Bayesian analysis of mass spectrometry proteomic data using wavelet-based functional mixed models

Summary .   In this article, we apply the recently developed Bayesian wavelet-based functional mixed model methodology to analyze MALDI-TOF mass spectrometry proteomic data. By modeling mass spectra as functions, this approach avoids reliance on peak detection methods. The flexibility of this framework in modeling nonparametric fixed and random effect functions enables it to model the effects of multiple factors simultaneously, allowing one to perform inference on multiple factors of interest using the same model fit, while adjusting for clinical or experimental covariates that may affect both the intensities and locations of peaks in the spectra. For example, this provides a straightforward way to account for systematic block and batch effects that characterize these data. From the model output, we identify spectral regions that are differentially expressed across experimental conditions, in a way that takes both statistical and clinical significance into account and controls the Bayesian false discovery rate to a prespecified level. We apply this method to two cancer studies.     

Biogenesis and maintenance of the apicoplast in model apicomplexan parasites

The apicoplast is a non-photosynthetic relict plastid of Apicomplexa that evolved from a secondary symbiotic system. During its evolution, most of the genes derived from its alga ancestor were lost. Only genes involved in several valuable metabolic pathways, such as the synthesis of isoprenoid precursors, heme, and fatty acids, have been transferred to the host genome and retained to help these parasites adapt to a complex life cycle and various living environments. The biological function of an apicoplast is essential for most apicomplexan parasites. Considering their potential as drug targets, the metabolic functions of this symbiotic organelle have been intensively investigated through computational and biological means. Moreover, we know that not only organellar metabolic functions are linked with other organelles, but also their biogenesis processes have developed and evolved to tailor their biological functions and proper inheritance. Several distinct features have been found in the biogenesis process of apicoplasts. For example, the apicoplast borrows a dynamin-related protein (DrpA) from its host to implement organelle division. The autophagy system has also been repurposed for linking the apicoplast and centrosome during replication and the division process. However, many vital questions remain to be answered about how these parasites maintain and properly inherit this symbiotic organelle. Here we review our current knowledge about its biogenesis process and discuss several critical questions remaining to be answered in this field.     

Ecosystem services–A tool for sustainable management of human–environment systems. Case study Finnish Forest Lapland

The concept of ecosystem services (ESs) is a relatively new scientific methodology, offering a possible approach to the prevention of ecological problems caused by human action and to the resolution of conflicts arising from land-use questions. Since ESs were launched as a major conceptual tool in the Millennium Ecosystem Assessment (MA, 2005), interest in them has been increasing. Despite the scientific as well as economic and political enthusiasm for the ES approach, only few case studies have as yet been published. We studied the interface between ESs and landscape planning in Forest Lapland, in northern Finland. In the article, we present a methodology and various databases which can be used in applied research on ESs. We classify the ESs offered by various biotopes of the study area, and examine the effects of different land-use forms on the provision of ESs. On the basis of our results, we suggest possible uses of the European CORINE land cover database in case studies.     

Bayesian calibration, validation, and uncertainty quantification of diffuse interface models of tumor growth

The idea that one can possibly develop computational models that predict the emergence, growth, or decline of tumors in living tissue is enormously intriguing as such predictions could revolutionize medicine and bring a new paradigm into the treatment and prevention of a class of the deadliest maladies affecting humankind. But at the heart of this subject is the notion of predictability itself, the ambiguity involved in selecting and implementing effective models, and the acquisition of relevant data, all factors that contribute to the difficulty of predicting such complex events as tumor growth with quantifiable uncertainty. In this work, we attempt to lay out a framework, based on Bayesian probability, for systematically addressing the questions of Validation, the process of investigating the accuracy with which a mathematical model is able to reproduce particular physical events, and Uncertainty quantification, developing measures of the degree of confidence with which a computer model predicts particular quantities of interest. For illustrative purposes, we exercise the process using virtual data for models of tumor growth based on diffuse-interface theories of mixtures utilizing virtual data.     

Tailor-made pharmacotherapy: future developments and ethical challenges in the field of pharmacogenomics

Pharmacogenomics is the study of the myriad interactions between genes and pharmacotherapy. Developments in pharmacogenomics have changed and will affect pharmaceutical research, drug development and the practice of medicine in a significant way. In this article, we make an inventory of the ethical implications that might arise as a result of possible developments in pharmacogenomics and investigate whether the present ethical framework will be able to adequately answer arising questions. We think that many of the questions related to the consequences of pharmacogenomics are answerable along the lines of present ethical thinking. We also believe, however, that many 'changes of degree' may result in a 'change of kind.' We therefore think that pharmacogenomics may potentially have such a profound influence on scientific research and the pharmaceutical industry, the practice of medicine and society at large, that this will generate its own unique dynamic, which will require new ethical research. We suggest that the notion of 'responsibility' will be a major focus of such research.     

Animal cell cultures as a source of hormones

Current methodology now makes it possible to establish in culture a variety of mammalian cells which perform organ-specific functions during serial propagation for periods of months or years. This report describes the results of experiments with animal and human cells that produce growth hormone, adrenocortical steroid hormones, thyrocalcitonin, and parathyroid hormone. Within the next decade it should be possible to use cell culture methods for manufacturing purposes to produce hormones and other valuable cellular products which are difficult to obtain in other ways. At first it may be necessary to use neoplastic cells for this purpose; but evidence is accumulating to suggest that it may eventually be possible to establish in culture normal, functional animal and human cells.     

occAssess: An R package for assessing potential biases in species occurrence data

Species occurrence records from a variety of sources are increasingly aggregated into heterogeneous databases and made available to ecologists for immediate analytical use. However, these data are typically biased, i.e. they are not a probability sample of the target population of interest, meaning that the information they provide may not be an accurate reflection of reality. It is therefore crucial that species occurrence data are properly scrutinised before they are used for research. In this article, we introduce occAssess, an R package that enables straightforward screening of species occurrence data for potential biases. The package contains a number of discrete functions, each of which returns a measure of the potential for bias in one or more of the taxonomic, temporal, spatial, and environmental dimensions. Users can opt to provide a set of time periods into which the data will be split; in this case separate outputs will be provided for each period, making the package particularly useful for assessing the suitability of a dataset for estimating temporal trends in species'' distributions. The outputs are provided visually (as ggplot2 objects) and do not include a formal recommendation as to whether data are of sufficient quality for any given inferential use. Instead, they should be used as ancillary information and viewed in the context of the question that is being asked, and the methods that are being used to answer it. We demonstrate the utility of occAssess by applying it to data on two key pollinator taxa in South America: leaf‐nosed bats (Phyllostomidae) and hoverflies (Syrphidae). In this worked example, we briefly assess the degree to which various aspects of data coverage appear to have changed over time. We then discuss additional applications of the package, highlight its limitations, and point to future development opportunities.     

Age estimation in subadult Egyptian remains

In anthropological analyses of past populations, it is very important to be able to accurately reconstruct the palaeodemographic profile in order to interpret infant mortality as an indicator of the environmental, social and cultural conditions. There are various methods to evaluate the age of immature individuals but some of these methods are strongly influenced by the different rates of skeletal development observed in populations from various geographical areas and/or from various time periods, as well as between the sexes. Clearly, there is a need for adopting a method of estimation of age at death, which will be the one most suitable for analysing the particular skeletal sample.In this study we investigated subadults from the Egyptian osteological collection housed in the Museum of Anthropology and Ethnography of the University of Turin. For each individual, the age at death was estimated based on the degree of eruption and mineralisation of the teeth. Then the estimated age at death was correlated with the measurements of the long bones and ilium.We showed that greater regularity and constancy of rates of skeletal growth could be assessed with measurements, alternative to using maximum length of diaphysis. Moreover, using alternative characters, it was possible to markedly increase the number of individuals whose age at death could be estimated. Our study also showed the need to use a reference sample consistent with the sample being analysed and, which was derived from similar biological-environmental context. Therefore, our proposed method can be used for the estimation of age at death in pre/protohistorical populations from the Mediterranean region.     

Statistical validation of surrogate endpoints: is bone density a valid surrogate for fracture?

In the treatment of osteoporosis using anti-resorptive agents there has been increasing interest in quantifying the relationship between fracture endpoints and surrogates such as bone mineral density (BMD) or bone turnover markers. Statistical methodology constitutes a critical component of assessing surrogate validity. Depending on study designs, data resources, and statistical methods used for analyses, one has to use caution when interpreting results from different analyses, especially when results are disparate. For example, analyses based on individual patient data reported that only a limited proportion of the anti-fracture efficacy was explained by BMD increases for agents such as alendronate, risedronate and raloxifene. Analyses employing meta-regression based on summary statistics, however, indicated that most of the anti-fracture benefits were due to improvements in BMD. In this paper, we review definitions of surrogate endpoints and requirements for their statistical validation. We evaluate whether BMD meets these requirements as a possible surrogate for fracture. Our review indicates that the actual BMD value is correlated with fracture risk and thus BMD is useful in identifying patients that might need treatment. There is limited evidence to support BMD increase with anti-resorptive agents as a reliable substitute for fracture risk reduction. Strengths and limitations for various statistical methods are discussed.     

