Phycobilisome-Deficient Strains of Synechocystis sp. PCC 6803 Have Reduced Size and Require Carbon-Limiting Conditions to Exhibit Enhanced Productivity.

Reducing excessive light harvesting in photosynthetic organisms may increase biomass yields by limiting photoinhibition and increasing light penetration in dense cultures. The cyanobacterium Synechocystis sp. PCC 6803 harvests light via the phycobilisome, which consists of an allophycocyanin core and six radiating rods, each with three phycocyanin (PC) discs. Via targeted gene disruption and alterations to the promoter region, three mutants with two (pcpcT→C) and one (ΔCpcC1C2:pcpcT→C) PC discs per rod or lacking PC (olive) were generated. Photoinhibition and chlorophyll levels decreased upon phycobilisome reduction, although greater penetration of white light was observed only in the PC-deficient mutant. In all strains cultured at high cell densities, most light was absorbed by the first 2 cm of the culture. Photosynthesis and respiration rates were also reduced in the ΔCpcC1C2:pcpcT→C and olive mutants. Cell size was smaller in the pcpcT→C and olive strains. Growth and biomass accumulation were similar between the wild-type and pcpcT→C under a variety of conditions. Growth and biomass accumulation of the olive mutant were poorer in carbon-saturated cultures but improved in carbon-limited cultures at higher light intensities, as they did in the ΔCpcC1C2:pcpcT→C mutant. This study shows that one PC disc per rod is sufficient for maximal light harvesting and biomass accumulation, except under conditions of high light and carbon limitation, and two or more are sufficient for maximal oxygen evolution. To our knowledge, this study is the first to measure light penetration in bulk cultures of cyanobacteria and offers important insights into photobioreactor design.

Single sensor boost converter-based maximum power point tracking algorithms

Two new maximum power point tracking algorithms are presented: the input voltage sensor, and duty ratio maximum power point tracking algorithm (ViSD algorithm); and the output voltage sensor, and duty ratio maximum power point tracking algorithm (VoSD algorithm). The ViSD and VoSD algorithms have the features, characteristics and advantages of the incremental conductance algorithm (INC); but, unlike the incremental conductance algorithm which requires two sensors (the voltage sensor and current sensor), the two algorithms are more desirable because they require only one sensor: the voltage sensor. Moreover, the VoSD technique is less complex; hence, it requires less computational processing. Both the ViSD and the VoSD techniques operate by maximising power at the converter output, instead of the input. The ViSD algorithm uses a voltage sensor placed at the input of a boost converter, while the VoSD algorithm uses a voltage sensor placed at the output of a boost converter. © 2011 IEEE.

Single-sensor maximum power point tracking algorithms

Single-sensor maximum power point tracking algorithms for photovoltaic systems are presented. The algorithms have the features, characteristics and advantages of the widely used incremental conductance (INC) algorithm. However; unlike the INC algorithm which requires two sensors (the voltage sensor and the current sensor), the single-sensor algorithms are more desirable because they require only one sensor: the voltage sensor. The algorithms operate by maximising power at the DC-DC converter output, instead of the input. © 2013 The Institution of Engineering and Technology.

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probability of a human pose occurring is used to incorporate likely human poses. This distribution is obtained offline, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this prior information with a random walk transition model to obtain an upper body model, suitable for use within a recursive Bayesian filtering framework. Our model can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information. Measurements are obtained using face detection and a simple skin colour hand detector, trained using the detected face. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. In addition, the use of the proposed upper body model allows reliable three-dimensional pose estimates to be obtained indirectly for a number of joints that are often difficult to detect using traditional object recognition strategies. Comparisons with Kinect sensor results and the state of the art in 2D pose estimation highlight the efficacy of the proposed approach.

A Bayesian approach to tracking in single molecule fluorescence microscopy

Using fluorescence microscopy with single molecule sensitivity it is now possible to follow the movement of individual fluorophore tagged molecules such as proteins and lipids in the cell membrane with nanometer precision. These experiments are important as they allow many key biological processes on the cell membrane and in the cell, such as transcription, translation and DNA replication, to be studied at new levels of detail. Computerized microscopes generate sequences of images (in the order of tens to hundreds) of the molecules diffusing and one of the challenges is to track these molecules to obtain reliable statistics such as speed distributions, diffusion patterns, intracellular positioning, etc. The data set is challenging because the molecules are tagged with a single or small number of fluorophores, which makes it difficult to distinguish them from the background, the fluorophore bleaches irreversibly over time, the number of tagged molecules are unknown and there is occasional loss of signal from the tagged molecules. All these factors make accurate tracking over long trajectories difficult. Also the experiments are technically difficulty to conduct and thus there is a pressing need to develop better algorithms to extract the maximum information from the data. For this purpose we propose a Bayesian approach and apply our technique to synthetic and a real experimental data set.