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.

Surface plasmon resonance-based inhibition assay for real-time detection of Cryptosporidium parvum oocyst

A surface plasmon resonance (SPR)-based inhibition assay method using a polyclonal anti-mouse IgM arrayed Cryptosporidium sensor chip was developed for the real-time detection of Cryptosporidium parvum oocysts. The Cryptosporidium sensor chip was fabricated by subsequent immobilization of streptavidin and polyclonal anti-mouse IgM (secondary antibody) onto heterogeneous self-assembled monolayers (SAMs). The assay consisted of the immunoreaction step between monoclonal anti-C. parvum oocyst (primary antibody) and oocysts, followed by the binding step of the unbound primary antibody onto the secondary antibody surface. It enhanced not only the immunoreaction yield of the oocysts by batch reaction but also the accessibility of analytes to the chip surface by antibody–antibody interaction. Furthermore, the use of optimum concentration of the primary antibody maximized its binding response on the chip. An inversely linear calibration curve for the oocyst concentration versus SPR signal was obtained in the range of 1×106–1×102 oocysts ml-1. The oocyst detection was also successfully achieved in natural water systems. These results indicate that the SPR-based inhibition assay using the Cryptosporidium sensor chip has high application potential for the real-time analysis of C. parvum oocyst in laboratory and field water monitoring.

Evaluation of a substructured soft Real-time Hybrid Test for performing Seismic analysis of complex structural systems

The hybrid test method is a relatively recently developed dynamic testing technique that uses numerical modelling combined with simultaneous physical testing. The concept of substructuring allows the critical or highly nonlinear part of the structure that is difficult to numerically model with accuracy to be physically tested whilst the remainder of the structure, that has a more predictable response, is numerically modelled. In this paper, a substructured soft-real time hybrid test is evaluated as an accurate means of performing seismic tests of complex structures. The structure analysed is a three-storey, two-by-one bay concentrically braced frame (CBF) steel structure subjected to seismic excitation. A ground storey braced frame substructure whose response is critical to the overall response of the structure is tested, whilst the remainder of the structure is numerically modelled. OpenSees is used for numerical modelling and OpenFresco is used for the communication between the test equipment and numerical model. A novel approach using OpenFresco to define the complex numerical substructure of an X-braced frame within a hybrid test is also presented. The results of the hybrid tests are compared to purely numerical models using OpenSees and a simulated test using a combination of OpenSees and OpenFresco. The comparative results indicate that the test method provides an accurate and cost effective procedure for performing
full scale seismic tests of complex structural systems.

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.

A network architectural style for real-time systems: NaSr

Inter-component communication has always been of great importance in the design of software architectures and connectors have been considered as first-class entities in many approaches [1][2][3]. We present a novel architectural style that is derived from the well-established domain of computer networks. The style adopts the inter-component communication protocol in a novel way that allows large scale software reuse. It mainly targets real-time, distributed, concurrent, and heterogeneous systems.

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.