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Abstract Techniques are developed for generating uncertainty estimates for convolutional neural network (CNN)-based methods for registering the locations of lesions between the craniocaudal (CC) and mediolateral oblique (MLO) mammographic X-ray image views. Multi-view lesion correspondence is an important task that clinicians perform for characterizing lesions during routine mammographic exams. Automated registration tools can aid in this task, yet if the tools also provide confidence estimates, they can be of greater value to clinicians, especially in cases involving dense tissue where lesions may be difficult to see. A set of deep ensemble-based techniques, which leverage a negative log-likelihood (NLL)-based cost function, are implemented for estimating uncertainties. The ensemble architectures involve significant modifications to an existing CNN dual-view lesion registration algorithm. Three architectural designs are evaluated, and different ensemble sizes are compared using various performance metrics. The techniques are tested on synthetic X-ray data, real 2D X-ray data, and slices from real 3D X-ray data. The ensembles generate covariance-based uncertainty ellipses that are correlated with registration accuracy, such that the ellipse sizes can give a clinician an indication of confidence in the mapping between the CC and MLO views. The results also show that the ellipse sizes can aid in improving computer-aided detection (CAD) results by matching CC/MLO lesion detects and reducing false alarms from both views, adding to clinical utility. The uncertainty estimation techniques show promise as a means for aiding clinicians in confidently establishing multi-view lesion correspondence, thereby improving diagnostic capability. 

Abstract Chemical signalling in the plant microbiome can have drastic effects on microbial community structure, and on host growth and development. Previously, we demonstrated that the auxin metabolic signal interference performed by the bacterial genusVariovoraxvia an auxin degradation locus was essential for maintaining stereotypic root development in an ecologically relevant bacterial synthetic community. Here, we dissect theVariovoraxauxin degradation locus to define the genesiadDEas necessary and sufficient for indole-3-acetic acid (IAA) degradation and signal interference. We determine the crystal structures and binding properties of the operon’s MarR-family repressor with IAA and other auxins. Auxin degradation operons were identified across the bacterial tree of life and we define two distinct types on the basis of gene content and metabolic products:iac-like andiad-like. The structures of MarRs from representatives of each auxin degradation operon type establish that each has distinct IAA-binding pockets. Comparison of representative IAA-degrading strains from diverse bacterial genera colonizingArabidopsisplants show that while all degrade IAA, only strains containingiad-like auxin-degrading operons interfere with auxin signalling in a complex synthetic community context. This suggests thatiad-like operon-containing bacterial strains, includingVariovoraxspecies, play a key ecological role in modulating auxins in the plant microbiome. 

Plant nucleotide-binding leucine-rich repeat receptors (NLRs) regulate immunity and cell death. InArabidopsis, a subfamily of “helper” NLRs is required by many “sensor” NLRs. Active NRG1.1 oligomerized, was enriched in plasma membrane puncta, and conferred cytoplasmic calcium ion (Ca²⁺) influx in plant and human cells. NRG1.1-dependent Ca²⁺influx and cell death were sensitive to Ca²⁺channel blockers and were suppressed by mutations affecting oligomerization or plasma membrane enrichment. Ca²⁺influx and cell death mediated by NRG1.1 and ACTIVATED DISEASE RESISTANCE 1 (ADR1), another helper NLR, required conserved negatively charged N-terminal residues. Whole-cell voltage-clamp recordings demonstrated thatArabidopsishelper NLRs form Ca²⁺-permeable cation channels to directly regulate cytoplasmic Ca²⁺levels and consequent cell death. Thus, helper NLRs transduce cell death signals directly.