Insights into the morphology of the Serra da Cangalha impact structure from geophysical modeling

Forward modeling is commonly applied to gravity field data of impact structures to determine the main gravity anomaly sources. In this context, we have developed 2.5-D gravity models of the Serra da Cangalha impact structure for the purpose of investigating geological bodies/structures underneath the crater. Interpretation of the models was supported by ground magnetic data acquired along profiles, as well as by high resolution aeromagnetic data. Ground magnetic data reveal the presence of short-wavelength anomalies probably related to shallow magnetic sources that could have been emplaced during the cratering process. Aeromagnetic data show that the basement underneath the crater occurs at an average depth of about 1.9 km, whereas in the region beneath the central uplift it is raised to 0.51 km below the current surface. These depths are also supported by 2.5-D gravity models showing a gentle relief for the basement beneath the central uplift area. Geophysical data were used to provide further constraints for numeral modeling of crater formation that provided important information on the structural modification that affected the rocks underneath the crater, as well as on shock-induced modifications of target rocks. The results showed that the morphology is consistent with the current observations of the crater and that Serra da Cangalha was formed by a meteorite of approximately 1.4 km diameter striking at 12 km s-1.

The Poisson-exponential regression model under different latent activation schemes

In this paper, a new family of survival distributions is presented. It is derived by considering that the latent number of failure causes follows a Poisson distribution and the time for these causes to be activated follows an exponential distribution. Three different activation schemes are also considered. Moreover, we propose the inclusion of covariates in the model formulation in order to study their effect on the expected value of the number of causes and on the failure rate function. Inferential procedure based on the maximum likelihood method is discussed and evaluated via simulation. The developed methodology is illustrated on a real data set on ovarian cancer.

Peptide fingerprinting of the neurotoxic fractions isolated from the secretions of sea anemones Stichodactyla helianthus and Bunodosoma granulifera. New members of the APETx-like family identified by a 454 pyrosequencing approach

Sea anemones are known to contain a wide diversity of biologically active peptides, mostly unexplored according to recent peptidomic and transcriptomic studies. In the present work, the neurotoxic fractions from the exudates of Stichodactyla helianthus and Bunodosoma granulifera were analyzed by reversed-phase chromatography and mass spectrometry. The first peptide fingerprints of these sea anemones were assessed, revealing the largest number of peptide components (156) so far found in sea anemone species, as well as the richer peptide diversity of B. granulifera in relation to S. helianthus. The transcriptomic analysis of B. granulifera, performed by massive cDNA sequencing with 454 pyrosequencing approach allowed the discovery of five new APETx-like peptides (U-AITX-Bg1a-e - including the full sequences of their precursors for four of them), which together with type 1 sea anemone sodium channel toxins constitute a very distinguishable feature of studied sea anemone species belonging to genus Bunodosoma. The molecular modeling of these new APETx-like peptides showed a distribution of positively charged and aromatic residues in putative contact surfaces as observed in other animal toxins. On the other hand, they also showed variable electrostatic potentials, thus suggesting a docking onto their targeted channels in different spatial orientations. Moreover several crab paralyzing toxins (other than U-AITX-Bg1a-e), which induce a variety of symptoms in crabs, were isolated. Some of them presumably belong to new classes of crab-paralyzing peptide toxins, especially those with molecular masses below 2 kDa, which represent the smallest peptide toxins found in sea anemones. (C) 2011 Elsevier Inc. All rights reserved.

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.

The impact of psychiatric diagnosis on treatment adherence and duration among victimized children and adolescents in Sao Paulo, Brazil

OBJECTIVE: Despite the high prevalence of substance abuse and mood disorders among victimized children and adolescents, few studies have investigated the association of these disorders with treatment adherence, represented by numbers of visits per month and treatment duration. We aimed to investigate the effects of substance abuse and mood disorders on treatment adherence and duration in a special programfor victimized children in Sao Paulo, Brazil. METHODS: A total of 351 participants were evaluated for psychiatric disorders and classified into one of five groups: mood disorders alone; substance abuse disorders alone; mood and substance abuse disorders; other psychiatric disorders; no psychiatric disorders. The associations between diagnostic classification and adherence to treatment and the duration of program participation were tested with logistic regression and survival analysis, respectively. RESULTS: Children with mood disorders alone had the highest rate of adherence (79.5%); those with substance abuse disorders alone had the lowest (40%); and those with both disorders had an intermediate rate of adherence (50%). Those with other psychiatric disorders and no psychiatric disorders also had high rates of adherence (75.6% and 72.9%, respectively). Living with family significantly increased adherence for children with substance abuse disorders but decreased adherence for those with no psychiatric disorders. The diagnostic correlates of duration of participation were similar to those for adherence. CONCLUSIONS: Mood and substance abuse disorders were strong predictive factors for treatment adherence and duration, albeit in opposite directions. Living with family seems to have a positive effect on treatment adherence for patients with substance abuse disorders. More effective treatment is needed for victimized substance-abusing youth.

