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1.
Int Urogynecol J ; 30(5): 683-692, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-30564874

RESUMO

INTRODUCTION: The use of lasers in urogynaecology has increased in recent years. Their use has been described in pelvic organ prolapse, urinary incontinence and genito-urinary symptoms of menopause. The aim of this study was to review the published literature on CO2 and erbium:YAG laser use in urogynaecological conditions. METHODS: An extensive search of literature databases (PubMed, EMBASE) was performed for publications (full text and abstracts) written in English up to July 2018. Relevant trials were selected and analysed by an independent reviewer. Twenty-five studies were identified in total. RESULTS: All studies were either prospective cohort or case-control studies. The results of individual studies indicate that both CO2 and erbium lasers are effective in treating urogynaecological conditions. Most studies use a vaginal approach with only two investigations of intraurethral application. CONCLUSION: The use of lasers to treat these conditions may seem appealing; however, the lack of good-quality evidence in the form of multi-centre randomised placebo-controlled trials is concerning. The safety and effectiveness of these laser devices have not been established. Use of lasers may lead to serious adverse events such as vaginal burns, scarring, dyspareunia and chronic pain. Randomised placebo-controlled trials in addition to formal evaluation of the laser devices are required before this treatment modality can be recommended.


Assuntos
Doenças dos Genitais Femininos/cirurgia , Lasers de Estado Sólido/uso terapêutico , Incontinência Urinária/cirurgia , Doenças Vaginais/cirurgia , Estudos de Casos e Controles , Feminino , Humanos , Menopausa , Estudos Prospectivos , Síndrome
2.
Proc Natl Acad Sci U S A ; 114(11): 2831-2835, 2017 03 14.
Artigo em Inglês | MEDLINE | ID: mdl-28193884

RESUMO

The prevailing view of western Atlantic hydrography during the Last Glacial Maximum (LGM) calls for transport and intermixing of deep southern and intermediate northern end members. However, δ13C and Δ14C results on foraminifera from a sediment core at 5.0 km in the northern subtropics show that there may have also been a northern source of relatively young, very dense, nutrient-depleted water during the LGM (18 ky to 21 ky ago). These results, when integrated with data from other western North Atlantic locations, indicate that the ocean was poorly ventilated at 4.2 km, with better ventilation above and below that depth. If this is a signal of water mass source and not nutrient storage, it would indicate that a previously unrecognized deep water end member originated along the western margin of the Labrador Sea, analogous to dense water formation today around Antarctica and in the Okhotsk Sea.

3.
PLoS One ; 9(9): e106524, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25191999

RESUMO

Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets.


Assuntos
Biologia Computacional/métodos , Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Análise por Conglomerados , Conjuntos de Dados como Assunto , Fusarium/genética , Triticum/genética
4.
Artif Intell Med ; 60(2): 103-12, 2014 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-24382423

RESUMO

OBJECTIVE: In this paper we present an evaluation of the role of reliability indicators in glaucoma severity prediction. In particular, we investigate whether it is possible to extract useful information from tests that would be normally discarded because they are considered unreliable. METHODS: We set up a predictive modelling framework to predict glaucoma severity from visual field (VF) tests sensitivities in different reliability scenarios. Three quality indicators were considered in this study: false positives rate, false negatives rate and fixation losses. Glaucoma severity was evaluated by considering a 3-levels version of the Advanced Glaucoma Intervention Study scoring metric. A bootstrapping and class balancing technique was designed to overcome problems related to small sample size and unbalanced classes. As a classification model we selected Naïve Bayes. We also evaluated Bayesian networks to understand the relationships between the different anatomical sectors on the VF map. RESULTS: The methods were tested on a data set of 28,778 VF tests collected at Moorfields Eye Hospital between 1986 and 2010. Applying Friedman test followed by the post hoc Tukey's honestly significant difference test, we observed that the classifiers trained on any kind of test, regardless of its reliability, showed comparable performance with respect to the classifier trained only considering totally reliable tests (p-value>0.01). Moreover, we showed that different quality indicators gave different effects on prediction results. Training classifiers using tests that exceeded the fixation losses threshold did not have a deteriorating impact on classification results (p-value>0.01). On the contrary, using only tests that fail to comply with the constraint on false negatives significantly decreased the accuracy of the results (p-value<0.01). Meaningful patterns related to glaucoma evolution were also extracted. CONCLUSIONS: Results showed that classification modelling is not negatively affected by the inclusion of less reliable tests in the training process. This means that less reliable tests do not subtract useful information from a model trained using only completely reliable data. Future work will be devoted to exploring new quantitative thresholds to ensure high quality testing and low re-test rates. This could assist doctors in tuning patient follow-up and therapeutic plans, possibly slowing down disease progression.


