1,084 research outputs found
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
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