Temporal trends and spatial distribution of unsafe abortion in Brazil, 1996-2012

OBJECTIVE To analyze temporal trends and distribution patterns of unsafe abortion in Brazil. METHODS Ecological study based on records of hospital admissions of women due to abortion in Brazil between 1996 and 2012, obtained from the Hospital Information System of the Ministry of Health. We estimated the number of unsafe abortions stratified by place of residence, using indirect estimate techniques. The following indicators were calculated: ratio of unsafe abortions/100 live births and rate of unsafe abortion/1,000 women of childbearing age. We analyzed temporal trends through polynomial regression and spatial distribution using municipalities as the unit of analysis. RESULTS In the study period, a total of 4,007,327 hospital admissions due to abortions were recorded in Brazil. We estimated a total of 16,905,911 unsafe abortions in the country, with an annual mean of 994,465 abortions (mean unsafe abortion rate: 17.0 abortions/1,000 women of childbearing age; ratio of unsafe abortions: 33.2/100 live births). Unsafe abortion presented a declining trend at national level (R2: 94.0%, p < 0.001), with unequal patterns between regions. There was a significant reduction of unsafe abortion in the Northeast (R2: 93.0%, p < 0.001), Southeast (R2: 92.0%, p < 0.001) and Central-West regions (R2: 64.0%, p < 0.001), whereas the North (R2: 39.0%, p = 0.030) presented an increase, and the South (R2: 22.0%, p = 0.340) remained stable. Spatial analysis identified the presence of clusters of municipalities with high values for unsafe abortion, located mainly in states of the North, Northeast and Southeast Regions. CONCLUSIONS Unsafe abortion remains a public health problem in Brazil, with marked regional differences, mainly concentrated in the socioeconomically disadvantaged regions of the country. Qualification of attention to women’s health, especially to reproductive aspects and attention to pre- and post-abortion processes, are necessary and urgent strategies to be implemented in the country.

Intimate partner violence after the diagnosis of sexually transmitted diseases

OBJECTIVE To assess the prevalence and factors associated with intimate partner violence after the diagnosis of sexually transmitted diseases.METHODS This cross-sectional study was conducted in Fortaleza, CE, Northeastern Brazil, in 2012 and involved 221 individuals (40.3% male and 59.7% female) attended to at reference health care units for the treatment of sexually transmitted diseases. Data were collected using a questionnaire applied during interviews with each participant. A multivariate analysis with a logistic regression model was conducted using the stepwise technique. Only the variables with a p value < 0.05 were included in the adjusted analysis. The odds ratio (OR) with 95% confidence interval (CI) was used as the measure of effect.RESULTS A total of 30.3% of the participants reported experiencing some type of violence (27.6%, psychological; 5.9%, physical; and 7.2%, sexual) after the diagnosis of sexually transmitted disease. In the multivariate analysis adjusted to assess intimate partner violence after the revelation of the diagnosis of sexually transmitted diseases, the following variables remained statistically significant: extramarital relations (OR = 3.72; 95%CI 1.91;7.26; p = 0.000), alcohol consumption by the partner (OR = 2.16; 95%CI 1.08;4.33; p = 0.026), history of violence prior to diagnosis (OR = 2.87; 95%CI 1.44;5.69; p = 0.003), and fear of disclosing the diagnosis to the partner (OR = 2.66; 95%CI 1.32;5.32; p = 0.006).CONCLUSIONS Individuals who had extramarital relations, experienced violence prior to the diagnosis of sexually transmitted disease, feared disclosing the diagnosis to the partner, and those whose partner consumed alcohol had an increased likelihood of suffering violence. The high prevalence of intimate partner violence suggests that this population is vulnerable and therefore intervention efforts should be directed to them. Referral health care services for the treatment of sexually transmitted diseases can be strategic places to identify and prevent intimate partner violence.

Machine learning Vasicek model calibration with Gaussian processes

In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.

Probability and statistics: selected problems

Probability and Statistics—Selected Problems is a unique book for senior undergraduate and graduate students to fast review basic materials in Probability and Statistics. Descriptive statistics are presented first, and probability is reviewed secondly. Discrete and continuous distributions are presented. Sample and estimation with hypothesis testing are presented in the last two chapters. The solutions for proposed excises are listed for readers to references.

