Background: Membrane potential (MP) plays a critical role in bacterial physiology. Existing methods for MP estimation by flow cytometry are neither accurate nor precise, due in part to the heterogeneity of size of the particles analyzed. The ratio of a size‐ and MP‐sensitive measurement, and an MP‐independent, size‐sensitive measurement, should provide a better estimate of MP. Methods: Flow cytometry and spectrofluorometry were used to detect red (488 → >600 nm) fluorescence associated with aggregates of diethyloxacarbocyanine (DiOC2(3)), which, in the monomeric state, is normally green (488 → 530 nm) fluorescent. Results: In bacteria incubated with 30 μM dye, aggregate formation increases with the magnitude of the interior‐negative membrane potential. Green fluorescence from stained bacteria predominantly reflects particle size, and is relatively independent of MP, whereas red fluorescence is highly dependent on both MP and size. The ratio of red to green fluorescence provides a measure of MP that is largely independent of cell size, with a low coefficient of variation (CV). Calibration with valinomycin and potassium demonstrates that the method is accurate over the range from −50 mV through −120 mV; it also accurately tracks reversible reductions in MP produced by incubation at 4°C and washing in glucose‐free medium. Conclusions: The ratiometric technique for MP estimation using DiOC2(3) is substantially more accurate and precise than those previously available, and may be useful in studies of bacterial physiology and in investigations of the effects of antibiotics and other agents on microorganisms. Cytometry 35:55–63, 1999. © 1999 Wiley‐Liss, Inc.

Although flow cytometry has been used to study antibiotic effects on bacterial membrane potential (MP) and membrane permeability, flow cytometric results are not always well correlated to changes in bacterial counts.Using new, precise techniques, we simultaneously measured MP, membrane permeability, and particle counts of antibiotic-treated and untreated Staphylococcus aureus and Micrococcus luteus cells. MP was calculated from the ratio of red and green fluorescence of diethyloxacarbocyanine [DiOC 2 (3)]. A normalized permeability parameter was calculated from the ratio of far red fluorescence of the nucleic acid dye TO-PRO-3 and green DiOC 2 (3) fluorescence. Bacterial counts were calculated by the addition of polystyrene beads to the sample at a known concentration. Amoxicillin increased permeability within 45 min. At concentrations of <1 g/ml, some organisms showed increased permeability but normal MP; this population disappeared after 4 h, while bacterial counts increased. At amoxicillin concentrations above 1 g/ml, MP decreased irreversibly and the particle counts did not increase. Tetracycline and erythromycin caused smaller, dose-and time-dependent decreases in MP. Tetracycline concentrations of <1 g/ml did not change permeability, while a tetracycline concentration of 4 g/ml permeabilized 50% of the bacteria; 4 g of erythromycin per ml permeabilized 20% of the bacteria. Streptomycin decreased MP substantially, with no effect on permeability; chloramphenicol did not change either permeability or MP. Erythromycin pretreatment of bacteria prevented streptomycin and amoxicillin effects. Flow cytometry provides a sensitive means of monitoring the dynamic cellular events that occur in bacteria exposed to antibacterial agents; however, it is probably simplistic to expect that changes in a single cellular parameter will suffice to determine the sensitivities of all species to all drugs.

Multispectral and hyperspectral flow cytometry (FC) instruments allow measurement of fluorescence or Raman spectra from single cells in flow. As with conventional FC, spectral overlap results in the measured signal in any given detector being a mixture of signals from multiple labels present in the analyzed cells. In contrast to traditional polychromatic FC, these devices utilize a number of detectors (or channels in multispectral detector arrays) that is larger than the number of labels, and no particular detector is a priori dedicated to the measurement of any particular label. This data-acquisition modality requires a rigorous study and understanding of signal formation as well as unmixing procedures that are employed to estimate labels abundance. The simplest extension of the traditional compensation procedure to multispectral data sets is equivalent to an ordinary least-square (LS) solution for estimating abundance of labels in individual cells. This process is identical to the technique employed for unmixing spectral data in various imaging fields. The present study shows that multispectral FC data violate key assumptions of the LS process, and use of the LS method may lead to unmixing artifacts, such as population distortion (spreading) and the presence of negative values in biomarker abundances. Various alternative unmixing techniques were investigated, including relative-error minimization and variance-stabilization transformations. The most promising results were obtained by performing unmixing using Poisson regression with an identity-link function within a generalized linear model framework. This formulation accounts for the presence of Poisson noise in the model of signal formation and subsequently leads to superior unmixing results, particularly for dim fluorescent populations. The proposed Poisson unmixing technique is demonstrated using simulated 8-channel, 2-fluorochrome data and real 32-channel, 6-fluorochrome data. The quality of unmixing is assessed by computing absolute and relative errors, as well as by calculating the symmetrized Kullback–Leibler divergence between known and approximated populations. These results are applicable to any flow-based system with more detectors than labels where Poisson noise is the dominant contributor to the overall system noise and highlight the fact that explicit incorporation of appropriate noise models is the key to accurately estimating the true label abundance on the cells.