The impact of psychiatric diagnosis on treatment adherence and duration among victimized children and adolescents in São Paulo, Brazil

OBJECTIVE: Despite the high prevalence of substance abuse and mood disorders among victimized children and adolescents, few studies have investigated the association of these disorders with treatment adherence, represented by numbers of visits per month and treatment duration. We aimed to investigate the effects of substance abuse and mood disorders on treatment adherence and duration in a special program for victimized children in São Paulo, Brazil. METHODS: A total of 351 participants were evaluated for psychiatric disorders and classified into one of five groups: mood disorders alone; substance abuse disorders alone; mood and substance abuse disorders; other psychiatric disorders; no psychiatric disorders. The associations between diagnostic classification and adherence to treatment and the duration of program participation were tested with logistic regression and survival analysis, respectively. RESULTS: Children with mood disorders alone had the highest rate of adherence (79.5%); those with substance abuse disorders alone had the lowest (40%); and those with both disorders had an intermediate rate of adherence (50%). Those with other psychiatric disorders and no psychiatric disorders also had high rates of adherence (75.6% and 72.9%, respectively). Living with family significantly increased adherence for children with substance abuse disorders but decreased adherence for those with no psychiatric disorders. The diagnostic correlates of duration of participation were similar to those for adherence. CONCLUSIONS: Mood and substance abuse disorders were strong predictive factors for treatment adherence and duration, albeit in opposite directions. Living with family seems to have a positive effect on treatment adherence for patients with substance abuse disorders. More effective treatment is needed for victimized substance-abusing youth

Her2p molecular modeling, mutant analysis and intramitochondrial localization

Bacterial GatCAB amidotransferases are responsible for the transamidation of mischarged glutamyl-tRNA(Gln) into glutaminyl-tRNA(Gln). Mitochondria matrix also has a multienzymatic complex necessary for the transamidation of glutamyl-tRNA(Gln). Gtf1p, Her2p and Pet112p are the constituents of mitochondrial GatFAB amidotransferase complex. Her2p is subunit A of GatFAB complex, while Gtf1p is subunit F, a connector protein between Pet112p (subunit B) and Her2p. Here we evaluate through molecular modeling and amino acid correlation analysis the HER2 protein family. Localization studies indicated that Her2p is predominantly localized in the mitochondrial outer membrane, but it is also located in the mitochondrial matrix where together with Pet112p and Gtf1p constitutes the GatFAB complex. Finally, HER2 random mutagenesis unveiled important residues that provide thermo stability for the complex and are differently suppressed by overexpression of GTF1 or PET112. For instance, her2/ts11 mutant showed its fermentative growth impaired, and poorly rescued by GTF1 indicating that Her2p unknown function in the mitochondria outer membrane affects cell viability.

Mathematical Modeling to Investigate Antiarrhythmic Drug Side Effects: Rate-Dependence Role in Ionic Currents and Action Potentials Shape in the O’Hara Model

Sudden cardiac death due to ventricular arrhythmia is one of the leading causes of mortality in the world. In the last decades, it has proven that anti-arrhythmic drugs, which prolong the refractory period by means of prolongation of the cardiac action potential duration (APD), play a good role in preventing of relevant human arrhythmias. However, it has long been observed that the “class III antiarrhythmic effect” diminish at faster heart rates and that this phenomenon represent a big weakness, since it is the precise situation when arrhythmias are most prone to occur. It is well known that mathematical modeling is a useful tool for investigating cardiac cell behavior. In the last 60 years, a multitude of cardiac models has been created; from the pioneering work of Hodgkin and Huxley (1952), who first described the ionic currents of the squid giant axon quantitatively, mathematical modeling has made great strides. The O’Hara model, that I employed in this research work, is one of the modern computational models of ventricular myocyte, a new generation began in 1991 with ventricular cell model by Noble et al. Successful of these models is that you can generate novel predictions, suggest experiments and provide a quantitative understanding of underlying mechanism. Obviously, the drawback is that they remain simple models, they don’t represent the real system. The overall goal of this research is to give an additional tool, through mathematical modeling, to understand the behavior of the main ionic currents involved during the action potential (AP), especially underlining the differences between slower and faster heart rates. In particular to evaluate the rate-dependence role on the action potential duration, to implement a new method for interpreting ionic currents behavior after a perturbation effect and to verify the validity of the work proposed by Antonio Zaza using an injected current as a perturbing effect.

