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.

Artificial urinary sphincter implantation in the irradiated patient:safety, efficacy and satisfaction

Objective To compare the long-term outcome of artificial urinary sphincter (AUS) implantation in patients after prostatectomy, with and with no history of previous irradiation.

Patients and methods The study included 98 men (mean age 68 years) with urinary incontinence after prostatectomy for prostate cancer (85 radical, 13 transurethral resection) who had an AUS implanted. Twenty-two of the patients had received adjuvant external beam irradiation before AUS implantation. Over a mean (range) follow-up of 46 (5-118) months, the complication and surgical revision rates were recorded and compared between irradiated and unirradiated patients. The two groups were also compared for the resolution of incontinence and satisfaction, assessed using a questionnaire.

Results Overall, surgical revision was equally common in irradiated (36%) and unirradiated (24%) patients. After activating the AUS, urethral atrophy, infection and erosion requiring surgical revision were more common in irradiated patients (41% vs 11%; P <0.05); 70% of patients reported a significant improvement in continence, regardless of previous irradiation. Patient satisfaction remained high, with >80% of patients stating that they would undergo surgery again and/or recommend it to others, despite previous Irradiation and/or the need for surgical revision.

Conclusions Despite higher complication and surgical revision rates in patients who have an AUS implanted and have a history of previous Irradiation, the long-term continence and patient satisfaction appear not to be adversely affected.

THE ARTIFICIAL URINARY SPHINCTER - A NEW SOLUTION FOR INCONTINENT PATIENTS

Treatment of urinary incontinence with the artificial urinary sphincter has been available in centres such as London and Liverpool for a number of years. This service is now available in the department of urology of the Belfast City Hospital. Twelve patients have had successful implantation of an artificial urinary sphincter for urinary incontinence, and ten are now fully continent. One patient with Wegener's granulomatosis developed active disease in his urethra which has precluded activation of the device. One patient has had the device removed because of erosion into the urethra.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

Late Holocene sea-level fall and turn-off of reef flat carbonate production: Rethinking bucket fill and coral reef growth models

Relative sea-level rise has been a major factor driving the evolution of reef systems during the Holocene. Most models of reef evolution suggest that reefs preferentially grow vertically during rising sea level then laterally from windward to leeward, once the reef flat reaches sea level. Continuous lagoonal sedimentation ("bucket fill") and sand apron progradation eventually lead to reef systems with totally filled lagoons. Lagoonal infilling of One Tree Reef (southern Great Barrier Reef) through sand apron accretion was examined in the context of late Holocene relative sea-level change. This analysis was conducted using sedimentological and digital terrain data supported by 50 radiocarbon ages from fossil microatolls, buried patch reefs, foraminifera and shells in sediment cores, and recalibrated previously published radiocarbon ages. This data set challenges the conceptual model of geologically continuous sediment infill during the Holocene through sand apron accretion. Rapid sand apron accretion occurred between 6000 and 3000 calibrated yr before present B.P. (cal. yr B.P.); followed by only small amounts of sedimentation between 3000 cal. yr B.P. and present, with no significant sand apron accretion in the past 2 k.y. This hiatus in sediment infill coincides with a sea-level fall of similar to 1-1.3 m during the late Holocene (ca. 2000 cal. yr B.P.), which would have caused the turn-off of highly productive live coral growth on the reef flats currently dominated by less productive rubble and algal flats, resulting in a reduced sediment input to back-reef environments and the cessation in sand apron accretion. Given that relative sea-level variations of similar to 1 m were common throughout the Holocene, we suggest that this mode of sand apron development and carbonate production is applicable to most reef systems.

Holocene “turn-on” and evolution of the Southern Great Barrier Reef: Revisiting reef cores from the Capricorn Bunker Group

