Cervical cancer precursors and hormonal contraceptive use in HIV-positive women: application of a causal model and semi-parametric estimation methods.

OBJECTIVE: To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. BACKGROUND: Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. METHODS: We developed a causal model of the factors related to combined oral contraceptive (COC) use and cervical intraepithelial neoplasia 2 or greater (CIN2+) and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. RESULTS: We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7%) were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9%) increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. CONCLUSION: Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an increased prevalence of CIN2+ associated with COC, evidence is insufficient to conclude causality. Priority areas for future studies to better satisfy causal criteria are identified.

Assessing the suitability of the minimum capture size and protection regimes in the gooseneck barnacle shellfishery

The suitability of a total-length-based, minimum capture-size and different protection regimes was investigated for the gooseneck barnacle Pollicipes pollicipes shellfishery in N Spain. For this analysis, individuals that were collected from 10 sites under different fishery protection regimes (permanently open, seasonally closed, and permanently closed) were used. First, we applied a non-parametric regression model to explore the relationship between the capitulum Rostro-Tergum (RT) size and the Total Length (TL). Important heteroskedastic disturbances were detected for this relationship, demon- strating a high variability of TL with respect to RT. This result substantiates the unsuitability of a TL-based minimum size by means of a mathematical model. Due to these disturbances, an alternative growth- based minimum capture size of 26.3 mm RT (23 mm RC) was estimated using the first derivative of a Kernel-based non-parametric regression model for the relationship between RT and dry weight. For this purpose, data from the permanently protected area were used to avoid bias due to the fishery. Second, the size-frequency distribution similarity was computed using a MDS analysis for the studied sites to evaluate the effectiveness of the protection regimes. The results of this analysis indicated a positive effect of the permanent protection, while the effect of the seasonal closure was not detected. This result needs to be interpreted with caution because the current harvesting based on a potentially unsuitable mini- mum capture size may dampen the efficacy of the seasonal protection regime.

Accuracy vs. simplicity: a complex trade-off

Inductive learning aims at finding general rules that hold true in a database. Targeted learning seeks rules for the predictions of the value of a variable based on the values of others, as in the case of linear or non-parametric regression analysis. Non-targeted learning finds regularities without a specific prediction goal. We model the product of non-targeted learning as rules that state that a certain phenomenon never happens, or that certain conditions necessitate another. For all types of rules, there is a trade-off between the rule's accuracy and its simplicity. Thus rule selection can be viewed as a choice problem, among pairs of degree of accuracy and degree of complexity. However, one cannot in general tell what is the feasible set in the accuracy-complexity space. Formally, we show that finding out whether a point belongs to this set is computationally hard. In particular, in the context of linear regression, finding a small set of variables that obtain a certain value of R2 is computationally hard. Computational complexity may explain why a person is not always aware of rules that, if asked, she would find valid. This, in turn, may explain why one can change other people's minds (opinions, beliefs) without providing new information.

Verenpaineen mittaamisen opettaminen sairaanhoitaja- ja terveydenhoitajaopiskelijoille

