Impact of geometric variations on delivered dose in highly focused single vocal cord IMRT

Purpose. To investigate the robustness of single vocal cord intensity modulated radiation therapy (IMRT) treatment plans for set-up errors, respiration, and deformation. Material and methods. Four-dimensional computed tomography (4D-CT) scans of 10 early glottic carcinoma patients, previously treated with conventional techniques, were used in this simulation study. For each patient a pre-treatment 4D-CT was used for IMRT planning, generating a reference dose distribution. Prescribed PTV dose was 66 Gy. The impact of systematic set-up errors was simulated by applying shifts of ± 2 mm to the planning CT scans, followed by dose re-calculation with original beam segments, MUs, etc. Effects of respiration and deformation were determined utilizing extreme inhale and exhale CT scans, and repeat scans acquired after 22 Gy, 44 Gy, and 66 Gy, respectively. All doses were calculated using Monte Carlo dose simulations. Results. Considering all investigated geometrical perturbations, reductions in the clinical target volume (CTV) V95%, D98%, D2%, and generalized equivalent uniform dose (gEUD) were limited to 1.2 ± 2.2%, 2.4 ± 2.9%, 0.2 ± 1.8%, and 0.6 ± 1.1 Gy, respectively. The near minimum dose, D98%, was always higher than 89%, and gEUD always remained higher than 66 Gy. Planned contra-lateral (CL) vocal cord DMean, gEUD, and V40 Gy were 38.2 ± 6.0 Gy, 43.4 ± 5.6 Gy, and 42.7 ± 14.9%. With perturbations these values changed by -0.1 ± 4.3 Gy, 0.1 ± 4.0 Gy, and -1.0 ± 9.6%, respectively. Conclusions. On average, CTV dose reductions due to geometrical perturbations were very low, and sparing of the CL vocal cord was maintained. In a few observations (6 of 103 simulated situations), the near-minimum CTV-dose was around 90%, requiring attention in deciding on a future clinical protocol. 

Single vocal cord irradiation:a competitive treatment strategy in early glottic cancer

INTRODUCTION: The treatment of choice for early glottic cancer is still being debated; ultimately it relies on the functional outcome. This paper reports on a novel sparing 4D conformal technique for single vocal cord irradiation (SVCI).

MATERIAL AND METHODS: The records of 164 T1a patients with SCC of the vocal cord, irradiated in the Erasmus MC between 2000 and 2008, were analyzed for local control and overall survival. The quality of life was determined by EORTC H&N35 questionnaires. Also the VHI (voice handicap index), and the TSH (thyroid stimulating hormone) blood levels, were established. On-line image guided SVCI, using cone beam CT or stereotactic radiation therapy (SRT) techniques, were developed.

RESULTS: A LC rate at five-years of 93% and a VHI of 12.7 (0-63) was determined. It appeared feasible to irradiate one vocal cord within 1-2mm accuracy. This way sparing of the contralateral (CL) vocal cord and CL normal tissues, could be achieved.

CONCLUSIONS: Given the accuracy (1-2mm) and small volume disease (CTV limited to one vocal cord), for the use of stereotactic RT techniques SVCI with large fraction sizes is currently being investigated in clinic. It is argued that hypofractionated SVCI can be a competitive alternative to laser surgery.

On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables

Influence diagrams are intuitive and concise representations of structured decision problems. When the problem is non-Markovian, an optimal strategy can be exponentially large in the size of the diagram. We can avoid the inherent intractability by constraining the size of admissible strategies, giving rise to limited memory influence diagrams. A valuable question is then how small do strategies need to be to enable efficient optimal planning. Arguably, the smallest strategies one can conceive simply prescribe an action for each time step, without considering past decisions or observations. Previous work has shown that finding such optimal strategies even for polytree-shaped diagrams with ternary variables and a single value node is NP-hard, but the case of binary variables was left open. In this paper we address such a case, by first noting that optimal strategies can be obtained in polynomial time for polytree-shaped diagrams with binary variables and a single value node. We then show that the same problem is NP-hard if the diagram has multiple value nodes. These two results close the fixed-parameter complexity analysis of optimal strategy selection in influence diagrams parametrized by the shape of the diagram, the number of value nodes and the maximum variable cardinality.

Solving Decision Problems with Limited Information

We present a new algorithm for exactly solving decision-making problems represented as an influence diagram. We do not require the usual assumptions of no forgetting and regularity, which allows us to solve problems with limited information. The algorithm, which implements a sophisticated variable elimination procedure, is empirically shown to outperform a state-of-the-art algorithm in randomly generated problems of up to 150 variables and 10^64 strategies.

Solving Limited Memory Influence Diagrams

We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 10^64 solutions. We show that these problems are NP-hard even if the underlying graph structure of the problem has low treewidth and the variables take on a bounded number of states, and that they admit no provably good approximation if variables can take on an arbitrary number of states.

Solving Limited Memory Influence Diagrams

We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 10^64 solutions. We show that the problem is NP-hard even if the underlying graph structure of the problem has small treewidth and the variables take on a bounded number of states, but that a fully polynomial time approximation scheme exists for these cases. Moreover, we show that the bound on the number of states is a necessary condition for any efficient approximation scheme.

Some current problems in perovskite nano-ferroelectrics and multiferroics: kinetically-limited systems of finite lateral size

We describe some unsolved problems of current interest; these involve quantum critical points in
ferroelectrics and problems which are not amenable to the usual density functional theory, nor to
classical Landau free energy approaches (they are kinetically limited), nor even to the Landau–
Kittel relationship for domain size (they do not satisfy the assumption of infinite lateral diameter)
because they are dominated by finite aperiodic boundary conditions.