Analysis and modeling techniques for ultrasonic tissue characterization

This thesis introduces new processing techniques for computer-aided interpretation of ultrasound images with the purpose of supporting medical diagnostic. In terms of practical application, the goal of this work is the improvement of current prostate biopsy protocols by providing physicians with a visual map overlaid over ultrasound images marking regions potentially affected by disease. As far as analysis techniques are concerned, the main contributions of this work to the state-of-the-art is the introduction of deconvolution as a pre-processing step in the standard ultrasonic tissue characterization procedure to improve the diagnostic significance of ultrasonic features. This thesis also includes some innovations in ultrasound modeling, in particular the employment of a continuous-time autoregressive moving-average (CARMA) model for ultrasound signals, a new maximum-likelihood CARMA estimator based on exponential splines and the definition of CARMA parameters as new ultrasonic features able to capture scatterers concentration. Finally, concerning the clinical usefulness of the developed techniques, the main contribution of this research is showing, through a study based on medical ground truth, that a reduction in the number of sampled cores in standard prostate biopsy is possible, preserving the same diagnostic power of the current clinical protocol.

Global modeling of pollutant transport and deposition from anthropogenic emission hot spots on global scale

Urban centers significantly contribute to anthropogenic air pollution, although they cover only a minor fraction of the Earth's land surface. Since the worldwide degree of urbanization is steadily increasing, the anthropogenic contribution to air pollution from urban centers is expected to become more substantial in future air quality assessments. The main objective of this thesis was to obtain a more profound insight in the dispersion and the deposition of aerosol particles from 46 individual major population centers (MPCs) as well as the regional and global influence on the atmospheric distribution of several aerosol types. For the first time, this was assessed in one model framework, for which the global model EMAC was applied with different representations of aerosol particles. First, in an approach with passive tracers and a setup in which the results depend only on the source location and the size and the solubility of the tracers, several metrics and a regional climate classification were used to quantify the major outflow pathways, both vertically and horizontally, and to compare the balance between pollution export away from and pollution build-up around the source points. Then in a more comprehensive approach, the anthropogenic emissions of key trace species were changed at the MPC locations to determine the cumulative impact of the MPC emissions on the atmospheric aerosol burdens of black carbon, particulate organic matter, sulfate, and nitrate. Ten different mono-modal passive aerosol tracers were continuously released at the same constant rate at each emission point. The results clearly showed that on average about five times more mass is advected quasi-horizontally at low levels than exported into the upper troposphere. The strength of the low-level export is mainly determined by the location of the source, while the vertical transport is mainly governed by the lifting potential and the solubility of the tracers. Similar to insoluble gas phase tracers, the low-level export of aerosol tracers is strongest at middle and high latitudes, while the regions of strongest vertical export differ between aerosol (temperate winter dry) and gas phase (tropics) tracers. The emitted mass fraction that is kept around MPCs is largest in regions where aerosol tracers have short lifetimes; this mass is also critical for assessing the impact on humans. However, the number of people who live in a strongly polluted region around urban centers depends more on the population density than on the size of the area which is affected by strong air pollution. Another major result was that fine aerosol particles (diameters smaller than 2.5 micrometer) from MPCs undergo substantial long-range transport, with about half of the emitted mass being deposited beyond 1000 km away from the source. In contrast to this diluted remote deposition, there are areas around the MPCs which experience high deposition rates, especially in regions which are frequently affected by heavy precipitation or are situated in poorly ventilated locations. Moreover, most MPC aerosol emissions are removed over land surfaces. In particular, forests experience more deposition from MPC pollutants than other land ecosystems. In addition, it was found that the generic treatment of aerosols has no substantial influence on the major conclusions drawn in this thesis. Moreover, in the more comprehensive approach, it was found that emissions of black carbon, particulate organic matter, sulfur dioxide, and nitrogen oxides from MPCs influence the atmospheric burden of various aerosol types very differently, with impacts generally being larger for secondary species, sulfate and nitrate, than for primary species, black carbon and particulate organic matter. While the changes in the burdens of sulfate, black carbon, and particulate organic matter show an almost linear response for changes in the emission strength, the formation of nitrate was found to be contingent upon many more factors, e.g., the abundance of sulfuric acid, than only upon the strength of the nitrogen oxide emissions. The generic tracer experiments were further extended to conduct the first risk assessment to obtain the cumulative risk of contamination from multiple nuclear reactor accidents on the global scale. For this, many factors had to be taken into account: the probability of major accidents, the cumulative deposition field of the radionuclide cesium-137, and a threshold value that defines contamination. By collecting the necessary data and after accounting for uncertainties, it was found that the risk is highest in western Europe, the eastern US, and in Japan, where on average contamination by major accidents is expected about every 50 years.

