Some problems and errors in cytogenetic biodosimetry

Human radiosensitivity is a quantitative trait that is generally subject to binomial distribution. Individual radiosensitivity, however, may deviate significantly from the mean (by 2-3 standard deviations). Thus, the same dose of radiation may result in different levels of genotoxic damage (commonly measured as chromosome aberration rates) in different individuals. There is significant genetic component in individual radiosensitivity. It is related to carriership of variant alleles of various single-nucleotide polymorphisms (most of these in genes coding for proteins functioning in DNA damage identification and repair); carriership of different number of alleles producing cumulative effects; amplification of gene copies coding for proteins responsible for radioresistance, mobile genetic elements, and others. Among the other factors influencing individual radioresistance are: radioadaptive response; bystander effect; levels of endogenous substances with radioprotective and antimutagenic properties and environmental factors such as lifestyle and diet, physical activity, psychoemotional state, hormonal state, certain drugs, infections and others. These factors may have radioprotective or sensibilising effects. Apparently, there are too many factors that may significantly modulate the biological effects of ionising radiation. Thus, conventional methodologies for biodosimetry (specifically, cytogenetic methods) may produce significant errors if personal traits that may affect radioresistance are not accounted for.

Genetic Algorithms for Parameter Estimation in Circadian Model

This paper proposes an algorithm to estimate two parameter values vs, transcription of frq gene, and vd, maximum rate of FRQ protein degradation for an existing 3rd order Neurospora model in literature. Details of the algorithm with simulation results are shown in this paper.

Personalization of Electronic Nicotine Delivery Systems Toxicological impact in in-vivo model

Electronic nicotine delivery systems (ENDS) use has recently grown. E-cig generates carcinogenic chemical compounds and reactive oxygen species (ROS). Carbonyls and ROS are formed when the liquid comes into contact with the heating element. In this study the chemical and biological effects of coil resistance applied on the same device were investigated. A preliminary in-vivo study the new heat-not-burn devices (IQOS®) has been conducted to evaluate the effect of the device on antioxidant biomarkers. The amount of formaldehyde, acetaldehyde, acrolein was measured by GC-MS analysis. The two e-liquids used for carbonyls detection differed only for the presence of nicotine. The nicotine-free liquid was then used for the detection of ROS in the aerosol. The impact of the non-nicotine vapor on cell viability in H1299 human lung carcinoma cells, as well as the biological effects in a rat model of e-cig aerosol exposure, were also evaluated. After the exposure of Sprague Dawley rats to e-cig and IQOS® aerosol, the effect of 28-day treatment was examined on enzymatic and non-enzymatic antioxidant response, lung inflammation, blood homeostasis and tissue damage by using scanning electron microscope (SEM) technique. The results show a significant correlation between the low resistance and the generation of higher concentrations of the selected carbonyls and ROS in aerosols. Cell viability was reduced with an inverse relation to coil resistance. The experimental model highlighted an impairment of the pulmonary antioxidant and detoxifying machinery. Frames from SEM show disorganization of alveolar and bronchial epithelium. IQOS® exposed animals shows a significant production of ROS related to the unbalance of antioxidant defense and alteration of macromolecule integrity. This research demonstrates how several toxicological aspects can potentially occur in e-cig consumers who use low resistance device coupled with nicotine-free liquid. ENDS may expose users to hazardous compounds, which, may promote chronic pathologies and degenerative diseases.

A 3D biomimetic in vitro model: a novel strategy to investigate glioma biology

Gliomas are one of the most frequent primary malignant brain tumors. Acquisition of stem-like features likely contributes to the malignant nature of high-grade gliomas and may be responsible for the initiation, growth, and recurrence of these tumors. In this regard, although the traditional 2D cell culture system has been widely used in cancer research, it shows limitations in maintaining the stemness properties of cancer and in mimicking the in vivo microenvironment. In order to overcome these limitations, different three-dimensional (3D) culture systems have been developed to mimic better the tumor microenvironment. Cancer cells cultured in 3D structures may represent a more reliable in vitro model due to increased cell-cell and cell-extracellular matrix (ECM) interaction. Several attempts to recreate brain cancer tissue in vitro are described in literature. However, to date, it is still unclear which main characteristics the ideal model should reproduce. The overall goal of this project was the development of a 3D in vitro model able to reproduce the brain ECM microenvironment and to recapitulate pathological condition for the study of tumor stroma interactions, tumor invasion ability, and molecular phenotype of glioma cells. We performed an in silico bioinformatic analysis using GEPIA2 Software to compare the expression level of seven matrix protein in the LGG tumors with healthy tissues. Then, we carried out a FFPE retrospective study in order to evaluate the percentage of expression of selected proteins. Thus, we developed a 3D scaffold composed by Hyaluronic Acid and Collagen IV in a ratio of 50:50. We used two astrocytoma cell lines, HTB-12 and HTB-13. In conclusion, we developed an in vitro 3D model able to reproduce the composition of brain tumor ECM, demonstrating that it is a feasible platform to investigate the interaction between tumor cells and the matrix.

