Spatial statistical models that use flow and stream distance

We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

Comparative clinical study of the effectiveness of different dental bleaching methods - two year follow-up

This study evaluated color change, stability, and tooth sensitivity in patients submitted to different bleaching techniques. Material and methods: In this study, 48 patients were divided into five groups. A half-mouth design was conducted to compare two in-office bleaching bleaching techniques (with and without light activation): G1: 35% hydrogen peroxide (HP) (Lase Peroxide - DMC Equipments, Sao Carlos, SP, Brazil) + hybrid light (HL) (LED/Diode Laser, Whitening Lase II DMC Equipments, Sao Carlos, SP, Brazil); G2: 35% HP; G3: 38% HP (X-traBoost - Ultradent, South Jordan UT, USA) + HL; G4: 38% HP; and G5: 15% carbamide peroxide (CP) (Opalescence PF - Ultradent, South Jordan UT, USA). For G1 and G3, HP was applied on the enamel surface for 3 consecutive applications activated by HL. Each application included 3x3' HL activations with 1' between each interval; for G2 and G4, HP was applied 3x15' with 15' between intervals; and for G5, 15% CP was applied for 120'/10 days at home. A spectrophotometer was used to measure color change before the treatment and after 24 h, 1 week, 1, 6, 12, 18 and 24 months. A VAS questionnaire was used to evaluate tooth sensitivity before the treatment, immediately following treatment, 24 h after and finally 1 week after. Results: Statistical analysis did not reveal any significant differences between in-office bleaching with or without HL activation related to effectiveness; nevertheless the time required was less with HL. Statistical differences were observed between the result after 24 h, 1 week and 1, 6, 12, 18 and 24 months (integroup). Immediately, in-office bleaching increased tooth sensitivity. The groups activated with HL required less application time with gel. Conclusion: All techniques and bleaching agents used were effective and demonstrated similar behaviors.

Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

In vivo accuracy of conventional and digital radiographic methods in confirming root canal working length determination by Root ZX

Objectives: To compare, in vivo, the accuracy of conventional and digital radiographic methods in determining root canal working length. Material and Methods: Twenty-five maxillary incisor or canine teeth from 22 patients were used in this study. Considering the preoperative radiographs as the baseline, a 25 K file was inserted into the root canal to the point where the Root ZX electronic apex locator indicated the APEX measurement in the screen. From this measurement, 1 mm was subtracted for positioning the file. The radiographic measurements were made using a digital sensor (Digora 1.51) or conventional type-E films, size 2, following the paralleling technique, to determine the distance of the file tip and the radiographic apex. Results: The Student "t" test indicated mean distances of 1.11 mm to conventional and 1.20 mm for the digital method and indicated a significant statistical difference (p<0.05). Conclusions: The conventional radiographic method was found to be superior to the digital one in determining the working length of the root canal.

Evaluation of the detection and quantification limits in electroanalysis using two popular methods: application in the case study of paraquat determination

In this work, the reduction reaction of paraquat herbicide was used to obtain analytical signals using electrochemical techniques of differential pulse voltammetry, square wave voltammetry and multiple square wave voltammetry. Analytes were prepared with laboratory purified water and natural water samples (from Mogi-Guacu River, SP). The electrochemical techniques were applied to 1.0 mol L-1 Na2SO4 solutions, at pH 5.5, and containing different concentrations of paraquat, in the range of 1 to 10 mu mol L-1, using a gold ultramicroelectrode. 5 replicate experiments were conducted and in each the mean value for peak currents obtained -0.70 V vs. Ag/AgCl yielded excellent linear relationships with pesticide concentrations. The slope values for the calibration plots (method sensitivity) were 4.06 x 10(-3), 1.07 x 10(-2) and 2.95 x 10(-2) A mol(-1) L for purified water by differential pulse voltammetry, square wave voltammetry and multiple square wave voltammetry, respectively. For river water samples, the slope values were 2.60 x 10(-3), 1.06 x 10(-2) and 3.35 x 10(-2) A mol(-1) L, respectively, showing a small interference from the natural matrix components in paraquat determinations. The detection limits for paraquat determinations were calculated by two distinct methodologies, i.e., as proposed by IUPAC and a statistical method. The values obtained with multiple square waves voltammetry were 0.002 and 0.12 mu mol L-1, respectively, for pure water electrolytes. The detection limit from IUPAC recommendations, when inserted in the calibration curve equation, an analytical signal (oxidation current) is smaller than the one experimentally observed for the blank solution under the same experimental conditions. This is inconsistent with the definition of detection limit, thus the IUPAC methodology requires further discussion. The same conclusion can be drawn by the analyses of detection limits obtained with the other techniques studied.

Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to insurance

In this paper, we carry out robust modeling and influence diagnostics in Birnbaum-Saunders (BS) regression models. Specifically, we present some aspects related to BS and log-BS distributions and their generalizations from the Student-t distribution, and develop BS-t regression models, including maximum likelihood estimation based on the EM algorithm and diagnostic tools. In addition, we apply the obtained results to real data from insurance, which shows the uses of the proposed model. Copyright (c) 2011 John Wiley & Sons, Ltd.

Comparison of different dosimetric methods for red marrow absorbed dose calculation in thyroid cancer therapy

Several dosimetric methods have been proposed for estimating red marrow absorbed dose (RMAD) when radionuclide therapy is planned for differentiated thyroid cancer, although to date, there is no consensus as to whether dose calculation should be based on blood-activity concentration or not. Our purpose was to compare RMADs derived from methods that require collecting patients' blood samples versus those involving OLINDA/EXM software, thereby precluding this invasive procedure. This is a retrospective study that included 34 patients under treatment for metastatic thyroid disease. A deviation of 10 between RMADs was found, when comparing the doses from the most usual invasive dosimetric methods and those from OLINDA/EXM. No statistical difference between the methods was discovered, whereby the need for invasive procedures when calculating the dose is questioned. The use of OLINDA/EXM in clinical routine could possibly diminish data collection, thus giving rise to a simultaneous reduction in time and clinical costs, besides avoiding any kind of discomfort on the part of the patients involved.

Evaluating different methods of microarray data normalization

Abstract Background With the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration. Results Here, we considered three commonly used normalization approaches, namely: Loess, Splines and Wavelets, and two non-parametric regression methods, which have yet to be used for normalization, namely, the Kernel smoothing and Support Vector Regression. The results obtained were compared using artificial microarray data and benchmark studies. The results indicate that the Support Vector Regression is the most robust to outliers and that Kernel is the worst normalization technique, while no practical differences were observed between Loess, Splines and Wavelets. Conclusion In face of our results, the Support Vector Regression is favored for microarray normalization due to its superiority when compared to the other methods for its robustness in estimating the normalization curve.

Comparison of two laboratory-developed PCR methods for the diagnosis of Pulmonary Tuberculosis in Brazilian patients with and without HIV infection

Abstract Background Direct smear examination with Ziehl-Neelsen (ZN) staining for the diagnosis of pulmonary tuberculosis (PTB) is cheap and easy to use, but its low sensitivity is a major drawback, particularly in HIV seropositive patients. As such, new tools for laboratory diagnosis are urgently needed to improve the case detection rate, especially in regions with a high prevalence of TB and HIV. Objective To evaluate the performance of two in house PCR (Polymerase Chain Reaction): PCR dot-blot methodology (PCR dot-blot) and PCR agarose gel electrophoresis (PCR-AG) for the diagnosis of Pulmonary Tuberculosis (PTB) in HIV seropositive and HIV seronegative patients. Methods A prospective study was conducted (from May 2003 to May 2004) in a TB/HIV reference hospital. Sputum specimens from 277 PTB suspects were tested by Acid Fast Bacilli (AFB) smear, Culture and in house PCR assays (PCR dot-blot and PCR-AG) and their performances evaluated. Positive cultures combined with the definition of clinical pulmonary TB were employed as the gold standard. Results The overall prevalence of PTB was 46% (128/277); in HIV+, prevalence was 54.0% (40/74). The sensitivity and specificity of PCR dot-blot were 74% (CI 95%; 66.1%-81.2%) and 85% (CI 95%; 78.8%-90.3%); and of PCR-AG were 43% (CI 95%; 34.5%-51.6%) and 76% (CI 95%; 69.2%-82.8%), respectively. For HIV seropositive and HIV seronegative samples, sensitivities of PCR dot-blot (72% vs 75%; p = 0.46) and PCR-AG (42% vs 43%; p = 0.54) were similar. Among HIV seronegative patients and PTB suspects, ROC analysis presented the following values for the AFB smear (0.837), Culture (0.926), PCR dot-blot (0.801) and PCR-AG (0.599). In HIV seropositive patients, these area values were (0.713), (0.900), (0.789) and (0.595), respectively. Conclusion Results of this study demonstrate that the in house PCR dot blot may be an improvement for ruling out PTB diagnosis in PTB suspects assisted at hospitals with a high prevalence of TB/HIV.

