A novel technique to demonstrate disturbed appetite profiles in haemodialysis patients

Background Malnutrition is common among dialysis patients and is associated with an adverse outcome. One cause of this is a persistent reduction in nutrient intake, suggesting an abnormality of appetite regulation. Methods We used a novel technique to describe the appetite profile in 46 haemodialysis (HD) patients and 40 healthy controls. The Electronic Appetite Rating System (EARS) employs a palmtop computer to collect hourly ratings of motivation to eat and mood. We collected data on hunger, desire to eat, fullness, and tiredness. HD subjects were monitored on the dialysis day and the interdialytic day. Controls were monitored for 1 or 2 days. Results Temporal profiles of motivation to eat for the controls were similar on both days. Temporal profiles of motivation to eat for the HD group were lower on the dialysis day. Mean HD scores were not significantly different from controls. Dietary records indicated that dialysis patients consumed less food than controls. Conclusions Our data indicate that the EARS can be used to monitor subjective appetite states continuously in a group of HD patients. A HD session reduces hunger and desire to eat. Patients feel more tired after dialysis. This does not correlate with their hunger score, but does correlate with their fullness rating. Nutrient intake is reduced, suggesting a resetting of appetite control for the HD group. The EARS may be useful for intervention studies.

Motivational interviewing

Co-existing mental health and drug and alcohol problems occur frequently in primary care and clinical settings. Despite this, health professionals rarely receive training in how to detect, assess and formulate interventions for co-existing problems and few clinical guidelines exist. This Handbook provides an exciting and highly useful addition to this area. Leading clinicians from the UK, the US and Australia provide practical descriptions of assessments and interventions for co-existing problems. These will enable professionals working with co-existing problems to understand best practice and ensure that people with co-existing problems receive optimal treatment. A range of overarching approaches are covered, including: • working within a cognitive behavioural framework; • provision of consultation-liaison services, training and supervision; • individual, group and family interventions; and • working with rurally isolated populations. The contributors also provide detailed descriptions of assessments and treatments for a range of disorders when accompanied by drug and alcohol problems, including anxiety, depression, schizophrenia, bipolar disorder and learning difficulties. The Clinical Handbook of Co-existing Mental Health and Drug and Alcohol Problems will enhance clinicians’ confidence in working with people with co-existing problems. It will prove a valuable resource for all psychologists, psychiatrists, counsellors, social workers and all those working in both primary and secondary care health settings.

Structural health monitoring of bridges : source localisation in acoustic emission technique

Managing the sustainability of urban infrastructure requires regular health monitoring of key infrastructure such as bridges. The process of structural health monitoring involves monitoring a structure over a period of time using appropriate sensors, extracting damage sensitive features from the measurements made by the sensors, and analysing these features to determine the current state of the structure. Various techniques are available for structural health monitoring of structures, and acoustic emission is one technique that is finding an increasing use in the monitoring of civil infrastructures such as bridges. Acoustic emission technique is based on the recording of stress waves generated by rapid release of energy inside a material, followed by analysis of recorded signals to locate and identify the source of emission and assess its severity. This chapter first provides a brief background of the acoustic emission technique and the process of source localization. Results from laboratory experiments conducted to explore several aspects of the source localization process are also presented. The findings from the study can be expected to enhance knowledge of the acoustic emission process, and to aid the development of effective bridge structure diagnostics systems.

The feasibility of vibration analysis as a technique to detect osseointegration of transfemoral implants

