Matrix-vector multiplication and triangular linear solver using GPGPU for symmetric positive definite matrices derived from elliptic equations

The modern GPUs are well suited for intensive computational tasks and massive parallel computation. Sparse matrix multiplication and linear triangular solver are the most important and heavily used kernels in scientific computation, and several challenges in developing a high performance kernel with the two modules is investigated. The main interest it to solve linear systems derived from the elliptic equations with triangular elements. The resulting linear system has a symmetric positive definite matrix. The sparse matrix is stored in the compressed sparse row (CSR) format. It is proposed a CUDA algorithm to execute the matrix vector multiplication using directly the CSR format. A dependence tree algorithm is used to determine which variables the linear triangular solver can determine in parallel. To increase the number of the parallel threads, a coloring graph algorithm is implemented to reorder the mesh numbering in a pre-processing phase. The proposed method is compared with parallel and serial available libraries. The results show that the proposed method improves the computation cost of the matrix vector multiplication. The pre-processing associated with the triangular solver needs to be executed just once in the proposed method. The conjugate gradient method was implemented and showed similar convergence rate for all the compared methods. The proposed method showed significant smaller execution time.

Study of using marker assisted selection on a beef cattle breeding program by model comparison

A data set of a commercial Nellore beef cattle selection program was used to compare breeding models that assumed or not markers effects to estimate the breeding values, when a reduced number of animals have phenotypic, genotypic and pedigree information available. This herd complete data set was composed of 83,404 animals measured for weaning weight (WW), post-weaning gain (PWG), scrotal circumference (SC) and muscle score (MS), corresponding to 116,652 animals in the relationship matrix. Single trait analyses were performed by MTDFREML software to estimate fixed and random effects solutions using this complete data. The additive effects estimated were assumed as the reference breeding values for those animals. The individual observed phenotype of each trait was adjusted for fixed and random effects solutions, except for direct additive effects. The adjusted phenotype composed of the additive and residual parts of observed phenotype was used as dependent variable for models' comparison. Among all measured animals of this herd, only 3160 animals were genotyped for 106 SNP markers. Three models were compared in terms of changes on animals' rank, global fit and predictive ability. Model 1 included only polygenic effects, model 2 included only markers effects and model 3 included both polygenic and markers effects. Bayesian inference via Markov chain Monte Carlo methods performed by TM software was used to analyze the data for model comparison. Two different priors were adopted for markers effects in models 2 and 3, the first prior assumed was a uniform distribution (U) and, as a second prior, was assumed that markers effects were distributed as normal (N). Higher rank correlation coefficients were observed for models 3_U and 3_N, indicating a greater similarity of these models animals' rank and the rank based on the reference breeding values. Model 3_N presented a better global fit, as demonstrated by its low DIC. The best models in terms of predictive ability were models 1 and 3_N. Differences due prior assumed to markers effects in models 2 and 3 could be attributed to the better ability of normal prior in handle with collinear effects. The models 2_U and 2_N presented the worst performance, indicating that this small set of markers should not be used to genetically evaluate animals with no data, since its predictive ability is restricted. In conclusion, model 3_N presented a slight superiority when a reduce number of animals have phenotypic, genotypic and pedigree information. It could be attributed to the variation retained by markers and polygenic effects assumed together and the normal prior assumed to markers effects, that deals better with the collinearity between markers. (C) 2012 Elsevier B.V. All rights reserved.