Effects of EDTA and Sodium Citrate on hormone measurements by fluorometric (FIA) and immunofluorometric (IFMA) methods

Abstract Background Measurements of hormonal concentrations by immunoassays using fluorescent tracer substance (Eu3+) are susceptible to the action of chemical agents that may cause alterations in its original structure. Our goal was to verify the effect of two types of anticoagulants in the hormone assays performed by fluorometric (FIA) or immunofluorometric (IFMA) methods. Methods Blood samples were obtained from 30 outpatients and were drawn in EDTA, sodium citrate, and serum separation Vacutainer®Blood Collection Tubes. Samples were analyzed in automatized equipment AutoDelfia™ (Perkin Elmer Brazil, Wallac, Finland) for the following hormones: Luteinizing hormone (LH), Follicle stimulating homone (FSH), prolactin (PRL), growth hormone (GH), Sex hormone binding globulin (SHBG), thyroid stimulating hormone (TSH), insulin, C peptide, total T3, total T4, free T4, estradiol, progesterone, testosterone, and cortisol. Statistical analysis was carried out by Kruskal-Wallis method and Dunn's test. Results No significant differences were seen between samples for LH, FSH, PRL and free T4. Results from GH, TSH, insulin, C peptide, SHBG, total T3, total T4, estradiol, testosterone, cortisol, and progesterone were significant different between serum and EDTA-treated samples groups. Differences were also identified between serum and sodium citrate-treated samples in the analysis for TSH, insulin, total T3, estradiol, testosterone and progesterone. Conclusions We conclude that the hormonal analysis carried through by FIA or IFMA are susceptible to the effects of anticoagulants in the biological material collected that vary depending on the type of assay.

Probing the statistical properties of unknown texts: application to the Voynich manuscript

While the use of statistical physics methods to analyze large corpora has been useful to unveil many patterns in texts, no comprehensive investigation has been performed on the interdependence between syntactic and semantic factors. In this study we propose a framework for determining whether a text (e.g., written in an unknown alphabet) is compatible with a natural language and to which language it could belong. The approach is based on three types of statistical measurements, i.e. obtained from first-order statistics of word properties in a text, from the topology of complex networks representing texts, and from intermittency concepts where text is treated as a time series. Comparative experiments were performed with the New Testament in 15 different languages and with distinct books in English and Portuguese in order to quantify the dependency of the different measurements on the language and on the story being told in the book. The metrics found to be informative in distinguishing real texts from their shuffled versions include assortativity, degree and selectivity of words. As an illustration, we analyze an undeciphered medieval manuscript known as the Voynich Manuscript. We show that it is mostly compatible with natural languages and incompatible with random texts. We also obtain candidates for keywords of the Voynich Manuscript which could be helpful in the effort of deciphering it. Because we were able to identify statistical measurements that are more dependent on the syntax than on the semantics, the framework may also serve for text analysis in language-dependent applications.

Kernel Methods for Tree Structured Data

Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of objects of a domain, the kernel function. Designing fast and good kernel functions is a challenging problem. In the case of tree structured data two issues become relevant: kernel for trees should not be sparse and should be fast to compute. The sparsity problem arises when, given a dataset and a kernel function, most structures of the dataset are completely dissimilar to one another. In those cases the classifier has too few information for making correct predictions on unseen data. In fact, it tends to produce a discriminating function behaving as the nearest neighbour rule. Sparsity is likely to arise for some standard tree kernel functions, such as the subtree and subset tree kernel, when they are applied to datasets with node labels belonging to a large domain. A second drawback of using tree kernels is the time complexity required both in learning and classification phases. Such a complexity can sometimes prevents the kernel application in scenarios involving large amount of data. This thesis proposes three contributions for resolving the above issues of kernel for trees. A first contribution aims at creating kernel functions which adapt to the statistical properties of the dataset, thus reducing its sparsity with respect to traditional tree kernel functions. Specifically, we propose to encode the input trees by an algorithm able to project the data onto a lower dimensional space with the property that similar structures are mapped similarly. By building kernel functions on the lower dimensional representation, we are able to perform inexact matchings between different inputs in the original space. A second contribution is the proposal of a novel kernel function based on the convolution kernel framework. Convolution kernel measures the similarity of two objects in terms of the similarities of their subparts. Most convolution kernels are based on counting the number of shared substructures, partially discarding information about their position in the original structure. The kernel function we propose is, instead, especially focused on this aspect. A third contribution is devoted at reducing the computational burden related to the calculation of a kernel function between a tree and a forest of trees, which is a typical operation in the classification phase and, for some algorithms, also in the learning phase. We propose a general methodology applicable to convolution kernels. Moreover, we show an instantiation of our technique when kernels such as the subtree and subset tree kernels are employed. In those cases, Direct Acyclic Graphs can be used to compactly represent shared substructures in different trees, thus reducing the computational burden and storage requirements.

