Nutritional status in Parkinson’s disease patients undergoing deep brain stimulation surgery : a pilot study

Objectives: People with Parkinson’s disease (PD) are at higher risk of malnutrition due to PD symptoms and pharmacotherapy side effects. When pharmacotherapy is no longer effective for symptom control, deep-brain stimulation (DBS) surgery may be considered. The aim of this study was to assess the nutritional status of people with PD who may be at higher risk of malnutrition related to unsatisfactory symptom management with optimised medical therapy. Design: This was an observational study using a convenience sample. Setting: Participants were seen during their hospital admission for their deep brain stimulation surgery. Participants: People with PD scheduled for DBS surgery were recruited from a Brisbane neurological clinic (n=15). Measurements: The Patient-Generated Subjective Global Assessment (PG-SGA), weight, height and body composition were assessed to determine nutritional status. Results: Six participants (40%) were classified as moderately malnourished (SGA-B). Eight participants (53%) reported previous unintentional weight loss (average loss of 13.3%). On average, participants classified as well-nourished (SGA-A) were younger, had shorter disease durations, lower PG-SGA scores, higher body mass (BMI) and fat free mass indices (FFMI) when compared to malnourished participants (SGA-B). Five participants had previously received dietetic advice but only one in relation to unintentional weight loss. Conclusion: Malnutrition remains unrecognised and untreated in this group despite unintentional weight loss and presence of nutrition impact symptoms. Improving nutritional status prior to surgery may improve surgical outcomes.

Talking of many things : using topical networks to study discussions in social media

This paper outlines a method for studying online activity using both qualitative and quantitative methods: topical network analysis. A topical network refers to "the collection of sites commenting on a particular event or issue, and the links between them" (Highfield, Kirchhoff, & Nicolai, 2011, p. 341). The approach is a complement for the analysis of large datasets enabling the examination and comparison of different discussions as a means of improving our understanding of the uses of social media and other forms of online communication. Developed for an analysis of political blogging, the method also has wider applications for other social media websites such as Twitter.

A qualitative study of stakeholder views of the conditions for and outcomes of successful clinical networks

The article informs on a research that analyzes the views of the stakeholders on the conditions required for the effective working on the clinical networks and the outcomes that marks the success of the networks. It is mentioned that clinical networks work to improve the health care access and outcome by undertaking innovations and projects based on local requirements. The factors for the success of clinical networks include building relationships, effective leadership and strategic workplans.

Funds of knowledge of sorting and patterning : networks of exchange in a Torres Strait Island community

The purpose of this article is to describe a project with one Torres Strait Islander Community. It provides some insights into parents’ funds of knowledge that are mathematical in nature, such as sorting shells and giving fish. The idea of funds of knowledge is based the premise that people are competent and have knowledge that has been historically and culturally accumulated into a body of knowledge and skills essential for their functioning and well-being. This knowledge is then practised throughout their lives and passed onto the next generation of children. Through using a community research approach, funds of knowledge that can be used to validate the community’s identities as knowledgeable people, can be used as foundations for future learnings for teachers, parents and children in the early years of school. They can be the bridge that joins a community’s funds of knowledge with schools validating that knowledge.

Combinatorial design of key distribution mechanisms for wireless sensor networks

Secure communications in wireless sensor networks operating under adversarial conditions require providing pairwise (symmetric) keys to sensor nodes. In large scale deployment scenarios, there is no prior knowledge of post deployment network configuration since nodes may be randomly scattered over a hostile territory. Thus, shared keys must be distributed before deployment to provide each node a key-chain. For large sensor networks it is infeasible to store a unique key for all other nodes in the key-chain of a sensor node. Consequently, for secure communication either two nodes have a key in common in their key-chains and they have a wireless link between them, or there is a path, called key-path, among these two nodes where each pair of neighboring nodes on this path have a key in common. Length of the key-path is the key factor for efficiency of the design. This paper presents novel deterministic and hybrid approaches based on Combinatorial Design for deciding how many and which keys to assign to each key-chain before the sensor network deployment. In particular, Balanced Incomplete Block Designs (BIBD) and Generalized Quadrangles (GQ) are mapped to obtain efficient key distribution schemes. Performance and security properties of the proposed schemes are studied both analytically and computationally. Comparison to related work shows that the combinatorial approach produces better connectivity with smaller key-chain sizes.