Robust bimodal person identification using face and speech with limited training data and corruption of both modalities

This paper presents a novel method of audio-visual fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there is a limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new representation and a modified cosine similarity are introduced for combining and comparing bimodal features with limited training data as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal data set created from the SPIDRE and AR databases with variable noise corruption of speech and occlusion in the face images. The new method has demonstrated improved recognition accuracy.

Forward and backward least angle regression for nonlinear system identification

A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR algorithm. This is achieved by introducing a replacement scheme using an additional backward LAR stage. The backward stage replaces insignificant model terms selected by forward LAR with more significant ones, leading to an improved model in terms of the model compactness and performance. A numerical example to construct four types of NARX models, namely polynomials, radial basis function (RBF) networks, neuro fuzzy and wavelet networks, is presented to illustrate the effectiveness of the proposed technique in comparison with some popular methods.

The paradox of invasion: Reeves’ muntjac deer invade the British Isles from a limited number of founding females

High levels of genetic diversity and high propagule pressure are favoured by conservation biologists as the basis for successful reintroductions and ensuring the persistence of populations. However, invasion ecologists recognize the ‘paradox of invasion’, as successful species introductions may often be characterized by limited numbers of individuals and associated genetic bottlenecks. In the present study, we used a combination of high-resolution nuclear and mitochondrial genetic markers to investigate the invasion history of Reeves' muntjac deer in the British Isles. This invasion has caused severe economic and ecological damage, with secondary spread currently a concern throughout Europe and potentially globally. Microsatellite analysis based on eight loci grouped all 176 introduced individuals studied from across the species' range in the UK into one genetic cluster, and seven mitochondrial D-loop haplotypes were recovered, two of which were present at very low frequency and were related to more common haplotypes. Our results indicate that the entire invasion can be traced to a single founding event involving a low number of females. These findings highlight the fact that even small releases of species may, if ignored, result in irreversible and costly invasion, regardless of initial genetic diversity or continual genetic influx.

On Variable Selection of a Nonlinear Non-parametric System with a Limited Data Set: A Stepwise Algorithm

his paper considers a problem of identification for a high dimensional nonlinear non-parametric system when only a limited data set is available. The algorithms are proposed for this purpose which exploit the relationship between the input variables and the output and further the inter-dependence of input variables so that the importance of the input variables can be established. A key to these algorithms is the non-parametric two stage input selection algorithm.

Scene From a Different Angle: Attempts to Internationalize Northern Ireland’s Conflict Experience

Reviews the books, Lessons From the Northern Ireland Peace Process edited by Timothy J. White (2013) and Human Rights as War by Other Means by Jennifer Curtis (2014). Edited by a U.S.-based academic with an enduring interest in Ireland, the first book draws together an interdisciplinary group of academics from across North America and the U.K. (though notably not Northern Ireland itself) to cover such topics as third party intervention, nationalism, grassroots change, and community development. The second text to be reviewed may be seen as a thorough analysis of this particular point: what is the role played by human rights in Northern Ireland’s peace process?

Geotechnical monitoring using a combination of LIDAR, real-time dGPS, and electrical tomography to assess geotechnical risk of a coastal landslide.

The Antrim Coast Road stretching from the seaport of Larne in the East of Northern Ireland has a well-deserved reputation for being one of the most spectacular roads in Europe (Day, 2006). However the problematic geology; Jurassic Lias Clay and Triassic Mudstone overlain by Cretaceous Limestone and Tertiary Basalt, and environmental variables result in frequent instances of slope instability manifested in both shallow debris flows and occasional massive rotational movements, creating a geotechnical risk to this highway. This paper describes how a variety of techniques are being used to both assess instability and monitor movement of these active slopes near one site at Straidkilly Point, Glenarm. An in-depth understanding of the geology was obtained via boreholes, resistivity surveys and laboratory testing. Environmental variables recorded by an on-site weather station were correlated with measured pore water pressure and soil moisture infiltration data. Terrestrial LiDAR (TLS), with surveys carried out on a bi-monthly basis allowed for the generation of Digital Elevation Models (DEMs) of difference, highlighting areas of recent movement, accumulation and depletion. Morphology parameters were generated from the DEMs and include slope, curvature and multiple measures of roughness. Changes in the structure of the slope coupled with morphological parameters were characterised and linked to progressive failures from the temporal monitoring. In addition to TLS monitoring, Aerial LiDAR datasets were used for the spatio-morphological characterisation of the slope on a macro scale. A Differential Global Positioning System (dGPS) was also deployed on site to provide a real-time warning system for gross movements, which were also correlated with environmental conditions. Frequent electrical resistivity tomography (ERT) surveys were also implemented to provide a better understanding of long-term changes in soil moisture and help to define the complex geology. The paper describes how the data obtained via a diverse range of methods has been combined to facilitate a more informed management regime of geotechnical risk by the Northern Ireland Roads Service.

Wide-Area Phase-Angle Measurements for Islanding Detection—An Adaptive Nonlinear Approach

The integration of an ever growing proportion of large scale distributed renewable generation has increased the probability of maloperation of the traditional RoCoF and vector shift relays. With reduced inertia due to non-synchronous penetration in a power grid, system wide disturbances have forced the utility industry to design advanced protection schemes to prevent system degradation and avoid cascading outages leading to widespread blackouts. This paper explores a novel adaptive nonlinear approach applied to islanding detection, based on wide area phase angle measurements. This is challenging, since the voltage phase angles from different locations exhibit not only strong nonlinear but also time-varying characteristics. The adaptive nonlinear technique, called moving window kernel principal component analysis is proposed to model the time-varying and nonlinear trends in the voltage phase angle data. The effectiveness of the technique is exemplified using both DigSilent simulated cases and real test cases recorded from the Great Britain and Ireland power systems by the OpenPMU project.