Teaching the measurement of blood pressure for both nursing and public health nursing students The purpose of this two-phase study was to develop the teaching of blood pressure measurement within the nursing degree programmes of the Universities of Applied Sciences. The first survey phase described what and how blood pressure measurement was taught within nursing degree programmes. The second intervention phase (2004-2005) evaluated first academic year nursing and public health nursing students’ knowledge and skills results for blood pressure measurement. Additionally, the effect on the Taitoviikko experimental group students’ blood pressure measurement knowledge and skills level. A further objective was to construct models for an instrument (RRmittTest) to evaluate nursing students measurement of blood pressure (2003-2009). The research data for the survey phase were collected from teachers (total sampling, N=107, response rate 77%) using a specially developed RRmittopetus-questionnaire. Quasi-experimental study data on the RRmittTest-instrument was collected from students (purposive sampling, experimental group, n=29, control group, n=44). The RRmittTest consisted of a test of knowledge (Tietotesti) and simulation-based test (TaitoSimkäsi and Taitovideo) of skills. Measurements were made immediately after the teaching and in clinical practice. Statistical methods were used to analyse the results and responses to open-ended questions were organised and classified. Due to the small amount of materials involved and the results of distribution tests of the variables, non-parametric analytic methods were mainly used. Experimental group and control group similar knowledge and skills teaching was based on the results of the national survey phase (RRmittopetus) questionnaire results. Experimental group teaching includes the supervised Taitoviikko teaching method. During Taitoviikko students studied blood pressure measurement at the municipal hospital in a real nursing environment, guided by a teacher and a clinical nursing professional. In order to evaluate both learning and teaching the processes and components of blood pressure measurement were clearly defined as follows: the reliability of measurement instruments, activities preceding blood pressure measurement, technical execution of the measurement, recording, lifestyle guidance and measurement at home (self-monitoring). According to the survey study, blood pressure measurement is most often taught at Universities of Applied Sciences, separately, as knowledge (teaching of theory, 2 hours) and skills (classroom practice, 4 hours). The teaching was implemented largely in a classroom and was based mainly on a textbook. In the intervention phase the students had good knowledge of blood pressure measurement. However, their blood pressure measurement skills were deficient and the control group students, in particular, were highly deficient. Following in clinical practice the experimental group and control group students’ blood pressure measurement recording knowledge improve and experimental groups declined lifestyle guidance. Skills did not improve within any of the components analysed. The control groups` skills on the whole, declined statistically.There was a significant decline amongst the experimental group although only in one component measured. The results describe the learning results for first academic year students and no parallel conclusions should be drawn when considering any learning results for graduating students. The results support the use and further development of the Taitoviiko teaching method. The RRmittTest developed for the study should be assessed and the results seen from a negative perspective. This evaluation tool needs to be developed and retested.

Correlacion entre lactato arterial y venoso central en niños con sepsis en estado critico

Este estudio fue conducido para evaluar la correlación entre lactato arterial y venoso central en niños con sepsis y choque séptico de una unidad de cuidado intensivo pediátrico. Se incluyeron 42 pacientes con edades comprendidas entre 1 mes y 17 años 364 días con diagnóstico de sepsis y choque séptico que ingresaron a la Unidad de Cuidado Intensivo en un hospital universitario de referencia. Se registró el valor del lactato obtenido de una muestra de sangre arterial y de sangre venosa central tomadas simultáneamente y dentro de las primeras 24 horas del ingreso a la unidad. Por medio de la prueba de Rho de Spearman se encontró una correlación de 0,872 (p<0,001) y se ajustó al uso de medicamentos, vasoactivos, edad y peso (modelo de regresión no paramétrico quantílico), manteniéndose una correlación fuerte y significativa.

Avaliação estatística de métodos de poda aplicados em neurônios intermediários da rede neural MLP

There are several papers on pruning methods in the artificial neural networks area. However, with rare exceptions, none of them presents an appropriate statistical evaluation of such methods. In this article, we proved statistically the ability of some methods to reduce the number of neurons of the hidden layer of a multilayer perceptron neural network (MLP), and to maintain the same landing of classification error of the initial net. They are evaluated seven pruning methods. The experimental investigation was accomplished on five groups of generated data and in two groups of real data. Three variables were accompanied in the study: apparent classification error rate in the test group (REA); number of hidden neurons, obtained after the application of the pruning method; and number of training/retraining epochs, to evaluate the computational effort. The non-parametric Friedman's test was used to do the statistical analysis.