CONTEXT TREE SELECTION AND LINGUISTIC RHYTHM RETRIEVAL FROM WRITTEN TEXTS

The starting point of this article is the question "How to retrieve fingerprints of rhythm in written texts?" We address this problem in the case of Brazilian and European Portuguese. These two dialects of Modern Portuguese share the same lexicon and most of the sentences they produce are superficially identical. Yet they are conjectured, on linguistic grounds, to implement different rhythms. We show that this linguistic question can be formulated as a problem of model selection in the class of variable length Markov chains. To carry on this approach, we compare texts from European and Brazilian Portuguese. These texts are previously encoded according to some basic rhythmic features of the sentences which can be automatically retrieved. This is an entirely new approach from the linguistic point of view. Our statistical contribution is the introduction of the smallest maximizer criterion which is a constant free procedure for model selection. As a by-product, this provides a solution for the problem of optimal choice of the penalty constant when using the BIC to select a variable length Markov chain. Besides proving the consistency of the smallest maximizer criterion when the sample size diverges, we also make a simulation study comparing our approach with both the standard BIC selection and the Peres-Shields order estimation. Applied to the linguistic sample constituted for our case study, the smallest maximizer criterion assigns different context-tree models to the two dialects of Portuguese. The features of the selected models are compatible with current conjectures discussed in the linguistic literature.

Parameter recovery for a skew-normal IRT model under a Bayesian approach: hierarchical framework, prior and kernel sensitivity and sample size

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is an interest in studying latent variables (or latent traits). Usually such latent traits are assumed to be random variables and a convenient distribution is assigned to them. A very common choice for such a distribution has been the standard normal. Recently, Azevedo et al. [Bayesian inference for a skew-normal IRT model under the centred parameterization, Comput. Stat. Data Anal. 55 (2011), pp. 353-365] proposed a skew-normal distribution under the centred parameterization (SNCP) as had been studied in [R. B. Arellano-Valle and A. Azzalini, The centred parametrization for the multivariate skew-normal distribution, J. Multivariate Anal. 99(7) (2008), pp. 1362-1382], to model the latent trait distribution. This approach allows one to represent any asymmetric behaviour concerning the latent trait distribution. Also, they developed a Metropolis-Hastings within the Gibbs sampling (MHWGS) algorithm based on the density of the SNCP. They showed that the algorithm recovers all parameters properly. Their results indicated that, in the presence of asymmetry, the proposed model and the estimation algorithm perform better than the usual model and estimation methods. Our main goal in this paper is to propose another type of MHWGS algorithm based on a stochastic representation (hierarchical structure) of the SNCP studied in [N. Henze, A probabilistic representation of the skew-normal distribution, Scand. J. Statist. 13 (1986), pp. 271-275]. Our algorithm has only one Metropolis-Hastings step, in opposition to the algorithm developed by Azevedo et al., which has two such steps. This not only makes the implementation easier but also reduces the number of proposal densities to be used, which can be a problem in the implementation of MHWGS algorithms, as can be seen in [R.J. Patz and B.W. Junker, A straightforward approach to Markov Chain Monte Carlo methods for item response models, J. Educ. Behav. Stat. 24(2) (1999), pp. 146-178; R. J. Patz and B. W. Junker, The applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses, J. Educ. Behav. Stat. 24(4) (1999), pp. 342-366; A. Gelman, G.O. Roberts, and W.R. Gilks, Efficient Metropolis jumping rules, Bayesian Stat. 5 (1996), pp. 599-607]. Moreover, we consider a modified beta prior (which generalizes the one considered in [3]) and a Jeffreys prior for the asymmetry parameter. Furthermore, we study the sensitivity of such priors as well as the use of different kernel densities for this parameter. Finally, we assess the impact of the number of examinees, number of items and the asymmetry level on the parameter recovery. Results of the simulation study indicated that our approach performed equally as well as that in [3], in terms of parameter recovery, mainly using the Jeffreys prior. Also, they indicated that the asymmetry level has the highest impact on parameter recovery, even though it is relatively small. A real data analysis is considered jointly with the development of model fitting assessment tools. The results are compared with the ones obtained by Azevedo et al. The results indicate that using the hierarchical approach allows us to implement MCMC algorithms more easily, it facilitates diagnosis of the convergence and also it can be very useful to fit more complex skew IRT models.

