Interaction between Insecticide Exposure and Trematode Infection across Four Wood Frog Populations

Amphibian populations are declining worldwide due to a number of stressors including pesticides and parasites. Conservation of these animals can be complicated because populations can differ dramatically in response to the same stressor. When consistently exposed to pesticides, some populations evolve tolerance through the process of natural selection acting across multiple generations. Alternatively, populations that are intermittently exposed to pesticides induce tolerance within a single generation. To date, however, there have been few studies examining the costs associated with these different stress tolerance mechanisms. In this study, we examined how difference in stress tolerance influence susceptibility to parasitic infections. We collected wood frog tadpoles from four different populations: two with evolved tolerance to pesticides and two with the ability to induce pesticide tolerance. We exposed tadpoles from each population to sublethal doses of carbaryl (0 and 5 ppm) for 5 days. Tadpoles were allowed to acclimate in pesticide-free water for 2 days. After this acclimation period, we then exposed tadpoles to 0 or 50 trematode parasites (Echinostoma trivolvis) for 2 days and counted the number of parasites encysted within the body. Exposure to sublethal carbaryl decreased susceptibility to trematodes for tadpole populations with evolved pesticide tolerance. In contrast, exposure to sublethal carbaryl increased susceptibility to trematodes for tadpole populations with induced pesticide tolerance. This suggests that populations with the ability to induce pesticide tolerance incur the cost of increased disease risk. This has important conservation implications for understanding a population’s history and defending against disease

MM Algorithms for Geometric and Signomial Programming

This paper derives new algorithms for signomial programming, a generalization of geometric programming. The algorithms are based on a generic principle for optimization called the MM algorithm. In this setting, one can apply the geometric-arithmetic mean inequality and a supporting hyperplane inequality to create a surrogate function with parameters separated. Thus, unconstrained signomial programming reduces to a sequence of one-dimensional minimization problems. Simple examples demonstrate that the MM algorithm derived can converge to a boundary point or to one point of a continuum of minimum points. Conditions under which the minimum point is unique or occurs in the interior of parameter space are proved for geometric programming. Convergence to an interior point occurs at a linear rate. Finally, the MM framework easily accommodates equality and inequality constraints of signomial type. For the most important special case, constrained quadratic programming, the MM algorithm involves very simple updates.Comment: 16 pages, 1 figur

Identification, Characterization, and Expression of a Novel P450 Gene Encoding CYP6AE25 from the Asian Corn Borer, Ostrinia furnacalis

An allele of the cytochrome P450 gene, CYP6AE14, named CYP6AE25 (GenBank accession no. EU807990) was isolated from the Asian com borer, Ostrinia fumacalis (Guenée) (Lepidoptera: Pyralidae) by RT-PCR. The cDNA sequence of CYP6AE25 is 2315 bp in length and contains a 1569 nucleotides open reading frame encoding a putative protein with 523 amino acid residues and a predicted molecular weight of 59.95 kDa and a theoretical pI of 8.31. The putative protein contains the classic heme-binding sequence motif F××G×××C×G (residues 451–460) conserved among all P450 enzymes as well as other characteristic motifs of all cytochrome P450s. It shares 52% identity with the previously published sequence of CYP6AE14 (GenBank accession no. DQ986461) from Helicoverpa armigera. Phylogenetic analysis of amino acid sequences from members of various P450 families indicated that CYP6AE25 has a closer phylogenetic relationship with CYP6AE14 and CYP6B1 that are related to metabolism of plant allelochemicals, CYP6D1 which is related to pyrethroid resistance and has a more distant relationship to CYP302A1 and CYP307A1 which are related to synthesis of the insect molting hormones. The expression level of the gene in the adults and immature stages of O. furnacalis by quantitative real-time PCR revealed that CYP6AE25 was expressed in all life stages investigated. The mRNA expression level in 3rd instar larvae was 12.8- and 2.97-fold higher than those in pupae and adults, respectively. The tissue specific expression level of CYP6AE25 was in the order of midgut, malpighian tube and fatty body from high to low but was absent in ovary and brain. The analysis of the CYP6AB25 gene using bioinformatic software is discussed

Mutant INS-Gene Induced Diabetes of Youth: Proinsulin Cysteine Residues Impose Dominant-Negative Inhibition on Wild-Type Proinsulin Transport