Utility of the Mild Brain Injury Atypical Symptoms Scale to detect symptom exaggeration : an analogue simulation study

Brief self-report symptom checklists are often used to screen for postconcussional disorder (PCD) and posttraumatic stress disorder (PTSD) and are highly susceptible to symptom exaggeration. This study examined the utility of the five-item Mild Brain Injury Atypical Symptoms Scale (mBIAS) designed for use with the Neurobehavioral Symptom Inventory (NSI) and the PTSD Checklist–Civilian (PCL–C). Participants were 85 Australian undergraduate students who completed a battery of self-report measures under one of three experimental conditions: control (i.e., honest responding, n = 24), feign PCD (n = 29), and feign PTSD (n = 32). Measures were the mBIAS, NSI, PCL–C, Minnesota Multiphasic Personality Inventory–2, Restructured Form (MMPI–2–RF), and the Structured Inventory of Malingered Symptomatology (SIMS). Participants instructed to feign PTSD and PCD had significantly higher scores on the mBIAS, NSI, PCL–C, and MMPI–2–RF than did controls. Few differences were found between the feign PCD and feign PTSD groups, with the exception of scores on the NSI (feign PCD > feign PTSD) and PCL–C (feign PTSD > feign PCD). Optimal cutoff scores on the mBIAS of ≥8 and ≥6 were found to reflect “probable exaggeration” (sensitivity = .34; specificity = 1.0; positive predictive power, PPP = 1.0; negative predictive power, NPP = .74) and “possible exaggeration” (sensitivity = .72; specificity = .88; PPP = .76; NPP = .85), respectively. Findings provide preliminary support for the use of the mBIAS as a tool to detect symptom exaggeration when administering the NSI and PCL–C.

A comparison of new and existing mild traumatic brain injury vignettes : recommendations for research into post-concussion syndrome

OBJECTIVE: To review and compare the mild traumatic brain injury (mTBI) vignettes used in postconcussion syndrome (PCS) research, and to develop 3 new vignettes. METHOD: The new vignettes were devised using World Health Organization (WHO) mTBI diagnostic criteria [1]. Each vignette depicted a very mild (VM), mild (M), or severe (S) brain injury. Expert review (N = 27) and readability analysis was used to validate the new vignettes and compare them to 5 existing vignettes. RESULTS: The response rate was 44%. The M vignette and existing vignettes were rated as depicting a mTBI; however, the fit-to-criteria of these vignettes differed significantly. The fit-to-criteria of the M vignette was as good as that of 3 existing vignettes and significantly better than 2 other vignettes. As expected, the VM and S vignettes were a poor fit-to-criteria. CONCLUSIONS: These new vignettes will assist PCS researchers to test the limits of important etiology factors by varying the severity of depicted injuries.

Systematic variation of the severity of motor vehicle accident-related traumatic brain injury vignettes produces different post-concussion symptom reports

This study investigated the specificity of the post-concussion syndrome (PCS) expectation-as-etiology hypothesis. Undergraduate students (n = 551) were randomly allocated to one of three vignette conditions. Vignettes depicted either a very mild (VMI), mild (MI), or moderate-to-severe (MSI) motor vehicle-related traumatic brain injury (TBI). Participants reported the PCS and PTSD symptoms that they imagined the depicted injury would produce. Secondary outcomes (knowledge of mild TBI, and the perceived undesirability of TBI) were also assessed. After data screening, the distribution of participants by condition was: VMI (n = 100), MI (n = 96), and MSI (n = 71). There was a significant effect of condition on PCS symptomatology, F(2, 264) = 16.55, p < .001. Significantly greater PCS symptomatology was expected in the MSI condition compared to the other conditions (MSI > VMI; medium effect, r = .33; MSI > MI; small-to-medium effect, r = .22). The same pattern of group differences was found for PTSD symptoms, F(2, 264) = 17.12, p < .001. Knowledge of mild TBI was not related to differences in expected PCS symptoms by condition; and the perceived undesirability of TBI was only associated with reported PCS symptomatology in the MSI condition. Systematic variation in the severity of a depicted TBI produces different PCS and PTSD symptom expectations. Even a very mild TBI vignette can elicit expectations of PCS symptoms.