Design of alternative energy storage systems for hybrid vehicles based on statistical processing of driving cycles information

Hybrid vehicles represent the future for automakers, since they allow to improve the fuel economy and to reduce the pollutant emissions. A key component of the hybrid powertrain is the Energy Storage System, that determines the ability of the vehicle to store and reuse energy. Though electrified Energy Storage Systems (ESS), based on batteries and ultracapacitors, are a proven technology, Alternative Energy Storage Systems (AESS), based on mechanical, hydraulic and pneumatic devices, are gaining interest because they give the possibility of realizing low-cost mild-hybrid vehicles. Currently, most literature of design methodologies focuses on electric ESS, which are not suitable for AESS design. In this contest, The Ohio State University has developed an Alternative Energy Storage System design methodology. This work focuses on the development of driving cycle analysis methodology that is a key component of Alternative Energy Storage System design procedure. The proposed methodology is based on a statistical approach to analyzing driving schedules that represent the vehicle typical use. Driving data are broken up into power events sequence, namely traction and braking events, and for each of them, energy-related and dynamic metrics are calculated. By means of a clustering process and statistical synthesis methods, statistically-relevant metrics are determined. These metrics define cycle representative braking events. By using these events as inputs for the Alternative Energy Storage System design methodology, different system designs are obtained. Each of them is characterized by attributes, namely system volume and weight. In the last part the work, the designs are evaluated in simulation by introducing and calculating a metric related to the energy conversion efficiency. Finally, the designs are compared accounting for attributes and efficiency values. In order to automate the driving data extraction and synthesis process, a specific script Matlab based has been developed. Results show that the driving cycle analysis methodology, based on the statistical approach, allows to extract and synthesize cycle representative data. The designs based on cycle statistically-relevant metrics are properly sized and have satisfying efficiency values with respect to the expectations. An exception is the design based on the cycle worst-case scenario, corresponding to same approach adopted by the conventional electric ESS design methodologies. In this case, a heavy system with poor efficiency is produced. The proposed new methodology seems to be a valid and consistent support for Alternative Energy Storage System design.

Statistical mechanics of protein complexed and condensed DNA

In this thesis I treat various biophysical questions arising in the context of complexed / ”protein-packed” DNA and DNA in confined geometries (like in viruses or toroidal DNA condensates). Using diverse theoretical methods I consider the statistical mechanics as well as the dynamics of DNA under these conditions. In the first part of the thesis (chapter 2) I derive for the first time the single molecule ”equation of state”, i.e. the force-extension relation of a looped DNA (Eq. 2.94) by using the path integral formalism. Generalizing these results I show that the presence of elastic substructures like loops or deflections caused by anchoring boundary conditions (e.g. at the AFM tip or the mica substrate) gives rise to a significant renormalization of the apparent persistence length as extracted from single molecule experiments (Eqs. 2.39 and 2.98). As I show the experimentally observed apparent persistence length reduction by a factor of 10 or more is naturally explained by this theory. In chapter 3 I theoretically consider the thermal motion of nucleosomes along a DNA template. After an extensive analysis of available experimental data and theoretical modelling of two possible mechanisms I conclude that the ”corkscrew-motion” mechanism most consistently explains this biologically important process. In chapter 4 I demonstrate that DNA-spools (architectures in which DNA circumferentially winds on a cylindrical surface, or onto itself) show a remarkable ”kinetic inertness” that protects them from tension-induced disruption on experimentally and biologically relevant timescales (cf. Fig. 4.1 and Eq. 4.18). I show that the underlying model establishes a connection between the seemingly unrelated and previously unexplained force peaks in single molecule nucleosome and DNA-toroid stretching experiments. Finally in chapter 5 I show that toroidally confined DNA (found in viruses, DNAcondensates or sperm chromatin) undergoes a transition to a twisted, highly entangled state provided that the aspect ratio of the underlying torus crosses a certain critical value (cf. Eq. 5.6 and the phase diagram in Fig. 5.4). The presented mechanism could rationalize several experimental mysteries, ranging from entangled and supercoiled toroids released from virus capsids to the unexpectedly short cholesteric pitch in the (toroidaly wound) sperm chromatin. I propose that the ”topological encapsulation” resulting from our model may have some practical implications for the gene-therapeutic DNA delivery process.