Inactivation of ferric uptake regulator (Fur) attenuates Helicobacter pylori J99 motility by disturbing the flagellar motor switch and autoinducer-2 production.

BACKGROUND: Flagellar motility of Helicobacter pylori has been shown to be important for the bacteria to establish initial colonization. The ferric uptake regulator (Fur) is a global regulator that has been identified in H. pylori which is involved in the processes of iron uptake and establishing colonization. However, the role of Fur in H. pylori motility is still unclear. MATERIALS AND METHODS: Motility of the wild-type, fur mutant, and fur revertant J99 were determined by a soft-agar motility assay and direct video observation. The bacterial shape and flagellar structure were evaluated by transmission electron microscopy. Single bacterial motility and flagellar switching were observed by phase-contrast microscopy. Autoinducer-2 (AI-2) production in bacterial culture supernatant was analyzed by a bioluminescence assay. RESULTS: The fur mutant showed impaired motility in the soft-agar assay compared with the wild-type J99 and fur revertant. The numbers and lengths of flagellar filaments on the fur mutant cells were similar to those of the wild-type and revertant cells. Phenotypic characterization showed similar swimming speed but reduction in switching rate in the fur mutant. The AI-2 production of the fur mutant was dramatically reduced compared with wild-type J99 in log-phase culture medium. CONCLUSIONS: These results indicate that Fur positively modulates H. pylori J99 motility through interfering with bacterial flagellar switching.

Uncertainty estimation for deep learning-based pectoral muscle segmentation via Monte Carlo dropout.

Objective. Deep Learning models are often susceptible to failures after deployment. Knowing when your model is producing inadequate predictions is crucial. In this work, we investigate the utility of Monte Carlo (MC) dropout and the efficacy of the proposed uncertainty metric (UM) for flagging of unacceptable pectoral muscle segmentations in mammograms.Approach. Segmentation of pectoral muscle was performed with modified ResNet18 convolutional neural network. MC dropout layers were kept unlocked at inference time. For each mammogram, 50 pectoral muscle segmentations were generated. The mean was used to produce the final segmentation and the standard deviation was applied for the estimation of uncertainty. From each pectoral muscle uncertainty map, the overall UM was calculated. To validate the UM, a correlation between the dice similarity coefficient (DSC) and UM was used. The UM was first validated in a training set (200 mammograms) and finally tested in an independent dataset (300 mammograms). ROC-AUC analysis was performed to test the discriminatory power of the proposed UM for flagging unacceptable segmentations.Main results. The introduction of dropout layers in the model improved segmentation performance (DSC = 0.95 ± 0.07 versus DSC = 0.93 ± 0.10). Strong anti-correlation (r= -0.76,p< 0.001) between the proposed UM and DSC was observed. A high AUC of 0.98 (97% specificity at 100% sensitivity) was obtained for the discrimination of unacceptable segmentations. Qualitative inspection by the radiologist revealed that images with high UM are difficult to segment.Significance. The use of MC dropout at inference time in combination with the proposed UM enables flagging of unacceptable pectoral muscle segmentations from mammograms with excellent discriminatory power.

Comparison of Escherichia coli surface attachment methods for single-cell microscopy.

For in vivo, single-cell imaging bacterial cells are commonly immobilised via physical confinement or surface attachment. Different surface attachment methods have been used both for atomic force and optical microscopy (including super resolution), and some have been reported to affect bacterial physiology. However, a systematic comparison of the effects these attachment methods have on the bacterial physiology is lacking. Here we present such a comparison for bacterium Escherichia coli, and assess the growth rate, size and intracellular pH of cells growing attached to different, commonly used, surfaces. We demonstrate that E. coli grow at the same rate, length and internal pH on all the tested surfaces when in the same growth medium. The result suggests that tested attachment methods can be used interchangeably when studying E. coli physiology.