Establishing initial trust in autonomous delay tolerant networks without centralised PKI

A Delay Tolerant Network (DTN) is one where nodes can be highly mobile, with long message delay times forming dynamic and fragmented networks. Traditional centralised network security is difficult to implement in such a network, therefore distributed security solutions are more desirable in DTN implementations. Establishing effective trust in distributed systems with no centralised Public Key Infrastructure (PKI) such as the Pretty Good Privacy (PGP) scheme usually requires human intervention. Our aim is to build and compare different de- centralised trust systems for implementation in autonomous DTN systems. In this paper, we utilise a key distribution model based on the Web of Trust principle, and employ a simple leverage of common friends trust system to establish initial trust in autonomous DTN’s. We compare this system with two other methods of autonomously establishing initial trust by introducing a malicious node and measuring the distribution of malicious and fake keys. Our results show that the new trust system not only mitigates the distribution of fake malicious keys by 40% at the end of the simulation, but it also improved key distribution between nodes. This paper contributes a comparison of three de-centralised trust systems that can be employed in autonomous DTN systems.

A reliable real-time transport protocol for control systems over wireless networks

Deploying networked control systems (NCSs) over wireless networks is becoming more and more popular. However, the widely-used transport layer protocols, Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), are not designed for real-time applications. Therefore, they may not be suitable for many NCS application scenarios because of their limitations on reliability and/or delay performance, which real-control systems concern. Considering a typical type of NCSs with periodic and sporadic real-time traffic, this paper proposes a highly reliable transport layer protocol featuring a packet loss-sensitive retransmission mechanism and a prioritized transmission mechanism. The packet loss-sensitive retransmission mechanism is designed to improve the reliability of all traffic flows. And the prioritized transmission mechanism offers differentiated services for periodic and sporadic flows. Simulation results show that the proposed protocol has better reliability than UDP and improved delay performance than TCP over wireless networks, particularly when channel errors and congestions occur.

Efficient route update and maintenance processes for multipath routing in large-scale industrial wireless sensor networks

Reliable communications is one of the major concerns in wireless sensor networks (WSNs). Multipath routing is an effective way to improve communication reliability in WSNs. However, most of existing multipath routing protocols for sensor networks are reactive and require dynamic route discovery. If there are many sensor nodes from a source to a destination, the route discovery process will create a long end-to-end transmission delay, which causes difficulties in some time-critical applications. To overcome this difficulty, the efficient route update and maintenance processes are proposed in this paper. It aims to limit the amount of routing overhead with two-tier routing architecture and introduce the combination of piggyback and trigger update to replace the periodic update process, which is the main source of unnecessary routing overhead. Simulations are carried out to demonstrate the effectiveness of the proposed processes in improvement of total amount of routing overhead over existing popular routing protocols.

Key Management in Wireless Sensor Networks

This chapter presents a comparative survey of recent key management (key distribution, discovery, establishment and update) solutions for wireless sensor networks. We consider both distributed and hierarchical sensor network architectures where unicast, multicast and broadcast types of communication take place. Probabilistic, deterministic and hybrid key management solutions are presented, and we determine a set of metrics to quantify their security properties and resource usage such as processing, storage and communication overheads. We provide a taxonomy of solutions, and identify trade-offs in these schemes to conclude that there is no one-size-fits-all solution.

Grouping people in social networks using a weighted multi-constraints clustering method

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.