A tripartite microbial reporter gene system for real-time assays of soil nutrient status

Plant-derived carbon is the substrate which drives the rate of microbial assimilation and turnover of nutrients, in particular N and P, within the rhizosphere. To develop a better understanding of rhizosphere dynamics, a tripartite reporter gene system has been developed. We used three lux-marked Pseudomonas fluorescens strains to report on soil (1) assimilable carbon, (2) N-status, and (3) P-status. In vivo studies using soil water, spiked with C, N and P to simulate rhizosphere conditions, showed that the tripartite reporter system can provide real-time assessment of carbon and nutrient status. Good quantitative agreement for bioluminescence output between reference material and soil water samples was found for the C and P reporters. With regard to soil nitrate, the minimum bioavailable concentration was found to be greater than that analytically detectable in soil water. This is the first time that bioavailable soil C, N and P have been quantified using a tripartite reporter gene system.

Real-time approach to the optical properties of solids and nanostructures: Time-dependent Bethe-Salpeter equation

Many-body effects are known to play a crucial role in the electronic and optical properties of solids and nanostructures. Nevertheless, the majority of theoretical and numerical approaches able to capture the influence of Coulomb correlations are restricted to the linear response regime. In this work, we introduce an approach based on a real-time solution of the electronic dynamics. The proposed approach reduces to the well-known Bethe-Salpeter equation in the linear limit regime and it makes it possible, at the same time, to investigate correlation effects in nonlinear phenomena. We show the flexibility and numerical stability of the proposed approach by calculating the dielectric constants and the effect of a strong pulse excitation in bulk h-BN.

Global down-regulation of HOX gene expression in PML-RARalpha + acute promyelocytic leukemia identified by small-array real-time PCR

Acute promyelocytic leukemia (APL) is associated with a reciprocal and balanced translocation involving the retinoic acid receptor-alpha (RARalpha). All-trans retinoic acid (ATRA) is used to treat APL and is a potent morphogen that regulates HOX gene expression in embryogenesis and organogenesis. HOX genes are also involved in hematopoiesis and leukemogenesis. Thirty-nine mammalian HOX genes have been identified and classified into 13 paralogous groups clustered on 4 chromosomes. They encode a complex network of transcription regulatory proteins whose precise targets remain poorly understood. The overall function of the network appears to be dictated by gene dosage. To investigate the mechanisms involved in HOX gene regulation in hematopoiesis and leukemogenesis by precise measurement of individual HOX genes, a small-array real-time HOX (SMART-HOX) quantitative polymerase chain reaction (PCR) platform was designed and validated. Application of SMART-HOX to 16 APL bone marrow samples revealed a global down-regulation of 26 HOX genes compared with normal controls. HOX gene expression was also altered during differentiation induced by ATRA in the PML-RARalpha(+) NB4 cell line. PML-RARalpha fusion proteins have been reported to act as part of a repressor complex during myeloid cell differentiation, and a model linking HOX gene expression to this PML-RARalpha repressor complex is now proposed.

Real-time GPU color-based segmentation of football players

In this paper, we propose a multi-camera application capable of processing high resolution images and extracting features based on colors patterns over graphic processing units (GPU). The goal is to work in real time under the uncontrolled environment of a sport event like a football match. Since football players are composed for diverse and complex color patterns, a Gaussian Mixture Models (GMM) is applied as segmentation paradigm, in order to analyze sport live images and video. Optimization techniques have also been applied over the C++ implementation using profiling tools focused on high performance. Time consuming tasks were implemented over NVIDIA's CUDA platform, and later restructured and enhanced, speeding up the whole process significantly. Our resulting code is around 4-11 times faster on a low cost GPU than a highly optimized C++ version on a central processing unit (CPU) over the same data. Real time has been obtained processing until 64 frames per second. An important conclusion derived from our study is the scalability of the application to the number of cores on the GPU. © 2011 Springer-Verlag.