Assuntos
Glaucoma/fisiopatologia , Modelos Teóricos , Indicadores de Qualidade em Assistência à Saúde , Índice de Gravidade de Doença , Humanos , Campos Visuais
5.
J Biomed Inform ; 46(2): 266-74, 2013 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-23200810

RESUMO

Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies give us a 'snapshot' of this disease process over a large number of people but do not allow us to model the temporal nature of disease, thereby allowing for modelling detailed prognostic predictions. The aim of this paper is to explore an extension of the temporal bootstrap to identify intermediate stages in a disease process and sub-categories of the disease exhibiting subtly different symptoms. Our approach is compared to a strawman method and investigated in its ability to explain the dynamics of progression on biomedical data from three diseases: Glaucoma, Breast Cancer and Parkinson's disease. We focus on creating reliable time-series models from large amounts of historical cross-sectional data using the temporal bootstrap technique. Two issues are explored: how to build time-series models from cross-sectional data, and how to automatically identify different disease states along these trajectories, as well as the transitions between them. Our approach of relabeling trajectories allows us to explore the temporal nature of how diseases progress even when time-series data is not available (if the cross-sectional study is large enough). We intend to expand this research to deal with multiple studies where we can combine both cross-sectional and longitudinal datasets and to focus on the junctions of the trajectories as key stages in the progression of disease.


Assuntos
Biologia Computacional/métodos , Progressão da Doença , Modelos Biológicos , Algoritmos , Neoplasias da Mama/patologia , Análise por Conglomerados , Simulação por Computador , Estudos Transversais , Mineração de Dados , Bases de Dados Factuais , Feminino , Glaucoma/patologia , Humanos , Cadeias de Markov , Doença de Parkinson/patologia
6.
Artigo em Inglês | MEDLINE | ID: mdl-23221081

RESUMO

Controlling regions in cortical networks, which serve as key nodes to control the dynamics of networks to a desired state, can be detected by minimizing the eigenratio R and the maximum imaginary part \sigma of an extended connection matrix. Until now, optimal selection of the set of controlling regions is still an open problem and this paper represents the first attempt to include two measures of controllability into one unified framework. The detection problem of controlling regions in cortical networks is converted into a constrained optimization problem (COP), where the objective function R is minimized and \sigma is regarded as a constraint. Then, the detection of controlling regions of a weighted and directed complex network (e.g., a cortical network of a cat), is thoroughly investigated. The controlling regions of cortical networks are successfully detected by means of an improved dynamic hybrid framework (IDyHF). Our experiments verify that the proposed IDyHF outperforms two recently developed evolutionary computation methods in constrained optimization field and some traditional methods in control theory as well as graph theory. Based on the IDyHF, the controlling regions are detected in a microscopic and macroscopic way. Our results unveil the dependence of controlling regions on the number of driver nodes l and the constraint r. The controlling regions are largely selected from the regions with a large in-degree and a small out-degree. When r=+ \infty, there exists a concave shape of the mean degrees of the driver nodes, i.e., the regions with a large degree are of great importance to the control of the networks when l is small and the regions with a small degree are helpful to control the networks when l increases. When r=0, the mean degrees of the driver nodes increase as a function of l. We find that controlling \sigma is becoming more important in controlling a cortical network with increasing l. The methods and results of detecting controlling regions in this paper would promote the coordination and information consensus of various kinds of real-world complex networks including transportation networks, genetic regulatory networks, and social networks, etc.


Assuntos
Córtex Cerebral/fisiologia , Biologia Computacional/métodos , Modelos Neurológicos , Rede Nervosa/fisiologia , Algoritmos , Animais , Gatos , Classificação , Simulação por Computador
7.
Algorithms Mol Biol ; 6: 22, 2011 Sep 22.
Artigo em Inglês | MEDLINE | ID: mdl-21939531

RESUMO

BACKGROUND: Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. RESULTS: We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. CONCLUSIONS: We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.

8.
Female Pelvic Med Reconstr Surg ; 16(4): 201-3, 2010 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-22453340
9.
J Comput Biol ; 14(10): 1327-41, 2007 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-18052773

RESUMO

Ensemble clustering methods have become increasingly important to ease the task of choosing the most appropriate cluster algorithm for a particular data analysis problem. The consensus clustering (CC) algorithm is a recognized ensemble clustering method that uses an artificial intelligence technique to optimize a fitness function. We formally prove the existence of a subspace of the search space for CC, which contains all solutions of maximal fitness and suggests two greedy algorithms to search this subspace. We evaluate the algorithms on two gene expression data sets and one synthetic data set, and compare the result with the results of other ensemble clustering approaches.


