The repertoire of G protein-coupled receptors in the human parasite Schistosoma mansoni and the model organism Schmidtea mediterranea.

Background

G protein-coupled receptors (GPCRs) constitute one of the largest groupings of eukaryotic proteins, and represent a particularly lucrative set of pharmaceutical targets. They play an important role in eukaryotic signal transduction and physiology, mediating cellular responses to a diverse range of extracellular stimuli. The phylum Platyhelminthes is of considerable medical and biological importance, housing major pathogens as well as established model organisms. The recent availability of genomic data for the human blood fluke Schistosoma mansoni and the model planarian Schmidtea mediterranea paves the way for the first comprehensive effort to identify and analyze GPCRs in this important phylum.

Results

Application of a novel transmembrane-oriented approach to receptor mining led to the discovery of 117 S. mansoni GPCRs, representing all of the major families; 105 Rhodopsin, 2 Glutamate, 3 Adhesion, 2 Secretin and 5 Frizzled. Similarly, 418 Rhodopsin, 9 Glutamate, 21 Adhesion, 1 Secretin and 11 Frizzled S. mediterranea receptors were identified. Among these, we report the identification of novel receptor groupings, including a large and highly-diverged Platyhelminth-specific Rhodopsin subfamily, a planarian-specific Adhesion-like family, and atypical Glutamate-like receptors. Phylogenetic analysis was carried out following extensive gene curation. Support vector machines (SVMs) were trained and used for ligand-based classification of full-length Rhodopsin GPCRs, complementing phylogenetic and homology-based classification.

Conclusions

Genome-wide investigation of GPCRs in two platyhelminth genomes reveals an extensive and complex receptor signaling repertoire with many unique features. This work provides important sequence and functional leads for understanding basic flatworm receptor biology, and sheds light on a lucrative set of anthelmintic drug targets.

Accuracy of LightCycler® SeptiFast for the detection and identification of pathogens in the blood of patients with suspected sepsis: a systematic review protocol

Background: There is growing interest in the potential utility of molecular diagnostics in improving the detection of life-threatening infection (sepsis). LightCycler® SeptiFast is a multipathogen probebased real-time PCR system targeting DNA sequences of bacteria and fungi present in blood samples within a few hours. We report here the protocol of the first systematic review of published clinical diagnostic accuracy studies of this technology when compared with blood culture in the setting of suspected sepsis. Methods/design: Data sources: the Cochrane Database of Systematic Reviews, the Database of Abstracts of Reviews of Effects (DARE), the Health Technology Assessment Database (HTA), the NHS Economic Evaluation Database (NHSEED), The Cochrane Library, MEDLINE, EMBASE, ISI Web of Science, BIOSIS Previews, MEDION and the Aggressive Research Intelligence Facility Database (ARIF). Study selection: diagnostic accuracy studies that compare the real-time PCR technology with standard culture results performed on a patient's blood sample during the management of sepsis. Data extraction: three reviewers, working independently, will determine the level of evidence, methodological quality and a standard data set relating to demographics and diagnostic accuracy metrics for each study. Statistical analysis/data synthesis: heterogeneity of studies will be investigated using a coupled forest plot of sensitivity and specificity and a scatter plot in Receiver Operator Characteristic (ROC) space. Bivariate model method will be used to estimate summary sensitivity and specificity. The authors will investigate reporting biases using funnel plots based on effective sample size and regression tests of asymmetry. Subgroup analyses are planned for adults, children and infection setting (hospital vs community) if sufficient data are uncovered. Dissemination: Recommendations will be made to the Department of Health (as part of an open-access HTA report) as to whether the real-time PCR technology has sufficient clinical diagnostic accuracy potential to move forward to efficacy testing during the provision of routine clinical care.

Identification of Burkholderia cenocepacia genes required for bacterial survival in vivo

Burkholderia cenocepacia (formerly Burkholderia cepacia complex genomovar III) causes chronic lung infections in patients with cystic fibrosis. In this work, we used a modified signature-tagged mutagenesis (STM) strategy for the isolation of B. cenocepacia mutants that cannot survive in vivo. Thirty-seven specialized plasposons, each carrying a unique oligonucleotide tag signature, were constructed and used to examine the survival of 2,627 B. cenocepacia transposon mutants, arranged in pools of 37 unique mutants, after a 10-day lung infection in rats by using the agar bead model. The recovered mutants were screened by real-time PCR, resulting in the identification of 260 mutants which presumably did not survive within the lungs. These mutants were repooled into smaller pools, and the infections were repeated. After a second screen, we isolated 102 mutants unable to survive in the rat model. The location of the transposon in each of these mutants was mapped within the B. cenocepacia chromosomes. We identified mutations in genes involved in cellular metabolism, global regulation, DNA replication and repair, and those encoding bacterial surface structures, including transmembrane proteins and cell surface polysaccharides. Also, we found 18 genes of unknown function, which are conserved in other bacteria. A subset of 12 representative mutants that were individually examined using the rat model in competition with the wild-type strain displayed reduced survival, confirming the predictive value of our STM screen. This study provides a blueprint to investigate at the molecular level the basis for survival and persistence of B. cenocepacia within the airways.