One of the main causes of above knee or transfemoral amputation (TFA) in the developed world is trauma to the limb. The number of people undergoing TFA due to limb trauma, particularly due to war injuries, has been increasing. Typically the trauma amputee population, including war-related amputees, are otherwise healthy, active and desire to return to employment and their usual lifestyle. Consequently there is a growing need to restore long-term mobility and limb function to this population. Traditionally transfemoral amputees are provided with an artificial or prosthetic leg that consists of a fabricated socket, knee joint mechanism and a prosthetic foot. Amputees have reported several problems related to the socket of their prosthetic limb. These include pain in the residual limb, poor socket fit, discomfort and poor mobility. Removing the socket from the prosthetic limb could eliminate or reduce these problems. A solution to this is the direct attachment of the prosthesis to the residual bone (femur) inside the residual limb. This technique has been used on a small population of transfemoral amputees since 1990. A threaded titanium implant is screwed in to the shaft of the femur and a second component connects between the implant and the prosthesis. A period of time is required to allow the implant to become fully attached to the bone, called osseointegration (OI), and be able to withstand applied load; then the prosthesis can be attached. The advantages of transfemoral osseointegration (TFOI) over conventional prosthetic sockets include better hip mobility, sitting comfort and prosthetic retention and fewer skin problems on the residual limb. However, due to the length of time required for OI to progress and to complete the rehabilitation exercises, it can take up to twelve months after implant insertion for an amputee to be able to load bear and to walk unaided. The long rehabilitation time is a significant disadvantage of TFOI and may be impeding the wider adoption of the technique. There is a need for a non-invasive method of assessing the degree of osseointegration between the bone and the implant. If such a method was capable of determining the progression of TFOI and assessing when the implant was able to withstand physiological load it could reduce the overall rehabilitation time. Vibration analysis has been suggested as a potential technique: it is a non destructive method of assessing the dynamic properties of a structure. Changes in the physical properties of a structure can be identified from changes in its dynamic properties. Consequently vibration analysis, both experimental and computational, has been used to assess bone fracture healing, prosthetic hip loosening and dental implant OI with varying degrees of success. More recently experimental vibration analysis has been used in TFOI. However further work is needed to assess the potential of the technique and fully characterise the femur-implant system. The overall aim of this study was to develop physical and computational models of the TFOI femur-implant system and use these models to investigate the feasibility of vibration analysis to detect the process of OI. Femur-implant physical models were developed and manufactured using synthetic materials to represent four key stages of OI development (identified from a physiological model), simulated using different interface conditions between the implant and femur. Experimental vibration analysis (modal analysis) was then conducted using the physical models. The femur-implant models, representing stage one to stage four of OI development, were excited and the modal parameters obtained over the range 0-5kHz. The results indicated the technique had limited capability in distinguishing between different interface conditions. The fundamental bending mode did not alter with interfacial changes. However higher modes were able to track chronological changes in interface condition by the change in natural frequency, although no one modal parameter could uniquely distinguish between each interface condition. The importance of the model boundary condition (how the model is constrained) was the key finding; variations in the boundary condition altered the modal parameters obtained. Therefore the boundary conditions need to be held constant between tests in order for the detected modal parameter changes to be attributed to interface condition changes. A three dimensional Finite Element (FE) model of the femur-implant model was then developed and used to explore the sensitivity of the modal parameters to more subtle interfacial and boundary condition changes. The FE model was created using the synthetic femur geometry and an approximation of the implant geometry. The natural frequencies of the FE model were found to match the experimental frequencies within 20% and the FE and experimental mode shapes were similar. Therefore the FE model was shown to successfully capture the dynamic response of the physical system. As was found with the experimental modal analysis, the fundamental bending mode of the FE model did not alter due to changes in interface elastic modulus. Axial and torsional modes were identified by the FE model that were not detected experimentally; the torsional mode exhibited the largest frequency change due to interfacial changes (103% between the lower and upper limits of the interface modulus range). Therefore the FE model provided additional information on the dynamic response of the system and was complementary to the experimental model. The small changes in natural frequency over a large range of interface region elastic moduli indicated the method may only be able to distinguish between early and late OI progression. The boundary conditions applied to the FE model influenced the modal parameters to a far greater extent than the interface condition variations. Therefore the FE model, as well as the experimental modal analysis, indicated that the boundary conditions need to be held constant between tests in order for the detected changes in modal parameters to be attributed to interface condition changes alone. The results of this study suggest that in a clinical setting it is unlikely that the in vivo boundary conditions of the amputated femur could be adequately controlled or replicated over time and consequently it is unlikely that any longitudinal change in frequency detected by the modal analysis technique could be attributed exclusively to changes at the femur-implant interface. Therefore further development of the modal analysis technique would require significant consideration of the clinical boundary conditions and investigation of modes other than the bending modes.

Development of a novel rolling-circle amplification technique to detect Banana streak virus which also discriminates between integrated and episomal virus sequences

Bananas are hosts to a large number of banana streak virus (BSV) species. However, diagnostic methods for BSV are inadequate because of the considerable genetic and serological diversity amongst BSV isolates and the presence of integrated BSV sequences in some banana cultivars which leads to false positives. In this study, a sequence non-specific, rolling-circle amplification (RCA) technique was developed and shown to overcome these limitations for the detection and subsequent characterisation of BSV isolates infecting banana. This technique was shown to discriminate between integrated and episomal BSV DNA, specifically detecting the latter in several banana cultivars known to contain episomal and/or integrated sequences of Banana streak Mysore virus (BSMyV), Banana streak OL virus (BSOLV) and Banana streak GF virus (BSGFV). Using RCA, the presence of BSMyV and BSOLV was confirmed in Australia, while BSOLV, BSGFV, Banana streak Uganda I virus (BSUgIV), Banana streak Uganda L virus (BSUgLV) and Banana streak Uganda M virus (BSUgMV) were detected in Uganda. This is the first confirmed report of episomally-derived BSUglV, BSUgLV and BSUgMV in Uganda. As well as its ability to detect BSV, RCA was shown to detect two other pararetroviruses, Sugarcane bacilliform virus in sugarcane and Cauliflower mosaic virus in turnip.

An intelligent data mining technique for product quality improvement

Advances in data mining have provided techniques for automatically discovering underlying knowledge and extracting useful information from large volumes of data. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large complex databases. Application of data mining to manufacturing is relatively limited mainly because of complexity of manufacturing data. Growing self organizing map (GSOM) algorithm has been proven to be an efficient algorithm to analyze unsupervised DNA data. However, it produced unsatisfactory clustering when used on some large manufacturing data. In this paper a data mining methodology has been proposed using a GSOM tool which was developed using a modified GSOM algorithm. The proposed method is used to generate clusters for good and faulty products from a manufacturing dataset. The clustering quality (CQ) measure proposed in the paper is used to evaluate the performance of the cluster maps. The paper also proposed an automatic identification of variables to find the most probable causative factor(s) that discriminate between good and faulty product by quickly examining the historical manufacturing data. The proposed method offers the manufacturers to smoothen the production flow and improve the quality of the products. Simulation results on small and large manufacturing data show the effectiveness of the proposed method.

The novice researcher : interviewing young children

Being a novice researcher undertaking research interviews with young children requires understandings of the interview process. By investigating the interaction between a novice researcher undertaking her first interview and a child participant, we attend to theoretical principles, such as the competence of young children as informants, and highlight practical matters when interviewing young children. A conversation analysis approach examines the talk preceding and following a sticker task. By highlighting the conversational features of a research interview, researchers can better understand the co-constructed nature of the interview. This paper provides insights into how to prepare for the interview and manage the interview context to recognize the active participation of child participants, and the value of artifacts to promote interaction. These insights make more transparent the interactional process of a research interview and become part of the researcher’s collection of devices to manage the conduct of research interviews.

A coupled meshless technique/molecular dynamics approach for deformation characterization of monocrystalline metal

This paper presents a multiscale study using the coupled Meshless technique/Molecular Dynamics (M2) for exploring the deformation mechanism of mono-crystalline metal (focus on copper) under uniaxial tension. In M2, an advanced transition algorithm using transition particles is employed to ensure the compatibility of both displacements and their gradients, and an effective local quasi-continuum approach is also applied to obtain the equivalent continuum strain energy density based on the atomistic poentials and Cauchy-Born rule. The key parameters used in M2 are firstly investigated using a benchmark problem. Then M2 is applied to the multiscale simulation for a mono-crystalline copper bar. It has found that the mono-crystalline copper has very good elongation property, and the ultimate strength and Young's modulus are much higher than those obtained in macro-scale.

Brief motivational interviewing for depression and anxiety

Motivational interviewing (MI)can be applied as a brief, low intensity (LI) intervention of 1-4 individualised sessions (typically 45-60 minutes in duration), including screening, assessment feedback, and psycho-education. MI is a client-centred, directive therapeutic style that enhances readiness for change by helping clients explore and resolve ambivalence (Miller and Rollnick 2002). A summary of the key components of brief MI interventions is provided in Table 16.1. There is a well-established evidence base for MI in the treatment of substance misuse (particularly alcohol misuse; Moyer et al. 2002), as well as a growing evidence for the use of MI in the treatment of other mental disorders (e.g. depression, PTSD, OCD), as well as suicidality and physical health problems (Hettema et al. 2005). Brief MI intervention can be delivered as a standalone treatment or as a motivational prelude to pharmacological and/or other psychological treatments (Hettema et al. 2005). MI has been used as an accompaniment to cognitive behavioural therapy (CBT) in the treatment of both depression and anxiety for resolving ambivalence about change and developing strategies for responding to resistance (e.g. treatment attendance, homework/medication compliance; Arkowitz et al. 2008a, 2008b). This chapter will describe how to apply brief MI interventions to the treatment of depression and anxiety as applied to the case of Megan (see Box 16.1) along with some of the challenges and potential solutions to applying MI in practice.

Use of acoustic emission technique for structural health monitoring of bridges

Bridges are valuable assets of every nation. They deteriorate with age and often are subjected to additional loads or different load patterns than originally designed for. These changes in loads can cause localized distress and may result in bridge failure if not corrected in time. Early detection of damage and appropriate retrofitting will aid in preventing bridge failures. Large amounts of money are spent in bridge maintenance all around the world. A need exists for a reliable technology capable of monitoring the structural health of bridges, thereby ensuring they operate safely and efficiently during the whole intended lives. Monitoring of bridges has been traditionally done by means of visual inspection. Visual inspection alone is not capable of locating and identifying all signs of damage, hence a variety of structural health monitoring (SHM) techniques is used regularly nowadays to monitor performance and to assess condition of bridges for early damage detection. Acoustic emission (AE) is one technique that is finding an increasing use in SHM applications of bridges all around the world. The chapter starts with a brief introduction to structural health monitoring and techniques commonly used for monitoring purposes. Acoustic emission technique, wave nature of AE phenomenon, previous applications and limitations and challenges in the use as a SHM technique are also discussed. Scope of the project and work carried out will be explained, followed by some recommendations of work planned in future.