In Vitro Study of Taenia solium Postoncospheral Form

Background

The transitional period between the oncosphere and the cysticercus of Taenia solium is the postoncospheral (PO) form, which has not yet been completely characterized. The aim of this work was to standardize a method to obtain T. solium PO forms by in vitro cultivation. We studied the morphology of the PO form and compared the expression of antigenic proteins among the PO form, oncosphere, and cysticerci stages.

Methodology/Principal Findings

T. solium activated oncospheres were co-cultured with ten cell lines to obtain PO forms, which we studied at three stages of development–days 15, 30, and 60. A high percentage (32%) of PO forms was obtained using HCT-8 cells in comparison to the other cell lines. The morphology was observed by bright field, scanning, and transmission electron microscopy. Morphology of the PO form changed over time, with the six hooks commonly seen in the oncosphere stage disappearing in the PO forms, and vesicles and microtriches observed in the tegument. The PO forms grew as they aged, reaching a diameter of 2.5 mm at 60 days of culture. 15–30 day PO forms developed into mature cysticerci when inoculated into rats. Antigenic proteins expressed in the PO forms are also expressed by the oncosphere and cysticerci stages, with more cysticerci antigenic proteins expressed as the PO forms ages.

Conclusions/Significance

This is the first report of an in vitro production method of T. solium PO forms. The changes observed in protein expression may be useful in identifying new targets for vaccine development. In vitro culture of PO form will aid in understanding the host-parasite relationship, since the structural changes of the developing PO forms may reflect the parasite’s immunoprotective mechanisms. A wider application of this method could significantly reduce the use of animals, and thus the costs and time required for further experimental investigations.     

High Resolution Melting Analysis Targeting hsp70 as a Fast and Efficient Method for the Discrimination of Leishmania Species

Background

Protozoan parasites of the genus Leishmania cause a large spectrum of clinical manifestations known as Leishmaniases. These diseases are increasingly important public health problems in many countries both within and outside endemic regions. Thus, an accurate differential diagnosis is extremely relevant for understanding epidemiological profiles and for the administration of the best therapeutic protocol.

Methods/Principal Findings

Exploring the High Resolution Melting (HRM) dissociation profiles of two amplicons using real time polymerase chain reaction (real-time PCR) targeting heat-shock protein 70 coding gene (hsp70) revealed differences that allowed the discrimination of genomic DNA samples of eight Leishmania species found in the Americas, including Leishmania (Leishmania) infantum chagasi, L. (L.) amazonensis, L. (L.) mexicana, L. (Viannia) lainsoni, L. (V.) braziliensis, L. (V.) guyanensis, L. (V.) naiffi and L. (V.) shawi, and three species found in Eurasia and Africa, including L. (L.) tropica, L. (L.) donovani and L. (L.) major. In addition, we tested DNA samples obtained from standard promastigote culture, naturally infected phlebotomines, experimentally infected mice and clinical human samples to validate the proposed protocol.

Conclusions/Significance

HRM analysis of hsp70 amplicons is a fast and robust strategy that allowed for the detection and discrimination of all Leishmania species responsible for the Leishmaniases in Brazil and Eurasia/Africa with high sensitivity and accuracy. This method could detect less than one parasite per reaction, even in the presence of host DNA.     

Inertia weight control strategies for particle swarm optimization

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.     

A taxonomic revision of <Emphasis Type="Italic">Bursera</Emphasis> subgen. <Emphasis Type="Italic">Bursera</Emphasis> in the Greater Antilles and the Bahamas,including a new species from Cuba

A revision of Bursera in the Greater Antilles and the Bahamas has confirmed that Commiphora does not occur in the region and that all but one species in the region belong to Bursera subgen. Bursera. Here we describe a new species, Bursera yaterensis; B. nashii is synonymized with B. glauca and B. ovata is synonymized with B. trinitensis; and we return five species from Commiphora to Bursera. A dichotomous key is provided using mostly vegetative characters due to the frequent lack of adequate reproductive material and the relative uniformity of most floral and fruit characters.     

Barcoding,population structure,and demographic history of Prodiplosis longifila associated with the Andes

Prodiplosis longifila Gagné (Diptera: Cecidomyiidae) is an insect pest that attacks various types of crops, including tomato, Solanum lycopersicum L. (Solanaceae), a vegetable with substantial economic significance worldwide. Prodiplosis longifila is a widely distributed pest in Colombia, Ecuador, and Peru, countries characterized by the presence of significant geographic barriers like the Andes Mountains. It has been reported that geographic barriers affect the dynamics and genetic differentiation of insect populations. Therefore, the aim of this study was to assess the diversity, genetic structure, and demographic history of P. longifila through the analysis of sequences within the mitochondrial region of cytochrome oxidase I (COI) and rDNA‐ITS2 in 27 populations located in Colombia and Ecuador. Analyses were performed on populations distributed in three geographic groups separated by the presence of the Andes Mountains. A total of 11 haplotypes were identified with the COI gene and only one haplotype in the rDNA‐ITS2 was found. Analyses of population structure and demographic history revealed that there is a structure associated with the Andes, which is reflected in an uneven distribution of the haplotype frequencies between regions, but even so, gene flow between populations was detected which produces low genetic differentiation. Because P. longifila has a short‐range dispersion that determines its territorial nature, it would be expected that other factors are producing the genetic exchange between populations. We suggest that the anthropogenic effect produced by farming practices, such as the use of seedlings as seed, which may carry P. longifila larvae, cause passive dispersal of pest throughout the Andes, particularly in Colombia.     

Hybridization and divergence in multi‐species oak (Quercus) communities

Oaks (Quercus: Fagaceae) commonly interbreed yet retain their morphological, genetic and ecological distinctiveness. Post‐zygotic isolation mechanisms, such as ecologically dependent selection on adaptive loci, may therefore limit introgression. To test this hypothesis, we quantified hybridization and genetic divergence across the contact zone of four red oaks (Quercus section Lobatae) in the Great Lakes region of North America using a suite of 259 amplified fragment length polymorphisms and 27 genic and genomic microsatellite markers. First, we identified hybrids using genetic structure analysis and confirmed the reliability of our assignments via simulations. Then, we identified candidate loci for species maintenance with three complementary tests for selection and obtained partial gene sequences linked to an outlier locus and three other loci. We detected evidence of recent hybridization among all species and considerable gene flow between Q. ellipsoidalis and Q. velutina. Overall, c. 20% of Q. velutina had recent ancestry from Q. ellipsoidalis, whereas nearly 30% of Q. ellipsoidalis had a Q. velutina ancestor. Most loci were negligibly to weakly differentiated among species, but two gene‐linked microsatellites deviated significantly from neutral expectations in multiple, complementary outlier tests. Both outlier loci were located in the same 15‐cM bin on an existing Q. robur linkage map, a region under divergent selection in other oak species. Adaptive loci in this highly differentiated genomic region may contribute to ecological divergence among species and limit introgression.