Extensive drilling of the Great Barrier Reef (GBR) in the 70s and 80s illuminated the main factors controlling reef growth during the Holocene. However, questions remain about: (1) the precise nature and timing of reef "turnon" or initiation, (2) whether consistent spatio-temporal patterns occur in the bio-sedimentologic response of the reef to Holocene sea-level rise then stability, and (3) how these factors are expressed in the context of the different evolutionary states (juvenile-mature-senile reefs). Combining 21 new C14-AMS and 146 existing recalibrated radiocarbon and U/Th ages, we investigated the detailed spatial and temporal variations in sedimentary facies and coralgal assemblages in fifteen cores across four reefs (Wreck, Fairfax, One Tree and Fitzroy) from the Southern GBR. Our newly defined facies and assemblages record distinct chronostratigraphic patterns in the cores, displaying both lateral zonation across the different reefs and shallowing upwards sequences, characterised by a transition from deep (Porites/faviids) to shallow (Acropora/Isopora) coral types. The revised reef accretion curves show a significant lag period, ranging from 0.7-2 ka, between flooding of the antecedent Pleistocene substrate and Holocene reef turn-on. This lag period and dominance of more environmentally tolerant early colonizers (e.g., domal Porites and faviids), suggests initial conditions that were unfavourable for coral growth. We contend that higher input of fine siliciclastic material from regional terrigenous sources, exposure to hydrodynamic forces and colonisation in deeper waters are the main factors influencing initially reduced growth and development. All four reefs record a time lag and we argue that the size and shape of the antecedent platform is most important in determining the duration between flooding and recolonisation of the Holocene reef. Finally, our study of Capricorn Bunker Group Holocene reefs suggests that the size and shape of the antecedent substrate has a greater impact on reef evolution and final evolutionary state (mature vs. senile), than substrate depth alone. 

Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.

Deglacial mesophotic reef demise on the Great Barrier Reef

Submerged reefs are important recorders of palaeo-environments and sea-level change, and provide a substrate for modern mesophotic (deep-water, light-dependent) coral communities. Mesophotic reefs are rarely, if ever, described from the fossil record and nothing is known of their long-term record on Great Barrier Reef (GBR). Sedimentological and palaeo-ecological analyses coupled with 67 14C AMS and U–Th radiometric dates from dredged coral, algae and bryozoan specimens, recovered from depths of 45 to 130 m, reveal two distinct generations of fossil mesophotic coral community development on the submerged shelf edge reefs of the GBR. They occurred from 13 to 10 ka and 8 ka to present. We identified eleven sedimentary facies representing both autochthonous (in situ) and allochthonous (detrital) genesis, and their palaeo-environmental settings have been interpreted based on their sedimentological characteristics, biological assemblages, and the distribution of similar modern biota within the dredges. Facies on the shelf edge represent deep sedimentary environments, primarily forereef slope and open platform settings in palaeo-water depths of 45–95 m. Two coral–algal assemblages and one non-coral encruster assemblage were identified: 1) Massive and tabular corals including Porites, Montipora and faviids associated with Lithophylloids and minor Mastophoroids, 2) platy and encrusting corals including Porites, Montipora and Pachyseris associated with melobesioids and Sporolithon, and 3) Melobesiods and Sporolithon with acervulinids (foraminifera) and bryozoans. Based on their modern occurrence on the GBR and Coral Sea and modern specimens collected in dredges, these are interpreted as representing palaeo-water depths of < 60 m, < 80–100 m and > 100 m respectively. The first mesophotic generation developed at modern depths of 85–130 m from 13 to 10.2 ka and exhibit a deepening succession of < 60 to > 100 m palaeo-water depth through time. The second generation developed at depths of 45–70 m on the shelf edge from 7.8 ka to present and exhibit stable environmental conditions through time. The apparent hiatus that interrupted the mesophotic coral communities coincided with the timing of modern reef initiation on the GBR as well as a wide-spread flux of siliciclastic sediments from the shelf to the basin. For the first time we have observed the response of mesophotic reef communities to millennial scale environmental perturbations, within the context of global sea-level rise and environmental changes.

Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

Being a new generation of green solvents and high-tech reaction media of the future, ionic liquids have increasingly attracted much attention. Of particular interest in this context are room temperature ionic liquids (in short as ILs in this paper). Due to the relatively high viscosity, ILs is expected to be used in the form of solvent diluted mixture with reduced viscosity in industrial application, where predicting the viscosity of IL mixture has been an important research issue. Different IL mixture and many modelling approaches have been investigated. The objective of this study is to provide an alternative model approach using soft computing technique, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of ILs [C _n-mim][NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0-328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity taking account of IL alkyl chain length, as well as temperature and compositions simultaneously, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. This illustrates the potential application of ANN in the case that the physical and thermodynamic properties are highly non-linear or too complex. © 2012 Copyright the authors.

Artificial neural network model to predict compositional viscosity over a broad range of temperatures

The objective of this study is to provide an alternative model approach, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of room temperature ionic liquids (in short as ILs) [C _n-mim] [NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity over a wide range of temperatures and more complex viscosity compositions, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. © 2010 IEEE.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.