A new method for studying human polycystic kidney disease epithelia in culture

A new method for studying human polycystic kidney disease epithelia in culture. Human polycystic kidney disease (PKD) epithelia were successfully grown in culture and expressed abnormal characteristics. Cysts lining epithelia of superficial and deep cysts were microdissected and compared to individual normal human proximal straight tubules (PST) and cortical collecting tubules (CCT) grown in defined media. PKD cyst epithelia differed from normal renal tubular epithelia in growth patterns and structural and functional properties. PKD epithelia grew more rapidly and showed cyst–like areas in otherwise confluent monolayers. Polygonal and elongate cells contained an epithelial–specific cytokeratin antigen and had polarized morphology. An extremely abnormal basement membrane morphology was seen and consisted of some banded collagen and numerous unique blebs or spheroids. These blebs were apparently extruded from intracellular vacuoles and stained with ruthenium red, suggesting a proteoglycan component. Cytochemistry of marker enzymes demonstrated the presence of NaK-ATPase and alkaline phosphatase, but a lack of γ-glutamyl transpeptidase. The response of adenylate cyclase activity to vasopressin, parathyroid hormone, and forskolin was significantly diminished in PKD cells as compared to PST and CCT. These studies suggest a defect in cell growth and basement membrane synthesis in human PKD. Cultured PKD epithelia provide a new tool for the study of the pathogenesis of this disease

Physical activity counselling : the application of motivational interviewing and brief negotiation.

The Department of Culture Media and Sport (2002) set a national target for sport and physical activity (PA) that 70% of the population be reasonably active by 2020. However, the proportion of the population meeting these levels of activity is currently only 30% (DoH, 2004a). There is now unequivocal evidence that the UK population is becoming increasingly inactive leading to increases in premature mortality and illness and disease. There is also clear evidence that increased PA can assist in both the avoidance and management of hypokinetic disease such as CHD and type II diabetes. Part of the health strategy for the UK includes the use of interventions such as PA referral schemes (PARS). Within such schemes specific techniques such as PA counselling are increasingly popular in both community and clinical settings (Tulloch et al., 2006).The aim of the thesis was to examine the context and efficacy of PARS, the prevalence of PA counselling and the levels of competence and consistency applied within empirical studies, and finally an assessment of the efficacy of behaviour change counselling in PARS settings based on Motivational Interviewing (Miller & Rollnick, 2002).The first study provided a systematic review of PA counselling from 1995 to 2006 and examined whether a theoretical framework was applied to each study reporting a PA counselling component and if so, which theory. Furthermore, it assessed the number of studies that report the use of a treatment fidelity framework in order to ensure internal validity of the intervention as well as an assessment of competence of the interventionist. Results indicated the dominant theory to be the transtheoretical model (TTM) and in particular stages of change (a sub-component of TTM). No studies applied a treatment fidelity framework with only 2 from 25 assessing competence of the PA counselling interventionist.Prior to delivering an MI intervention, the second study followed a treatment fidelity framework and assessed the competence of the investigator in delivering MI. This applied validated tools with regards to levels of MI competency and proficiency. Results indicated that the investigator demonstrated proficiency across MI global ratings of empathy and spirit and used commensurate levels of open to closed questions and complex to simple reflections.Having assessed the competency and consistency of the MI intervention Study 3 examined the impact of MI applied to a randomly allocated patient group referred to a PARS by GP's The results of the intervention, as compared to a control group receiving traditional PARS interventions only, were equivocal. Additional measures such as patient 'readiness to change' and 'exercise motivation' were also recorded and it appears from the current study that 'pure' MI is not appropriate for those patients reporting a high level of readiness.The final study assessed the impact of a 2-day training workshop in MI to an experienced PARS officer with little or no previous counselling training. The assessment of competence was carried out using the same measure as Study 2 for comparison. The impact of the training was assessed by applying a similar design to that of Study 3. Competency tests indicated the 2-day training did not create competence and proficiency across all facets of MI though adaptations were recorded. The impact on the patient adherence rates in the PARS was similarly equivocal to the previous study

