Chiton phylogeny (Mollusca Polyplacophora) and the placement of the enigmatic species Choriplax grayi (H. Adams & Angas)

Shallow marine chitons (Mollusca:Polyplacophora:Chitonida) are widespread and well described from established morphoanatomical characters, yet key aspects of polyplacophoran phylogeny have remained unresolved. Several species, including Hemiarthrum setulosum Carpenter in Dall, 1876, and especially the rare and enigmatic Choriplax grayi (Adams & Angas, 1864), defy systematic placement. Choriplax is known from only a handful of specimens and its morphology is a mosaic of key taxonomic features from two different clades. Here, new molecular evidence provides robust support for its correct association with a third different clade: Choriplax is placed in the superfamily Mopalioidea. Hemiarthrum is included in Cryptoplacoidea, as predicted from morphological evidence. Our multigene analysis of standard nuclear and mitochondrial markers demonstrates that the topology of the order Chitonida is divided into four clades, which have also been recovered in previous studies: Mopalioidea is sister to Cryptoplacoidea, forming a clade Acanthochitonina. The family Callochitonidae is sister to Acanthochitonina. Chitonoidea is resolved as the earliest diverging group within Chitonida. Consideration of this unexpected result for Choriplax and our well-supported phylogeny has revealed differing patterns of shell reduction separating the two superfamilies within Acanthochitonina. As in many molluscs, shell reduction as well as the de novo development of key shell features has occurred using different mechanisms, in multiple lineages of chitons.

The forgiving tree: a self-healing distributed data structure

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that the following process continues for up to n rounds where n is the total number of nodes initially in the network: the adversary deletesan arbitrary node from the network, then the network responds by quickly adding a small number of new edges.

We present a distributed data structure that ensures two key properties. First, the diameter of the network is never more than O(log Delta) times its original diameter, where Delta is the maximum degree of the network initially. We note that for many peer-to-peer systems, Delta is polylogarithmic, so the diameter increase would be a O(loglog n) multiplicative factor. Second, the degree of any node never increases by more than 3 over its original degree. Our data structure is fully distributed, has O(1) latency per round and requires each node to send and receive O(1) messages per round. The data structure requires an initial setup phase that has latency equal to the diameter of the original network, and requires, with high probability, each node v to send O(log n) messages along every edge incident to v. Our approach is orthogonal and complementary to traditional topology-based approaches to defending against attack.

Asking the Critical Questions: An Evaluation of Social Work Students' Experiences in an International Placement

Preparing social work students for the demands of changing social environments and to promote student mobility and interest in overseas employment opportunities have resulted in an increasing demand for international social work placements. The literature describes numerous examples of social work programmes that offer a wide variety of international placements. However, research about the actual benefit of undertaking an overseas placement is scant with limited empirical evidence on the profile of students participating, their experience of the tasks offered, the supervisory practice and the outcomes for students' professional learning and career. This study contributes to the existing body of literature by exploring the relevance of international field placements for students and is unique in that it draws its sample from students who have graduated so provides a distinctive perspective in which to compare their international placement with their other placement/s as well as evaluating what were the benefits and drawbacks for them in terms of their careers, employment opportunities and current professional practice.

Urethral atrophy after artificial urinary sphincter placement:Is cuff downsizing effective?

Purpose: We reviewed the outcome of cuff downsizing with an artificial urinary sphincter for treating recurrent incontinence due to urethral atrophy.

Materials and Methods: We analyzed the records of 17 patients in a 7-year period in whom clinical, radiological and urodynamic evidence of urethral atrophy was treated with cuff downsizing. Cuff downsizing was accomplished by removing the existing cuff and replacing it with a 4 cm. cuff within the established false capsule. Incontinence and satisfaction parameters before and after the procedure were assessed by a validated questionnaire.

Results: Mean patient age was 70 years (range 62 to 79). Average time to urethral atrophy was 31 months (range 5 to 96) after primary sphincter implantation. Mean followup after downsizing was 22 months (range 1 to 64). Cuff downsizing caused a mean decrease of 3.9 to 0.5 pads daily. The number of severe leakage episodes decreased from a mean of 5.4 to 2.1 The mean SEAPI (stress leakage, emptying, anatomy, protection, inhibition) score decreased from 8.2 to 2.4. Patient satisfaction increased from 15% to 80% after cuff downsizing. In 1 patient an infected cuff required complete removal of the device.

Conclusions: Patient satisfaction and continence parameters improved after cuff downsizing. We believe that this technique is a simple and effective method of restoring continence after urethral atrophy.

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

A tree augmented classifier based on Extreme Imprecise Dirichlet Model

We present TANC, a TAN classifier (tree-augmented naive) based on imprecise probabilities. TANC models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM). A first contribution of this paper is the experimental comparison between EDM and the global Imprecise Dirichlet Model using the naive credal classifier (NCC), with the aim of showing that EDM is a sensible approximation of the global IDM. TANC is able to deal with missing data in a conservative manner by considering all possible completions (without assuming them to be missing-at-random), but avoiding an exponential increase of the computational time. By experiments on real data sets, we show that TANC is more reliable than the Bayesian TAN and that it provides better performance compared to previous TANs based on imprecise probabilities. Yet, TANC is sometimes outperformed by NCC because the learned TAN structures are too complex; this calls for novel algorithms for learning the TAN structures, better suited for an imprecise probability classifier.

Bayesian network data imputation with application to survival tree analysis

Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation–maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.

NP-hardness of MAP in Ternary Tree Bayesian Networks

This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem in Bayesian networks with topology of trees (every variable has at most one parent) and variable cardinality at most three. MAP is the problem of querying the most probable state configuration of some (not necessarily all) of the network variables given evidence. It is demonstrated that the problem remains hard even in such simplistic networks.