Characterisation of adventitious lung sounds in people with dementia

Introduction: Lower Respiratory Tract Infections (LRTIs) are highly prevalent in institutionalised people with dementia, constituting an important cause of morbidity and mortality. Computerised auscultation of Adventitious Lung Sounds (ALS) has shown to be objective and reliable to assess and monitor respiratory diseases, however its application in people with dementia is unknown. Aim: This study characterised ALS (crackles and wheezes) in institutionalised people with dementia. Methods: An exploratory descriptive study, including 6 long-term care institutions was conducted. The sample included a dementia group (DG) of 30 people with dementia and a match healthy group (HG) of 30 elderly people. Socio-demographic and anthropometric data, cognition, type and severity of dementia, cardio-respiratory parameters, balance, mobility and activities and participation were collected. Lung sounds were recorded with a digital stethoscope following Computerised Respiratory Sound Analysis (CORSA) guidelines. Crackles’ location, number (N), frequency (F), two-cycle duration (2CD), initial deflection width (IDW) and largest deflection width (LDW) and wheezes’ number (N), ratio (R) and frequency (F) were analysed per breathing phase. Statistical analyses were performed using PASW Statistics(v.19). Results: There were no significant differences between the two groups in relation to the mean N of crackles during inspiration and expiration in both trachea and thorax. DG trachea crackles had significant higher F during inspiration and lower IDW, 2CD and LDW during expiration when compared with HG. At the thorax, the LDW during inspiration was also significantly lower in the DG. A significant higher N of inspiratory wheezes was found in the HG. Both groups had a low ratio of high frequency wheezes. Conclusion: Computerised analyses of ALS informed on the respiratory system and function of people with dementia and elderly people. Hence, this could be the step towards prevention, early diagnosis and continuous monitoring of respiratory diseases in people with cognitive impairment.

Vigilância activa de eventos após vacinação

A vigilância de efeitos indesejáveis após a vacinação é complexa. Existem vários actores de confundimento que podem dar origem a associações espúrias, meramente temporais mas que podem provocar uma percepção do risco alterada e uma consequente desconfiança generalizada acerca do uso das vacinas. Com efeito as vacinas são medicamentos complexos com características únicas cuja vigilância necessita de abordagens metodológicas desenvolvidas para esse propósito. Do exposto se entende que, desde o desenvolvimento da farmacovigilância se tem procurado desenvolver novas metodologias que sejam concomitantes aos Sistemas de Notificação Espontânea que já existem. Neste trabalho propusemo-nos a desenvolver e testar um modelo de vigilância de reacções adversas a vacinas, baseado na auto-declaração pelo utente de eventos ocorridos após a vacinação e testar a capacidade de gerar sinais aplicando cálculos de desproporção a datamining. Para esse efeito foi constituída uma coorte não controlada de utentes vacinados em Centros de Saúde que foram seguidos durante quinze dias. A recolha de eventos adversos a vacinas foi efectuada pelos próprios utentes através de um diário de registo. Os dados recolhidos foram objecto de análise descritiva e análise de data-mining utilizando os cálculos Proportional Reporting Ratio e o Information Component. A metodologia utilizada permitiu gerar um corpo de evidência suficiente para a geração de sinais. Tendo sido gerados quatro sinais. No âmbito do data-mining a utilização do Information Component como método de geração de sinais parece aumentar a eficiência científica ao permitir reduzir o número de ocorrências até detecção de sinal. A informação reportada pelos utentes parece válida como indicador de sinais de reacções adversas não graves, o que permitiu o registo de eventos sem incluir o viés da avaliação da relação causal pelo notificador. Os principais eventos reportados foram eventos adversos locais (62,7%) e febre (31,4%).------------------------------------------ABSTRACT: The monitoring of undesirable effects following vaccination is complex. There are several confounding factors that can lead to merely temporal but spurious associations that can cause a change in the risk perception and a consequent generalized distrust about the safe use of vaccines. Indeed, vaccines are complex drugs with unique characteristics so that its monitoring requires specifically designed methodological approaches. From the above-cited it is understandable that since the development of Pharmacovigilance there has been a drive for the development of new methodologies that are concomitant with Spontaneous Reporting Systems already in place. We proposed to develop and test a new model for vaccine adverse reaction monitoring, based on self-report by users of events following vaccination and to test its capability to generate disproportionality signals applying quantitative methods of signal generation to data-mining. For that effect we set up an uncontrolled cohort of users vaccinated in Healthcare Centers,with a follow-up period of fifteen days. Adverse vaccine events we registered by the users themselves in a paper diary The data was analyzed using descriptive statistics and two quantitative methods of signal generation: Proportional Reporting Ratio and Information Component. themselves in a paper diary The data was analyzed using descriptive statistics and two quantitative methods of signal generation: Proportional Reporting Ratio and Information Component. The methodology we used allowed for the generation of a sufficient body of evidence for signal generation. Four signals were generated. Regarding the data-mining, the use of Information Component as a method for generating disproportionality signals seems to increase scientific efficiency by reducing the number of events needed to signal detection. The information reported by users seems valid as an indicator of non serious adverse vaccine reactions, allowing for the registry of events without the bias of the evaluation of the casual relation by the reporter. The main adverse events reported were injection site reactions (62,7%) and fever (31,4%).