A working memory model for serial order that stores information in the intrinsic excitability properties of neurons

Models for temporary information storage in neuronal populations are dominated by mechanisms directly dependent on synaptic plasticity. There are nevertheless other mechanisms available that are well suited for creating short-term memories. Here we present a model for working memory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. We show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity. For this we introduce a functional class of neurons, named gate interneurons, which can store information in their membrane dynamics and can literally act as gates routing the flow of activations in the principal neurons population. The presented model exhibits properties which are in close agreement with experimental results in working memory. Namely, the recall process plays an important role in stabilizing and prolonging the memory trace. This means that the stored information is correctly maintained as long as it is being used. Moreover, the working memory model is adequate for storing completely new information, in time windows compatible with the notion of “one-shot” learning (hundreds of milliseconds).     

Data mining tools for biological sequences

We describe a methodology, as well as some related data mining tools, for analyzing sequence data. The methodology comprises three steps: (a) generating candidate features from the sequences, (b) selecting relevant features from the candidates, and (c) integrating the selected features to build a system to recognize specific properties in sequence data. We also give relevant techniques for each of these three steps. For generating candidate features, we present various types of features based on the idea of k-grams. For selecting relevant features, we discuss signal-to-noise, t-statistics, and entropy measures, as well as a correlation-based feature selection method. For integrating selected features, we use machine learning methods, including C4.5, SVM, and Naive Bayes. We illustrate this methodology on the problem of recognizing translation initiation sites. We discuss how to generate and select features that are useful for understanding the distinction between ATG sites that are translation initiation sites and those that are not. We also discuss how to use such features to build reliable systems for recognizing translation initiation sites in DNA sequences.     

Functions and analysis of the seminal fluid proteins of male Drosophila melanogaster fruit flies

The study of insect seminal fluid proteins provides a unique window upon adaptive evolution in action. The seminal fluid of Drosophila melanogaster contains over 80 proteins and peptides, which are transferred together with sperm by mating males. The functions of many of these substances are not yet known. However, those that have been characterized have marked effects on the reproductive success of males and females. For example, seminal fluid proteins and peptides can decrease female receptivity, can increase egg production and can increase sperm storage, and are necessary for sperm transfer and success in sperm competition. In this review we focus on the currently known functions of seminal fluid molecules and on new technologies and approaches that are enabling novel questions about their form and function to be addressed. We discuss how techniques for disrupting the production of seminal fluid proteins, such as homologous recombination and RNA interference, along with the use of microarrays and yeast two hybrid systems, should allow us to address ever more sophisticated questions about seminal fluid protein function. These and similar techniques promise to reveal the function of naturally-occurring variants of these proteins and hence the evolutionary significance of genetic variation for them.     

Leibnizian organisms,nested individuals,and units of selection

Summary Leibniz developed a new notion of individuality, according to which individuals are nested one within another, thereby abandoning the Aristotelian formula at the heart of substantialist metaphysics, ‘one body, one substance’. On this model, the level of individuality is determined by the degree of activity, and partly defined by its relations with other individuals. In this article, we show the importance of this new notion of individuality for some persisting questions in theoretical biology. Many evolutionary theorists presuppose a model of individuality that will eventually reduce to spatiotemporal mechanisms, and some still look for an exclusive level or function to determine a unit of selection. In recent years, a number of alternatives to these exclusive approaches have emereged, and no consensus can be foreseen. It is for this reason that we propose the model of nested individuals. This model supports pluralistic multi-level selection and rejects an exclusive level or function for a unit of selection. Since activity is essential to the unity of an individual, this model focuses on integrating processes of interaction and replication instead of choosing between them. In addition, the model of nested individuals may also be seen as a distinct perspective among the various alternative models for the unit of selection. This model stresses activity and pluralism: it accepts simultaneuous co-existence of individuals at different levels, nested one within the other. Our aim in this article is to show now a chapter of the history of metaphysics may be fruitfully brought to bear on the current debate over the unit of selection in evolutionary biology.     

Ciclograma: a tool for detection of rhythmicities in sleep/wake cycles

The Fourier spectral analysis of binary time series (or rectangular signals) causes methodological problems, due to the fact that it is based on sinusoidal functions. We propose a new tool for the detection of periodicities in binary time series, focusing on sleep/wake cycles. This methodology is based on a weighted histogram of cycle durations. In this paper, we compare our methodology with the Fourier spectral analysis on the basis of simulated and real binary data sets of various lengths. We also provide an approach to statistical validation of the periodicities determined with our methodology. Furthermore, we analyze the discriminating power of both methods in terms of standard deviation. Our results indicate that the Ciclograma is much more powerful than Fourier analysis when applied on this type of time series.     