Assuntos
Algoritmos , Bases de Dados Genéticas , Perfilação da Expressão Gênica/métodos , Animais , Análise por Conglomerados , Análise Multivariada , Plasmodium falciparum/genética , Saccharomyces cerevisiae/genética
10.
J Biomed Inform ; 40(6): 698-706, 2007 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-17395545

RESUMO

High-throughput technologies such as DNA microarray are in the process of revolutionizing the way modern biological research is being done. Bioinformatics tools are becoming increasingly important to assist biomedical scientists in their quest in understanding complex biological processes. Gene expression analysis has attracted a large amount of attention over the last few years mostly in the form of algorithms, exploring cluster and regulatory relationships among genes of interest, and programs that try to display the multidimensional microarray data in appropriate formats so that they make biological sense. To reduce the dimensionality of microarray data and make the corresponding analysis more biologically relevant, in this paper we propose a biologically-led approach to biochemical pathway analysis using microarray data and relevant biological knowledge. The method selects a subset of genes for each pathway that describes the behaviour of the pathway at a given experimental condition, and transforms them into pathway signatures. The metabolic pathways of Escherichia coli are used as a case study.


Assuntos
Inteligência Artificial , Bases de Dados Genéticas , Proteínas de Escherichia coli/metabolismo , Escherichia coli/metabolismo , Modelos Biológicos , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Transdução de Sinais/fisiologia , Algoritmos , Simulação por Computador , Sistemas de Gerenciamento de Base de Dados , Perfilação da Expressão Gênica/métodos , Armazenamento e Recuperação da Informação
11.
Science ; 312(5776): 1016-20, 2006 May 19.
Artigo em Inglês | MEDLINE | ID: mdl-16627698

RESUMO

Sampling an intact sequence of oceanic crust through lavas, dikes, and gabbros is necessary to advance the understanding of the formation and evolution of crust formed at mid-ocean ridges, but it has been an elusive goal of scientific ocean drilling for decades. Recent drilling in the eastern Pacific Ocean in Hole 1256D reached gabbro within seismic layer 2, 1157 meters into crust formed at a superfast spreading rate. The gabbros are the crystallized melt lenses that formed beneath a mid-ocean ridge. The depth at which gabbro was reached confirms predictions extrapolated from seismic experiments at modern mid-ocean ridges: Melt lenses occur at shallower depths at faster spreading rates. The gabbros intrude metamorphosed sheeted dikes and have compositions similar to the overlying lavas, precluding formation of the cumulate lower oceanic crust from melt lenses so far penetrated by Hole 1256D.

12.
IEEE Trans Syst Man Cybern B Cybern ; 35(6): 1156-67, 2005 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-16366242

RESUMO

Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with the k-means algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results.


Assuntos
Algoritmos , Inteligência Artificial , Análise por Conglomerados , Armazenamento e Recuperação da Informação/métodos , Reconhecimento Automatizado de Padrão/métodos , Simulação por Computador , Modelos Genéticos , Modelos Estatísticos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
13.
Evol Comput ; 13(4): 477-99, 2005.
Artigo em Inglês | MEDLINE | ID: mdl-16297280

RESUMO

There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree. We introduce a new representation for grouping genetic algorithms, the restricted growth function genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman optimisation method and a well-established statistical clustering algorithm, with encouraging results.


Assuntos
Algoritmos , Evolução Biológica , Simulação por Computador , Modelos Genéticos , Classificação , Mutação
14.
Genome Biol ; 5(11): R94, 2004.
Artigo em Inglês | MEDLINE | ID: mdl-15535870

RESUMO

Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene-expression analysis. Here we introduce consensus clustering, which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel genes regulated by NFkappaB and the unfolded protein response in certain B-cell lymphomas.


Assuntos
Sequência Consenso/fisiologia , Perfilação da Expressão Gênica/estatística & dados numéricos , Regulação da Expressão Gênica/fisiologia , Análise em Microsséries/estatística & dados numéricos , Modelos Genéticos , Análise por Conglomerados , Simulação por Computador , Perfilação da Expressão Gênica/métodos , Análise em Microsséries/métodos
15.
Artif Intell Med ; 24(1): 5-24, 2002 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-11779682

RESUMO

In bio-medical domains there are many applications involving the modelling of multivariate time series (MTS) data. One area that has been largely overlooked so far is the particular type of time series where the dataset consists of a large number of variables but with a small number of observations. In this paper, we describe the development of a novel computational method based on genetic algorithms that bypasses the size restrictions of traditional statistical MTS methods, makes no distribution assumptions, and also locates the order and associated parameters as a whole step. We apply this method to the prediction and modelling of glaucomatous visual field deterioration.


Assuntos
Algoritmos , Simulação por Computador , Predisposição Genética para Doença/genética , Glaucoma/genética , Modelos Genéticos , Modelos Estatísticos , Campos Visuais/fisiologia , Glaucoma/fisiopatologia , Humanos , Análise Multivariada , Valor Preditivo dos Testes
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