Relative to the previous nomograms (10 predictors or 71% accuracy) our tool relies on fewer variables (6) and shows superior accuracy in European men. Accuracy in American men is substantially lower. Racial, clinical and biochemical differences may explain the observed discrepancy in predictive accuracy.

Introduction: To assess the impact on subjective symptoms and flow patterns of a new surgical technique designed to correct strictures of the female distal urethra and urethral meatus. Materials and Methods: Seventeen patients (mean age 41.2 years) with symptomatic strictures of either the distal urethra or the urethral meatus entered the study. Patients reporting an AUA score >20, a diagnosis of bladder outlet obstruction according to the Abrams-Griffiths nomogram and the Schaefer linPURR diagram, urethral calibration <20 F and radiologic evidence of the stricture, were considered eligible for surgery. A pedicled flap isolated from the vaginal vestibule was anastomosed with two longitudinal running sutures along the two edges of an opened urethra. Results: In all cases, diffuse fibrosis of the urethral wall was demonstrated at histological examination. Mean (± SE) preoperative and 12-month follow-up results were as follows: AUA score 25.2 ± 2.1 vs. 8.4 ± 1.2 (p < 0.0001); peak flow rate (ml/s) 13.2 ± 1.2 vs. 36 ± 1.5 (p < 0.0001); detrusor pressure at Q_max (cm H₂O) 45 ± 5 vs. 17 ± 3; residual urine volume (ml) 120 ± 5 vs. 20 ± 5 (p < 0.0001). Fifteen patients (88%) showed an unobstructed Abrams-Griffiths nomogram and a Schaefer linPURR diagram postoperatively. All but 2 cases (88%) could be calibrated at 28 F postoperatively and showed a normal urethral lumen at voiding cystourethrography. Complications were never noted. Conclusions: Female patients with symptomatic strictures of the distal urethra or urethral meatus may be treated efficaciously and safely with vestibular flap urethroplasty. Although this technique must be performed under optical magnification it is easy to perform and is not associated with complications.

In this manuscript the fixed-lag smoothing problem for conditionally linear Gaussian state-space models is investigated from a factor graph perspective. More specifically, after formulating Bayesian smoothing for an arbitrary state-space model as forward-backward message passing over a factor graph, we focus on the above mentioned class of models and derive two novel particle smoothers for it. Both the proposed techniques are based on the well known two-filter smoothing approach and employ marginalized particle filtering in their forward pass. However, on the one hand, the first smoothing technique can only be employed to improve the accuracy of state estimates with respect to that achieved by forward filtering. On the other hand, the second method, that belongs to the class of Rao-Blackwellized particle smoothers, provides also a point mass approximation of the so called joint smoothing distribution. Finally, our smoothing algorithms are compared, in terms of estimation accuracy and computational requirements, with a Rao-Blackwellized particle smoother recently proposed by Lindsten et al. in [20].

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Abstract-Establishing bounds on the accuracy achievable by localization techniques represents a fundamental technical issue. Bounds on localization accuracy have been derived for cases in which the position of an agent is estimated on the basis of a set of observations and, possibly, of some a priori information related to them (e.g., information about anchor positions and properties of the communication channel). In this manuscript new bounds are derived under the assumption that the localization system is map-aware, i.e., it can benefit not only from the availability of observations, but also from the a priori knowledge provided by the map of the environment where it operates. Our results show that: a) map-aware estimation accuracy can be related to some features of the map (e.g., its shape and area) even though, in general, the relation is complicated; b) maps are really useful in the presence of some combination of low signal-to-noise ratios and specific geometrical features of the map (e.g., the size of obstructions); c) in most cases, there is no need of refined maps since additional details do not improve estimation accuracy.

Vulvodynia is a clinical syndrome that may include unexplained vulvar pain, sexual dysfunction, and psychological disability. It is a multifactorial syndrome that should be diagnosed, if possible, with an intradisciplinary approach. This article discusses the diagnosis and treatment of vulvodynia, starting with a summary of the complex nervous system within the pelvis. Different clinical pictures and different subtypes of the syndrome have been described in order to identify the etiologic aspects that are essential for diagnosis and subsequent treatment. Clinical evaluation should stress attention to detailed "pain-mapping" and evaluation of past and present history. The gynecological examination should be an overall patient evaluation, incorporating global physical impression, change in posture due to pain and careful examination of the pelvic floor. Examination of the pelvic floor is frequently omitted. Leading to an incorrect diagnosis of psychogenic pain. Such a misdiagnosis can result in the dismissal of appropriate treatment. Proper evaluation requires a comprehensive, multidisciplinary approach that includes medical, rehabilitative, and psychological issues.

In this manuscript, a general method for deriving filtering algorithms that involve a network of interconnected Bayesian filters is proposed. This method is based on the idea that the processing accomplished inside each of the Bayesian filters and the interactions between them can be represented as message passing algorithms over a proper graphical model. The usefulness of our method is exemplified by developing new filtering techniques, based on the interconnection of a particle filter and an extended Kalman filter, for conditionally linear Gaussian systems. Numerical results for two specific dynamic systems evidence that the devised algorithms can achieve a better complexity-accuracy tradeoff than marginalized particle filtering and multiple particle filtering. particle filter are run over partially overlapped state vectors. In both cases, however, two heterogeneous filtering methods are combined in a way that the resulting overall algorithm is forward only and, within each of its recursions, both methods are executed only once. Another class of solutions, known as multiple particle filtering (MPF), is based on the idea of partitioning the state vector into multiple substates and running multiple particle filters in parallel, one on each subspace [9], [12]- [15]. The resulting network of particle filters requires the mutual exchange of statistical information (in the form of estimates/predictions of the tracked substates or parametric distributions), so that, within each filter, the unknown portion of the state vector can be integrated out in both weight computation and particle propagation. In principle, MPF can be employed only when the selected substates are separable in the state equation, even if approximate solutions can be devised to circumvent this problem [15]. Moreover, the technical literature about MPF has raised three interesting technical issues that have received limited attention until now. The first issue refers to the possibility of coupling an extended Kalman filter with each particle filter of the network; the former filter should provide the latter one with the statistical information required for integrating out the unknown portion of the state vector (see [14, Par. 3.2]). The second one concerns the use of filters having partially overlapped substates (see [13, Sec.1]). The third (and final) issue, instead, concerns the iterative exchange of statistical information among the interconnected filters of the network. Some work related to the first issue can be found in [16], where the application of MPF to target tracking in a cognitive radar network has been investigated. In this case, however, the proposed solution is based on Rao-Blackwellisation; for this reason, each particle filter of the network is not coupled with a single extended Kalman filter, but with a bank of Kalman filters. The second issue has not been investigated at all, whereas limited attention has been paid to the third one; in fact, the last problem has been investigated only in [12], where a specific iterative method ba...

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