Quality assurance in the 22991 EORTC ROG trial in localized prostate cancer: dummy run and individual case review.

INTRODUCTION: EORTC trial 22991 was designed to evaluate the addition of concomitant and adjuvant short-term hormonal treatments to curative radiotherapy in terms of disease-free survival for patients with intermediate risk localized prostate cancer. In order to assess the compliance to the 3D conformal radiotherapy protocol guidelines, all participating centres were requested to participate in a dummy run procedure. An individual case review was performed for the largest recruiting centres as well. MATERIALS AND METHODS: CT-data of an eligible prostate cancer patient were sent to 30 centres including a description of the clinical case. The investigator was requested to delineate the volumes of interest and to perform treatment planning according to the protocol. Thereafter, the investigators of the 12 most actively recruiting centres were requested to provide data on five randomly selected patients for an individual case review. RESULTS: Volume delineation varied significantly between investigators. Dose constraints for organs at risk (rectum, bladder, hips) were difficult to meet. In the individual case review, no major protocol deviations were observed, but a number of dose reporting problems were documented for centres using IMRT. CONCLUSIONS: Overall, results of this quality assurance program were satisfactory. The efficacy of the combination of a dummy run procedure with an individual case review is confirmed in this study, as none of the evaluated patient files harboured a major protocol deviation. Quality assurance remains a very important tool in radiotherapy to increase the reliability of the trial results. Special attention should be given when designing quality assurance programs for more complex irradiation techniques.

A Model-to-model analysis of the repeated Prisoners' Dilemma: genetic algorithms vs. evolutionary dynamics

We study the properties of the well known Replicator Dynamics when applied to a finitely repeated version of the Prisoners' Dilemma game. We characterize the behavior of such dynamics under strongly simplifying assumptions (i.e. only 3 strategies are available) and show that the basin of attraction of defection shrinks as the number of repetitions increases. After discussing the difficulties involved in trying to relax the 'strongly simplifying assumptions' above, we approach the same model by means of simulations based on genetic algorithms. The resulting simulations describe a behavior of the system very close to the one predicted by the replicator dynamics without imposing any of the assumptions of the analytical model. Our main conclusion is that analytical and computational models are good complements for research in social sciences. Indeed, while on the one hand computational models are extremely useful to extend the scope of the analysis to complex scenar

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Fast numerical algorithms for the computation of invariant tori in Hamiltonian systems

In this paper, we develop numerical algorithms that use small requirements of storage and operations for the computation of invariant tori in Hamiltonian systems (exact symplectic maps and Hamiltonian vector fields). The algorithms are based on the parameterization method and follow closely the proof of the KAM theorem given in [LGJV05] and [FLS07]. They essentially consist in solving a functional equation satisfied by the invariant tori by using a Newton method. Using some geometric identities, it is possible to perform a Newton step using little storage and few operations. In this paper we focus on the numerical issues of the algorithms (speed, storage and stability) and we refer to the mentioned papers for the rigorous results. We show how to compute efficiently both maximal invariant tori and whiskered tori, together with the associated invariant stable and unstable manifolds of whiskered tori. Moreover, we present fast algorithms for the iteration of the quasi-periodic cocycles and the computation of the invariant bundles, which is a preliminary step for the computation of invariant whiskered tori. Since quasi-periodic cocycles appear in other contexts, this section may be of independent interest. The numerical methods presented here allow to compute in a unified way primary and secondary invariant KAM tori. Secondary tori are invariant tori which can be contracted to a periodic orbit. We present some preliminary results that ensure that the methods are indeed implementable and fast. We postpone to a future paper optimized implementations and results on the breakdown of invariant tori.

Localized, diffuse, and aggregative-adhering Escherichia coli from infants with acute diarrhea and matched-controls

Of 126 infants under 2 years, enrolled in a study on the etiology of acute diarrhea in Recife, Brazil, we selected 37 from whom no recognized enteropathogens, except classic enteropathogenic Escherichia coli, were identified. For comparison, we also examined 37 matched-control infants without diarrhea seen at the same hospital setting. This paper had the purpose to determine the prevalence of localized, diffuse, and aggregative-adhering E. coli strains in both groups. Three to five fecal E. coli colonies, of each case and control, were tested individually for adherence to HeLa cells by using the one step 3-h incubation assay. Strains of E. coli showing localized adherence were found significantly more often in patients (37.8%) than in controls (13.5%), p < 0.02, and they were pratically confined to EPEC serovars 055:H-, 0111:H2, and 119:H6. In contrast, E. coli isolates exhibiting the diffuse or aggregative patterns of adherence were restricted to non-EPEC serogroups and were more frequently encountred among controls.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Hard instances of algorithms and proof systems

"Vegeu el resum a l'inici del document del fitxer adjunt."