Adesão ao tratamento da malária: um estudo em comunidades do entorno da Usina Hidrelétrica de Tucuruí - Pará

Malária, doença infecciosa causada pelo protozoário Plasmodium, transmitida ao homem pela picada de mosquito fêmea do gênero Anopheles, atualmente põe em risco 40% da população mundial. No Brasil, ocorre sobretudo na Região Amazônica, onde estão concentrados 99,7% dos casos. Nas comunidades localizadas no entorno do lago de Tucuruí, a ocorrência de malária é elevada e os moradores não contam com serviços eficientes que proporcionem profilaxia e terapia adequados. Esta pesquisa teve a finalidade de analisar a ocorrência de comportamentos de adesão ao tratamento medicamentoso e de prevenção da malária em indivíduos residentes em comunidades do entorno da Usina Hidrelétrica de Tucuruí, Estado do Pará, por meio da comparação de três condições de intervenção: Rotina (n=10), Monitoramento (n=9) e Informação com monitoramento (n=10). Para avaliar quantitativamente os efeitos da intervenção, os comportamentos adotados nas três condições foram comparados por testes não-paramétricos (Qui-Quadrado e teste Binomial). A adesão ao tratamento nas condições Rotina e Monitoramento foi inexpressiva, enquanto, na condição Informação com monitoramento após a intervenção, 80% dos participantes apresentaram relatos de adesão ao tratamento significativamente superior, evidenciando eficácia da intervenção. Quanto ao conhecimento da malária na condição Informação com monitoramento, a intervenção promoveu aumento no nível de conhecimento dos participantes sobre a malária. A análise da mudança no repertório comportamental foi realizada em treze itens. Foram alcançados resultados mais expressivos na condição Informação com monitoramento; em nove itens foi observada mudança significativa de atitude dos participantes. A comparação entre as condições Monitoramento e Informação com monitoramento apresentou diferença significativa em oito itens: usar mosquiteiro, notificar o agente de saúde, manter cortadas ou podadas as árvores, não tomar banho no rio em horários de risco, usar roupa adequada para adentrar à mata, usar roupa adequada para pescar, não ficar no relento e usar repelentes como andiroba ou similares. Em síntese, conclui-se que a intervenção Informação com monitoramento foi eficaz para melhorar a adesão ao tratamento da malária e o nível de conhecimento sobre a doença.

Geospatial analysis of western juniper (Juniperus occidentalis) encroachment utilizing remotely sensed data

Utilizing remote sensing methods to assess landscape-scale ecological change are rapidly becoming a dominant force in the natural sciences. Powerful and robust non-parametric statistical methods are also actively being developed to compliment the unique characteristics of remotely sensed data. The focus of this research is to utilize these powerful, robust remote sensing and statistical approaches to shed light on woody plant encroachment into native grasslands--a troubling ecological phenomenon occurring throughout the world. Specifically, this research investigates western juniper encroachment within the sage-steppe ecosystem of the western USA. Western juniper trees are native to the intermountain west and are ecologically important by means of providing structural diversity and habitat for many species. However, after nearly 150 years of post-European settlement changes to this threatened ecosystem, natural ecological processes such as fire regimes no longer limit the range of western juniper to rocky refugia and other areas protected from short fire return intervals that are historically common to the region. Consequently, sage-steppe communities with high juniper densities exhibit negative impacts, such as reduced structural diversity, degraded wildlife habitat and ultimately the loss of biodiversity. Much of today's sage-steppe ecosystem is transitioning to juniper woodlands. Additionally, the majority of western juniper woodlands have not reached their full potential in both range and density. The first section of this research investigates the biophysical drivers responsible for juniper expansion patterns observed in the sage-steppe ecosystem. The second section is a comprehensive accuracy assessment of classification methods used to identify juniper tree cover from multispectral 1 m spatial resolution aerial imagery.