Numerical integration errors in deep space orbit determination

Il progetto di tesi è stato sviluppato durante il periodo di tirocinio svolto all’interno del “Laboratorio di Radio Scienza ed Esplorazione Planetaria” da un'esperienza da cui prende il nome lo stesso elaborato: ”Numerical integration errors in deep space orbit determination”. Lo scopo del sopraccitato laboratorio è stato quello di studiare in modo approfondito il problema kepleriano dei due corpi, per poi passare ad un’analisi del problema dei tre corpi e successivamente a n corpi (con particolare attenzione alle orbite dei satelliti medicei di Giove). Lo studio è stato affiancato ad un costante utilizzo della piattaforma di programmazione Matlab per l’elaborazione e la stesura di codici per il calcolo di traiettorie orbitali ed errori numerici. Infatti, il fulcro del lavoro è stato proprio il confronto di vari integratori e degli errori numerici derivanti dall’integrazione. Nella tesi, dapprima, viene introdotto il sistema Gioviano, vengono presentati i satelliti medicei, delineate le caratteristiche fisiche fondamentali e i principali motivi che portano ad avere particolare interesse nel conoscere lo sviluppo orbitale di tale sistema. In seguito, l'elaborato, dopo una dettagliata descrizione teorica del problema dei due corpi, presenta un codice per la rappresentazione di orbite kepleriane e il calcolo dei relativi errori commessi dal metodo numerico rispetto a quello analitico. Nell'ultimo capitolo, invece, il problema è esteso a più corpi dotati di massa e a tal proposito viene proposto un codice per la rappresentazione delle orbite descritte nel tempo da n corpi, date le condizioni iniziali, e il calcolo dei rispettivi errori nel sistema di riferimento (r,t,n). In merito a ciò, vengono infine testati diversi integratori per cercare quello con le migliori performance e sono poi analizzati alcuni parametri in input al problema per verificare sotto quali condizioni l’integratore lavora meglio.

Looking for Validity or Testing It? The Perils of Stepwise Regression, Extreme-Scores Analysis, Heteroscedasticity, and Measurement Error

When researchers introduce a new test they have to demonstrate that it is valid, using unbiased designs and suitable statistical procedures. In this article we use Monte Carlo analyses to highlight how incorrect statistical procedures (i.e., stepwise regression, extreme scores analyses) or ignoring regression assumptions (e.g., heteroscedasticity) contribute to wrong validity estimates. Beyond these demonstrations, and as an example, we re-examined the results reported by Warwick, Nettelbeck, and Ward (2010) concerning the validity of the Ability Emotional Intelligence Measure (AEIM). Warwick et al. used the wrong statistical procedures to conclude that the AEIM was incrementally valid beyond intelligence and personality traits in predicting various outcomes. In our re-analysis, we found that the reliability-corrected multiple correlation of their measures with personality and intelligence was up to .69. Using robust statistical procedures and appropriate controls, we also found that the AEIM did not predict incremental variance in GPA, stress, loneliness, or well-being, demonstrating the importance for testing validity instead of looking for it.

Retrieval of phytoplankton size from bio-optical measurements: theory and applications

The absorption coefficient of a substance distributed as discrete particles in suspension is less than that of the same material dissolved uniformly in a medium—a phenomenon commonly referred to as the flattening effect. The decrease in the absorption coefficient owing to flattening effect depends on the concentration of the absorbing pigment inside the particle, the specific absorption coefficient of the pigment within the particle, and on the diameter of the particle, if the particles are assumed to be spherical. For phytoplankton cells in the ocean, with diameters ranging from less than 1 µm to more than 100 µm, the flattening effect is variable, and sometimes pronounced, as has been well documented in the literature. Here, we demonstrate how the in vivo absorption coefficient of phytoplankton cells per unit concentration of its major pigment, chlorophyll a, can be used to determine the average cell size of the phytoplankton population. Sensitivity analyses are carried out to evaluate the errors in the estimated diameter owing to potential errors in the model assumptions. Cell sizes computed for field samples using the model are compared qualitatively with indirect estimates of size classes derived from high performance liquid chromatography data. Also, the results are compared quantitatively against measurements of cell size in laboratory cultures. The method developed is easy-to-apply as an operational tool for in situ observations, and has the potential for application to remote sensing of ocean colour data.