Block of the hERG channel by bupivacaine: Electrophysiological and modeling insights towards stereochemical optimization

The hERG voltage-gated potassium channel mediates the cardiac I(Kr) current, which is crucial for the duration of the cardiac action potential. Undesired block of the channel by certain drugs may prolong the QT interval and increase the risk of malignant ventricular arrhythmias. Although the molecular determinants of hERG block have been intensively studied, not much is known about its stereoselectivity. Levo-(S)-bupivacaine was the first drug reported to have a higher affinity to block hERG than its enantiomer. This study strives to understand the principles underlying the stereoselectivity of bupivacaine block with the help of mutagenesis analyses and molecular modeling simulations. Electrophysiological measurements of mutated hERG channels allowed for the identification of residues involved in bupivacaine binding and stereoselectivity. Docking and molecular mechanics simulations for both enantiomers of bupivacaine and terfenadine (a non-stereoselective blocker) were performed inside an open-state model of the hERG channel. The predicted binding modes enabled a clear depiction of ligand-protein interactions. Estimated binding affinities for both enantiomers were consistent with electrophysiological measurements. A similar computational procedure was applied to bupivacaine enantiomers towards two mutated hERG channels (Tyr652Ala and Phe656Ala). This study confirmed, at the molecular level, that bupivacaine stereoselectively binds the hERG channel. These results help to lay the foundation for structural guidelines to optimize the cardiotoxic profile of drug candidates in silico.

MODELING FUNCTIONAL DATA WITH SPATIALLY HETEROGENEOUS SHAPE CHARACTERISTICS

We propose a novel class of models for functional data exhibiting skewness or other shape characteristics that vary with spatial or temporal location. We use copulas so that the marginal distributions and the dependence structure can be modeled independently. Dependence is modeled with a Gaussian or t-copula, so that there is an underlying latent Gaussian process. We model the marginal distributions using the skew t family. The mean, variance, and shape parameters are modeled nonparametrically as functions of location. A computationally tractable inferential framework for estimating heterogeneous asymmetric or heavy-tailed marginal distributions is introduced. This framework provides a new set of tools for increasingly complex data collected in medical and public health studies. Our methods were motivated by and are illustrated with a state-of-the-art study of neuronal tracts in multiple sclerosis patients and healthy controls. Using the tools we have developed, we were able to find those locations along the tract most affected by the disease. However, our methods are general and highly relevant to many functional data sets. In addition to the application to one-dimensional tract profiles illustrated here, higher-dimensional extensions of the methodology could have direct applications to other biological data including functional and structural MRI.

Modeling of diesel particulate filter filtration and regeneration for transient driving schedules

