Introduction: Leishmaniasis is an infectious and parasitic zoonotic, non-contagious, vectorborne disease caused by protozoa of the genus Leishmania. In Brazil, the major vector of Leishmania (Leishmania) infantum chagasi (Cunha & Chagas, 1934) is Lutzomyia longipalpis. Barra do Garças, State of Mato Grosso, was designated as a priority area by the Brazilian Ministry of Health for american visceral leishmaniasis, and it is important to identify the vector species present in this municipality. Our objective was to raise sandflies and study the influence of environmental variables on the vector density of Lutzomyia longipalpis. Methods: We performed entomological monitoring in 3 districts using Centers for Disease Control and Prevention (CDC) light traps and recorded human cases of american visceral leishmaniasis in the city. We calculated the relative frequency and richness of sandflies and applied a transfer function model to the vector density correlate with relative humidity. Results: The sandfly population was composed of 2 genera and 27 species, totaling 8,097 individuals. Monitoring identified Lutzomyia longipalpis (44%), followed by Lutzomyia lenti (18.9%), Lutzomyia whitmani (13.9%), Lutzomyia carmelinoi (9.1%), Lutzomyia evandroi (5.1%), Lutzomyia termitophila (3.3%), Lutzomyia sordellii (1.9%), and 20 other species (<4%). The male:female ratio was 3.5:1. We observed high species diversity (Dα = 6.65). Lutzomyia longipalpis showed occurrence peaks during the rainy season; there was a temporal correlation with humidity, but not with frequency or temperature. Conclusions: The presence of Lutzomyia longipalpis in the urban area of Barra do Garças underscores the changing disease profile, which was previously restricted to the wild environment.

Shallow lakes of temperate areas experience seasonal and inter-annual variability in weather conditions, impacting on their biological communities. Here, we studied the temporal fluctuation of the zooplankton community in a highly eutrophic shallow lake, Laguna Chascomu ´s. Rotifers and the cyclopoid copepod Acanthocyclops robustus dominated the community. The most important rotifers were Brachionus caudatus, B. havanaensis, and Keratella tropica. The abundance of the two Brachionus species reached maximum values in late summer/early autumn. In contrast, K. tropica and A. robustus did not display seasonal patterns. A prolonged period of low water temperature resulted in a massive fish winterkill event (in 2007), which seemingly allowed the development of unusually dense populations of cladocerans. We used vector autoregressive models to analyze the rotifer time series. The model accounted for 76% of the variance in rotifer abundance and provided evidence of their dependence on temperature and chlorophyll a. In addition, the impact of the fish winterkill on rotifer abundance could be assessed through intervention analysis. The evidence collected here suggests that the zooplankton community structure is controlled by fish planktivory, while rotifers population dynamics are mostly driven by temperature and available food. Both processes seem highly responsive to forcing weather variables.

In this paper, a novel statistical test is introduced to compare two locally stationary time series. The proposed approach is a Wald test considering time-varying autoregressive modeling and function projections in adequate spaces. The covariance structure of the innovations may be also time-varying. In order to obtain function estimators for the time-varying autoregressive parameters, we consider function expansions in splines and wavelet bases. Simulation studies provide evidence that the proposed test has a good performance. We also assess its usefulness when applied to a financial time series.

BackgroundThe interactions between pathogen proteins and their hosts allow pathogens to manipulate host cellular mechanisms to their advantage. The identification of host proteins that are targeted by virulent pathogen proteins is crucial to increase our understanding of infection mechanisms and to propose new therapeutics that target pathogens. Understanding the virulence mechanisms of pathogens requires a detailed molecular description of the proteins involved, but acquiring this knowledge is time consuming and prohibitively expensive. Therefore, we develop a statistical method based on hypothesis testing to compare the time series obtained from conversion of the physicochemical characteristics of the amino acids that form the primary structure of proteins and thus to propose potential functional relation between proteins. We called this algorithm the multiple spectral comparison algorithm (MSCA); the MSCA was inspired by the BLASTP tool and was implemented in R code. The algorithm compares and relates multiple time series according to their spectral similarities, and the biological relation between them could be interpreted as either a similar function or protein-protein interaction (PPI).ResultsA simulation study showed that the MSCA works satisfactorily well when we compare unequal time series generated from ARMA processes because its power was close to 1. The MSCA presented a 70% average accuracy of detecting protein interactions using a threshold of 0.7 for our spectral measure, indicating that this algorithm could predict novel PPIs and pathogen-host interactions (PHIs) with acceptable confidence. The MSCA also was validated by its identification of well-known interactions of the human proteins MAGI1, SCRIB and JAK1, as well as interactions of the virulence proteins ROP16, ROP18, ROP17 and ROP5. We verified the spectral similarities for human intraspecific PPIs and PHIs that were previously demonstrated experimentally by other authors. We suggest that human GBP (GTPase group induced by interferon) and the CREB transcription factor family could be human substrates for the complex of ROP18, ROP17 and ROP5.ConclusionsUsing multiple-hypothesis testing between the spectral densities of a set of unequal time series, we developed an algorithm that is able to identify the similarities or interactions between a set of proteins.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0599-8) contains supplementary material, which is available to authorized users.

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