These results indicate that Fur positively modulates H. pylori J99 motility through interfering with bacterial flagellar switching.

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. Microscopy has been a powerful tool for studying biological processes on the cellular level, ever since the first discovery of microorganisms by Antonie van Leeuwenhoek back in 17th century 1. Recently employed single-cell imaging allowed scientists to study population diversity 2 , physiology 3 , sub-cellular features 4 , and protein dynamics 5 in real-time. Single cell imaging of bacteria is particularly dependent on immobilisation, as majority of bacteria are small in size and capable of swimming. Immobilisation methods vary depending on the application, but typically fall into one of the two categories: use of physical confinement or attachment to the surface via specific molecules. The former group includes microfluidic platforms capable of mechanical trapping 6,7 , where some popular examples include the "mother machine" 8 , CellASIC 9 or MACS 10 devices, and porous membranes such as agarose gel pads 2,11-13. Physical confinement methods, while higher in throughput, can have drawbacks. For example, agarose gel pads do not allow fast medium exchange, and when mechanically confining bacteria the choice of enclosure dimensions should be done carefully in order to avoid influencing the growth and morphology with mechanical forces 14. Furthermore, mechanically confined bacteria cannot be used for studies of bacterial motility or energetics via detection of bacteria flagellar motor rotation 15-17. Chemical attachment methods rely on the interaction of various adhesive molecules, deposited on the cover glass surface, with the cell itself. Adhesion can be a result of electrostatic (polyethylenimine (PEI) 18,19 , poly-L-lysine (PLL) 15,17,19) or covalent interactions (3-aminopropyltriethoxysilane (APTES) 19), or a combination, such as with polyphenolic proteins (Cell-Tak) 19. Time scales on which researchers perform single-cell experiments vary. For example, scanning methods, like atomic force microscopy (AFM) or confocal laser scanning microscopy (CLSM) 19,20 , require enough time to probe each point of the sample, and stochastic approaches of super resolution microscopy (e.g. PALM and STORM) use low activation rate of fluorophores to achieve a single fluorophore localisati...

We study the dynamics of self-propelled chains with the excluded volume interaction via the Brownian dynamics simulation, in which the bending elasticity of chains is varied. The changes of the bending elasticity lead to various characteristics of the clustering behavior in the short-chainonly system. When a long self-propelled chain is mixed with these short chains, it can fold into the spiral-coil, a steadily rotating spiral conformation, either at a high density of short chains or at a low density if the long chain itself is sufficiently flexible. Our results qualitatively support the speculation on that the formation of the spiral-coil in the swarm of Vibrio alginolyticus is triggered by collisions from the clusters of the shorter bacteria. arXiv:1805.05357v2 [cond-mat.soft]

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.

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. &#xD;&#xD;Approach: Segmentation of pectoral muscle was performed with modified ResNet18 Convolutional Neural Network (CNN). 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.&#xD;&#xD;Main results: The introduction of dropout layers in the model improved segmentation performance (DSC=0.95±0.07 vs. 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. &#xD;&#xD;Significance: The use of Monte Carlo dropout at inference time in combination with the proposed uncertainty metric enables flagging of unacceptable pectoral muscle segmentations from mammograms with excellent discriminatory power.&#xD;