Recently, a syndrome of Mutant INS-gene-induced Diabetes of Youth (MIDY, derived from one of 26 distinct mutations) has been identified as a cause of insulin-deficient diabetes, resulting from expression of a misfolded mutant proinsulin protein in the endoplasmic reticulum (ER) of insulin-producing pancreatic beta cells. Genetic deletion of one, two, or even three alleles encoding insulin in mice does not necessarily lead to diabetes. Yet MIDY patients are INS-gene heterozygotes; inheritance of even one MIDY allele, causes diabetes. Although a favored explanation for the onset of diabetes is that insurmountable ER stress and ER stress response from the mutant proinsulin causes a net loss of beta cells, in this report we present three surprising and interlinked discoveries. First, in the presence of MIDY mutants, an increased fraction of wild-type proinsulin becomes recruited into nonnative disulfide-linked protein complexes. Second, regardless of whether MIDY mutations result in the loss, or creation, of an extra unpaired cysteine within proinsulin, Cys residues in the mutant protein are nevertheless essential in causing intracellular entrapment of co-expressed wild-type proinsulin, blocking insulin production. Third, while each of the MIDY mutants induces ER stress and ER stress response; ER stress and ER stress response alone appear insufficient to account for blockade of wild-type proinsulin. While there is general agreement that ultimately, as diabetes progresses, a significant loss of beta cell mass occurs, the early events described herein precede cell death and loss of beta cell mass. We conclude that the molecular pathogenesis of MIDY is initiated by perturbation of the disulfide-coupled folding pathway of wild-type proinsulin

Towards a data publishing framework for primary biodiversity data: challenges and potentials for the biodiversity informatics community

Background: Currently primary scientific data, especially that dealing with biodiversity, is neither easily discoverable nor accessible. Amongst several impediments, one is a lack of professional recognition of scientific data publishing efforts. A possible solution is establishment of a ‘Data Publishing Framework’ which would encourage and recognise investments and efforts by institutions and individuals towards management, and publishing of primary scientific data potentially on a par with recognitions received for scholarly publications. Discussion: This paper reviews the state-of-the-art of primary biodiversity data publishing, and conceptualises a ‘Data Publishing Framework’ that would help incentivise efforts and investments by institutions and individuals in facilitating free and open access to biodiversity data. It further postulates the institutionalisation of a ‘Data Usage Index (DUI)’, that would attribute due recognition to multiple players in the data collection/creation, management and publishing cycle. Conclusion: We believe that institutionalisation of such a ‘Data Publishing Framework’ that offers socio-cultural, legal, technical, economic and policy environment conducive for data publishing will facilitate expedited discovery and mobilisation of an exponential increase in quantity of ‘fit-for-use’ primary biodiversity data, much of which is currently invisible

Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders

Personality is influenced by genetic and environmental factors1 and associated with mental health. However, the underlying genetic determinants are largely unknown. We identified six genetic loci, including five novel loci2,3, significantly associated with personality traits in a meta-analysis of genome-wide association studies (N = 123,132–260,861). Of these genomewide significant loci, extraversion was associated with variants in WSCD2 and near PCDH15, and neuroticism with variants on chromosome 8p23.1 and in L3MBTL2. We performed a principal component analysis to extract major dimensions underlying genetic variations among five personality traits and six psychiatric disorders (N = 5,422–18,759). The first genetic dimension separated personality traits and psychiatric disorders, except that neuroticism and openness to experience were clustered with the disorders. High genetic correlations were found between extraversion and attention-deficit– hyperactivity disorder (ADHD) and between openness and schizophrenia and bipolar disorder. The second genetic dimension was closely aligned with extraversion–introversion and grouped neuroticism with internalizing psychopathology (e.g., depression or anxiety)

Learning deterministic probabilistic automata from a model checking perspective

Probabilistic automata models play an important role in the formal design and analysis of hard- and software systems. In this area of applications, one is often interested in formal model-checking procedures for verifying critical system properties. Since adequate system models are often difficult to design manually, we are interested in learning models from observed system behaviors. To this end we adopt techniques for learning finite probabilistic automata, notably the Alergia algorithm. In this paper we show how to extend the basic algorithm to also learn automata models for both reactive and timed systems. A key question of our investigation is to what extent one can expect a learned model to be a good approximation for the kind of probabilistic properties one wants to verify by model checking. We establish theoretical convergence properties for the learning algorithm as well as for probability estimates of system properties expressed in linear time temporal logic and linear continuous stochastic logic. We empirically compare the learning algorithm with statistical model checking and demonstrate the feasibility of the approach for practical system verification