De novo transcriptome assembly for a non-model species, the blood-sucking bug Triatoma brasiliensis, a vector of Chagas disease

High throughput sequencing (HTS) provides new research opportunities for work on non-model organisms, such as differential expression studies between populations exposed to different environmental conditions. However, such transcriptomic studies first require the production of a reference assembly. The choice of sampling procedure, sequencing strategy and assembly workflow is crucial. To develop a reliable reference transcriptome for Triatoma brasiliensis, the major Chagas disease vector in Northeastern Brazil, different de novo assembly protocols were generated using various datasets and software. Both 454 and Illumina sequencing technologies were applied on RNA extracted from antennae and mouthparts from single or pooled individuals. The 454 library yielded 278 Mb. Fifteen Illumina libraries were constructed and yielded nearly 360 million RNA-seq single reads and 46 million RNA-seq paired-end reads for nearly 45 Gb. For the 454 reads, we used three assemblers, Newbler, CAP3 and/or MIRA and for the Illumina reads, the Trinity assembler. Ten assembly workflows were compared using these programs separately or in combination. To compare the assemblies obtained, quantitative and qualitative criteria were used, including contig length, N50, contig number and the percentage of chimeric contigs. Completeness of the assemblies was estimated using the CEGMA pipeline. The best assembly (57,657 contigs, completeness of 80 %, < 1 % chimeric contigs) was a hybrid assembly leading to recommend the use of (1) a single individual with large representation of biological tissues, (2) merging both long reads and short paired-end Illumina reads, (3) several assemblers in order to combine the specific advantages of each.

A hybrid method for the probabilistic maximal covering location-allocation problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The benefits of soft sensor and multi-rate control for the implementation of wireless networked control systems

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

Genetic identification of interspecific hybrid of Neotropical catfish species (Pseudoplatystoma corruscans vs. Pseudoplatystoma reticulatum) in rivers of Mato Grosso do Sul State, Brazil

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A hybrid method for the solution of a sparse power system matrices on vector computers

This paper describes a methodology for solving a linear system of equations on vector computer. The methodology combines direct and inverse factors. The decomposition and implementation of the direct solution in a CRAY Y-MPZE/232, and the performance results are discussed.

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

SNIIC: a plataforma digital de cultura brasileira sob a perspectiva das convergências entre o Design e a Ciência da Informação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

United Way and University partnerships in community-wide human services planning and plan implementation: The case of Lincoln/Lancaster County, Nebraska.

Given the similar interests of United Way organizations and universities in planning, implementation, and evaluation of human services, the two social institutions could be extensively and effectively partnering with one another. However, there is little documentation that such cooperative efforts are taking place. This article describes one such collaboration in Lincoln, Nebraska. The purpose of the article is to show the potential of such collaboration to improve community-wide coordination and outcomes by following the principles of a community-engagement model, to generate more effective use of evaluative tools that can assist in developing evidence-based practices in community planning, and to connect areas of study within the university to United Way efforts.

SNIIC: a plataforma digital de cultura brasileira sob a perspectiva das convergências entre o Design e a Ciência da Informação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Regeneration and characterization of somatic hybrids combining sweet orange and mandarin/mandarin hybrid cultivars for citrus scion improvement

Protoplast fusion between sweet orange and mandarin/mandarin hybrids scion cultivars was performed following the model "diploid embryogenic callus protoplast + diploid mesophyll-derived protoplast". Protoplasts were isolated from embryogenic calli of 'Pera' and 'Westin' sweet orange cultivars (Citrus sinensis) and from young leaves of 'Fremont', Nules', and 'Thomas' mandarins (C. reticulata), and 'Nova' tangelo [C. reticulata x (C. paradisi x C. reticulata)]. The regenerated plants were characterized based on their leaf morphology (thickness), ploidy level, and simple sequence repeat (SSR) molecular markers. Plants were successfully generated only when 'Pera' sweet orange was used as the embryogenic parent. Fifteen plants were regenerated being 7 tetraploid and 8 diploid. Based on SSR molecular markers analyses all 7 tetraploid regenerated plants revealed to be allotetraploids (somatic hybrids), including 2 from the combination of 'Pera' sweet orange + 'Fremont' mandarin, 3 'Pera' sweet orange + 'Nules' mandarin, and 2 'Pera' sweet orange + 'Nova' tangelo, and all the diploid regenerated plants showed the 'Pera' sweet orange marker profile. Somatic hybrids were inoculated with Alternaria alternata and no disease symptoms were detected 96 h post-inoculation. This hybrid material has the potential to be used as a tetraploid parent in interploid crosses for citrus scion breeding.