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-time bject detection and dry-Electroencephalography (EEG) based human cortical brain bio-signals decoding. We employ recent advances in dry-EEG technology to stream and collect the cortical waveforms from subjects while they fixate on variable Steady State Visual Evoked Potential (SSVEP) stimuli generated directly from the environment the robot is navigating. To these ends, we propose the use of novel variable BCI stimuli by utilising the real-time video streamed via the on-board robot camera as visual input for SSVEP, where the CNN detected natural scene objects are altered and flickered with differing frequencies (10Hz, 12Hz and 15Hz). These stimuli are not akin to traditional stimuli - as both the dimensions of the flicker regions and their on-screen position changes depending on the scene objects detected. On-screen object selection via such a dry-EEG enabled SSVEP methodology, facilitates the on-line decoding of human cortical brain signals, via a specialised secondary CNN, directly into teleoperation robot commands (approach object, move in a specific direction: right, left or back). This SSVEP decoding model is trained via a priori offline experimental data in which very similar visual input is present for all subjects. The resulting classification demonstrates high performance with mean accuracy of 85% for the real-time robot navigation experiment across multiple test subjects.Comment: Accepted as a full paper at the 2019 International Conference on Robotics and Automation (ICRA

Properties of the Broad-Range Nematic Phase of a Laterally Linked H-Shaped Liquid Crystal Dimer

In search for novel nematic materials, a laterally linked H-shaped liquid crystal dimer have been synthesized and characterized. The distinct feature of the material is a very broad temperature range (about 50 oC) of the nematic phase, which is in contrast with other reported H-dimers that show predominantly smectic phases. The material exhibits interesting textural features at the scale of nanometers (presence of smectic clusters) and at the macroscopic scales. Namely, at a certain temperature, the flat samples of the material show occurrence of domain walls. These domain walls are caused by the surface anchoring transition and separate regions with differently tilted director. Both above and below this transition temperature the material represents a uniaxial nematic, as confirmed by the studies of defects in flat samples and samples with colloidal inclusions, freely suspended drops, X-ray diffraction and transmission electron microscopy.Comment: 30 pages (including Supplementary Information), 7 Figure

Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR)

Currently, most land Intelligence, Surveillance and Reconnaissance (ISR) assets (e.g. EO/IR cameras) are simply data collectors. Understanding, decision making and sensor control are performed by the human operators, involving high cognitive load. Any automation in the system has traditionally involved bespoke design of centralised systems that are highly specific for the assets/targets/environment under consideration, resulting in complex, non-flexible systems that exhibit poor interoperability. We address a concept of Autonomous Sensor Modules (ASMs) for land ISR, where these modules have the ability to make low-level decisions on their own in order to fulfil a higher-level objective, and plug in, with the minimum of preconfiguration, to a High Level Decision Making Module (HLDMM) through a middleware integration layer. The dual requisites of autonomy and interoperability create challenges around information fusion and asset management in an autonomous hierarchical system, which are addressed in this work. This paper presents the results of a demonstration system, known as Sensing for Asset Protection with Integrated Electronic Networked Technology (SAPIENT), which was shown in realistic base protection scenarios with live sensors and targets. The SAPIENT system performed sensor cueing, intelligent fusion, sensor tasking, target hand-off and compensation for compromised sensors, without human control, and enabled rapid integration of ISR assets at the time of system deployment, rather than at design-time. Potential benefits include rapid interoperability for coalition operations, situation understanding with low operator cognitive burden and autonomous sensor management in heterogenous sensor systems

Real-time Classification of Vehicle Types within Infra-red Imagery

Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios

Extended patch prioritization for depth filling within constrained exemplar-based RGB-D image completion.

We address the problem of hole filling in depth images, obtained from either active or stereo sensing, for the purposes of depth image completion in an exemplar-based framework. Most existing exemplar-based inpainting techniques, designed for color image completion, do not perform well on depth information with object boundaries obstructed or surrounded by missing regions. In the proposed method, using both color (RGB) and depth (D) information available from a common-place RGB-D image, we explicitly modify the patch prioritization term utilized for target patch ordering to facilitate improved propagation of complex texture and linear structures within depth completion. Furthermore, the query space in the source region is constrained to increase the efficiency of the approach compared to other exemplar-driven methods. Evaluations demonstrate the efficacy of the proposed method compared to other contemporary completion techniques