Diagnosis of inflammatory lesions by high-wavenumber FT-Raman spectroscopy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

THE UBV(RI)(C) COLORS OF THE SUN

Photometric data in the UBV(RI)(C) system have been acquired for 80 solar analog stars for which we have previously derived highly precise atmospheric parameters T-eff, log g, and [Fe/H] using high-resolution, high signal-to-noise ratio spectra. UBV and (RI)(C) data for 46 and 76 of these stars, respectively, are published for the first time. Combining our data with those from the literature, colors in the UBV(RI) C system, with similar or equal to 0.01 mag precision, are now available for 112 solar analogs. Multiple linear regression is used to derive the solar colors from these photometric data and the spectroscopically derived T-eff, log g, and [Fe/H] values. To minimize the impact of systematic errors in the model-dependent atmospheric parameters, we use only the data for the 10 stars that most closely resemble our Sun, i.e., the solar twins, and derive the following solar colors: (B - V)(circle dot) = 0.653 +/- 0.005, (U - B)(circle dot) = 0.166 +/- 0.022, (V - R)(circle dot) = 0.352 +/- 0.007, and (V - I)(circle dot) = 0.702 +/- 0.010. These colors are consistent, within the 1 sigma errors, with those derived using the entire sample of 112 solar analogs. We also derive the solar colors using the relation between spectral-line-depth ratios and observed stellar colors, i.e., with a completely model-independent approach, and without restricting the analysis to solar twins. We find (B - V)(circle dot) = 0.653 +/- 0.003, (U - B)(circle dot) = 0.158 +/- 0.009, (V - R)(circle dot) = 0.356 +/- 0.003, and (V - I)(circle dot) = 0.701 +/- 0.003, in excellent agreement with the model-dependent analysis.

[impact Variables On The Decline In Infant Mortality In The State Of São Paulo, Brazil: 1998-2008].

This is an ecological, analytical and retrospective study comprising the 645 municipalities in the State of São Paulo, the scope of which was to determine the relationship between socioeconomic, demographic variables and the model of care in relation to infant mortality rates in the period from 1998 to 2008. The ratio of average annual change for each indicator per stratum coverage was calculated. Infant mortality was analyzed according to the model for repeated measures over time, adjusted for the following correction variables: the city's population, proportion of Family Health Programs (PSFs) deployed, proportion of Growth Acceleration Programs (PACs) deployed, per capita GDP and SPSRI (São Paulo social responsibility index). The analysis was performed by generalized linear models, considering the gamma distribution. Multiple comparisons were performed with the likelihood ratio with chi-square approximate distribution, considering a significance level of 5%. There was a decrease in infant mortality over the years (p < 0.05), with no significant difference from 2004 to 2008 (p > 0.05). The proportion of PSFs deployed (p < 0.0001) and per capita GDP (p < 0.0001) were significant in the model. The decline of infant mortality in this period was influenced by the growth of per capita GDP and PSFs.

Analysis and application of an equilibrium model for in vitro bioassay systems with three components: Receptor, hormone and hormone-binding-protein

A simple theoretical framework is presented for bioassay studies using three component in vitro systems. An equilibrium model is used to derive equations useful for predicting changes in biological response after addition of hormone-binding-protein or as a consequence of increased hormone affinity. Sets of possible solutions for receptor occupancy and binding protein occupancy are found for typical values of receptor and binding protein affinity constants. Unique equilibrium solutions are dictated by the initial condition of total hormone concentration. According to the occupancy theory of drug action, increasing the affinity of a hormone for its receptor will result in a proportional increase in biological potency. However, the three component model predicts that the magnitude of increase in biological potency will be a small fraction of the proportional increase in affinity. With typical initial conditions a two-fold increase in hormone affinity for its receptor is predicted to result in only a 33% increase in biological response. Under the same conditions an Ii-fold increase in hormone affinity for receptor would be needed to produce a two-fold increase in biological potency. Some currently used bioassay systems may be unrecognized three component systems and gross errors in biopotency estimates will result if the effect of binding protein is not calculated. An algorithm derived from the three component model is used to predict changes in biological response after addition of binding protein to in vitro systems. The algorithm is tested by application to a published data set from an experimental study in an in vitro system (Lim et al., 1990, Endocrinology 127, 1287-1291). Predicted changes show good agreement (within 8%) with experimental observations. (C) 1998 Academic Press Limited.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.

Robust model-order reduction of complex biological processes

This paper addresses robust model-order reduction of a high dimensional nonlinear partial differential equation (PDE) model of a complex biological process. Based on a nonlinear, distributed parameter model of the same process which was validated against experimental data of an existing, pilot-scale BNR activated sludge plant, we developed a state-space model with 154 state variables in this work. A general algorithm for robustly reducing the nonlinear PDE model is presented and based on an investigation of five state-of-the-art model-order reduction techniques, we are able to reduce the original model to a model with only 30 states without incurring pronounced modelling errors. The Singular perturbation approximation balanced truncating technique is found to give the lowest modelling errors in low frequency ranges and hence is deemed most suitable for controller design and other real-time applications. (C) 2002 Elsevier Science Ltd. All rights reserved.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.