PROSTATIC INTRAEPITHELIAL NEOPLASIA (PIN) - PERFORMANCE OF BAYESIAN BELIEF NETWORK FOR DIAGNOSIS AND GRADING

Prostatic intraepithelial neoplasia (PIN) diagnosis and grading are affected by uncertainties which arise from the fact that almost all knowledge of PIN histopathology is expressed in concepts, descriptive linguistic terms, and words. A Bayesian belief network (BBN) was therefore used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependences between elements in the reasoning sequence. A shallow network was used with an open-tree topology, with eight first-level descendant nodes for the diagnostic clues (evidence nodes), each independently linked by a conditional probability matrix to a root node containing the diagnostic alternatives (decision node). One of the evidence nodes was based on the tissue architecture and the others were based on cell features. The system was designed to be interactive, in that the histopathologist entered evidence into the network in the form of likelihood ratios for outcomes at each evidence node. The efficiency of the network was tested on a series of 110 prostate specimens, subdivided as follows: 22 cases of non-neoplastic prostate or benign prostatic tissue (NP), 22 PINs of low grade (PINlow), 22 PINs of high grade (PINhigh), 22 prostatic adenocarcinomas with cribriform pattern (PACcri), and 22 prostatic adenocarcinomas with large acinar pattern (PAClgac). The results obtained in the benign and malignant categories showed that the belief for the diagnostic alternatives is very high, the values being in general more than 0.8 and often close to 1.0. When considering the PIN lesions, the network classified and graded most of the cases with high certainty. However, there were some cases which showed values less than 0.8 (13 cases out of 44), thus indicating that there are situations in which the feature changes are intermediate between contiguous categories or grades. Discrepancy between morphological grading and the BBN results was observed in four out of 44 PIN cases: one PINlow was classified as PINhigh and three PINhigh were classified as PINlow. In conclusion, the network can grade PlN lesions and differentiate them from other prostate lesions with certainty. In particular, it offers a descriptive classifier which is readily implemented and which allows the use of linguistic, fuzzy variables.

Middleware mechanisms for agent mobility in wireless sensor and actuator networks

This paper describes middleware-level support for agent mobility, targeted at hierarchically structured wireless sensor and actuator network applications. Agent mobility enables a dynamic deployment and adaptation of the application on top of the wireless network at runtime, while allowing the middleware to optimize the placement of agents, e.g., to reduce wireless network traffic, transparently to the application programmer. The paper presents the design of the mechanisms and protocols employed to instantiate agents on nodes and to move agents between nodes. It also gives an evaluation of a middleware prototype running on Imote2 nodes that communicate over ZigBee. The results show that our implementation is reasonably efficient and fast enough to support the envisioned functionality on top of a commodity multi-hop wireless technology. Our work is to a large extent platform-neutral, thus it can inform the design of other systems that adopt a hierarchical structuring of mobile components. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

In Defiance of Duverger: The Class Cleavage and the Emergence of District-Level Multiparty Systems in Western Europe

At its core, Duverger’s Law—holding that the number of viable parties in first-past-the-post systems should not exceed two—applies primarily at the district level. While the number of parties nationally may exceed two, district-level party system fragmentation should not. Given that a growing body of research shows that district-level party system fragmentation can indeed exceed two in first-past-the-post systems, I explore whether the major alternative explanation for party system fragmentation—the social cleavage approach—can explain such violations of Duverger’s Law. Testing this argument in several West European elections prior to the adoption of proportional representation, I find evidence favouring a social cleavage explanation: with the expansion of the class cleavage, the average district-level party system eventually came to violate the two-party predictions associated with Duverger’s Law. This suggests that sufficient social cleavage diversity may produce multiparty systems in other first-past-the-post systems.

Is Our Students Learning - QuestionMark: Extending E-Assessment Systems for Adapative Marking and Feedback

Our key contribution is a flexible, automated marking system that adds desirable functionality to existing E-Assessment systems. In our approach, any given E-Assessment system is relegated to a data-collection mechanism, whereas marking and the generation and distribution of personalised per-student feedback is handled separately by our own system. This allows content-rich Microsoft Word feedback documents to be generated and distributed to every student simultaneously according to a per-assessment schedule.

The feedback is adaptive in that it corresponds to the answers given by the student and provides guidance on where they may have gone wrong. It is not limited to simple multiple choice which are the most prescriptive question type offered by most E-Assessment Systems and as such most straightforward to mark consistently and provide individual per-alternative feedback strings. It is also better equipped to handle the use of mathematical symbols and images within the feedback documents which is more flexible than existing E-Assessment systems, which can only handle simple text strings.

As well as MCQs the system reliably and robustly handles Multiple Response, Text Matching and Numeric style questions in a more flexible manner than Questionmark: Perception and other E-Assessment Systems. It can also reliably handle multi-part questions where the response to an earlier question influences the answer to a later one and can adjust both scoring and feedback appropriately.

New question formats can be added at any time provided a corresponding marking method conforming to certain templates can also be programmed. Indeed, any question type for which a programmatic method of marking can be devised may be supported by our system. Furthermore, since the student’s response to each is question is marked programmatically, our system can be set to allow for minor deviations from the correct answer, and if appropriate award partial marks.

A realist synthesis of educational interventions to improve nutrition care competencies and delivery by doctors and other healthcare professionals

Objective: To determine what, how, for whom, why, and in what circumstances educational interventions to improve the delivery of nutrition care by doctors and other healthcare professionals work?

Design: Realist synthesis following a published protocol and reported following Realist and Meta-narrative Evidence Synthesis: Evolving Standards (RAMESES) guidelines. A multidisciplinary team searched Medline, CINAHL, ERIC, EMBASE, PsyINFO, Sociological Abstracts, Web of Science, Google Scholar, and Science Direct for published and unpublished (grey) literature. The team identified studies with varied designs; appraised their ability to answer the review question; identified relationships between contexts, mechanisms, and outcomes (CMOs); and entered them into a spreadsheet configured for the purpose. The final synthesis identified commonalities across CMO configurations.

Results: Over half of the 46 studies from which we extracted data originated from the US. Interventions that improved the delivery of nutrition care improved skills and attitudes rather than just knowledge; provided opportunities for superiors to model nutrition care; removed barriers to nutrition care in health systems; provided participants with local, practically relevant tools and messages; and incorporated non-traditional, innovative teaching strategies. Operating in contexts where student and qualified healthcare professionals provided nutrition care in both developed and developing countries, these interventions yielded health outcomes by triggering a range of mechanisms, which included: feeling competent; feeling confident and comfortable; having greater self-efficacy; being less inhibited by barriers in healthcare systems; and feeling that nutrition care was accepted and recognised.

Conclusion: These findings show how important it is to move education for nutrition care beyond the simple acquisition of knowledge. They show how educational interventions embedded within systems of healthcare can improve patients’ health by helping health students and professionals to appreciate the importance of delivering nutrition care and feel competent to deliver it.