On monetary policy and stock market anomalies

This study utilizes a macro-based VAR framework to investigate whether stock portfolios formedon the basis of their value, size and past performance characteristics are affected in a differentialmanner by unexpected US monetary policy actions during the period 1967-2007. Full sample results show that value, small capitalization and past loser stocks are more exposed to monetary policy shocks in comparison to growth, big capitalization and past winner stocks. Subsample analysis, motivated by variation in the realized premia and parameter instability, reveals that monetary policy shocks’ impact on these portfolios is significant and pronounced only during the pre-1983 period.

Molecular analysis of yellow fever virus 17DD vaccine strain

The Oswaldo Cruz Foundation produces most of the yellow fever (YF) vaccine prepared world wide. As part of a broader approach to determine the genetic variability in YF l7D seeds and vaccines and its relevance to viral attenuation the 17DD virus was purifed directly from chick embryo homogenates which is the source of virus used for vaccination of millions of people in Brazil and other countries for half a century. Neutralization and hemagglutination tests showed that the purified virus is similar to the original stock. Furthermore, radioimmune precipitation of 35S-methionine-labeled viral proteins using mouse hyperimmune ascitic fluid revealed identical patterns for the purified 17DD virus and the YF l7D-204 strain except for the 17DD E protein which migrated slower on SDS-PAGE. This difference is likely to be due to N-linked glycosylation. Finally, comparison by northern blot nybridization of virion RNAs of purified 17DD with two other strains of YF virus only fenome-sized molecules for all three viruses. These observations suggest that vaccine phenotype is primarily associated with the accumulation of mutations.

Molecular karyotype analysis and mapping of housekeeping genes to chromosomes of selected species complexes of Leishmania

The molecular karyotypes for 20 reference strais of species complexes of Leishmania were determined by contour-clamped homogeneous eletric field (CHEF) electrosphoresis. Determination of number/position of chromosome-sized bands and chromosomal DNA locations of house-keeping genes were the two criteria used for differentiating and classifying the Leishmania species. We have established two gel running conditions of optimal separation of chromosomes, wich resolved DNA molecules as large as 2,500 kilobase pairs (kb). Chromosomes were polymorphic in number (22-30) and size (200-2,500 kb) of bands among members of five complexes of Leishmania. Although each stock had a distinct karyotype, in general the differences found between strains and/or species within each complex were not clear enough for parasite identification. However, each group showed a specific number of size-concordant DNA molecules, wich allowed distinction among the Leishmania complex parasites. Clear differences between the Old and New world groups of parasites or among some New World Leishmania species were also apparent in relation to the chromosome locations of beta-tubulin genes. Based on these results as well as data from other published studies the potencial of using DNA karyotype for identifying and classifying leishmanial field isolates is discussed.

Mixture of bivariate Poisson regression models with an application to insurance

In a recent paper Bermúdez [2009] used bivariate Poisson regression models for ratemaking in car insurance, and included zero-inflated models to account for the excess of zeros and the overdispersion in the data set. In the present paper, we revisit this model in order to consider alternatives. We propose a 2-finite mixture of bivariate Poisson regression models to demonstrate that the overdispersion in the data requires more structure if it is to be taken into account, and that a simple zero-inflated bivariate Poisson model does not suffice. At the same time, we show that a finite mixture of bivariate Poisson regression models embraces zero-inflated bivariate Poisson regression models as a special case. Additionally, we describe a model in which the mixing proportions are dependent on covariates when modelling the way in which each individual belongs to a separate cluster. Finally, an EM algorithm is provided in order to ensure the models’ ease-of-fit. These models are applied to the same automobile insurance claims data set as used in Bermúdez [2009] and it is shown that the modelling of the data set can be improved considerably.

Mexican Trypanosoma cruzi stocks: analysis of minicircle kDNA homologies by cross-hybridization

Homologies of minicircle kDNA of 27 Mexican stocks were studied by cross-hybridization with four kDNA probes derived from three reference stocks belonging to groups Trypanosoma cruzi I (SO34 cl4 and Silvio) and T. cruzi II (MN) and one Mexican stock. High homologies were only observed with Silvio (six stocks) and Mexican probes (11 stocks). After 30 min exposure (low homology) additional stocks were recognized with SO34 cl4 (three stocks) and Silvio (six stocks) probes; with the Mexican probe only five stocks remained non-reactive. All the stocks were typed by isoenzyme (16 loci) and Mexican stocks belonged to T. cruzi I. Hybridization patterns were not strictly correlated with the observed clustering and cross-hybridization of kDNA minicircles is not available to distinct Mexican stocks.