Health monitoring of a new hysteretic damper subjected to earthquakes on a shaking table

This paper presents the experimental results obtained by applying frequency-domain structural health monitoring techniques to assess the damage suffered on a special type of damper called Web Plastifying Damper (WPD). The WPD is a hysteretic type energy dissipator recently developed for the passive control of structures subjected to earthquakes. It consists of several I-section steel segments connected in parallel. The energy is dissipated through plastic deformations of the web of the I-sections, which constitute the dissipative parts of the damper. WPDs were subjected to successive histories of dynamically-imposed cyclic deformations of increasing magnitude with the shaking table of the University of Granada. To assess the damage to the web of the I-section steel segments after each history of loading, a new damage index called Area Index of Damage (AID) was obtained from simple vibration tests. The vibration signals were acquired by means of piezoelectric sensors attached on the I-sections, and non-parametric statistical methods were applied to calculate AID in terms of changes in frequency response functions. The damage index AID was correlated with another energy-based damage index-ID- which past research has proven to accurately characterize the level of mechanical damage. The ID is rooted in the decomposition of the load-displacement curve experienced by the damper into the so-called skeleton and Bauschinger parts. ID predicts the level of damage and the proximity to failure of the damper accurately, but it requires costly instrumentation. The experiments reported in this paper demonstrate a good correlation between AID and ID in a realistic seismic loading scenario consisting of dynamically applied arbitrary cyclic loads. Based on this correlation, it is possible to estimate ID indirectly from the AID, which calls for much simpler and less expensive instrumentation.

Spatial and temporal variability in somatic growth of green sea turtles (Chelonia mydas) resident in the Hawaiian Archipelago

The somatic growth dynamics of green turtles ( Chelonia mydas) resident in five separate foraging grounds within the Hawaiian Archipelago were assessed using a robust non-parametric regression modelling approach. The foraging grounds range from coral reef habitats at the north-western end of the archipelago, to coastal habitats around the main islands at the southeastern end of the archipelago. Pelagic juveniles recruit to these neritic foraging grounds from ca. 35 cm SCL or 5 kg ( similar to 6 years of age), but grow at foraging-ground-specific rates, which results in quite different size- and age-specific growth rate functions. Growth rates were estimated for the five populations as change in straight carapace length ( cm SCL year) 1) and, for two of the populations, also as change in body mass ( kg year) 1). Expected growth rates varied from ca. 0 - 2.5 cm SCL year) 1, depending on the foraging-ground population, which is indicative of slow growth and decades to sexual maturity, since expected size of first-time nesters is greater than or equal to 80 cm SCL. The expected size- specific growth rate functions for four populations sampled in the southeastern archipelago displayed a non-monotonic function, with an immature growth spurt at ca. 50 - 53 cm SCL ( similar to 18 - 23 kg) or ca. 13 - 19 years of age. The growth spurt for the Midway atoll population in the northwestern archipelago occurs at a much larger size ( ca. 65 cm SCL or 36 kg), because of slower immature growth rates that might be due to a limited food stock and cooler sea surface temperature. Expected age-at-maturity was estimated to be ca. 35 - 40 years for the four populations sampled at the south-eastern end of the archipelago, but it might well be > 50 years for the Midway population. The Hawaiian stock comprises mainly the same mtDNA haplotype, with no differences in mtDNA stock composition between foraging-ground populations, so that the geographic variability in somatic growth rates within the archipelago is more likely due to local environmental factors rather than genetic factors. Significant temporal variability was also evident, with expected growth rates declining over the last 10 - 20 years, while green turtle abundance within the archipelago has increased significantly since the mid-1970s. This inverse relationship between somatic growth rates and population abundance suggests a density-dependent effect on somatic growth dynamics that has also been reported recently for a Caribbean green turtle stock. The Hawaiian green turtle stock is characterised by slow growth rates displaying significant spatial and temporal variation and an immature growth spurt. This is consistent with similar findings for a Great Barrier Reef green turtle stock that also comprises many foraging-ground populations spanning a wide geographic range.

Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity

The standard reference clinical score quantifying average Parkinson's disease (PD) symptom severity is the Unified Parkinson's Disease Rating Scale (UPDRS). At present, UPDRS is determined by the subjective clinical evaluation of the patient's ability to adequately cope with a range of tasks. In this study, we extend recent findings that UPDRS can be objectively assessed to clinically useful accuracy using simple, self-administered speech tests, without requiring the patient's physical presence in the clinic. We apply a wide range of known speech signal processing algorithms to a large database (approx. 6000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial) and propose a number of novel, nonlinear signal processing algorithms which reveal pathological characteristics in PD more accurately than existing approaches. Robust feature selection algorithms select the optimal subset of these algorithms, which is fed into non-parametric regression and classification algorithms, mapping the signal processing algorithm outputs to UPDRS. We demonstrate rapid, accurate replication of the UPDRS assessment with clinically useful accuracy (about 2 UPDRS points difference from the clinicians' estimates, p < 0.001). This study supports the viability of frequent, remote, cost-effective, objective, accurate UPDRS telemonitoring based on self-administered speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments.

Density forecasts of crude-oil prices using option-implied and ARCH-type models

The predictive accuracy of competing crude-oil price forecast densities is investigated for the 1994–2006 period. Moving beyond standard ARCH type models that rely exclusively on past returns, we examine the benefits of utilizing the forward-looking information that is embedded in the prices of derivative contracts. Risk-neutral densities, obtained from panels of crude-oil option prices, are adjusted to reflect real-world risks using either a parametric or a non-parametric calibration approach. The relative performance of the models is evaluated for the entire support of the density, as well as for regions and intervals that are of special interest for the economic agent. We find that non-parametric adjustments of risk-neutral density forecasts perform significantly better than their parametric counterparts. Goodness-of-fit tests and out-of-sample likelihood comparisons favor forecast densities obtained by option prices and non-parametric calibration methods over those constructed using historical returns and simulated ARCH processes. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:727–754, 2011

Monte Carlo Method for Reconstruction of Densities

2000 Mathematics Subject Classification: 65C05

Comparison of two non-bronchoscopic methods for evaluating inflammation in patients with acute hypoxaemic respiratory failure

INTRODUCTION: The simple bedside method for sampling undiluted distal pulmonary edema fluid through a normal suction catheter (s-Cath) has been experimentally and clinically validated. However, there are no data comparing non-bronchoscopic bronchoalveolar lavage (mini-BAL) and s-Cath for assessing lung inflammation in acute hypoxaemic respiratory failure. We designed a prospective study in two groups of patients, those with acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) and those with acute cardiogenic lung edema (ACLE), designed to investigate the clinical feasibility of these techniques and to evaluate inflammation in both groups using undiluted sampling obtained by s-Cath. To test the interchangeability of the two methods in the same patient for studying the inflammation response, we further compared mini-BAL and s-Cath for agreement of protein concentration and percentage of polymorphonuclear cells (PMNs). METHODS: Mini-BAL and s-Cath sampling was assessed in 30 mechanically ventilated patients, 21 with ALI/ARDS and 9 with ACLE. To analyse agreement between the two sampling techniques, we considered only simultaneously collected mini-BAL and s-Cath paired samples. The protein concentration and polymorphonuclear cell (PMN) count comparisons were performed using undiluted sampling. Bland-Altman plots were used for assessing the mean bias and the limits of agreement between the two sampling techniques; comparison between groups was performed by using the non-parametric Mann-Whitney-U test; continuous variables were compared by using the Student t-test, Wilcoxon signed rank test, analysis of variance or Student-Newman-Keuls test; and categorical variables were compared by using chi-square analysis or Fisher exact test. RESULTS: Using protein content and PMN percentage as parameters, we identified substantial variations between the two sampling techniques. When the protein concentration in the lung was high, the s-Cath was a more sensitive method; by contrast, as inflammation increased, both methods provided similar estimates of neutrophil percentages in the lung. The patients with ACLE showed an increased PMN count, suggesting that hydrostatic lung edema can be associated with a concomitant inflammatory process. CONCLUSIONS: There are significant differences between the s-Cath and mini-BAL sampling techniques, indicating that these procedures cannot be used interchangeably for studying the lung inflammatory response in patients with acute hypoxaemic lung injury.

Robust regression applied to fractal/multifractal analysis.

Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn?t be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don?t have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: ? Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R pvalue. In this way we consider the implications of reducing the number of points. ? Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology.