When Should Epidemiologic Regressions Use Random Coefficients?

Regression models with random coefficients arise naturally in both frequentist and Bayesian approaches to estimation problems. They are becoming widely available in standard computer packages under the headings of generalized linear mixed models, hierarchical models, and multilevel models. I here argue that such models offer a more scientifically defensible framework for epidemiologic analysis than the fixed-effects models now prevalent in epidemiology. The argument invokes an antiparsimony principle attributed to L. J. Savage, which is that models should be rich enough to reflect the complexity of the relations under study. It also invokes the countervailing principle that you cannot estimate anything if you try to estimate everything (often used to justify parsimony). Regression with random coefficients offers a rational compromise between these principles as well as an alternative to analyses based on standard variable-selection algorithms and their attendant distortion of uncertainty assessments. These points are illustrated with an analysis of data on diet, nutrition, and breast cancer.     

Are Random Drift and Natural Selection Conceptually Distinct?

The latter half of thetwentieth century has been marked by debates inevolutionary biology over the relativesignificance of natural selection and randomdrift: the so-called ``neutralist/selectionist'debates. Yet John Beatty has argued that it isdifficult, if not impossible, to distinguishthe concept of random drift from the concept ofnatural selection, a claim that has beenaccepted by many philosophers of biology. Ifthis claim is correct, then theneutralist/selectionist debates seem at bestfutile, and at worst, meaningless. I reexaminethe issues that Beatty raises, and argue thatrandom drift and natural selection, conceivedas processes, can be distinguished from one another.     

The origin of adaptive mutants: Random or nonrandom?

Several recent reports have claimed that adaptive mutants in bacteria and yeast are induced by selective conditions. The results of these reports suggest that mutants can arise nonrandomly with respect to fitness, contrary to what has been widely accepted. In several cases that have received careful experimental reexamination, however, the detection of seemingly nonrandom mutation has been explained as an experimental artifact. In the remaining cases, there is no evidence to suggest that cells have the capacity to direct or choose which genetic variants will arise. Instead, current models propose processes by which genetic variants persist as mutations only if they enable cell growth and DNA replication. Most of these models are apparently contradicted by experimental data. One model, the hypermutable state model, has recently received limited circumstantial support. However, in this model the origin of adaptive mutants is random; the apparent nonrandomness of mutation is merely a consequence of natural selection. The critical distinction between the origin of genetic variation (mutation) and the possible consequence of that variation (selection) has been neglected by proponents of directed mutation.     

Random fluctuations in readings of measurement devices: cosmophysic effects?

Fluctuations of "computer time" provided by the PC quartz generator were studied. It was shown that variations in the yearly course of the quartz generator frequency occur with periodicities of 13-15 days and 28-30 days. Regular fluctuations with periods of 0.5 and 1.0 min are typical of the short-term spectral component. Impulses of sharp and intense deceleration of "computer time" are often observed against the background of these regular fluctuations. The conclusion is made that fluctuations of "computer time" are of cosmophysical origin.     

Random Motion of Chromatin Is Influenced by Lamin A Interconnections

Using fluorescence correlation spectroscopy in single-plane illumination microscopy, we investigated the dynamics of chromatin in interphase mouse adult fibroblast cell nuclei under the influence of the intermediate filament protein lamin A. We find that 1) lamin A-eGFP and histone H2A-mRFP show significant comobility, indicating that their motions are clearly interconnected in the nucleus, and 2) that the random motion of histones H2A within the chromatin network is subdiffusive, i.e., the effective diffusion coefficient decreases for slow timescales. Knocking out lamin A changes the diffusion back to normal. Thus, lamin A influences the dynamics of the entire chromatin network. Our conclusion is that lamin A plays a central role in determining the viscoelasticity of the chromatin network and helping to maintain local ordering of interphase chromosomes.     

大鼠RAPD标记的观察 Random Amplified Polymorphic DNA Analysis in Rat

采用随机扩增多态DNA(RAPD)技术, 分析SD和Wistar二种大鼠的基因多态性, 探讨用RAPD标记鉴别二种大鼠及其血标本实验中的认证。 结果表明, 二种大鼠表现出了各自不同的多态性RAPD标记, 作为大鼠的分子标记, 可在基因水平区别二种大鼠。故认为是一种大鼠研究的分子依据。 Abstract　Random Amplified Polymorphic DNA (RAPD) was developed for analysing genetic polymorphism in SD and Wistar rats. The results show characteristic RAPD patterns between two strain rats. As molecular markers of rats, it could be used to distinguish species of rats between SD and Wistar rats in genetic level. The RAPD marker might be a potential tool for the study of rats.     

Use of Random Amplified Polymorphic DNA Analysis for Economically Important Food Crops

The objective of this review is to summarize numerous studies on the use of the random amplified polymorphic DNA （RAPD） technique on rice, corn, wheat, sorghum, barley, rye, and oats to examine its feasibility and validity for assessment of genetic variation, population genetics, mapping, linkage and marker assisted selection, phylogenetic analysis, and the detection of somaclonal variation. Also we discuss the advantages and limitations of RAPD. Molecular markers have entered the scene of genetic improvement in different fields of agricultural research. The simplicity of the RAPD technique made it ideal for genetic mapping, plant and animal breeding programs, and DNA fingerprinting, with particular utility in the field of population genetics.     

Efficient or Inaccurate? Analytical and Numerical Modelling of Random Search Strategies

A large number of observational and theoretical studies have investigated animal movement strategies for finding randomly located food items. Many of these studies have claimed that a particular strategy is advantageous over other strategies or that the spatial distribution of the food items affects the search efficiency. Here, we study a deliberately idealised problem, in which a blind forager searches for re-visitable food items. We show analytically that the forager’s efficiency is completely independent of both its movement strategy and the spatial pattern of the food items and depends only on the density of food in the environment. However, in some cases, apparent optima in search strategies can arise as artefacts of inappropriate and inaccurate numerical simulations. We discuss modifications to the idealised foraging problem that can confer an advantage on certain strategies, including when the forager has some memory or knowledge of the environment; when the food items are non-revisitable; and when the problem is viewed in an evolutionary context.     

Random Bi Bi机制的非稳态酶动力学布尔函数图论研究

本文以非稳态酶动力学的布尔函数图形方法^[1],来研究一类Random Bi Bi机制的非稳态酶动力学问题,推导同此类反应的非稳态酶动力学方程,并对此动力学方程进行了讨论,分析了此类Random Bi Bi机制酶反应体系的非稳态酶动力学方程。     

Functional Brain Networks: Random, “Small World” or Deterministic?

Lately the problem of connectivity in brain networks is being approached frequently by graph theoretical analysis. In several publications based on bivariate estimators of relations between EEG channels authors reported random or “small world” structure of networks. The results of these works often have no relation to other evidence based on imaging, inverse solutions methods, physiological and anatomical data. Herein we try to find reasons for this discrepancy. We point out that EEG signals are very much interdependent, thus bivariate measures applied to them may produce many spurious connections. In fact, they may outnumber the true connections. Giving all connections equal weights, as it is usual in the framework of graph theoretical analysis, further enhances these spurious links. In effect, close to random and disorganized patterns of connections emerge. On the other hand, multivariate connectivity estimators, which are free of the artificial links, show specific, well determined patterns, which are in a very good agreement with other evidence. The modular structure of brain networks may be identified by multivariate estimators based on Granger causality and formalism of assortative mixing. In this way, the strength of coupling may be evaluated quantitatively. During working memory task, by means of multivariate Directed Transfer Function, it was demonstrated that the modules characterized by strong internal bonds exchange the information by weaker connections.     

Random selection—A simple model based on linear birth and death processes

Linear birth and death processes are used to derive simple expressions for sequential extinction times and gene fixation probabilities in asexual populations.     

A Non-Lévy Random Walk in Chacma Baboons: What Does It Mean?