INFERENCES FOR THE CHANGE-POINT OF THE EXPONENTIATED WEIBULL HAZARD FUNCTION

In many applications of lifetime data analysis, it is important to perform inferences about the change-point of the hazard function. The change-point could be a maximum for unimodal hazard functions or a minimum for bathtub forms of hazard functions and is usually of great interest in medical or industrial applications. For lifetime distributions where this change-point of the hazard function can be analytically calculated, its maximum likelihood estimator is easily obtained from the invariance properties of the maximum likelihood estimators. From the asymptotical normality of the maximum likelihood estimators, confidence intervals can also be obtained. Considering the exponentiated Weibull distribution for the lifetime data, we have different forms for the hazard function: constant, increasing, unimodal, decreasing or bathtub forms. This model gives great flexibility of fit, but we do not have analytic expressions for the change-point of the hazard function. In this way, we consider the use of Markov Chain Monte Carlo methods to get posterior summaries for the change-point of the hazard function considering the exponentiated Weibull distribution.

CRITICAL APPLICATIONS OF SW 846 US EPA METHODS TO EVALUATION OF MARINE SAMPLES QUALITY

Technical evaluation of analytical data is of extreme relevance considering it can be used for comparisons with environmental quality standards and decision-making as related to the management of disposal of dredged sediments and the evaluation of salt and brackish water quality in accordance with CONAMA 357/05 Resolution. It is, therefore, essential that the project manager discusses the environmental agency`s technical requirements with the laboratory contracted for the follow-up of the analysis underway and even with a view to possible re-analysis when anomalous data are identified. The main technical requirements are: (1) method quantitation limits (QLs) should fall below environmental standards; (2) analyses should be carried out in laboratories whose analytical scope is accredited by the National Institute of Metrology (INMETRO) or qualified or accepted by a licensing agency; (3) chain of custody should be provided in order to ensure sample traceability; (4) control charts should be provided to prove method performance; (5) certified reference material analysis or, if that is not available, matrix spike analysis, should be undertaken and (6) chromatograms should be included in the analytical report. Within this context and with a view to helping environmental managers in analytical report evaluation, this work has as objectives the discussion of the limitations of the application of SW 846 US EPA methods to marine samples, the consequences of having data based on method detection limits (MDL) and not sample quantitation limits (SQL), and present possible modifications of the principal method applied by laboratories in order to comply with environmental quality standards.

Comparison Between Micro- and Macrosurgical Techniques for the Treatment of Localized Gingival Recessions Using Coronally Positioned Flaps and Enamel Matrix Derivative

Background: The aim of this study is to compare the macro- and microsurgery techniques for root coverage using a coronally positioned flap (CPF) associated with enamel matrix derivative (EMD). Methods: Thirty patients were selected for the treatment of localized gingival recessions (GRs) using CPF associated to EMD. Fifteen patients were randomly assigned to the test group (TG), and 15 patients were randomly assigned to the control group (CG). The microsurgical approach was performed in the TG, and the conventional macrosurgical technique was performed in the CG. The clinical parameters evaluated before surgery and after 6 months were GR, probing depth, relative clinical attachment level, width of keratinized tissue (WKT), and thickness of keratinized tissue (TKT). The discomfort evaluation was performed 1 week postoperative. Results: There were no statistically significant differences between groups for all parameters at baseline. At 6 months, there was no statistically significant difference between the techniques in achieving root coverage. The percentage of root coverage was 92% and 83% for TG and CG, respectively. After 6 months, there was a statistically significant increase of WKT and TKT in TG only. Both procedures were well tolerated by all patients. Conclusions: The macro- and microsurgery techniques provided a statistically significant reduction in GR height. After 6 months, there was no statistically significant difference between the techniques regarding root coverage, and the microsurgical technique demonstrated a statistically significant increase in WKT and TKT. J Periodontol 2010;81:1572-1579.

Fuzzy linguistic model for bioelectrical impedance vector analysis

Backgrounds Ea aims: The boundaries between the categories of body composition provided by vectorial analysis of bioimpedance are not well defined. In this paper, fuzzy sets theory was used for modeling such uncertainty. Methods: An Italian database with 179 cases 18-70 years was divided randomly into developing (n = 20) and testing samples (n = 159). From the 159 registries of the testing sample, 99 contributed with unequivocal diagnosis. Resistance/height and reactance/height were the input variables in the model. Output variables were the seven categories of body composition of vectorial analysis. For each case the linguistic model estimated the membership degree of each impedance category. To compare such results to the previously established diagnoses Kappa statistics was used. This demanded singling out one among the output set of seven categories of membership degrees. This procedure (defuzzification rule) established that the category with the highest membership degree should be the most likely category for the case. Results: The fuzzy model showed a good fit to the development sample. Excellent agreement was achieved between the defuzzified impedance diagnoses and the clinical diagnoses in the testing sample (Kappa = 0.85, p < 0.001). Conclusions: fuzzy linguistic model was found in good agreement with clinical diagnoses. If the whole model output is considered, information on to which extent each BIVA category is present does better advise clinical practice with an enlarged nosological framework and diverse therapeutic strategies. (C) 2012 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

A relaxed constant positive linear dependence constraint qualification and applications

In this work we introduce a relaxed version of the constant positive linear dependence constraint qualification (CPLD) that we call RCPLD. This development is inspired by a recent generalization of the constant rank constraint qualification by Minchenko and Stakhovski that was called RCRCQ. We show that RCPLD is enough to ensure the convergence of an augmented Lagrangian algorithm and that it asserts the validity of an error bound. We also provide proofs and counter-examples that show the relations of RCRCQ and RCPLD with other known constraint qualifications. In particular, RCPLD is strictly weaker than CPLD and RCRCQ, while still stronger than Abadie's constraint qualification. We also verify that the second order necessary optimality condition holds under RCRCQ.

Acellular dermal matrix graft with or without enamel matrix derivative for root coverage in smokers: a randomized clinical study

Aim: The aim of this randomized controlled clinical study was to compare the use of an acellular dermal matrix graft (ADMG) with or without the enamel matrix derivative (EMD) in smokers to evaluate which procedure would provide better root coverage. Material and Methods: Nineteen smokers with bilateral Miller Class I or II gingival recessions >= 3 mm were selected. The test group was treated with an association of ADMG and EMD, and the control group with ADMG alone. Probing depth, relative clinical attachment level, gingival recession height, gingival recession width, keratinized tissue width and keratinized tissue thickness were evaluated before the surgeries and after 6 months. Wilcoxon test was used for the statistical analysis at significance level of 5%. Results: No significant differences were found between groups in all parameters at baseline. The mean gain recession height between baseline and 6 months and the complete root coverage favored the test group (p = 0.042, p = 0.019 respectively). Conclusion: Smoking may negatively affect the results achieved through periodontal plastic procedures; however, the association of ADMG and EMD is beneficial in the root coverage of gingival recessions in smokers, 6 months after the surgery.

miR-21 may acts as an oncomir by targeting RECK, a matrix metalloproteinase regulator, in prostate cancer

Background: Prognosis of prostate cancer (PCa) is based mainly in histological aspects together with PSA serum levels that not always reflect the real aggressive potential of the neoplasia. The micro RNA (miRNA) mir-21 has been shown to regulate invasiveness in cancer through translational repression of the Metaloproteinase (MMP) inhibitor RECK. Our aim is to investigate the levels of expression of RECK and miR-21 in PCa comparing with classical prognostic factors and disease outcome and also test if RECK is a target of miR-21 in in vitro study using PCa cell line. Materials and methods: To determine if RECK is a target of miR-21 in prostate cancer we performed an in vitro assay with PCa cell line DU-145 transfected with pre-miR-21 and anti-miR-21. To determine miR-21 and RECK expression levels in PCa samples we performed quantitative real-time polymerase chain reaction (qRT-PCR). Results: The in vitro assays showed a decrease in expression levels of RECK after transfection with pre-miR-21, and an increase of MMP9 that is regulated by RECK compared to PCa cells treated with anti-miR-21. We defined three profiles to compare the prognostic factors. The first was characterized by miR-21 and RECK underexpression (N = 25) the second was characterized by miR-21 overexpression and RECK underexpression (N = 12), and the third was characterized by miR-21 underexpression and RECK overexpression (N = 16). From men who presented the second profile (miR-21 overexpression and RECK underexpression) 91.7% were staged pT3. For the other two groups 48.0%, and 46.7% of patients were staged pT3 (p = 0.025). Conclusions: Our results demonstrate RECK as a target of miR-21. We believe that miR-21 may be important in PCa progression through its regulation of RECK, a known regulator of tumor cell invasion.