Non-parametric nonlinear system identification: A data-driven orthogonal basis function approach

In this paper, a data driven orthogonal basis function approach is proposed for non-parametric FIR nonlinear system identification. The basis functions are not fixed a priori and match the structure of the unknown system automatically. This eliminates the problem of blindly choosing the basis functions without a priori structural information. Further, based on the proposed basis functions, approaches are proposed for model order determination and regressor selection along with their theoretical justifications. © 2008 IEEE.

Automated object identification and position estimation for airport lighting quality assessment

The development of an automated system for the quality assessment of aerodrome ground lighting (AGL), in accordance with associated standards and recommendations, is presented. The system is composed of an image sensor, placed inside the cockpit of an aircraft to record images of the AGL during a normal descent to an aerodrome. A model-based methodology is used to ascertain the optimum match between a template of the AGL and the actual image data in order to calculate the position and orientation of the camera at the instant the image was acquired. The camera position and orientation data are used along with the pixel grey level for each imaged luminaire, to estimate a value for the luminous intensity of a given luminaire. This can then be compared with the expected brightness for that luminaire to ensure it is operating to the required standards. As such, a metric for the quality of the AGL pattern is determined. Experiments on real image data is presented to demonstrate the application and effectiveness of the system.

Modelling 3D camera movement for vibration characterisation and multiple object identification with application to lighting assessment

Utilising cameras as a means to survey the surrounding environment is becoming increasingly popular in a number of different research areas and applications. Central to using camera sensors as input to a vision system, is the need to be able to manipulate and process the information captured in these images. One such application, is the use of cameras to monitor the quality of airport landing lighting at aerodromes where a camera is placed inside an aircraft and used to record images of the lighting pattern during the landing phase of a flight. The images are processed to determine a performance metric. This requires the development of custom software for the localisation and identification of luminaires within the image data. However, because of the necessity to keep airport operations functioning as efficiently as possible, it is difficult to collect enough image data to develop, test and validate any developed software. In this paper, we present a technique to model a virtual landing lighting pattern. A mathematical model is postulated which represents the glide path of the aircraft including random deviations from the expected path. A morphological method has been developed to localise and track the luminaires under different operating conditions. © 2011 IEEE.

How position, velocity, and temporal information combine in the prospective control of catching:data and model

The cerebral cortex contains circuitry for continuously computing properties of the environment and one's body, as well as relations among those properties. The success of complex perceptuomotor performances requires integrated, simultaneous use of such relational information. Ball catching is a good example as it involves reaching and grasping of visually pursued objects that move relative to the catcher. Although integrated neural control of catching has received sparse attention in the neuroscience literature, behavioral observations have led to the identification of control principles that may be embodied in the involved neural circuits. Here, we report a catching experiment that refines those principles via a novel manipulation. Visual field motion was used to perturb velocity information about balls traveling on various trajectories relative to a seated catcher, with various initial hand positions. The experiment produced evidence for a continuous, prospective catching strategy, in which hand movements are planned based on gaze-centered ball velocity and ball position information. Such a strategy was implemented in a new neural model, which suggests how position, velocity, and temporal information streams combine to shape catching movements. The model accurately reproduces the main and interaction effects found in the behavioral experiment and provides an interpretation of recently observed target motion-related activity in the motor cortex during interceptive reaching by monkeys. It functionally interprets a broad range of neurobiological and behavioral data, and thus contributes to a unified theory of the neural control of reaching to stationary and moving targets.

Illumination invariant facial recognition using a piecewise-constant lighting model

In this paper we demonstrate a simple and novel illumination model that can be used for illumination invariant facial recognition. This model requires no prior knowledge of the illumination conditions and can be used when there is only a single training image per-person. The proposed illumination model separates the effects of illumination over a small area of the face into two components; an additive component modelling the mean illumination and a multiplicative component, modelling the variance within the facial area. Illumination invariant facial recognition is performed in a piecewise manner, by splitting the face image into blocks, then normalizing the illumination within each block based on the new lighting model. The assumptions underlying this novel lighting model have been verified on the YaleB face database. We show that magnitude 2D Fourier features can be used as robust facial descriptors within the new lighting model. Using only a single training image per-person, our new method achieves high (in most cases 100%) identification accuracy on the YaleB, extended YaleB and CMU-PIE face databases.

Enhancing family physician capacity to deliver quality palliative home care:An end-of-life, shared-care model

PROBLEM BEING ADDRESSED: Family physicians face innumerable challenges to delivering quality palliative home care to meet the complex needs of end-of-life patients and their families. OBJECTIVE OF PROGRAM: To implement a model of shared care to enhance family physicians' ability to deliver quality palliative home care, particularly in a community-based setting. PROGRAM DESCRIPTION: Family physicians in 3 group practices (N = 21) in Ontario's Niagara West region collaborated with an interprofessional palliative care team (including a palliative care advanced practice nurse, a palliative medicine physician, a bereavement counselor, a psychosocial-spiritual advisor, and a case manager) in a shared-care partnership to provide comprehensive palliative home care. Key features of the program included systematic and timely identification of end-of-life patients, needs assessments, symptom and psychosocial support interventions, regular communication between team members, and coordinated care guided by outcome-based assessment in the home. In addition, educational initiatives were provided to enhance family physicians' knowledge and skills. CONCLUSION: Because of the program, participants reported improved communication, effective interprofessional collaboration, and the capacity to deliver palliative home care, 24 hours a day, 7 days a week, to end-of-life patients in the community.

