A hierarchical Bayesian model to estimate the unobservable predation rate on sawfly cocoons by small mammals.

Predation by small mammals has been reported as an important mortality factor for the cocoons of sawfly species. However, it is difficult to provide an accurate estimate of newly spun cocoons and subsequent predation rates by small mammals for several reasons. First, all larvae do not spin cocoons at the same time. Second, cocoons are exposed to small mammal predation immediately after being spun. Third, the cocoons of the current generation are indistinguishable from those of the previous generation. We developed a hierarchical Bayesian model to estimate these values from annual one-time soil sampling datasets. To apply this model to an actual data set, field surveys were conducted in eight stands of larch plantations in central Hokkaido (Japan) from 2009 to 2012. Ten 0.04-m(2) soil samples were annually collected from each site in mid-October. The abundance of unopened cocoons (I), cocoons emptied by small-mammal predation (M), and empty cocoons caused by something other than small-mammal predation (H) were determined. The abundance of newly spun cocoons, the predation rate by small mammals before and after cocoon sampling, and the annual rate of empty cocoons that remained were estimated. A posterior predictive check yielded Bayesian P-values of 0.54, 0.48, and 0.07 for I, M, and H, respectively. Estimated predation rates showed a significant positive correlation with the number of trap captures of small mammals. Estimates of the number of newly spun cocoons had a significant positive correlation with defoliation intensity. These results indicate that our model showed an acceptable fit, with reasonable estimates. Our model is expected to be widely applicable to all hymenopteran and lepidopteran insects that spin cocoons in soil.

PAAQD: Predicting immunogenicity of MHC class I binding peptides using amino acid pairwise contact potentials and quantum topological molecular similarity descriptors.

Prediction of peptide immunogenicity is a promising approach for novel vaccine discovery. Conventionally, epitope prediction methods have been developed to accelerate the process of vaccine production by searching for candidate peptides from pathogenic proteins. However, recent studies revealed that peptides with high binding affinity to major histocompatibility complex molecules (MHCs) do not always result in high immunogenicity. Therefore, it is promising to predict the peptide immunogenicity rather than epitopes in order to discover new vaccines more effectively. To this end, we developed a novel T-cell reactivity predictor which we call PAAQD. Nonapeptides were encoded numerically, using combining information of amino acid pairwise contact potentials (AAPPs) and quantum topological molecular similarity (QTMS) descriptors. Encoded data were used in the construction of our classification model. Our numerical experiments suggested that the predictive performance of PAAQD is at least comparable with POPISK, one of the pioneering techniques for T-cell reactivity prediction. Also, our experiment suggested that the first and eighth positions of nonapeptides are the most important for immunogenicity and most of the anchor residues in epitope prediction were not important in T-cell reactivity prediction. The R implementation of PAAQD is available at http://pirun.ku.ac.th/~fsciiok/PAAQD.rar.

EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information.

BACKGROUND: Epitope identification is an essential step toward synthetic vaccine development since epitopes play an important role in activating immune response. Classical experimental approaches are laborious and time-consuming, and therefore computational methods for generating epitope candidates have been actively studied. Most of these methods, however, are based on sophisticated nonlinear techniques for achieving higher predictive performance. The use of these techniques tend to diminish their interpretability with respect to binding potential: that is, they do not provide much insight into binding mechanisms. RESULTS: We have developed a novel epitope prediction method named EpicCapo and its variants, EpicCapo(+) and EpicCapo(+REF). Nonapeptides were encoded numerically using a novel peptide-encoding scheme for machine learning algorithms by utilizing 40 amino acid pairwise contact potentials (referred to as AAPPs throughout this paper). The predictive performances of EpicCapo(+) and EpicCapo(+REF) outperformed other state-of-the-art methods without losing interpretability. Interestingly, the most informative AAPPs estimated by our study were those developed by Micheletti and Simons while previous studies utilized two AAPPs developed by Miyazawa & Jernigan and Betancourt & Thirumalai. In addition, we found that all amino acid positions in nonapeptides could effect on performances of the predictive models including non-anchor positions. Finally, EpicCapo(+REF) was applied to identify candidates of promiscuous epitopes. As a result, 67.1% of the predicted nonapeptides epitopes were consistent with preceding studies based on immunological experiments. CONCLUSIONS: Our method achieved high performance in testing with benchmark datasets. In addition, our study identified a number of candidates of promiscuous CTL epitopes consistent with previously reported immunological experiments. We speculate that our techniques may be useful in the development of new vaccines. The R implementation of EpicCapo(+REF) is available at http://pirun.ku.ac.th/~fsciiok/EpicCapoREF.zip. Datasets are available at http://pirun.ku.ac.th/~fsciiok/Datasets.zip.

Wideband digital frequency detector with subtraction-based phase comparator for frequency modulation atomic force microscopy.

We have developed a wideband digital frequency detector for high-speed frequency modulation atomic force microscopy (FM-AFM). We used a subtraction-based phase comparator (PC) in a phase-locked loop circuit instead of a commonly used multiplication-based PC, which has enhanced the detection bandwidth to 100 kHz. The quantitative analysis of the noise performance revealed that the internal noise from the developed detector is small enough to provide the theoretically limited noise performance in FM-AFM experiments in liquid. FM-AFM imaging of mica in liquid was performed with the developed detector, showing its stability and applicability to true atomic-resolution imaging in liquid.

Changes of phospho-growth-associated protein 43 (phospho-GAP43) in the zebrafish retina after optic nerve injury: a long-term observation.

The major model animal of optic nerve regeneration in fish is goldfish. A closely related zebrafish is the most popular model system for genetic and developmental studies of vertebrate central nervous system. A few challenging works of optic nerve regeneration have been done with zebrafish. However, knowledge concerning the long term of optic nerve regeneration apparently lacks in zebrafish. In the present study, therefore, we followed changes of zebrafish behavior and phosphorylated form of growth-associated protein 43 (phospho-GAP43) expression in the zebrafish retina over 100 days after optic nerve transection. Optomotor response was fast recovered by 20-25 days after axotomy whereas chasing behavior (a schooling behavior) was slowly recovered by 80-100 days after axotomy. The temporal pattern of phospho-GAP43 expression showed a biphasic increase, a short-peak (12 folds) at 1-2 weeks and a long-plateau (4 folds) at 1-2 months after axotomy. The recovery of optomotor response well correlated with projection of growing axons to the tectum, whereas the recovery of chasing behavior well correlated with synaptic refinement of retinotectal topography. The present data strongly suggest that phospho-GAP43 plays an active role in both the early and late stages of optic nerve regeneration in fish.