A Randomized comparative clinical study of two surgical procedures to improve root coverage with the acellular dermal matrix graft

Aim This randomized, controlled, clinical study compared two surgical techniques for root coverage with the acellular dermal matrix graft (ADMG) to evaluate which procedure could provide better root coverage and greater amounts of keratinized tissue. Materials and Methods Fifteen pairs of bilateral Miller Class I or II gingival recessions were treated and assigned randomly to the test group, and the contra-lateral recessions were assigned to the control group. The ADMG was used in both groups. In the control group, the graft and flap were positioned at the level of the cemento-enamel junction (CEJ), and in the test group, the graft was positioned 1 mm apical to the CEJ and the flap 1 mm coronal to the CEJ. The clinical parameters were taken before the surgeries and after 6 months. The gingival recession area, a new parameter, was measured in standardized photographs through a special device and software. Results There were statistically significant differences favouring the proposed technique for all parameters except for the amount of keratinized tissue at 6 months. Conclusions The proposed test technique is more suitable for root coverage procedures with ADMG, and the new parameter evaluated appears valuable for root coverage analysis. (Clinicaltrials.gov Identifier: NCT01175720).

Bioelectrical impedance vector analysis (BIVA) in stable preterm newborns

Objective: To observe the behavior of the plotted vectors on the RXc (R - resistance - and Xc - reactance corrected for body height/length) graph through bioelectrical impedance analysis (BIVA) and phase angle (PA) values in stable premature infants, considering the hypothesis that preterm infants present vector behavior on BIVA suggestive of less total body water and soft tissues, compared to reference data for term infants. Methods: Cross-sectional study, including preterm neonates of both genders, in-patients admitted to an intermediate care unit at a tertiary care hospital. Data on delivery, diet and bioelectrical impedance (800 mA, 50 kHz) were collected. The graphs and vector analysis were performed with the BIVA software. Results: A total of 108 preterm infants were studied, separated according to age (< 7 days and >= 7 days). Most of the premature babies were without the normal range (above the 95% tolerance intervals) existing in literature for term newborn infants and there was a tendency to dispersion of the points in the upper right quadrant, RXc plan. The PA was 4.92 degrees (+/- 2.18) for newborns < 7 days and 4.34 degrees (+/- 2.37) for newborns >= 7 days. Conclusion: Premature infants behave similarly in terms of BIVA and most of them have less absolute body water, presenting less fat free mass and fat mass in absolute values, compared to term newborn infants.

Venomics Profiling of Thamnodynastes strigatus Unveils Matrix Metalloproteinases and Other Novel Proteins Recruited to the Toxin Arsenal of Rear-Fanged Snakes

Rear-fanged and aglyphous snakes are usually considered not dangerous to humans because of their limited capacity of injecting venom. Therefore, only a few studies have been dedicated to characterizing the venom of the largest parcel of snake fauna. Here, we investigated the venom proteome of the rear-fanged snake Thamnodynastes strigatus, in combination with a transcriptomic evaluation of the venom gland. About 60% of all transcripts code for putative venom components. A striking finding is that the most abundant type of transcript (similar to 47%) and also the major protein type in the venom correspond to a new kind of matrix metalloproteinase (MMP) that is unrelated to the classical snake venom metalloproteinases found in all snake families. These enzymes were recently suggested as possible venom components, and we show here that they are proteolytically active and probably recruited to venom from a MMP-9 ancestor. Other unusual proteins were suggested to be venom components: a protein related to lactadherin and an EGF repeat-containing transcript. Despite these unusual molecules, seven toxin classes commonly found in typical venomous snakes are also present in the venom. These results support the evidence that the arsenals of these snakes are very diverse and harbor new types of biologically important molecules.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.