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There is a long standing interest in measuring complete emission spectra from individual cells in flow cytometry. We have developed flow cytometry instruments and analysis approaches to enable this to be done routinely and robustly. Our spectral flow cytometers use a holographic grating to disperse light from single cells onto a CCD for high speed, wavelength-resolved detection. Customized software allows the single cell spectral data to be displayed and analyzed to produce new spectra-derived parameters. We show that familiar reference and calibration beads can be employed to quantitatively assess instrument performance. We use microspheres stained with six different quantum dots to compare a virtual bandpass filter approach with classic least squares (CLS) spectral unmixing, and then use antibody capture beads and CLS unmixing to demonstrate immunophenotyping of peripheral blood mononuclear cells using spectral flow cytometry. Finally, we characterize and evaluate several near infrared (NIR) emitting fluorophores for use in spectral flow cytometry. Spectral flow cytometry offers a number of attractive features for single cell analysis, including a simplified optical path, high spectral resolution, and streamlined approaches to quantitative multiparameter measurements. The availability of robust instrumentation, software, and analysis approaches will facilitate the development of spectral flow cytometry applications.

ML and near-ML MIMO detectors have attracted a lot of interest in recent years. However, almost all of the reported implementations are delivered in ASIC or FPGA. Our contribution is to co-optimize the near-ML MIMO detector algorithm and implementation for parallel programmable baseband architectures, such as DSPs with VLIW, SIMD or vector processing features. Although for hardware the architecture can be tuned to fit algorithms, for programmable platforms the algorithm must be elaborately designed to fit the given architecture, so that efficient resource-utilizations can be achieved. By thoroughly analyzing and exploiting the interaction between algorithms and architectures, we propose the SSFE (Selective Spanning with Fast Enumeration) as an architecture-friendly near-ML MIMO detector. The SSFE has a distributed and greedy algorithmic structure that brings a completely deterministic and regular dataflow. The SSFE has been evaluated for coded OFDM transmissions over 802.11n channels and 3GPP channels. Under the same performance constraints, the complexity of the SSFE is significantly lower than the K-Best, the most popular detector implemented in hardware. More importantly, SSFE can be easily parallelized and efficiently mapped on programmable baseband architectures. With TI TMS320C6416, the SSFE delivers 37.4 -125.3 Mbps throughput for 4x4 64QAM transmissions. To the best of our knowledge, this is the first reported near-ML MIMO detector explicitly designed for parallel programmable architectures and demonstrated on a real-life platform.

Single-ISA heterogeneous multicore processors have gained increasing popularity with the introduction of recent technologies such as ARM big.LITTLE. These processors offer increased energy efficiency through combining low power inorder cores with high performance out-of-order cores. Efficiently exploiting this attractive feature requires careful management so as to meet the demands of targeted applications. In this paper, we explore the design of those architectures based on the ARM big.LITTLE technology by modeling performance and power in gem5 and McPAT frameworks. Our models are validated w.r.t. the Samsung Exynos 5 Octa (5422) chip. We show average errors of 20% in execution time, 13% for power consumption and 24% for energy-to-solution.

Flow cytometry data analysis routinely includes the use of one-or two-parameter histograms to visualize the data. These histograms have traditionally been plotted with either a linear or logarithmic scale. However, the recent trend of performing the logarithmic conversion in software has made apparent some limitations of the traditional visual presentation of logarithmic data. This review discusses the mathematics of presenting data on a histogram and emphasizes the difference between scaling and binning. The review introduces the concept of an effective resolution to describe how the bin width changes in a variable bin-width histogram. The change in effective resolution is used to explain the commonly observed valley and picket fencing artifacts. These result from the effective resolution of the display histogram being too high for the data being presented. Recently, several different binning transformations have been described that are becoming more popular because they allow one to view a large dynamic range of data on a single plot, while allowing the display of negative data values. While each of the transforms is based upon different equations, they all exhibit very similar properties. All of the transforms bin the data logarithmically at high channel values and linearly at low channel values. The linear scaling of the lower channels serves to limit the effective resolution of the histogram, thus minimizing the valley and picket fencing artifacts. The newer transformations are not without their own limitations and recommendations for the appropriate manner of presenting flow cytometry data using these newer transformations are discussed. '