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Stress Ratio Effects on the fatigue Properties of Biaxially Loaded Tubular FRP-Specimens – Experimental Study

In this work tubular fiber reinforced specimens are tested for fatigue life. The specimens are biaxially loaded with tension and shear stresses, with a load angle β of 30° and 60° and a load ratio of R=0,1. There are many factors that affect fatigue life of a fiber reinforced material and the main goal of this work is to study the effects of load ratio R by obtaining S-N curves and compare them to the previous works (1). All the other parameters, such as specimen production, fatigue loading frequency and temperature, will be the same as for the previous tests. For every specimen, stiffness, temperature of the specimen during testing, crack counting and final fracture mode are obtained. Prior to testing, a study if the literature regarding the load ratio effects on composites fatigue life and with that review estimate the initial stresses to be applied in testing. In previous works (1) similar specimens have only been tested for a load ratio of R=-1 and therefore the behaviour of this tubular specimens for a different load ratio is unknown. All the data acquired will be analysed and compared to the previous works, emphasizing the differences found and discussing the possible explanations for those differences. The crack counting software, developed at the institute, has shown useful before, however different adjustments to the software parameters lead to different cracks numbers for the same picture, and therefore a better methodology will be discussed to improve the crack counting results. After the specimen’s failure, all the data will be collected and stored and fibre volume content for every specimen is also determinate. The number of tests required to make the S-N curves are obtained according to the existent standards. Additionally are also identified some improvements to the testing machine setup and to the procedures for future testing.

Epidemiological and clinical aspects of snAkebite in Belo Horizonte, Southeast Brazil

Epidemiologic and clinical aspects of 310 hospitalized snakebite patients and 310 matched controls were described, over a seven years period, from an emergency hospital in Belo Horizonte, Southeast Brazil. The diagnosis was based upon clinical picture or actual snake identification. Fifty six percent of victims were bitten by the snakes of genus Bothrops, 32.0% by Crotalus, 1.0% by Lachesis and 10.0% undetermined. During the study period, stable number of cases and marked seasonal variation were noted. In comparing cases of snakebite and controls, those from a rural area or who were involved in agricultural labor activity were identified as a high risk group, with an odds ratio (OR) of 14.7 and 6.7, respectively, in favor of being bitten. Upon treatment, snakebite patients were 13.5 times more likely to have had early anaphylactic reactions than their controls, with a higher association in the age group ³ 20 years (OR = 30.3). Increased risks were also detected for pyrexia (OR = 11.7), with a marked association in the group under 19 years old (OR = 16.6). Severe cases of snakebite are an important treatable cause of morbidity in Brazil but therapy may be potentially life threatening. The higher case-fatality ratio encountered, compared to national statistics may be due the representativeness of the more severe cases who sought hospitalization. Preventing snakebite and early referral of those who are bitten is proposed

Extremum estimators and stochastic optimization methods

Submitted in partial fulfillment for the Requirements for the Degree of PhD in Mathematics, in the Speciality of Statistics in the Faculdade de Ciências e Tecnologia

Digit Ratio (2D:4D) in Newborns: Influences of Prenatal Testosterone and Maternal Environment