Robust Multimodal Person Identification With Limited Training Data

This paper presents a novel method of audio-visual feature-level fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there are limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new multimodal feature representation and a modified cosine similarity are introduced to combine and compare bimodal features with limited training data, as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal dataset created from the SPIDRE speaker recognition database and AR face recognition database with variable noise corruption of speech and occlusion in the face images. The system's speaker identification performance on the SPIDRE database, and facial identification performance on the AR database, is comparable with the literature. Combining both modalities using the new method of multimodal fusion leads to significantly improved accuracy over the unimodal systems, even when both modalities have been corrupted. The new method also shows improved identification accuracy compared with the bimodal systems based on multicondition model training or missing-feature decoding alone.

The identification of p130cas-binding proteins and their role in cellular transformation

Adaptor proteins play an important role in signal transduction by regulating the establishment and maintenance of functionally important protein complexes. A recently described member of this group of proteins is p130cas (CAS), which contains numerous sequence motifs predicted to be involved in mediating protein-protein interactions. We propose that adaptor molecules like CAS may help determine the response of a cell to a particular signal by interacting with specific subsets of cellular proteins. To test this hypothesis, we have identified potential binding partners of CAS that may play a rote in cellular transformation by the oncoproteins v-SRC and/or v-CRK. We show that individual domains of CAS associate with specific subsets of proteins in vitro, and that many of these interactions are dependent on the state of tyrosine-phosphorylation of CAS. Sequences necessary for interacting with the focal adhesion kinase pp125FAK (FAK), v-SRC and v-CRK have been mapped to distinct regions of CAS. In addition, the identification of a number of putative CAS-binding partners that are present in crk-transformed cell extracts but undetectable in normal and src-transformed cell extracts supports a model in which unique protein complexes are formed in response to different signals.

Identification of damping in a bridge using a moving instrumented vehicle

In recent years, there has been a significant increase in the number of bridges which are being instrumented and monitored on an ongoing basis. This is in part due to the introduction of bridge management systems designed to provide a high level of protection to the public and early warning if the bridge becomes unsafe. This paper investigates a novel alternative; a low-cost method consisting of the use of a vehicle fitted with accelerometers on its axles to monitor the dynamic behaviour of bridges. A simplified half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the damping ratio of the bridge. The method is tested for a range of bridge spans and vehicle velocities using theoretical simulations and the influences of road roughness, initial vibratory condition of the vehicle, signal noise, modelling errors and frequency matching on the accuracy of the results are investigated.

A drive-by inspection system via vehicle moving force identification

This paper presents a novel method to carry out monitoring of transport infrastructure such as pavements and bridges through the analysis of vehicle accelerations. An algorithm is developed for the identification of dynamic vehicle-bridge interaction forces using the vehicle response. Moving force identification theory is applied to a vehicle model in order to identify these dynamic forces between the vehicle and the road and/or bridge. A coupled half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the forces. The potential of the method to identify the global bending stiffness of the bridge and to predict the pavement roughness is presented. The method is tested for a range of bridge spans using theoretical simulations and the influences of road roughness and signal noise on the accuracy of the results are investigated.

Dynamic Axle Force and Road Profile Identification Using a Moving Vehicle

In the interaction between vehicles, pavements and bridges, it is essential to aim towards a reduction of vehicle axle forces to promote longer pavement life spans and to prevent bridges loads becoming too high. Moreover, as the road surface roughness affects the vehicle dynamic forces, an efficient monitoring of pavement condition is also necessary to achieve this aim. This paper uses a novel algorithm to identify the dynamic interaction forces and pavement roughness from vehicle accelerations in both theoretical simulations and a laboratory experiment; moving force identification theory is applied to a vehicle model for this purpose. Theoretical simulations are employed to evaluate the ability of the algorithm to predict forces over a range of bridge spans and to evaluate the influence of road roughness level on the accuracy of the results. Finally, in addressing the challenge for the real-world problem, the effects of vehicle configuration and speed on the predicted road roughness are also investigated in a laboratory experiment.

3D Morphable Model Construction for Robust Ear and Face Recognition

Recent work suggests that the human ear varies significantly between different subjects and can be used for identification. In principle, therefore, using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views. The paper describes work that investigates this hypothesis using an approach based on the construction of a 3D morphable model of the head and ear. One issue with creating a model that includes the ear is that existing training datasets contain noise and partial occlusion. Rather than exclude these regions manually, a classifier has been developed which automates this process. When combined with a robust registration algorithm the resulting system enables full head morphable models to be constructed efficiently using less constrained datasets. The algorithm has been evaluated using registration consistency, model coverage and minimalism metrics, which together demonstrate the accuracy of the approach. To make it easier to build on this work, the source code has been made available online.