Due to their high thermal efficiency, diesel engines have excellent fuel economy and have been widely used as a power source for many vehicles. Diesel engines emit less greenhouse gases (carbon dioxide) compared with gasoline engines. However, diesel engines emit large amounts of particulate matter (PM) which can imperil human health. The best way to reduce the particulate matter is by using the Diesel Particulate Filter (DPF) system which consists of a wall-flow monolith which can trap particulates, and the DPF can be periodically regenerated to remove the collected particulates. The estimation of the PM mass accumulated in the DPF and total pressure drop across the filter are very important in order to determine when to carry out the active regeneration for the DPF. In this project, by developing a filtration model and a pressure drop model, we can estimate the PM mass and the total pressure drop, then, these two models can be linked with a regeneration model which has been developed previously to predict when to regenerate the filter. There results of this project were: 1 Reproduce a filtration model and simulate the processes of filtration. By studying the deep bed filtration and cake filtration, stages and quantity of mass accumulated in the DPF can be estimated. It was found that the filtration efficiency increases faster during the deep-bed filtration than that during the cake filtration. A “unit collector” theory was used in our filtration model which can explain the mechanism of the filtration very well. 2 Perform a parametric study on the pressure drop model for changes in engine exhaust flow rate, deposit layer thickness, and inlet temperature. It was found that there are five primary variables impacting the pressure drop in the DPF which are temperature gradient along the channel, deposit layer thickness, deposit layer permeability, wall thickness, and wall permeability. 3 Link the filtration model and the pressure drop model with the regeneration model to determine the time to carry out the regeneration of the DPF. It was found that the regeneration should be initiated when the cake layer is at a certain thickness, since a cake layer with either too big or too small an amount of particulates will need more thermal energy to reach a higher regeneration efficiency. 4 Formulate diesel particulate trap regeneration strategies for real world driving conditions to find out the best desirable conditions for DPF regeneration. It was found that the regeneration should be initiated when the vehicle’s speed is high and during which there should not be any stops from the vehicle. Moreover, the regeneration duration is about 120 seconds and the inlet temperature for the regeneration is 710K.

DEVELOPMENT OF A PREDICTIVE COMBUSTION MODEL OF A SPARK IGNITED ENGINE WITH GASOLINE DIRECT INJECTION, VARIABLE VALVE TIMING, DURATION AND LIFT TECHNOLOGIES

There is a need by engine manufactures for computationally efficient and accurate predictive combustion modeling tools for integration in engine simulation software for the assessment of combustion system hardware designs and early development of engine calibrations. This thesis discusses the process for the development and validation of a combustion modeling tool for Gasoline Direct Injected Spark Ignited Engine with variable valve timing, lift and duration valvetrain hardware from experimental data. Data was correlated and regressed from accepted methods for calculating the turbulent flow and flame propagation characteristics for an internal combustion engine. A non-linear regression modeling method was utilized to develop a combustion model to determine the fuel mass burn rate at multiple points during the combustion process. The computational fluid dynamic software Converge ©, was used to simulate and correlate the 3-D combustion system, port and piston geometry to the turbulent flow development within the cylinder to properly predict the experimental data turbulent flow parameters through the intake, compression and expansion processes. The engine simulation software GT-Power © is then used to determine the 1-D flow characteristics of the engine hardware being tested to correlate the regressed combustion modeling tool to experimental data to determine accuracy. The results of the combustion modeling tool show accurate trends capturing the combustion sensitivities to turbulent flow, thermodynamic and internal residual effects with changes in intake and exhaust valve timing, lift and duration.

PHYLOGENETIC ANALYSES, GENE DOSAGE EFFECT AND SELECTION IN MEMBERS OF THE LATERAL BOUNDARY DOMAIN (LBD) GENES TRANSCRIPTION FACTOR FAMILY

Wood formation is an economically and environmentally important process and has played a significant role in the evolution of terrestrial plants. Despite its significance, the molecular underpinnings of the process are still poorly understood. We have previously shown that four Lateral Boundary Domain (LBD) transcription factors have important roles in the regulation of wood formation with two (LBD1 and LBD4) involved in secondary phloem and ray cell development and two (LBD15 and LBD18) in secondary xylem formation. Here, we used comparative phylogenetic analyses to test potential roles of the four LBD genes in the evolution of woodiness. We studied the copy number and variation in DNA and amino acid sequences of the four LBDs in a wide range of woody and herbaceous plant taxa with fully sequenced and annotated genomes. LBD1 showed the highest gene copy number across the studied species, and LBD1 gene copy number was strongly and significantly correlated with the level of ray seriation. The lianas, cucumber and grape, with multiseriate ray cells showed the highest gene copy number (12 and 11, respectively). Because lianas’ growth habit requires significant twisting and bending, the less lignified ray parenchyma cells likely facilitate stem flexibility and maintenance of xylem conductivity. We further demonstrate conservation of amino acids in the LBD18 protein sequences that are specific to woody taxa. Neutrality tests showed evidence for strong purifying selection on these gene regions across various orders, indicating adaptive convergent evolution of LBD18. Structural modeling demonstrates that the conserved amino acids have a significant impact on the tertiary protein structure and thus are likely of significant functional importance.