Candida dubliniensis identification in Brazilian yeast stock collection

We investigated the presence of Candida dubliniensis among isolates previously identified as Candida albicans and maintained in a yeast stock collection from 1994 to 2000. All isolates were serotyped and further evaluated for antifungal susceptibility profile. After doing a screening test for C. dubliniensis isolates based on the capability of colonies to grow at 42°C, its final identification was obtained by randomly amplified polymorphic DNA (RAPD) analysis using three different primers. A total of 46 out of 548 screened isolates did not exhibit growth at 42°C and were further genotyped by RAPD. Eleven isolates were identified as C. dubliniensis with RAPD analysis. Regarding serotypes, 81.5% of C. albicans and all C. dubliniensis isolates belonged to serotype A. Of note, 9 out of 11 C. dubliniensis isolates were obtained from patients with acquired immunodeficiency syndrome (Aids) and all of them were susceptible to azoles and amphotericin B. We found 17 (3%) C. albicans isolates that were dose-dependent susceptibility or resistant to azoles. In conclusion, we found a low rate of C. dubliniensis isolates among stock cultures of yeasts previously identified as C. albicans. Most of these isolates were recovered from oral samples of Aids patients and exhibited high susceptibility to amphotericin B and azoles. C. albicans serotype A susceptible to all antifungal drugs is the major phenotype found in our stock culture.

Carbon isotopic ratio analysis by gas chromatography/combustion/isotope ratio mass spectrometry for the detection of gamma-hydroxybutyric acid (GHB) administration to humans.

Since GHB (gamma-hydroxybutyric acid) is naturally produced in the human body, clinical and forensic toxicologists must be able to discriminate between endogenous levels and a concentration resulting from exposure. To suggest an alternative to the use of interpretative concentration cut-offs, the detection of exogenous GHB in urine specimens was investigated by means of gas chromatography/combustion/isotope ratio mass spectrometry (GC/C/IRMS). GHB was isolated from urinary matrix by successive purification on Oasis MCX and Bond Elute SAX solid-phase extraction (SPE) cartridges prior to high-performance liquid chromatography (HPLC) fractioning using an Atlantis dC18 column eluted with a mixture of formic acid and methanol. Subsequent intramolecular esterification of GHB leading to the formation of gamma-butyrolactone (GBL) was carried out to avoid introduction of additional carbon atoms for carbon isotopic ratio analysis. A precision of 0.3 per thousand was determined using this IRMS method for samples at GHB concentrations of 10 mg/L. The (13)C/(12)C ratios of GHB in samples of subjects exposed to the drug ranged from -32.1 to -42.1 per thousand, whereas the results obtained for samples containing GHB of endogenous origin at concentration levels less than 10 mg/L were in the range -23.5 to -27.0 per thousand. Therefore, these preliminary results show that a possible discrimination between endogenous and exogenous GHB can be made using carbon isotopic ratio analyses.

Compositional data analysis: where we are and where should we be heading?

We take stock of the present position of compositional data analysis, of what has beenachieved in the last 20 years, and then make suggestions as to what may be sensibleavenues of future research. We take an uncompromisingly applied mathematical view,that the challenge of solving practical problems should motivate our theoreticalresearch; and that any new theory should be thoroughly investigated to see if it mayprovide answers to previously abandoned practical considerations. Indeed a main themeof this lecture will be to demonstrate this applied mathematical approach by a number ofchallenging examples

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Analysis of multi-scale systemic risk in Brazil's financial market

This work analyzes whether the relationship between risk and returns predicted by the Capital Asset Pricing Model (CAPM) is valid in the Brazilian stock market. The analysis is based on discrete wavelet decomposition on different time scales. This technique allows to analyze the relationship between different time horizons, since the short-term ones (2 to 4 days) up to the long-term ones (64 to 128 days). The results indicate that there is a negative or null relationship between systemic risk and returns for Brazil from 2004 to 2007. As the average excess return of a market portfolio in relation to a risk-free asset during that period was positive, it would be expected this relationship to be positive. That is, higher systematic risk should result in higher excess returns, which did not occur. Therefore, during that period, appropriate compensation for systemic risk was not observed in the Brazilian market. The scales that proved to be most significant to the risk-return relation were the first three, which corresponded to short-term time horizons. When treating differently, year-by-year, and consequently separating positive and negative premiums, some relevance is found, during some years, in the risk/return relation predicted by the CAPM. However, this pattern did not persist throughout the years. Therefore, there is not any evidence strong enough confirming that the asset pricing follows the model.