Evaluation of an algorithm to guide patients with type 1 diabetes treated with continuous subcutaneous insulin infusion on how to respond to real-time continuous glucose levels:A randomized controlled trial

OBJECTIVE - To evaluate an algorithm guiding responses of continuous subcutaneous insulin infusion (CSII)-treated type 1 diabetic patients using real-time continuous glucose monitoring (RT-CGM). RESEARCH DESIGN AND METHODS - Sixty CSII-treated type 1 diabetic participants (aged 13-70 years, including adult and adolescent subgroups, with A1C =9.5%) were randomized in age-, sex-, and A1C-matched pairs. Phase 1 was an open 16-week multicenter randomized controlled trial. Group A was treated with CSII/RT-CGM with the algorithm, and group B was treated with CSII/RT-CGM without the algorithm. The primary outcome was the difference in time in target (4-10 mmol/l) glucose range on 6-day masked CGM. Secondary outcomes were differences in A1C, low (=3.9 mmol/l) glucose CGM time, and glycemic variability. Phase 2 was the week 16-32 follow-up. Group A was returned to usual care, and group B was provided with the algorithm. Glycemia parameters were as above. Comparisons were made between baseline and 16 weeks and 32 weeks. RESULTS - In phase 1, after withdrawals 29 of 30 subjects were left in group A and 28 of 30 subjects were left in group B. The change in target glucose time did not differ between groups. A1C fell (mean 7.9% [95% CI 7.7-8.2to 7.6% [7.2-8.0]; P <0.03) in group A but not in group B (7.8% [7.5-8.1] to 7.7 [7.3-8.0]; NS) with no difference between groups. More subjects in group A achieved A1C =7% than those in group B (2 of 29 to 14 of 29 vs. 4 of 28 to 7 of 28; P = 0.015). In phase 2, one participant was lost from each group. In group A, A1C returned to baseline with RT-CGM discontinuation but did not change in group B, who continued RT-CGM with addition of the algorithm. CONCLUSIONS - Early but not late algorithm provision to type 1 diabetic patients using CSII/RT-CGM did not increase the target glucose time but increased achievement of A1C =7%. Upon RT-CGM cessation, A1C returned to baseline. © 2010 by the American Diabetes Association.

Real-Time Face Recognition from Surveillance Video

This chapter describes an experimental system for the recognition of human faces from surveillance video. In surveillance applications, the system must be robust to changes in illumination, scale, pose and expression. The system must also be able to perform detection and recognition rapidly in real time. Our system detects faces using the Viola-Jones face detector, then extracts local features to build a shape-based feature vector. The feature vector is constructed from ratios of lengths and differences in tangents of angles, so as to be robust to changes in scale and rotations in-plane and out-of-plane. Consideration was given to improving the performance and accuracy of both the detection and recognition steps.

NanoStreams: Advancing the Hardware and Software Stack for Real-Time Analytics on Fast Data Streams

NanoStreams is a consortium project funded by the European Commission under its FP7 programme and is a major effort to address the challenges of processing vast amounts of data in real-time, with a markedly lower carbon footprint than the state of the art. The project addresses both the energy challenge and the high-performance required by emerging applications in real-time streaming data analytics. NanoStreams achieves this goal by designing and building disruptive micro-server solutions incorporating real-silicon prototype micro-servers based on System-on-Chip and reconfigurable hardware technologies.

Real time measurement and control of viscosity for extrusion processes using recycled materials

The aim of this paper is to develop a new generation of extruder control system for recycled materials which has ability to automatically maintain constant a polymer melt viscosity of mixed recycled polymers during extrusion, regardless of variations in the Melt Flow Index (MFI) of recycled mixed grade high density polyethylene (HDPE) feedstock. The variations in MFI are due to differences in the source of the recycled material used. The work describes how melt viscosity for specific extruder/die system is calculated in real time using the rheological properties of the materials, the pressure drop through the extruder die and the actual throughput measurements using a gravimetric loss-in-weight hopper feeder. A closed-loop controller is also developed to automatically regulate screw speed and barrel temperature profile to achieve constant viscosity and enable consistent processing of variable grade recycled HDPE materials. Such a system will improve processability of mixed MFI polymers may also reduce the risk of polymer melt degradation, reduce producing large volumes of scrap/waste and lead to improvement in product quality. The experimental results of real time viscosity measurement and control using a 38 mm single screw extruder with different recycled HDPEs with widely different MFIs are reported in this work.