Approximation algorithms for two-state anti-ferromagnetic spin systems on bounded degree graphs

In a seminal paper [10], Weitz gave a deterministic fully polynomial approximation scheme for counting exponentially weighted independent sets (which is the same as approximating the partition function of the hard-core model from statistical physics) in graphs of degree at most d, up to the critical activity for the uniqueness of the Gibbs measure on the innite d-regular tree. ore recently Sly [8] (see also [1]) showed that this is optimal in the sense that if here is an FPRAS for the hard-core partition function on graphs of maximum egree d for activities larger than the critical activity on the innite d-regular ree then NP = RP. In this paper we extend Weitz's approach to derive a deterministic fully polynomial approximation scheme for the partition function of general two-state anti-ferromagnetic spin systems on graphs of maximum degree d, up to the corresponding critical point on the d-regular tree. The main ingredient of our result is a proof that for two-state anti-ferromagnetic spin systems on the d-regular tree, weak spatial mixing implies strong spatial mixing. his in turn uses a message-decay argument which extends a similar approach proposed recently for the hard-core model by Restrepo et al [7] to the case of general two-state anti-ferromagnetic spin systems.

Sensitivity of a Vacuum Aspiratory Culture Technique for Diagnosis of Localized Cutaneous Leishmaniasis in an Endemic Area of Leishmania (Viannia) braziliensis Transmission

Sixty eight patients with localized cutaneous leishmaniasis from an area with Leishmania (Viannia) braziliensis transmission had cultures performed with a modified Marzochi´s vacuum aspiratory puncture technique to establish sensitivity and contamination rate with this new method. Overall sensitivity of three aspirates was 47.1%; (CI95% 39.4; 59.4) significantly greater than the sensitivity of a single one aspirate. Fungal contamination was observed in 6/204 (2.9%) inoculated culture tubes. We recommend that this useful technique should be adopted as routine for primary isolation of L. (V.) braziliensis from localized cutaneous ulcers.

Sensitivity of Lymph Node Aspiration in Localized Cutaneous Leishmaniasis Due to Leishmania (Viannia) braziliensis

Twenty nine patients with localized cutaneous leishmaniasis had lymph node and skin ulcer aspirations for culture of Leishmania with the modified Marzochi´s vacuum aspiratory technique. Sensitivity of lymph node aspiration was 58.6% and 34.5% for skin ulcer aspiration (P=0.06). Combined sensitivity of the two methods was 79.3%. There was no agreement between methods (Kappa Index = -0.084; CI95% -0,45; 0,28) showing the potential complementary roles in diagnostic approach.

MR spectroscopy of the human brain with enhanced signal intensity at ultrashort echo times on a clinical platform at 3T and 7T.

Recently, the spin-echo full-intensity acquired localized (SPECIAL) spectroscopy technique was proposed to unite the advantages of short TEs on the order of milliseconds (ms) with full sensitivity and applied to in vivo rat brain. In the present study, SPECIAL was adapted and optimized for use on a clinical platform at 3T and 7T by combining interleaved water suppression (WS) and outer volume saturation (OVS), optimized sequence timing, and improved shimming using FASTMAP. High-quality single voxel spectra of human brain were acquired at TEs below or equal to 6 ms on a clinical 3T and 7T system for six volunteers. Narrow linewidths (6.6 +/- 0.6 Hz at 3T and 12.1 +/- 1.0 Hz at 7T for water) and the high signal-to-noise ratio (SNR) of the artifact-free spectra enabled the quantification of a neurochemical profile consisting of 18 metabolites with Cramér-Rao lower bounds (CRLBs) below 20% at both field strengths. The enhanced sensitivity and increased spectral resolution at 7T compared to 3T allowed a two-fold reduction in scan time, an increased precision of quantification for 12 metabolites, and the additional quantification of lactate with CRLB below 20%. Improved sensitivity at 7T was also demonstrated by a 1.7-fold increase in average SNR (= peak height/root mean square [RMS]-of-noise) per unit-time.