Advantages of extended bottom-up proteomics using sap9 for analysis of monoclonal antibodies.

Despite the recent advances in structural analysis of monoclonal antibodies with bottom-up, middle-down, and top-down mass spectrometry (MS), further improvements in analysis accuracy, depth, and speed are needed. The remaining challenges include quantitatively accurate assignment of post-translational modifications, reduction of artifacts introduced during sample preparation, increased sequence coverage per liquid chromatography (LC) MS experiment, and ability to extend the detailed characterization to simple antibody cocktails and more complex antibody mixtures. Here, we evaluate the recently introduced extended bottom-up proteomics (eBUP) approach based on proteolysis with secreted aspartic protease 9, Sap9, for analysis of monoclonal antibodies. Key findings of the Sap9-based proteomics analysis of a single antibody include: (i) extensive antibody sequence coverage with up to 100% for the light chain and up to 99-100% for the heavy chain in a single LC-MS run; (ii) connectivity of complementarity-determining regions (CDRs) via Sap9-produced large proteolytic peptides (3.4 kDa on average) containing up to two CDRs per peptide; (iii) reduced artifact introduction (e. g., deamidation) during proteolysis with Sap9 compared to conventional bottom-up proteomics workflows. The analysis of a mixture of six antibodies via Sap9-based eBUP produced comparable results. Due to the reasons specified above, Sap9-produced proteolytic peptides improve the identification confidence of antibodies from the mixtures compared to conventional bottom-up proteomics dealing with shorter proteolytic peptides.

Measures of fit in multiple correspondence analysis of crisp and fuzzy coded data

When continuous data are coded to categorical variables, two types of coding are possible: crisp coding in the form of indicator, or dummy, variables with values either 0 or 1; or fuzzy coding where each observation is transformed to a set of "degrees of membership" between 0 and 1, using co-called membership functions. It is well known that the correspondence analysis of crisp coded data, namely multiple correspondence analysis, yields principal inertias (eigenvalues) that considerably underestimate the quality of the solution in a low-dimensional space. Since the crisp data only code the categories to which each individual case belongs, an alternative measure of fit is simply to count how well these categories are predicted by the solution. Another approach is to consider multiple correspondence analysis equivalently as the analysis of the Burt matrix (i.e., the matrix of all two-way cross-tabulations of the categorical variables), and then perform a joint correspondence analysis to fit just the off-diagonal tables of the Burt matrix - the measure of fit is then computed as the quality of explaining these tables only. The correspondence analysis of fuzzy coded data, called "fuzzy multiple correspondence analysis", suffers from the same problem, albeit attenuated. Again, one can count how many correct predictions are made of the categories which have highest degree of membership. But here one can also defuzzify the results of the analysis to obtain estimated values of the original data, and then calculate a measure of fit in the familiar percentage form, thanks to the resultant orthogonal decomposition of variance. Furthermore, if one thinks of fuzzy multiple correspondence analysis as explaining the two-way associations between variables, a fuzzy Burt matrix can be computed and the same strategy as in the crisp case can be applied to analyse the off-diagonal part of this matrix. In this paper these alternative measures of fit are defined and applied to a data set of continuous meteorological variables, which are coded crisply and fuzzily into three categories. Measuring the fit is further discussed when the data set consists of a mixture of discrete and continuous variables.

An investigation of the potential of DIP-STR markers for DNA mixture analyses

The genetic characterization of unbalanced mixed stains remains an important area where improvement is imperative. In fact, using the standard tools of forensic DNA profiling (i.e., STR markers), the profile of the minor contributor in mixed DNA stains cannot be successfully detected if its quantitative share of DNA is less than 10% of the mixed trace. This is due to the fact that the major contributor's profile "masks" that of the minor contributor. Besides known remedies to this problem, such as Y-STR analysis, a new compound genetic marker that consists of a Deletion/Insertion Polymorphism (DIP) linked to a Short Tandem Repeat (STR) polymorphism, has recently been developed and proposed [1]. These novel markers are called DIP-STR markers. This paper compares, from a statistical and forensic perspective, the potential usefulness of these novel DIP-STR markers (i) with traditional STR markers in cases of moderately unbalanced mixtures, and (ii) with Y-STR markers in cases of female-male mixtures. This is done through a comparison of the distribution of 100,000 likelihood ratio values obtained using each method on simulated mixtures. This procedure is performed assuming, in turn, the prosecution's and the defence's point of view.