Quantitative Analyses of Cell Division in Plants

At the molecular level regulatory interactions between cell cycle genes are being uncovered rapidly, but less progress is made in unravelling how these molecular events regulate growth processes at the level of cells and of the whole organism. The main obstacle is the absence of a set of analytical tools that are powerful enough to determine pertinent parameters and, at the same time, relatively easy to use by non-specialized laboratories. Appropriate methodology to obtain this type of data has been pioneered in the first half of the last century and is now commonly defined as ‘kinematic analysis’. Unfortunately, the laborious nature of these analyses and the relatively complex numerical methods used, have limited their use to only a handful of specialized research groups. In this article we attempt to present an accessible entry to this methodology, particularly in terms of the mathematical framework. We start describing the simplest possible system, i.e., a virtually homogenous cell suspension culture. Then, we outline the analysis of dicotyledonous leaves, root tips, monocotyledonous leaves, and finally shoot apical meristems. For each of these systems we discuss the details of the calculation of cell division parameters such as cell cycle duration, size of the meristem and number of cells contained in it, which enables answering fundamental questions about the relative contribution of differences in cell production and cell size to variation in growth. In addition, we discuss the assumptions and limitations of these and alternative methodologies with the aim to facilitate the choice of appropriate analyses depending on the specific research question.     

Placebo controls: historical, methodological and general aspects

Control conditions were introduced through the trial of Mesmerism in Paris. Placebo controls became codified standard in 1946. Although seemingly unchallenged, there are various problems with this received view. The notion of a placebo is only defined from the negative. A positive notion proposed that placebo effects are effects owing to the meaning an intervention has for an individual. Thus, placebo effects are individualized, whereas standard research paradigms reveal only grossly averaged behaviour. Also, placebo effects are context sensitive, dependent on psychological factors such as expectancy, relief of stress and anxiety, and hence can generate strong and long-lasting treatment effects. These, however, are not predictable. Such a situation can lead to the efficacy paradox: sometimes, sham interventions can be more powerful than proved, evidence-based treatments. This situation has methodological consequences. Placebo-controlled randomized trials reveal only part of the answer, whether an intervention is effective. This is valuable information for regulators, but not necessarily also for patients and of limited value for providers. Hence, I have argued that we need to complement the hierarchical model of evidence by a circular one, in which various methods are employed on equal footing to answer different questions.     

Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Networks offer a powerful tool for understanding and visualizing inter-species ecological and evolutionary interactions. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for this methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining methodology allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases.     

Robustness,flexibility, and the role of lateral inhibition in the neurogenic network

BACKGROUND: Many gene networks used by developing organisms have been conserved over long periods of evolutionary time. Why is that? We showed previously that a model of the segment polarity network in Drosophila is robust to parameter variation and is likely to act as a semiautonomous patterning module. Is this true of other networks as well? RESULTS: We present a model of the core neurogenic network in Drosophila. Our model exhibits at least three related pattern-resolving behaviors that the real neurogenic network accomplishes during embryogenesis in Drosophila. Furthermore, we find that it exhibits these behaviors across a wide range of parameter values, with most of its parameters able to vary more than an order of magnitude while it still successfully forms our test patterns. With a single set of parameters, different initial conditions (prepatterns) can select between different behaviors in the network's repertoire. We introduce two new measures for quantifying network robustness that mimic recombination and allelic divergence and use these to reveal the shape of the domain in the parameter space in which the model functions. We show that lateral inhibition yields robustness to changes in prepatterns and suggest a reconciliation of two divergent sets of experimental results. Finally, we show that, for this model, robustness confers functional flexibility. CONCLUSIONS: The neurogenic network is robust to changes in parameter values, which gives it the flexibility to make new patterns. Our model also offers a possible resolution of a debate on the role of lateral inhibition in cell fate specification.     

Analysis of biochemical genetic data on Jewish populations: II. Results and interpretations of heterogeneity indices and distance measures with respect to standards.

A nonparametric statistical methodology is used for the analysis of biochemical frequency data observed on a series of nine Jewish and six non-Jewish populations. Two categories of statistics are used: heterogeneity indices and various distance measures with respect to a standard. The latter are more discriminating in exploiting historical, geographical and culturally relevant information. A number of partial orderings and distance relationships among the populations are determined. Our concern in this study is to analyze similarities and differences among the Jewish populations, in terms of the gene frequency distributions for a number of genetic markers. Typical questions discussed are as follows: These Jewish populations differ in certain morphological and anthropometric traits. Are there corresponding differences in biochemical genetic constitution? How can we assess the extent of heterogeneity between and within groupings? Which class of markers (blood typings or protein loci) discriminates better among the separate populations? The results are quite surprising. For example, we found the Ashkenazi, Sephardi and Iraqi Jewish populations to be consistently close in genetic constitution and distant from all the other populations, namely the Yemenite and Cochin Jews, the Arabs, and the non-Jewish German and Russian populations. We found the Polish Jewish community the most heterogeneous among all Jewish populations. The blood loci discriminate better than the protein loci. A number of possible interpretations and hypotheses for these and other results are offered. The method devised for this analysis should prove useful in studying similarities and differences for other groups of populations for which substantial biochemical polymorphic data are available.