Rationale and knowledge for the universal implementation of sanitation in areas of social vulnerability

The adoption of principles of equality and universality stipulated in legislation for the sanitation sector requires discussions on innovation. The existing model was able to meet sanitary demands, but was unable to attend all areas causing disparities in vulnerable areas. The universal implementation of sanitation requires identification of the know-how that promotes it and analysis of the model adopted today to establish a new method. Analysis of how different viewpoints on the restructuring process is necessary for the definition of public policy, especially in health, and understanding its complexities and importance in confirming social practices and organizational designs. These are discussed to contribute to universal implementation of sanitation in urban areas by means of a review of the literature and practices in the industry. By way of conclusion, it is considered that accepting a particular concept or idea in sanitation means choosing some effective interventions in the network and on the lives of individual users, and implies a redefinition of the space in which it exercises control and management of sewerage networks, such that connected users are perceived as groups with different interests.

Framing the difficulties resulting from implementing a Participatory Management Model in a public hospital

Objective: This study aims to address difficulties reported by the nursing team during the process of changing the management model in a public hospital in Brazil. Methods: This qualitative study used thematic content analysis as proposed by Bardin, and data were analyzed using the theoretical framework of Bolman and Deal. Results: The vertical implementation of Participatory Management contradicted its underlying philosophy and thereby negatively influenced employee acceptance of the change. The decentralized structure of the Participatory Management Model was implemented but shared decision-making was only partially utilized. Despite facilitation of the communication process within the unit, more significant difficulties arose from lack of communication inter-unit. Values and principals need to be shared by teams, however, that will happens only if managers restructure accountabilities changing job descriptions of all team members. Conclusion: Innovative management models that depart from the premise of decentralized decision-making and increased communication encourage accountability, increased motivation and satisfaction, and contribute to improving the quality of care. The contribution of the study is that it describes the complexity of implementing an innovative management model, examines dissent and intentionally acknowledges the difficulties faced by employees in the organization.

Towards a hybrid approach for adapting web graphical user interfaces to heterogeneous devices using context

Ubiquitous Computing promises seamless access to a wide range of applications and Internet based services from anywhere, at anytime, and using any device. In this scenario, new challenges for the practice of software development arise: Applications and services must keep a coherent behavior, a proper appearance, and must adapt to a plenty of contextual usage requirements and hardware aspects. Especially, due to its interactive nature, the interface content of Web applications must adapt to a large diversity of devices and contexts. In order to overcome such obstacles, this work introduces an innovative methodology for content adaptation of Web 2.0 interfaces. The basis of our work is to combine static adaption - the implementation of static Web interfaces; and dynamic adaptation - the alteration, during execution time, of static interfaces so as for adapting to different contexts of use. In hybrid fashion, our methodology benefits from the advantages of both adaptation strategies - static and dynamic. In this line, we designed and implemented UbiCon, a framework over which we tested our concepts through a case study and through a development experiment. Our results show that the hybrid methodology over UbiCon leads to broader and more accessible interfaces, and to faster and less costly software development. We believe that the UbiCon hybrid methodology can foster more efficient and accurate interface engineering in the industry and in the academy.

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.

MODEL PARTITIONS AND COMPACT TEST CASE SUITES

We present a generalized test case generation method, called the G method. Although inspired by the W method, the G method, in contrast, allows for test case suite generation even in the absence of characterization sets for the specification models. Instead, the G method relies on knowledge about the index of certain equivalences induced at the implementation models. We show that the W method can be derived from the G method as a particular case. Moreover, we discuss some naturally occurring infinite classes of FSM models over which the G method generates test suites that are exponentially more compact than those produced by the W method.