Metabolite and transcriptome analysis of Campylobacter jejuni in vitro growth reveals a stationary-phase physiological switch.

Campylobacter jejuni is a prevalent cause of food-borne diarrhoeal illness in humans. Understanding of the physiological and metabolic capabilities of the organism is limited. We report a detailed analysis of the C. jejuni growth cycle in batch culture. Combined transcriptomic, phenotypic and metabolic analysis demonstrates a highly dynamic 'stationary phase', characterized by a peak in motility, numerous gene expression changes and substrate switching, despite transcript changes that indicate a metabolic downshift upon the onset of stationary phase. Video tracking of bacterial motility identifies peak activity during stationary phase. Amino acid analysis of culture supernatants shows a preferential order of amino acid utilization. Proton NMR (1H-NMR) highlights an acetate switch mechanism whereby bacteria change from acetate excretion to acetate uptake, most probably in response to depletion of other substrates. Acetate production requires pta (Cj0688) and ackA (Cj0689), although the acs homologue (Cj1537c) is not required. Insertion mutants in Cj0688 and Cj0689 maintain viability less well during the stationary and decline phases of the growth cycle than wild-type C. jejuni, suggesting that these genes, and the acetate pathway, are important for survival.

On-line topographic analysis in the SEM

A novel method for on-line topographic analysis of rough surfaces in the SEM has been investigated. It utilises a digital minicomputer configured to act as a programmable scan generator and automatic focusing unit. The computer is coupled to the microscope through digital-to-analogue converters which enable it to generate ramp waveforms allowing the beam to be scanned over a small sub-region of the field under program control. A further digital-to-analogue converter regulates the current supply to the objective lens of the microscope. The video signal is sampled by means of an analogue-to-digital converter and the resultant binary code stored in the computer's memory as an array of numbers describing relative image intensity. Computations based on the intensity gradient of the image allow the objective lens current to be found for the in-focus condition, which may be related to the working distance through a previous calibration experiment. The sensitivity of the method for detecting small height changes is theoretically of the order of 1 μm. In practice the operator specifies features of interest by means of a mobile spot cursor injected into the SEM display screen, or he may scan the specimen at sub-regions corresponding to pre-determined points on a regular grid defined by him. The operation then proceeds under program control. | A novel method for on-line topographic analysis of rough surfaces in the SEM has been investigated. It utilizes a digital minicomputer configured to act as a programmable scan generator and automatic focusing unit. A further digital-to-analog converter regulates the current supply to the objective lens of the microscope. The video signal is sampled by means of an analog-to-digital converter and the resultant binary code stored in the computer's memory as an array of numbers describing relative image intensity. The sensitivity of the method for detecting small height changes is theroretically of the order of 1 mu m.

Topographical analysis in the SEM using an automatic focusing technique

A novel technique for automated topographical analysis in the SEM has been investigated. It utilizes a 16-bit minicomputer arranged to act as an automatic focusing unit. The computer is coupled to the objective lens of the microscope, by means of a digital to analogue converter, and may regulate the excitation of the lens under program control. Further digital-to-analogue converters allow the computer to act as a programmable scan generator by applying ramp waveforms to the scan amplifiers, permitting the beam to be swept over a small sub-region of the field of interest. The video signal is sampled and applied to an analogue-to-digital converter; the resultant binary numbers are stored in computer memory as an array of values representing relative image intensities within a subregion. A differencing algorithm applied to the collected data allows the level of objective lens excitation to be found at which the sharpness of the image is optimized, and the excitation may be related to the working distance for that subregion through a previous calibration experiment. The sensitivity of the method for detecting small height changes is theoretically of the order of 1 μm.

Semi-supervised video segmentation using tree structured graphical models

We present a novel, implementation friendly and occlusion aware semi-supervised video segmentation algorithm using tree structured graphical models, which delivers pixel labels alongwith their uncertainty estimates. Our motivation to employ supervision is to tackle a task-specific segmentation problem where the semantic objects are pre-defined by the user. The video model we propose for this problem is based on a tree structured approximation of a patch based undirected mixture model, which includes a novel time-series and a soft label Random Forest classifier participating in a feedback mechanism. We demonstrate the efficacy of our model in cutting out foreground objects and multi-class segmentation problems in lengthy and complex road scene sequences. Our results have wide applicability, including harvesting labelled video data for training discriminative models, shape/pose/articulation learning and large scale statistical analysis to develop priors for video segmentation. © 2011 IEEE.

Video segmentation with superpixels

Due to its importance, video segmentation has regained interest recently. However, there is no common agreement about the necessary ingredients for best performance. This work contributes a thorough analysis of various within- and between-frame affinities suitable for video segmentation. Our results show that a frame-based superpixel segmentation combined with a few motion and appearance-based affinities are sufficient to obtain good video segmentation performance. A second contribution of the paper is the extension of [1] to include motion-cues, which makes the algorithm globally aware of motion, thus improving its performance for video sequences. Finally, we contribute an extension of an established image segmentation benchmark [1] to videos, allowing coarse-to-fine video segmentations and multiple human annotations. Our results are tested on BMDS [2], and compared to existing methods. © 2013 Springer-Verlag.

Warping trajectories for video synchronization

Temporal synchronization of multiple video recordings of the same dynamic event is a critical task in many computer vision applications e.g. novel view synthesis and 3D reconstruction. Typically this information is implied, since recordings are made using the same timebase, or time-stamp information is embedded in the video streams. Recordings using consumer grade equipment do not contain this information; hence, there is a need to temporally synchronize signals using the visual information itself. Previous work in this area has either assumed good quality data with relatively simple dynamic content or the availability of precise camera geometry. In this paper, we propose a technique which exploits feature trajectories across views in a novel way, and specifically targets the kind of complex content found in consumer generated sports recordings, without assuming precise knowledge of fundamental matrices or homographies. Our method automatically selects the moving feature points in the two unsynchronized videos whose 2D trajectories can be best related, thereby helping to infer the synchronization index. We evaluate performance using a number of real recordings and show that synchronization can be achieved to within 1 sec, which is better than previous approaches. Copyright 2013 ACM.