be ill-suited to experimental tasks, such as clinical classification, where outcome does not depend on parameter-estimation accuracy or precision alone. Current assessment metrics evaluate experiments' ability to faithfully recover microstructural parameters rather than their task performance. The method we propose addresses this shortcoming. For a given MRI experimental design (protocol, parameter-estimation method, model, etc.), experiments are simulated start-to-finish and task performance is computed from receiver operating characteristic (ROC) curves and associated summary metrics (e.g. area under the curve (AUC)). Two experiments were performed: first, a validation of the pipeline's task performance predictions against clinical results, comparing in-silico predictions to real-world ROC/AUC; and second, a demonstration of the pipeline's advantages over traditional CED approaches, using two simulated clinical classification tasks. Comparison with clinical datasets validates our method's predictions of (a) the qualitative form of ROC curves, (b) the relative task performance of different experimental designs, and (c) the absolute performance (AUC) of each experimental design. Furthermore, we show that our method outperforms traditional task-agnostic assessment methods, enabling improved, more useful experimental design. Our pipeline produces accurate, quantitative predictions of real-world task performance. Compared to current approaches, such task-driven assessment is more likely to identify experimental designs that perform well in practice. Our method is not limited to diffusion MRI; the pipeline generalises to any task-based quantitative MRI application, and provides the foundation for developing future task-driven end-to end CED frameworks.

OBJECTIVE:To identify the whole-body MRI (WB-MRI) image type(s) with the highest value for assessment of multiple myeloma, in order to optimise acquisition protocols and read times. METHODS:Thirty patients with clinically-suspected MM underwent WB-MRI at 3 Tesla. Unenhanced Dixon images [fat-only (FO) and water-only (WO)], post contrast Dixon [fat-only plus contrast (FOC) and water-only plus contrast (WOC)] and diffusion weighted images (DWI) of the pelvis from all 30 patients were randomised and read by three experienced readers. For each image type, each reader identified and labelled all visible myeloma lesions. Each identified lesion was compared with a composite reference standard achieved by review of a complete imaging dataset by a further experienced consultant radiologist to determine truly positive lesions. Lesion count, true positives, sensitivity, and positive predictive value were determined. Time to read each scan set was recorded. Confidence for a diagnosis of myeloma was scored using a Likert scale. Conspicuity of focal lesions was assessed in terms of percent contrast and contrast to noise ratio (CNR). RESULTS:Lesion count, true positives, sensitivity and confidence scores were significantly higher when compared to other image types for DWI (P

The ParB protein forms DNA bridging interactions around parS to condense DNA and earmark the bacterial chromosome for segregation. The molecular mechanism underlying the formation of these ParB networks is unclear. We show here that while the central DNA binding domain is essential for anchoring at parS, this interaction is not required for DNA condensation. Structural analysis of the C-terminal domain reveals a dimer with a lysine-rich surface that binds DNA non-specifically and is essential for DNA condensation in vitro. Mutation of either the dimerisation or the DNA binding interface eliminates ParB-GFP foci formation in vivo. Moreover, the free C-terminal domain can rapidly decondense ParB networks independently of its ability to bind DNA. Our work reveals a dual role for the C-terminal domain of ParB as both a DNA binding and bridging interface, and highlights the dynamic nature of ParB networks in Bacillus subtilis.

To determine which of four Dixon image types [in-phase (IP), out-of-phase (OP), fat only (FO) and water-only (WO)] is most sensitive for detecting multiple myeloma (MM) focal lesions on whole body MRI (WB-MRI) images.Thirty patients with clinically-suspected MM underwent WB-MRI at 3 Tesla. Unenhanced IP, OP, FO and WO Dixon images were generated and read by four radiologists. On each image type, each radiologist identified and labelled all visible myeloma lesions in the bony pelvis. Each identified lesion was compared with a reference standard consisting of pre- and post-contrast Dixon and diffusion weighted imaging (read by a further consultant radiologist) to determine whether the lesion was truly positive. Lesion count, true positives, sensitivity, and positive predictive value were compared across the four Dixon image types.Lesion count, true positives, sensitivity and confidence scores were all significantly higher on FO images than on IP images (p>0.05).FO images are more sensitive than other Dixon image types for MM focal lesions, and should be preferentially read by radiologists to improve diagnostic accuracy and reporting efficiency.

Prokaryotic and ciliate communities of healthy and aquarium White Syndrome (WS)-affected coral fragments were screened using denaturing gradient gel electrophoresis (DGGE). A significant difference (R = 0.907, p < 0.001) in 16S rRNA prokaryotic diversity was found between healthy (H), sloughed tissue (ST), WS-affected (WSU) and antibiotic treated (WST) samples. Although 3 Vibrio spp were found in WS-affected samples, two of these species were eliminated following ampicillin treatment, yet lesions continued to advance, suggesting they play a minor or secondary role in the pathogenesis. The third Vibrio sp increased slightly in relative abundance in diseased samples and was abundant in non-diseased samples. Interestingly, a Tenacibaculum sp showed the greatest increase in relative abundance between healthy and WS-affected samples, demonstrating consistently high abundance across all WS-affected and treated samples, suggesting Tenacibaculum sp could be a more likely candidate for pathogenesis in this instance. In contrast to previous studies bacterial abundance did not vary significantly (ANOVA, F2, 6 = 1.000, p = 0.422) between H, ST, WSU or WST. Antimicrobial activity (assessed on Vibrio harveyi cultures) was limited in both H and WSU samples (8.1% ±8.2 and 8.0% ±2.5, respectively) and did not differ significantly (Kruskal-Wallis, χ2 (2) = 3.842, p = 0.146). A Philaster sp, a Cohnilembus sp and a Pseudokeronopsis sp. were present in all WS-affected samples, but not in healthy samples. The exact role of ciliates in WS is yet to be determined, but it is proposed that they are at least responsible for the neat lesion boundary observed in the disease.

BET family proteins are epigenetic regulators known to control expression of genes involved in cell growth and oncogenesis. Selective inhibitors of BET proteins exhibit potent anti-proliferative activity in a number of hematologic cancer models, in part through suppression of the MYC oncogene and downstream Myc-driven pathways. However, little is currently known about the activity of BET inhibitors in solid tumor models, and whether down-regulation of MYC family genes contributes to sensitivity. Here we provide evidence for potent BET inhibitor activity in neuroblastoma, a pediatric solid tumor associated with a high frequency of MYCN amplifications. We treated a panel of neuroblastoma cell lines with a novel small molecule inhibitor of BET proteins, GSK1324726A (I-BET726), and observed potent growth inhibition and cytotoxicity in most cell lines irrespective of MYCN copy number or expression level. Gene expression analyses in neuroblastoma cell lines suggest a role of BET inhibition in apoptosis, signaling, and N-Myc-driven pathways, including the direct suppression of BCL2 and MYCN. Reversal of MYCN or BCL2 suppression reduces the potency of I-BET726-induced cytotoxicity in a cell line-specific manner; however, neither factor fully accounts for I-BET726 sensitivity. Oral administration of I-BET726 to mouse xenograft models of human neuroblastoma results in tumor growth inhibition and down-regulation MYCN and BCL2 expression, suggesting a potential role for these genes in tumor growth. Taken together, our data highlight the potential of BET inhibitors as novel therapeutics for neuroblastoma, and suggest that sensitivity is driven by pleiotropic effects on cell growth and apoptotic pathways in a context-specific manner.