The Lévy walk is found from amoebas to humans and has been described as the optimal strategy for food research. Recent results, however, have generated controversy about this conclusion since animals also display alternatives to the Lévy walk such as the Brownian walk or mental maps and because movement patterns found in some species only seem to depend on food patches distribution. Here I show that movement patterns of chacma baboons do not follow a Lévy walk but a Brownian process. Moreover this Brownian walk is not the main process responsible for movement patterns of baboons. Findings about their speed and trajectories show that baboons use metal maps and memory to find resources. Thus the Brownian process found in this species appears to be more dependent on the environment or might be an alternative when known food patches are depleted and when animals have to find new resources.     

Bivalent Associations in Mus domesticus 2n=40 Spermatocytes. Are They Random?

The establishment of associations between bivalents from Mus domesticus \(2n=40\) spermatocytes is a common phenomenon that shows up during the first prophase of meiotic nuclei. In each nucleus, a seemingly random display of variable size clusters of bivalents in association is observed. These associations originate a particular nuclear architecture and determine the probability of encounters between chromosome domains. Hence, the type of randomness in associations between bivalents has nontrivial consequences. We explore different models for randomness and the associated bivalent probability distributions and find that a simple model based on randomly coloring a subset of vertices of a 6-regular graph provides best agreement with microspreads observations. The notion of randomness is thereby explained in conjunction with the underlying local geometry of the nuclear envelope.     

Random variability of map distances based on Kosambi’s and Haldane’s mapping functions

The majority of published genetic maps are based on Kosambi distances or on Haldane distances. For a comparison of both map distance measures, their random variability is of particular interest. For the statistic ‘variance’, this paper presents a relationship between Kosambi distances and Haldane distances. The results suggest that Kosambi distances exhibit a smaller random variability. The theoretical results are applied to an experimental data set for molecular AFLP markers linked to the bolting gene of sugar beet (Beta vulgaris L.).     

Evolution and structure of the fibrinogen genes: Random insertion of introns or selective loss?

Chromosomal linkage as well as sequence homologies provide unequivocal evidence that the genes for the alpha, beta and gamma chains of fibrinogen arose by successive duplication of a single ancestral gene. Yet, when the three fibrinogen chains are aligned by amino acid homology, the positions of intervening sequences coincide at only two positions for all three chains. While one additional intron occurs at a homologous site in the beta and gamma chains, none of the positions of the remaining 11 introns in the three genes is shared. This arrangement of introns in the three fibrinogen genes suggests that either introns were selectively lost, implying that there is essential information in the retained introns, or the common introns were present in the ancestral fibrinogen gene and introns have been randomly inserted since the triplication of the original gene. The more likely possibility of selective loss of introns implies that the ancestral gene, as it existed about one billion years ago, must have been composed of numerous small exons.     

Characterization of Hoxd1 Protein–DNA-Binding Specificity Using Affinity Chromatography and Random DNA Oligomer Selection

1. Hoxd1 is member of the labial subfamily of Hox genes that has a conserved 60 amino acid homeodomain region. The homeodomain is an important determining factor in the binding of the protein to specific DNA sequence(s). DNA-binding specificity for the Hoxd1 protein has not been determined previously.2. We have employed a rapid affinity chromatography method to determine optimal DNA binding sequences for the 109 amino acid Hoxd1 peptide, comprising the homeodomain and the entire carboxy terminal region of the Hoxd1 protein.3. Labial Hox proteins have intrinsically weak DNA-binding activity that has been attributed to the nonbasic residues at positions 2 and 3 in the N-terminal arm of the homeodomain. The presence of the Hoxd1 carboxy terminal region negated the influence of the nonbasic residues and facilitated Hoxd1 DNA-binding specificity.4. DNA sequences bound to the Hoxd1 peptide-affinity column were separated from a random pool of oligonucleotide sequences by gradient elution and enriched by polymerase chain reaction. Preferred sequences were identified on 5w and 3 of a TAAT core, extending the binding site to T/AT/gTAATTGTA.5. Stability and specificity of optimal DNA-binding sequence for Hoxd1 homeodomain were determined by equilibrium and kinetic studies. Dissociation coefficient constant (K _D) was estimated to be 8.6 × 10^–9 M and the DNA–Hoxd1 homeodomain complex has a half life (t _1/2) of 12.7 min.6. A molecular model of Hoxd1 homeodomain–-DNA interaction based on the X-ray coordinates of Antennapedia homeodomain–DNA complex has revealed novel interactions of key Hoxd1 residues at the protein–DNA interface.     

Reconstructing Local Population Dynamics in Noisy Metapopulations—The Role of Random Catastrophes and Allee Effects

Reconstructing the dynamics of populations is complicated by the different types of stochasticity experienced by populations, in particular if some forms of stochasticity introduce bias in parameter estimation in addition to error. Identification of systematic biases is critical when determining whether the intrinsic dynamics of populations are stable or unstable and whether or not populations exhibit an Allee effect, i.e., a minimum size below which deterministic extinction should follow. Using a simulation model that allows for Allee effects and a range of intrinsic dynamics, we investigated how three types of stochasticity—demographic, environmental, and random catastrophes— affect our ability to reconstruct the intrinsic dynamics of populations. Demographic stochasticity aside, which is only problematic in small populations, we find that environmental stochasticity—positive and negative environmental fluctuations—caused increased error in parameter estimation, but bias was rarely problematic, except at the highest levels of noise. Random catastrophes, events causing large-scale mortality and likely to be more common than usually recognized, caused immediate bias in parameter estimates, in particular when Allee effects were large. In the latter case, population stability was predicted when endogenous dynamics were actually unstable and the minimum viable population size was overestimated in populations with small or non-existent Allee effects. Catastrophes also generally increased extinction risk, in particular when endogenous Allee effects were large. We propose a method for identifying data points likely resulting from catastrophic events when such events have not been recorded. Using social spider colonies (Anelosimus spp.) as models for populations, we show that after known or suspected catastrophes are accounted for, reconstructed growth parameters are consistent with intrinsic dynamical instability and substantial Allee effects. Our results are applicable to metapopulation or time series data and are relevant for predicting extinction in conservation applications or the management of invasive species.     

Prediction of Protein–Protein Interaction Sites in Sequences and 3D Structures by Random Forests

Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras–Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.     

Geometrical versus Random β-TCP Scaffolds: Exploring the Effects on Schwann Cell Growth and Behavior

Numerous studies have demonstrated that Schwann cells (SCs) play a role in nerve regeneration; however, their role in innervating a bioceramic scaffold for potential application in bone regeneration is still unknown. Here we report the cell growth and functional behavior of SCs on β-tricalcium phosphate (β-TCP) scaffolds arranged in 3D printed-lattice (P-β-TCP) and randomly-porous, template-casted (N-β-TCP) structures. Our results indicate that SCs proliferated well and expressed the phenotypic markers p75^LNGFR and the S100-β subunit of SCs as well as displayed growth morphology on both scaffolds, but SCs showed spindle-shaped morphology with a significant degree of SCs alignment on the P-β-TCP scaffolds, seen to a lesser degree in the N-β-TCP scaffold. The gene expressions of nerve growth factor (β-ngf), neutrophin–3 (nt–3), platelet-derived growth factor (pdgf-bb), and vascular endothelial growth factor (vegf-a) were higher at day 7 than at day 14. While no significant differences in protein secretion were measured between these last two time points, the scaffolds promoted the protein secretion at day 3 compared to that on the cell culture plates. These results together imply that the β-TCP scaffolds can support SC cell growth and that the 3D-printed scaffold appeared to significantly promote the alignment of SCs along the struts. Further studies are needed to investigate the early and late stage relationship between gene expression and protein secretion of SCs on the scaffolds with refined characteristics, thus better exploring the potential of SCs to support vascularization and innervation in synthetic bone grafts.