1,720,988 research outputs found
A class-based search for the in-core fuel management optimization of a pressurized water reactor
No full textThe In-Core Fuel Management Optimization (ICFMO) is a prominent problem in nuclear engineering, with high complexity and studied for more than 40 years. Besides manual optimization and knowledge-based methods, optimization metaheuristics such as Genetic Algorithms, Ant Colony Optimization and Particle Swarm Optimization have yielded outstanding results for the ICFMO. In the present article, the Class-Based Search (CBS) is presented for application to the ICFMO. It is a novel metaheuristic approach that performs the search based on the main nuclear characteristics of the fuel assemblies, such as reactivity. The CBS is then compared to the one of the state-of-art algorithms applied to the ICFMO, the Particle Swarm Optimization. Experiments were performed for the optimization of Angra 1 Nuclear Power Plant, located at the Southeast of Brazil. The CBS presented noticeable performance, providing Loading Patterns that yield a higher average of Effective Full Power Days in the simulation of Angra 1 NPP operation, according to our methodology
Introduction to the special issue on statistical and computational methods for genomics and integrative genomics
No full textBayesian network data imputation with application to survival tree analysis
No full textRetrospective 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)
Discovering Subgroups of Patients from DNA Copy Number Data Using NMF on Compacted Matrices
No full textIn the study of complex genetic diseases, the identification of subgroups of patients sharing similar genetic characteristics represents a challenging task, for example, to improve treatment decision. One type of genetic lesion, frequently investigated in such disorders, is the change of the DNA copy number (CN) at specific genomic traits. Non-negative Matrix Factorization (NMF) is a standard technique to reduce the dimensionality of a data set and to cluster data samples, while keeping its most relevant information in meaningful components. Thus, it can be used to discover subgroups of patients from CN profiles. It is however computationally impractical for very high dimensional data, such as CN microarray data. Deciding the most suitable number of subgroups is also a challenging problem. The aim of this work is to derive a procedure to compact high dimensional data, in order to improve NMF applicability without compromising the quality of the clustering. This is particularly important for analyzing high-resolution microarray data. Many commonly used quality measures, as well as our own measures, are employed to decide the number of subgroups and to assess the quality of the results. Our measures are based on the idea of identifying robust subgroups, inspired by biologically/clinically relevance instead of simply aiming at well-separated clusters. We evaluate our procedure using four real independent data sets. In these data sets, our method was able to find accurate subgroups with individual molecular and clinical features and outperformed the standard NMF in terms of accuracy in the factorization fitness function. Hence, it can be useful for the discovery of subgroups of patients with similar CN profiles in the study of heterogeneous diseases
Assessment of neural networks training strategies for histomorphometric analysis of synchrotron radiation medical images RID C-4349-2011 RID B-9316-2011
No full textMicro-computed tomography (mu CT) obtained by synchrotron radiation (SR) enables magnified images with a high space resolution that might be used as a non-invasive and non-destructive technique for the quantitative analysis of medical images, in particular the histomorphometry (HMM) of bony mass. In the preprocessing of such images, conventional operations such as binarization and morphological filtering are used before calculating the stereological parameters related, for example, to the trabecular bone microarchitecture. However, there is no standardization of methods for HMM based on mu CT images, especially the ones obtained with SR X-ray. Notwithstanding the several uses of artificial neural networks (ANNs) in medical imaging, their application to the HMM of SR-mu CT medical images is still incipient, despite the potential of both techniques. The contribution of this paper is the assessment and comparison of well-known training algorithms as well as the proposal of training strategies (combinations of training algorithms, sub-image kernel and symmetry information) for feed-forward ANNs in the task of bone pixels recognition in SR-mu CT medical images. For a quantitative comparison, the results of a cross validation and a statistical analysis of the results for 36 training strategies are presented. The ANNs demonstrated both very low mean square errors in the validation, and good quality segmentation of the image of interest for application to HMM in SR-mu CT medical images
National validation and proposed revision of REM sleep behavior disorder screening questionnaire (RBDSQ)