An Event Driven Fusion Approach for Enjoyment Recognition in Real-time

Social signals and interpretation of carried information is of high importance in Human Computer Interaction. Often used for affect recognition, the cues within these signals are displayed in various modalities. Fusion of multi-modal signals is a natural and interesting way to improve automatic classification of emotions transported in social signals. Throughout most present studies, uni-modal affect recognition as well as multi-modal fusion, decisions are forced for fixed annotation segments across all modalities. In this paper, we investigate the less prevalent approach of event driven fusion, which indirectly accumulates asynchronous events in all modalities for final predictions. We present a fusion approach, handling short-timed events in a vector space, which is of special interest for real-time applications. We compare results of segmentation based uni-modal classification and fusion schemes to the event driven fusion approach. The evaluation is carried out via detection of enjoyment-episodes within the audiovisual Belfast Story-Telling Corpus.

Rapid single-nucleotide polymorphism-based identification of clonal Pseudomonas aeruginosa isolates from patients with cystic fibrosis by the use of real-time PCR and high-resolution melting curve analysis

Pseudomonas aeruginosa genotyping relies mainly upon DNA fingerprinting methods, which can be subjective, expensive and time-consuming. The detection of at least three different clonal P. aeruginosa strains in patients attending two cystic fibrosis (CF) centres in a single Australian city prompted the design of a non-gel-based PCR method to enable clinical microbiology laboratories to readily identify these clonal strains. We designed a detection method utilizing heat-denatured P. aeruginosa isolates and a ten-single-nucleotide polymorphism (SNP) profile. Strain differences were detected by SYBR Green-based real-time PCR and high-resolution melting curve analysis (HRM10SNP assay). Overall, 106 P. aeruginosa sputum isolates collected from 74 patients with CF, as well as five reference strains, were analysed with the HRM10SNP assay, and the results were compared with those obtained by pulsed-field gel electrophoresis (PFGE). The HRM10SNP assay accurately identified all 45 isolates as members of one of the three major clonal strains characterized by PFGE in two Brisbane CF centres (Australian epidemic strain-1, Australian epidemic strain-2 and P42) from 61 other P. aeruginosa strains from Australian CF patients and two representative overseas epidemic strain isolates. The HRM10SNP method is simple, is relatively inexpensive and can be completed in <3 h. In our setting, it could be made easily available for clinical microbiology laboratories to screen for local P. aeruginosa strains and to guide infection control policies. Further studies are needed to determine whether the HRM10SNP assay can also be modified to detect additional clonal strains that are prevalent in other CF centres.

Identification of Pseudomonas aeruginosa by a duplex real-time polymerase chain reaction assay targeting the ecfX and the gyrB genes

Phenotypic identification of Gram-negative bacteria from respiratory specimens of patients with cystic fibrosis carries a high risk of misidentification. Molecular identification techniques that use single-gene targets are also susceptible to error, including cross-reaction issues with other Gram-negative organisms. In this study, we have designed a Pseudomonas aeruginosa duplex real-time polymerase chain reaction (PCR) (PAduplex) assay targeting the ecfX and the gyrB genes. The PAduplex was evaluated against a panel of 91 clinical and environmental isolates that were presumptively identified as P. aeruginosa. The results were compared with those obtained using a commercial biochemical identification kit and several other P. aeruginosa PCR assays. The results showed that the PAduplex assay is highly suitable for routine identification of P. aeruginosa isolates from clinical or environmental samples. The 2-target format provides simultaneous confirmation of P. aeruginosa identity where both the ecfX and gyrB PCR reactions are positive and may also reduce the potential for false negatives caused by sequence variation in primer or probe targets.