Association mapping of blood pressure levels in a longitudinal framework using binomial regression

Heritable quantitative characters underline complex genetic traits. However, a single quantitative phenotype may not be a suitably good surrogate for a clinical end point trait. It may be more optimal to use a multivariate phenotype vector correlated with the end point trait to carry out an association analysis. Existing methods, such as variance components and principal components, suffer from inherent limitations, such as lack of robustness or difficulty in biological interpretation of association findings. In an effort to circumvent these limitations, we propose a novel regression approach based on a conditional binomial model to detect association between a single-nucleotide polymorphism and a multivariate phenotype vector. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. We find that the bivariate analysis of the two phenotypes yields more promising results in terms of lower p-values compared to univariate analyses

Unsupervised Hebbian Learning on Point Sets in StarCraft II

Learning the evolution of real-time strategy (RTS) game is a challenging problem in artificial intelligent (AI) system. In this paper, we present a novel Hebbian learning method to extract the global feature of point sets in StarCraft II game units, and its application to predict the movement of the points. Our model includes encoder, LSTM, and decoder, and we train the encoder with the unsupervised learning method. We introduce the concept of neuron activity aware learning combined with k-Winner-Takes-All. The optimal value of neuron activity is mathematically derived, and experiments support the effectiveness of the concept over the downstream task. Our Hebbian learning rule benefits the prediction with lower loss compared to self-supervised learning. Also, our model significantly saves the computational cost such as activations and FLOPs compared to a frame-based approach.Comment: Accepted in International Joint Conference on Neural Networks (IJCNN) 202

Fluorescence-Activated Cell Sorting of EGFP-Labeled Neural Crest Cells From Murine Embryonic Craniofacial Tissue

During the early stages of embryogenesis, pluripotent neural crest cells (NCC) are known to migrate from the neural folds to populate multiple target sites in the embryo where they differentiate into various derivatives, including cartilage, bone, connective tissue, melanocytes, glia, and neurons of the peripheral nervous system. The ability to obtain pure NCC populations is essential to enable molecular analyses of neural crest induction, migration, and/or differentiation. Crossing Wnt1-Cre and Z/EG transgenic mouse lines resulted in offspring in which the Wnt1-Cre transgene activated permanent EGFP expression only in NCC. The present report demonstrates a flow cytometric method to sort and isolate populations of EGFP-labeled NCC. The identity of the sorted neural crest cells was confirmed by assaying expression of known marker genes by TaqMan Quantitative Real-Time Polymerase Chain Reaction (QRT-PCR). The molecular strategy described in this report provides a means to extract intact RNA from a pure population of NCC thus enabling analysis of gene expression in a defined population of embryonic precursor cells critical to development

Anti-bacterial activity of inorganic nanomaterials and their antimicrobial peptide conjugates against resistant and non-resistant pathogens

This review details the antimicrobial applications of inorganic nanomaterials of mostly metallic form, and the augmentation of activity by surface conjugation of peptide ligands. The review is subdivided into three main sections, of which the first describes the antimicrobial activity of inorganic nanomaterials against gram-positive, gram-negative and multidrug-resistant bacterial strains. The second section highlights the range of antimicrobial peptides and the drug resistance strategies employed by bacterial species to counter lethality. The final part discusses the role of antimicrobial peptide-decorated inorganic nanomaterials in the fight against bacterial strains that show resistance. General strategies for the preparation of antimicrobial peptides and their conjugation to nanomaterials are discussed, emphasizing the use of elemental and metallic oxide nanomaterials. Importantly, the permeation of antimicrobial peptides through the bacterial membrane is shown to aid the delivery of nanomaterials into bacterial cells. By judicious use of targeting ligands, the nanomaterial becomes able to differentiate between bacterial and mammalian cells and, thus, reduce side effects. Moreover, peptide conjugation to the surface of a nanomaterial will alter surface chemistry in ways that lead to reduction in toxicity and improvements in biocompatibility

Inference and optimal design in Bayes and classical problems

The emphasis in this work is on derivation of optimal Bayes inferences and designs in relatively unexplored models or formulations. Linear and non-linear regression models are considered for finding Bayes as well as classical optimal designs. The work focuses principally on the three following families of problems: Part I: Inference. In this work, we consider inference problems for location parameters. The idea is that if one can produce priors for which the posterior densities are uniformly close to the likelihood function, then the corresponding Bayesian inference should also be close to classical inference, at least for location parameters. We describe a large family of prior distributions meeting this goal. Apart from obtaining approximations for the posterior density itself, we also derive uniform approximations to the Bayes rule and the posterior expected loss. We also demonstrate that for these priors, the sampling distributions of the Bayes rule and the classical unbiased estimate are close uniformly in the parameter and that all $100(1 - \alpha)$% Bayesian HPD sets have a classical coverage probabilities uniformly close to $1 - \alpha$ as well. Part II: Design. (i) Compromise designs. Multiple linear regression models are considerd with emphasis on construction of designs that provide a guaranteed prespecified efficiency simultaneously for each of a collection of different statistical problems. It is usually always the case that the experimenter is simultaneously interested in more than one inference problem using the same data and the sheer difference in the nature of the different problems makes combining them in terms of a single loss function undesirable. On the other hand, designs optimal in one problem may be inefficient in other similar problems and yet the statistician has to select one single design. Various heteroscedastic models are studied and the design problem described above is addressed and solved. These problems are addressed in Bayesian as well as classical frameworks and the solutions are compared. (ii) Nonlinear regression models and construction of Bayesian optimal designs. We consider exponential growth models which are of great practical use for describing growth of organisms over time. We have successfully used Tchebycheff-system and convexity arguments in some cases. We consider D-optimum designs with or without prior information and give some qualitative understanding of the nature of the optimal design depending on the situation. In particular, we try to draw parallels with the linear case. (Abstract shortened by UMI.