Nearest Neighbor Classification Rule for Mixtures of Discrete and Continuous Random Variables

In this paper very simple nonparametric classification rule for mixtures of discrete and continuous random variables is described. It is based on the method of nearest neighbor proposed by Cover and Hart (1967). The bounds on the limit of the nearest neighbor rule risks are given. Both lower and upper bound depend on the Bayes risk and the loss function. Finally the method is compared with other existing methods on some practical data set.     

Regional excitatory and inhibitory amino acid levels in epileptic El mouse brain

Inbred mutant El mice are highly susceptible to convulsive seizures upon tossing stimulation. The levels of excitatory (e.g. glutamate and aspartate) and inhibitory amino acids [e.g. -aminobutyrate (GABA)] were examined in discrete regions of stimulated El mice [El(+)] non-stimulated El mice [El(-)] and ddY mice, which do not have convulsive disposition. In comparison with ddY, a general increased levels of aspartate, glutamate, glutamine, and taurine were detected in brain regions of El(-). The levels of GABA and glycine were almost the same in ddY and El(-). Compared to El(+), the levels of aspartate, glutamate, glutamine, and GABA in El(-) were either the same or higher. In the case of taurine and glycine, the levels in El(-) were either the same or lower than El(+). Alanine is special in that El(-) have a higher level than El(+) in hippocampus but lower in cerebellum. Furthermore, while marked changes were registered in several brain regions, none of the amino acids investigated showed any significant differences in the hypothalamus of three different groups of mice.     

Validation of discrete time-to-event prediction models in the presence of competing risks

Clinical prediction models play a key role in risk stratification, therapy assignment and many other fields of medical decision making. Before they can enter clinical practice, their usefulness has to be demonstrated using systematic validation. Methods to assess their predictive performance have been proposed for continuous, binary, and time-to-event outcomes, but the literature on validation methods for discrete time-to-event models with competing risks is sparse. The present paper tries to fill this gap and proposes new methodology to quantify discrimination, calibration, and prediction error (PE) for discrete time-to-event outcomes in the presence of competing risks. In our case study, the goal was to predict the risk of ventilator-associated pneumonia (VAP) attributed to Pseudomonas aeruginosa in intensive care units (ICUs). Competing events are extubation, death, and VAP due to other bacteria. The aim of this application is to validate complex prediction models developed in previous work on more recently available validation data.     

Partially incorrect fossil data augment analyses of discrete trait evolution in living species

Ancestral state reconstruction of discrete character traits is often vital when attempting to understand the origins and homology of traits in living species. The addition of fossils has been shown to alter our understanding of trait evolution in extant taxa, but researchers may avoid using fossils alongside extant species if only few are known, or if the designation of the trait of interest is uncertain. Here, I investigate the impacts of fossils and incorrectly coded fossils in the ancestral state reconstruction of discrete morphological characters under a likelihood model. Under simulated phylogenies and data, likelihood-based models are generally accurate when estimating ancestral node values. Analyses with combined fossil and extant data always outperform analyses with extant species alone, even when around one quarter of the fossil information is incorrect. These results are especially pronounced when model assumptions are violated, such as when there is a trend away from the root value. Fossil data are of particular importance when attempting to estimate the root node character state. Attempts should be made to include fossils in analysis of discrete traits under likelihood, even if there is uncertainty in the fossil trait data.     

A hidden aggregation‐prone structure in the heart of hypoxia inducible factor prolyl hydroxylase

Prolyl hydroxylase domain‐containing protein 2 (PHD2), as one of the most important regulators of angiogenesis and metastasis of cancer cells, is a promising target for cancer therapy drug design. Progressive studies imply that abnormality in PHD2 function may be due to misfolding. Therefore, study of the PHD2 unfolding pathway paves the way for a better understanding of the influence of PHD2 mutations and cancer cell metabolites on the protein folding pathway. We study the unfolding of the PHD2 catalytic domain using differential scanning calorimetry (DSC), fluorescence spectroscopy, and discrete molecular dynamics simulations (DMD). Using computational and experimental techniques, we find that PHD2 undergoes four transitions along the thermal unfolding pathway. To illustrate PHD2 unfolding events in atomic detail, we utilize DMD simulations. Analysis of computational results indicates an intermediate species in the PHD2 unfolding pathway that may enhance aggregation propensity, explaining mutation‐independent PHD2 malfunction. Proteins 2016; 84:611–623. © 2016 Wiley Periodicals, Inc.     

A modified resonant recognition model to predict protein-protein interaction

Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement. This paper briefly introduced the original Resonant Recognition Model (RRM), and then modified it by using the wavelet transform to acquire the Modified Resonant Recognition Model (MRRM). The key characteristic of the new model is that it can predict directly the protein-protein interaction from the primary sequence, and the MRRM is more suitable than the RRM for this prediction. The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction. Translated from Journal of Shanghai University (Natural Science), 2006, 12(1): 69–73 [译自: 上海大学学报(自然科学版)]     

Heat and water transfer in a rotating drum containing solid substrate particles

In previous work we reported on the simulation of mixing behavior of a slowly rotating drum for solid-state fermentation (SSF) using a discrete particle model. In this investigation the discrete particle model is extended with heat and moisture transfer. Heat transfer is implemented in the model via interparticle contacts and the interparticle heat transfer coefficient is determined experimentally. The model is shown to accurately predict heat transfer and resulting temperature gradients in a mixed wheat grain bed. In addition to heat transfer, the addition and subsequent distribution of water in the substrate bed is also studied. The water is added to the bed via spray nozzles to overcome desiccation of the bed during evaporative cooling. The development of moisture profiles in the bed during spraying and mixing are studied experimentally with a water-soluble fluorescent tracer. Two processes that affect the water distribution are considered in the model: the intraparticle absorption process, and the interparticle transfer of free water. It is found that optimum distribution can be achieved when the free water present at the surface of the grains is quickly distributed in the bed, for example, by fast mixing. Alternatively, a short spraying period, followed by a period of mixing without water addition, can be applied. The discrete particle model developed is used successfully to examine the influence of process operation on the moisture distribution (e.g., fill level and rotation rate). It is concluded that the extended discrete particle model can be used as a powerful predictive tool to derive operating strategies and criteria for design and scale-up for mixed SSF and other processes with granular media.     

Statistical inference for familial disease clusters

In many epidemiologic studies, the first indication of an environmental or genetic contribution to the disease is the way in which the diseased cases cluster within the same family units. The concept of clustering is contrasted with incidence. We assume that all individuals are exchangeable except for their disease status. This assumption is used to provide an exact test of the initial hypothesis of no familial link with the disease, conditional on the number of diseased cases and the distribution of the sizes of the various family units. New parametric generalizations of binomial sampling models are described to provide measures of the effect size of the disease clustering. We consider models and an example that takes covariates into account. Ascertainment bias is described and the appropriate sampling distribution is demonstrated. Four numerical examples with real data illustrate these methods.