No full textWe validated the Italian version of the rapid eye movement sleep behavior disorder (RBD) screening questionnaire (RBDSQ) and calculated its cut-off value for discriminating RBD group from other sleep disorders and healthy controls (HC). 380 patients with sleep disorders and 101 HC were enrolled. RBDSQ achieved an acceptable Cronbach’s α value of 0.787 and item 10 was the only one with a very low item-total biserial correlation (0.141). At ROC analysis, we obtained an AUC of 0.888, denoting a good performance of the RBDSQ total score for predicting the RBD status. The optimal cut-off value was 8 and it achieved good values of both sensitivity and specificity (0.842 and 0.780, respectively). Due to the poor performance of item 10 in our sample, we analyzed the RBDSQ without this item (called “revised RBDSQ”). We obtained a good Cronbach’s α of 0.802. When evaluating the performance of the revised score in predicting the RBD status, we obtained an increased value of AUC (0.899). The optimal cut-off value was still 8 (sensitivity = 0.829; specificity = 0.820). The Italian version of RBDSQ is a sensitive tool for the identification of RBD patients. An improvement of the instrument could be obtained by removing item 10 and define a higher cut-off value of 8. The “revised RBDSQ” represents a reliable screening questionnaire for primary care physicians and neurologists and its employment may facilitate the choice of subjects that should undergo a PSG that confirms the diagnosis of RBD, thus avoiding polysomnographic exams when not needed
Bayesian DNA copy number analysis
Full text linkAbstract Background Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents. Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments. Results We derived an improved version of the BPCR (called mBPCR) and BRC, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of BPCR and BRC with other regression methods, showing that our improved version of BPCR generally outperformed all the others. Similar results were also observed on real data. Conclusion We propose an improved method for DNA copy number estimation, mBPCR, which performed very well compared to previously published algorithms. In particular, mBPCR was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples.</p
Integrating Dimensional and Discrete Theories of Emotions: A New Set of Anger- and Fear-Eliciting Stimuli for Children
No full textThe selection of appropriate stimuli for inducing specific emotional states has become one of the most challenging topics in psychological research. In the literature there is a lack of affective picture database specifically suited to investigate emotional response in children. Here the authors present the methodology that led us to create a new database (called Anger- and Fear-Eliciting Stimuli for Children) of affective stimuli inducing experiences of 3 target emotions (neutral, anger, and fear) to use in experimental session involving children. A total of 84 children were asked to (a) indicate the perceived emotion and its intensity and (b) rate the three affective dimensions of the Self-Assessment Manikin (SAM). Based on concordance between labeled and expected target emotion, the authors decided to select 15 stimuli to be included in Multivariate modeling techniques were applied to evaluate the association between expected target emotion and SAM ratings. The authors found that the hit rate for the neutral pictures was good (greater than 81%), for fear-eliciting pictures it was greater than 64%, and for anger-eliciting pictures it was moderate (between 45% and 56%). The study results reveal also an age effect only in the arousal scale. However, the authors did not find significant gender-related differences in SAM ratings
Incidence, risk factors and outcome of histological transformation in follicular lymphoma
No full textHistological transformation (HT) into diffuse large B-cell lymphoma (DLBCL) was documented in 37 of the 281 (13%; 95% CI, 9-18) follicular lymphoma (FL) patients treated at our institute from 1979 to 2007. HT occurred at a median of 2·75 years from initial FL diagnosis and HT rate was 15% at 10 years and 26% at 14 years, with a plateau from that point onward. Patients with bulky or extranodal disease, or those diagnosed before 1990 had a significantly higher risk of HT. When initial treatment strategies were taken into account, a reduced HT risk was seen in the patients initially managed with a 'watch and wait' policy, while the risk appeared significantly increased in the small subset of 18 patients initially managed with rituximab plus chemotherapy (P = 0·0005). HT was associated with a significantly shorter cause-specific survival (P = 0·0002). Predictors of survival after HT were the Follicular Lymphoma International Prognostic Index at diagnosis, as well as age and performance status at the time of HT. Our data confirm the adverse clinical outcome of FL after HT. In keeping with previous isolated reports, our findings suggest that there is a subgroup of patients in whom HT may not occur
Quality of life and chemotherapy: Predictive factors in a sample of gynaecological cancer patients
No full textDiagnosis and treatment of gynaecological cancer still entail significant impairment in quality of life. The present study aims at monitoring it during chemotherapy treatment and at identifying variables significantly associated with it. Methods. 87 patients who attended the San Raffaele Hospital completed the EORTC QLQ-C30 and the Multidimensional Scale of Perceived Social Support before their first and third chemotherapy infusion. A self-report questionnaire was created in order to collect socio-demographic and medical information. Results. Social and emotional functioning and global quality of life show a significant improvement between the first and third chemotherapy infusion; age and perceived social support appear significantly associated with emotional functioning. A chemotherapy regimen with medium or high emetogenic potential and having a full-time job predict an improvement over time in global quality of life and role functioning, respectively. Discussion. These variables should be taken into consideration in patients care, in order to detect potential difficulties and promote a better adjustment to the disease and its treatment
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