Introduction: The 2D:4D digit ratio is sexually-dimorphic, probably due to testosterone action through the perinatal period. We characterize the 2D:4D ratio in newborn (NB) infants, in between the pre- and postnatal surges of testosterone, and relate it to the mother's 2D:4D and to testosterone levels in the amniotic fluid (AF). Subjects and methods: Testosterone was assayed in samples of maternal plasma and AF collected at amniocentesis. Shortly after birth, 106 NBs and their mothers were measured for 2D:4D ratio. Results: NB males had lower mean 2D:4D ratios than females but this dimorphism was significant only for the left hand (males: 0.927; females: 0.950; p=0.004). Mothers who had sons had lower 2D:4D ratios than those who had daughters and the mother's 2D:4D were higher than those of NBs regardless of sex. Both hands of NB females were negatively correlated with AF testosterone and positively correlated with the mother's 2D:4D, but males showed no significant associations. Maternal plasma testosterone also showed a negative weak correlation with NB's digit ratio in both sexes. Conclusions: Sexual dimorphism at birth was only significant for the left hand, in contrast with reports of greater right hand dimorphism, suggesting that postnatal testosterone is determinant for 2D:4D stabilization. The lower 2D:4D ratios in mothers who had sons support claims that hormone levels in parents are influential for determining their children's sex. NB female's digit ratio, but not males', was associated to the level of AF testosterone. The mother's 2D:4D ratios were positively correlated with their daughters' 2D:4D, but the same was not observed for male NBs, suggesting that prenatal testosterone levels in male fetus lead their 2D:4D ratios to stray from their mothers' with high individual variability.

Antibiotics Utilization Ratio in a Nicu

Introduction: Antibiotics are one of the most common prescribed drugs in the NICU; despite this, studies on its use are scarce. Aim: To assess antibiotics utilization ratio in a medical surgical NICU. Methods: Prospective, observational study. Daily registry of antibiotics given to newborn infants; two periods of two months, 2010; data collected every day after the second medical round. Variables: treated patients, days on antibiotics, treatment/patient days, number of courses, number of antibiotics. Antibiotics utilization ratio – ratio days on antibiotics/days at the NICU. Results: Patients enrolled - 113; admission days – 1722; length of stay - 15.2 days; 85 newborn infants were given antibiotics; days on antibiotics - 771; antibiotics utilization ratio – 44.8; 292 antibiotics were prescribed; 61.8% of patients were given more than two antibiotics and 15.3% had more than one course. The most frequents were gentamicin, cefotaxime, ampicillin, vancomycin and metronidazole. Conclusion: Antibiotics utilization ratio should be subject of audits and a quality criteria on NICUs evaluation.

Occurrence of Ancylostoma in dogs, cats and public places from Andradina city, São Paulo state, Brazil

The aim of this study was to determine the frequency and intensity of Ancylostoma spp. in 33 dogs and 52 cats by means of coproparasitological examinations and parasitological necropsy, and assess the presence of contaminated feces with eggs of that parasite in public places of Andradina Municipality, São Paulo State, Brazil. Willis-Mollay and Sedimentation methods indicated Ancylostoma spp. eggs in 87.8% (29/33) dogs and 94.2% (49/52) cats. The species A. caninum and A. braziliense were found in 63.6% (21/33) and 30.3% (10/33) of dogs, respectively. Considering cats, 67.3% (35/52) were parasitized by A. braziliense, 21.1% (11/52) by A. caninum, and 9.6% (5/52) by A. tubaeforme. Forty-two canine fecal samples were collected from public environments, including 23 squares/gardens and 19 streets/sidewalks. Positive samples for Ancylostoma spp. accounted for 64.3% (27/42); squares/gardens had 60.9% (14/23) positive samples, and streets and sidewalks, 68.4% (13/19). No association was observed between the number of Ancylostoma spp parasites and age, sex and breed of the animals and also the ratio of EPG counts and the parasitic intensity observed at necropsy (p > 0.05). Based on the high occurrence of hookworm in dogs and cats in this study, the treatment with anti helminthics are needed even in those animals with negative stool tests, besides adopting control of the number of animals in public places, in order to decrease the likelihood of environmental contamination, since this parasite represents a potential hazard to human and animal health.

Maximum likelihood detection for OFDM signals with strong nonlinear distortion effects

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores