Selective Sampling of Species and Fossils Influences Age Estimates Under the Fossilized Birth-Death Model

The fossilized birth-death (FBD) model allows the estimation of species divergence times from molecular and fossil information in a coherent framework of diversification and fossil sampling. Some assumptions of the FBD model, however, are difficult to meet in phylogenetic analyses of highly diverse groups. Here, I use simulations to assess the impact of extreme model violations, including diversified sampling of species and the exclusive use of the oldest fossils per clade, on divergence times estimated with the FBD model. My results demonstrate that selective sampling of fossils can produce dramatically overestimated divergence times when the FBD model is used for inference, due to an interplay of underestimates for the model parameters net diversification rate, turnover, and fossil-sampling proportion. In contrast, divergence times estimated with CladeAge, a method that uses information about the oldest fossils per clade together with estimates of sampling and diversification rates, are accurate under these conditions. Practitioners of Bayesian divergence-time estimation should therefore ensure that the dataset conforms to the expectations of the FBD model, or estimates of sampling and diversification rates should be obtained a priori so that CladeAge can be used for the inference.

Congruence of morphological and molecular phylogenies of the rove beetle subfamily Staphylininae (Coleoptera: Staphylinidae)

Staphylininae is the third largest subfamily of the enormous family Staphylinidae. Monophyly of Staphylininae and its sister relationship to the subfamily Paederinae have been broadly accepted according to both conventional morphology- and molecular-based phylogenies until the last three years. Recent molecular phylogenies rejected monophyly of Staphylininae and regarded Paederinae as a clade within it. This paper re-evaluates the recent molecular work, aiming to clarify the relationship between Staphylininae and Paederinae and resolve intertribal relationships within Staphylininae. Based on a new six-gene data set (5707 bp) for 92 taxa including Oxyporinae (outgroup), representatives of Paederinae, and members of all extant tribes of Staphylininae from published DNA data in GenBank, we generated a well-resolved phylogeny of Staphylininae with all deep nodes (intertribal relationships) strongly supported, and reassert the hypothesis that Staphylininae is monophyletic and indeed the sister group to Paederinae using both Bayesian and maximum likelihood inference. Additionally, our study is a case-study to show that both outgroup selection and completeness of nucleotide data can influence the outcome of a molecular phylogeny. With an increasing number of staphylinid fossils being discovered, the robust phylogeny of Staphylininae inferred by our research will provide a good framework for understanding the early evolution of this group.

The Generative Adversarial Brain

The idea that the brain learns generative models of the world has been widely promulgated. Most approaches have assumed that the brain learns an explicit density model that assigns a probability to each possible state of the world. However, explicit density models are difficult to learn, requiring approximate inference techniques that may find poor solutions. An alternative approach is to learn an implicit density model that can sample from the generative model without evaluating the probabilities of those samples. The implicit model can be trained to fool a discriminator into believing that the samples are real. This is the idea behind generative adversarial algorithms, which have proven adept at learning realistic generative models. This paper develops an adversarial framework for probabilistic computation in the brain. It first considers how generative adversarial algorithms overcome some of the problems that vex prior theories based on explicit density models. It then discusses the psychological and neural evidence for this framework, as well as how the breakdown of the generator and discriminator could lead to delusions observed in some mental disorders.

Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference

Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here, we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high-resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in GBM patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and, thus, is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.

Evidencing How Experience and Problem Format Affect Probabilistic Reasoning Through Interaction Analysis

This paper examines the role that lived experience plays in the human capacity to reason about uncertainty. Previous research shows that people are more likely to provide accurate responses in Bayesian tasks when the data are presented in natural frequencies, the problem in question describes a familiar event, and the values of the data are in line with beliefs. Precisely why these factors are important remains open to debate. We elucidate the issue in two ways. Firstly, we hypothesize that in a task that requires people to reason about conditional probabilities, they are more likely to respond accurately when the values of the problem reflect their own lived experience, than when they reflect the experience of the average participant. Secondly, to gain further understanding of the underlying reasoning process, we employ a novel interaction analysis method that tracks mouse movements in an interactive web application and applies transition analysis to model how the approach to reasoning differs depending on whether data are presented using percentages or natural frequencies. We find (1) that the closer the values of the data in the problem are to people's self-reported lived experience, the more likely they are to provide a correct answer, and (2) that the reasoning process employed when data are presented using natural frequencies is qualitatively different to that employed when data are presented using percentages. The results indicate that the benefits of natural frequency presentation are due to a clearer representation of the relationship between sets and that the prior humans acquire through experience has an overwhelming influence on their ability to reason about uncertainty.

Variable precision in visual perception

Given the same sensory stimuli in the same task, human observers do not always make the same response. Well-known sources of behavioral variability are sensory noise and guessing. Visual short-term memory (STM) studies have suggested that the precision of the sensory noise is itself variable. However, it is unknown whether precision is also variable in perceptual tasks without a memory component. We searched for evidence for variable precision in 11 visual perception tasks with a single relevant feature, orientation. We specifically examined the effect of distractor stimuli: distractors were absent, homogeneous and fixed across trials, homogeneous and variable, or heterogeneous and variable. We first considered 4 models: with and without guessing, and with and without variability in precision. We quantified the importance of both factors using 6 metrics: factor knock-in difference, factor knock-out difference, and log factor posterior ratio, each based on AIC or BIC. According to all 6 metrics, we found strong evidence for variable precision in 5 experiments. Next, we extended our model space to include potential confounding factors: the oblique effect and decision noise. This left strong evidence for variable precision in only 1 experiment, in which distractors were homogeneous but variable. Finally, when we considered suboptimal decision rules, the evidence also disappeared in this experiment. Our results provide little evidence for variable precision overall and only a hint when distractors are variable. Methodologically, the results underline the importance of including multiple factors in factorial model comparison: Testing for only 2 factors would have yielded an incorrect conclusion. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Bayesian Weighing of Electron Cryo-Microscopy Data for Integrative Structural Modeling

Cryo-electron microscopy (cryo-EM) has become a mainstream technique for determining the structures of complex biological systems. However, accurate integrative structural modeling has been hampered by the challenges in objectively weighing cryo-EM data against other sources of information due to the presence of random and systematic errors, as well as correlations, in the data. To address these challenges, we introduce a Bayesian scoring function that efficiently and accurately ranks alternative structural models of a macromolecular system based on their consistency with a cryo-EM density map as well as other experimental and prior information. The accuracy of this approach is benchmarked using complexes of known structure and illustrated in three applications: the structural determination of the GroEL/GroES, RNA polymerase II, and exosome complexes. The approach is implemented in the open-source Integrative Modeling Platform (http://integrativemodeling.org), thus enabling integrative structure determination by combining cryo-EM data with other sources of information.

Probabilistic Inference for Dynamical Systems

A general framework for inference in dynamical systems is described, based on the language of Bayesian probability theory and making use of the maximum entropy principle. Taking the concept of a path as fundamental, the continuity equation and Cauchy's equation for fluid dynamics arise naturally, while the specific information about the system can be included using the maximum caliber (or maximum path entropy) principle.

Occam's razor: from Ockham's via moderna to modern data science

The principle of parsimony, also known as 'Occam's razor', is a heuristic dictum that is thoroughly familiar to virtually all practitioners of science: Aristotle, Newton, and many others have enunciated it in some form or other. Even though the principle is not difficult to comprehend as a general heuristic guideline, it has proved surprisingly resistant to being put on a rigorous footing - a difficulty that has become more pressing and topical with the 'big data' explosion. We review the significance of Occam's razor in the philosophical and theological writings of William of Ockham, and survey modern developments of parsimony in data science.

Online Bayesian Phylogenetic Inference: Theoretical Foundations via Sequential Monte Carlo

Phylogenetics, the inference of evolutionary trees from molecular sequence data such as DNA, is an enterprise that yields valuable evolutionary understanding of many biological systems. Bayesian phylogenetic algorithms, which approximate a posterior distribution on trees, have become a popular if computationally expensive means of doing phylogenetics. Modern data collection technologies are quickly adding new sequences to already substantial databases. With all current techniques for Bayesian phylogenetics, computation must start anew each time a sequence becomes available, making it costly to maintain an up-to-date estimate of a phylogenetic posterior. These considerations highlight the need for an online Bayesian phylogenetic method which can update an existing posterior with new sequences. Here, we provide theoretical results on the consistency and stability of methods for online Bayesian phylogenetic inference based on Sequential Monte Carlo (SMC) and Markov chain Monte Carlo. We first show a consistency result, demonstrating that the method samples from the correct distribution in the limit of a large number of particles. Next, we derive the first reported set of bounds on how phylogenetic likelihood surfaces change when new sequences are added. These bounds enable us to characterize the theoretical performance of sampling algorithms by bounding the effective sample size (ESS) with a given number of particles from below. We show that the ESS is guaranteed to grow linearly as the number of particles in an SMC sampler grows. Surprisingly, this result holds even though the dimensions of the phylogenetic model grow with each new added sequence.

Estimating Age-Dependent Extinction: Contrasting Evidence from Fossils and Phylogenies

The estimation of diversification rates is one of the most vividly debated topics in modern systematics, with considerable controversy surrounding the power of phylogenetic and fossil-based approaches in estimating extinction. Van Valen's seminal work from 1973 proposed the "Law of constant extinction," which states that the probability of extinction of taxa is not dependent on their age. This assumption of age-independent extinction has prevailed for decades with its assessment based on survivorship curves, which, however, do not directly account for the incompleteness of the fossil record, and have rarely been applied at the species level. Here, we present a Bayesian framework to estimate extinction rates from the fossil record accounting for age-dependent extinction (ADE). Our approach, unlike previous implementations, explicitly models unobserved species and accounts for the effects of fossil preservation on the observed longevity of sampled lineages. We assess the performance and robustness of our method through extensive simulations and apply it to a fossil data set of terrestrial Carnivora spanning the past 40 myr. We find strong evidence of ADE, as we detect the extinction rate to be highest in young species and declining with increasing species age. For comparison, we apply a recently developed analogous ADE model to a dated phylogeny of extant Carnivora. Although the phylogeny-based analysis also infers ADE, it indicates that the extinction rate, instead, increases with increasing taxon age. The estimated mean species longevity also differs substantially, with the fossil-based analyses estimating 2.0 myr, in contrast to 9.8 myr derived from the phylogeny-based inference. Scrutinizing these discrepancies, we find that both fossil and phylogeny-based ADE models are prone to high error rates when speciation and extinction rates increase or decrease through time. However, analyses of simulated and empirical data show that fossil-based inferences are more robust. This study shows that an accurate estimation of ADE from incomplete fossil data is possible when the effects of preservation are jointly modeled, thus allowing for a reassessment of Van Valen's model as a general rule in macroevolution.

Tamoxifen Pharmacovigilance: Implications for Safe Use in the Future

OBJECTIVE

To survey the status of current tamoxifen pharmacovigilance documentation reflecting tamoxifen use in an academic outpatient multispecialty practice in older adults. This data will help provide information to develop improved pharmacovigilance for a growing cohort of older adult users. The data will be utilized by an interdisciplinary team developing new methods of identifying factors for individualized pharmacovigilance in older adults.

DESIGN

Retrospective chart review to gather descriptive and quantitative data on tamoxifen pharmacovigilance.

SETTING

Multi-specialty clinic.

PATIENTS

Ninety-three patients 60 years of age and older.

MAIN OUTCOME MEASURES

Quantitative report of tamoxifen monitoring as well as descriptive analysis of individual cases.

RESULTS

We found 19 cases of serious adverse events possibly related to tamoxifen (thrombi, uterine malignancies). There were 15 cases with no documentation of pharmacovigilance. All cases had incomplete pharmacovigilance documented. There were two cases of hypercalcemia. There was one case of tamoxifen discontinuation resulting from muscle pain and with chronic muscle pain complaints while receiving tamoxifen. We observed a correlation in older age or high comorbidity burden patients and adverse events patients.

CONCLUSION

Some studies direct the important pharmacovigilance toward prevention of thrombi, uterine malignancies, and hypercalcemia; however, it is not easy to identify recommendations for frequency or focus of monitoring to prevent adverse events for individual older adults based on existing recommendations. The data collected and presented in this study serve to heighten awareness of tamoxifen pharmacovigilance and as a starting point for the application of machine learning techniques and modeling to identify high-risk patients and individualized pharmacovigilance recommendations.

A New Species of the Rare Genus Anguillonema Fuchs, 1938 (Nematoda: Hexatylina, Sphaerularioidea) with Its Molecular Phylogenetic Study

Anguillonema amolensis n. sp. is described and illustrated based on its morphological, morphometric, and molecular characters. The new species is characterized by its 575 to 820 μm long and wide body (body width at vulva = 30 to 59 μm), irregularly ventrally curved after fixation, five to six lines in lateral fields, 6.0 to 7.5 μm long stylet with small rounded knobs, pharynx lacking a median bulb, pharyngo-intestinal junction anterior to nerve ring and excretory pore, females with monodelphic-prodelphic reproductive system, 15 to 19 μm long conical tail with broad rounded tip, and males absent. The new species is compared with two known species of the genus, Anguillonema poligraphi and A. crenati. Molecular phylogenetic studies of the new species using partial sequences of small subunit (SSU) rDNA revealed that it forms a clade with an unidentified nematode species and two species of the genus Howardula. In phylogenetic analyses using partial sequences of the 28S rDNA (D2-D3 segment), the new species formed a monophyletic group with species belonging to two genera Howardula and Parasitylenchus.

Structure in the variability of the basic reproductive number (R0) for Zika epidemics in the Pacific islands

Before the outbreak that reached the Americas in 2015, Zika virus (ZIKV) circulated in Asia and the Pacific: these past epidemics can be highly informative on the key parameters driving virus transmission, such as the basic reproduction number (R0). We compare two compartmental models with different mosquito representations, using surveillance and seroprevalence data for several ZIKV outbreaks in Pacific islands (Yap, Micronesia 2007, Tahiti and Moorea, French Polynesia 2013-2014, New Caledonia 2014). Models are estimated in a stochastic framework with recent Bayesian techniques. R0 for the Pacific ZIKV epidemics is estimated between 1.5 and 4.1, the smallest islands displaying higher and more variable values. This relatively low range of R0 suggests that intervention strategies developed for other flaviviruses should enable as, if not more effective control of ZIKV. Our study also highlights the importance of seroprevalence data for precise quantitative analysis of pathogen propagation, to design prevention and control strategies.

DOI:http://dx.doi.org/10.7554/eLife.19874.001

Zika virus is an infectious disease primarily transmitted between people by mosquitoes. While most people develop mild flu-like symptoms, infection during pregnancy can interfere with how the baby’s head and brain develop. Until recently, the virus had only been seen sporadically in Africa and Asia, but since 2007, outbreaks have been recorded on several Pacific islands. In 2015, the Zika virus reached the Americas, and within six months over 1.5 million cases had been reported in Brazil alone.

There is an urgent need to understand how the Zika virus moves within a population in order to help policymakers, and public health professionals, plan treatment and control of outbreaks of the disease. Researchers often use predictive models to estimate how a disease will spread. A parameter commonly calculated by these models is the “basic reproductive number”, or R₀, which represents the average number of additional cases of the disease caused by one infected individual.

Using models that incorporated data from Zika virus outbreaks that occurred on several Pacific islands, Champagne et al. have produced estimates of R₀ that range from 1.5-4.1.

The R₀ values are greater than one, indicating that infection will spread within a population, but in the same range as those obtained for dengue fever, another closely related mosquito-borne disease. This suggests that by taking appropriate measures, the spread of Zika and dengue can be controlled to similar extents.

A closer look at the relationship between the population size and the predicted R₀ value for each Pacific island revealed an unexpected inverse relationship: the smaller the population, the larger the value of R₀. Since other regional factors may also explain these large differences between settings, further work is needed to disentangle context-specific from disease-specific factors. In this respect, data about seroprevalence (the number of people whose blood shows evidence of a past infection) in different populations is crucial for precisely analyzing the spread of Zika virus.

DOI:http://dx.doi.org/10.7554/eLife.19874.002

Active SAmpling Protocol (ASAP) to Optimize Individual Neurocognitive Hypothesis Testing: A BCI-Inspired Dynamic Experimental Design

The relatively young field of Brain-Computer Interfaces has promoted the use of electrophysiology and neuroimaging in real-time. In the meantime, cognitive neuroscience studies, which make extensive use of functional exploration techniques, have evolved toward model-based experiments and fine hypothesis testing protocols. Although these two developments are mostly unrelated, we argue that, brought together, they may trigger an important shift in the way experimental paradigms are being designed, which should prove fruitful to both endeavors. This change simply consists in using real-time neuroimaging in order to optimize advanced neurocognitive hypothesis testing. We refer to this new approach as the instantiation of an Active SAmpling Protocol (ASAP). As opposed to classical (static) experimental protocols, ASAP implements online model comparison, enabling the optimization of design parameters (e.g., stimuli) during the course of data acquisition. This follows the well-known principle of sequential hypothesis testing. What is radically new, however, is our ability to perform online processing of the huge amount of complex data that brain imaging techniques provide. This is all the more relevant at a time when physiological and psychological processes are beginning to be approached using more realistic, generative models which may be difficult to tease apart empirically. Based upon Bayesian inference, ASAP proposes a generic and principled way to optimize experimental design adaptively. In this perspective paper, we summarize the main steps in ASAP. Using synthetic data we illustrate its superiority in selecting the right perceptual model compared to a classical design. Finally, we briefly discuss its future potential for basic and clinical neuroscience as well as some remaining challenges.

Examining the Impact of Prior Models in Transmural Electrophysiological Imaging: A Hierarchical Multiple-Model Bayesian Approach

Noninvasive cardiac electrophysiological (EP) imaging aims to mathematically reconstruct the spatiotemporal dynamics of cardiac sources from body-surface electrocardiographic (ECG) data. This ill-posed problem is often regularized by a fixed constraining model. However, a fixed-model approach enforces the source distribution to follow a pre-assumed structure that does not always match the varying spatiotemporal distribution of actual sources. To understand the model-data relation and examine the impact of prior models, we present a multiple-model approach for volumetric cardiac EP imaging where multiple prior models are included and automatically picked by the available ECG data. Multiple models are incorporated as an Lp-norm prior for sources, where p is an unknown hyperparameter with a prior uniform distribution. To examine how different combinations of models may be favored by different measurement data, the posterior distribution of cardiac sources and hyperparameter p is calculated using a Markov Chain Monte Carlo (MCMC) technique. The importance of multiple-model prior was assessed in two sets of synthetic and real-data experiments, compared to fixed-model priors (using Laplace and Gaussian priors). The results showed that the posterior combination of models (the posterior distribution of p) as determined by the ECG data differed substantially when reconstructing sources with different sizes and structures. While the use of fixed models is best suited in situations where the prior assumption fits the actual source structures, the use of an automatically adaptive set of models may have the ability to better address model-data mismatch and to provide consistent performance in reconstructing sources with different properties.

40 years of progress in female cancer death risk: a Bayesian spatio-temporal mapping analysis in Switzerland

BACKGROUND

In the past decades, mortality of female gender related cancers declined in Switzerland and other developed countries. Differences in the decrease and in spatial patterns within Switzerland have been reported according to urbanisation and language region, and remain controversial. We aimed to investigate geographical and temporal trends of breast, ovarian, cervical and uterine cancer mortality, assess whether differential trends exist and to provide updated results until 2011.

METHODS

Breast, ovarian, cervical and uterine cancer mortality and population data for Switzerland in the period 1969-2011 was retrieved from the Swiss Federal Statistical office (FSO). Cases were grouped into <55 year olds, 55-74 year olds and 75+ year olds. The geographical unit of analysis was the municipality. To explore age- specific spatio-temporal patterns we fitted Bayesian hierarchical spatio-temporal models on subgroup-specific death rates indirectly standardized by national references. We used linguistic region and degree of urbanisation as covariates.

RESULTS

Female cancer mortality continuously decreased in terms of rates in all age groups and cancer sites except for ovarian cancer in 75+ year olds, especially since 1990 onwards. Contrary to other reports, we found no systematic difference between language regions. Urbanisation as a proxy for access to and quality of medical services, education and health consciousness seemed to have no influence on cancer mortality with the exception of uterine and ovarian cancer in specific age groups. We observed no obvious spatial pattern of mortality common for all cancer sites. Rate reduction in cervical cancer was even stronger than for other cancer sites.

CONCLUSIONS

Female gender related cancer mortality is continuously decreasing in Switzerland since 1990. Geographical differences are small, present on a regional or canton-overspanning level, and different for each cancer site and age group. No general significant association with cantonal or language region borders could be observed.

Natural frequencies facilitate diagnostic inferences of managers

In Bayesian inference tasks, information about base rates as well as hit rate and false-alarm rate needs to be integrated according to Bayes' rule after the result of a diagnostic test became known. Numerous studies have found that presenting information in a Bayesian inference task in terms of natural frequencies leads to better performance compared to variants with information presented in terms of probabilities or percentages. Natural frequencies are the tallies in a natural sample in which hit rate and false-alarm rate are not normalized with respect to base rates. The present research replicates the beneficial effect of natural frequencies with four tasks from the domain of management, and with management students as well as experienced executives as participants. The percentage of Bayesian responses was almost twice as high when information was presented in natural frequencies compared to a presentation in terms of percentages. In contrast to most tasks previously studied, the majority of numerical responses were lower than the Bayesian solutions. Having heard of Bayes' rule prior to the study did not affect Bayesian performance. An implication of our work is that textbooks explaining Bayes' rule should teach how to represent information in terms of natural frequencies instead of how to plug probabilities or percentages into a formula.

Normative amplitude-integrated EEG measures in preterm infants

OBJECTIVE

Assessing qualitative patterns of amplitude-integrated electroencephalography (aEEG) maturation of preterm infants requires personnel with training in interpretation and an investment of time. Quantitative algorithms provide a method for rapidly and reproducibly assessing an aEEG recording independent of provider skill level. Although there are several qualitative and quantitative normative data sets in the literature, this study provides the broadest array of quantitative aEEG measures in a carefully selected and followed cohort of preterm infants with mild or no visible injury on term-equivalent magnetic resonance imaging (MRI) and subsequently normal neurodevelopment at 2 and 7 years of age.

STUDY DESIGN

A two-channel aEEG recording was obtained on days 4, 7, 14 and 28 of life for infants born ⩽30 weeks estimated gestational age. Measures of amplitude and continuity, spectral edge frequency, percentage of trace in interburst interval (IBI), IBI length and frequency counts of smooth delta waves, delta brushes and theta bursts were obtained. MRI was obtained at term-equivalent age and neurodevelopmental testing was conducted at 2 and 7 years of corrected age.

RESULT

Correlations were found between increasing postmenstrual age (PMA) and decreasing maximum amplitude (R= -0.23, P=0.05), increasing minimum amplitude (R=0.46, P=0.002) and increasing spectral edge frequency (R=0.78, P=4.17 × 10(-14)). Negative correlations were noted between increasing PMA and counts of smooth delta waves (R= -0.39, P=0.001), delta brushes (R= -0.37, P=0.003) and theta bursts (R= -0.61, P=5.66 × 10(-8)). Increasing PMA was also associated with a decreased amount of time spent in the IBI (R= -0.38, P=0.001) and a shorter length of the maximum IBI (R= -0.27, P=0.03).

CONCLUSION

This analysis supports a strong correlation between quantitatively determined aEEG measures and PMA, in a cohort of preterm infants with normal term-equivalent age neuroimaging and neurodevelopmental outcomes at 7 years of age, which is both predictable and reproducible. These 'normative' quantitative values support the pattern of maturation previously identified by qualitative analysis.

Localisation of an unknown number of land mines using a network of vapour detectors

We consider the problem of localising an unknown number of land mines using concentration information provided by a wireless sensor network. A number of vapour sensors/detectors, deployed in the region of interest, are able to detect the concentration of the explosive vapours, emanating from buried land mines. The collected data is communicated to a fusion centre. Using a model for the transport of the explosive chemicals in the air, we determine the unknown number of sources using a Principal Component Analysis (PCA)-based technique. We also formulate the inverse problem of determining the positions and emission rates of the land mines using concentration measurements provided by the wireless sensor network. We present a solution for this problem based on a probabilistic Bayesian technique using a Markov chain Monte Carlo sampling scheme, and we compare it to the least squares optimisation approach. Experiments conducted on simulated data show the effectiveness of the proposed approach.

Evolutionary analysis of the cystatin family in three Schistosoma species

The cystatin family comprises cysteine protease inhibitors distributed in 3 subfamilies (I25A–C). Family members lacking cystatin activity are currently unclassified. Little is known about the evolution of Schistosoma cystatins, their physiological roles, and expression patterns in the parasite life cycle. The present study aimed to identify cystatin homologs in the predicted proteome of three Schistosoma species and other Platyhelminthes. We analyzed the amino acid sequence diversity focused in the identification of protein signatures and to establish evolutionary relationships among Schistosoma and experimentally validated human cystatins. Gene expression patterns were obtained from different developmental stages in Schistosoma mansoni using microarray data. In Schistosoma, only I25A and I25B proteins were identified, reflecting little functional diversification. I25C and unclassified subfamily members were not identified in platyhelminth species here analyzed. The resulting phylogeny placed cystatins in different clades, reflecting their molecular diversity. Our findings suggest that Schistosoma cystatins are very divergent from their human homologs, especially regarding the I25B subfamily. Schistosoma cystatins also differ significantly from other platyhelminth homologs. Finally, transcriptome data publicly available indicated that I25A and I25B genes are constitutively expressed thus could be essential for schistosome life cycle progression. In summary, this study provides insights into the evolution, classification, and functional diversification of cystatins in Schistosoma and other Platyhelminthes, improving our understanding of parasite biology and opening new frontiers in the identification of novel therapeutic targets against helminthiases.

Bayesian inference of local trees along chromosomes by the sequential Markov coalescent

We propose a genealogy-sampling algorithm, Sequential Markov Ancestral Recombination Tree (SMARTree), that provides an approach to estimation from SNP haplotype data of the patterns of coancestry across a genome segment among a set of homologous chromosomes. To enable analysis across longer segments of genome, the sequence of coalescent trees is modeled via the modified sequential Markov coalescent (Marjoram and Wall, Genetics 7:16, 2006). To assess performance in estimating these local trees, our SMARTree implementation is tested on simulated data. Our base data set is of the SNPs in 10 DNA sequences over 50 kb. We examine the effects of longer sequences and of more sequences, and of a recombination and/or mutational hotspot. The model underlying SMARTree is an approximation to the full recombinant-coalescent distribution. However, in a small trial on simulated data, recovery of local trees was similar to that of LAMARC (Kuhner et al. Genetics 156:1393-1401, 2000a), a sampler which uses the full model.

Multiple comparisons in genetic association studies: a hierarchical modeling approach

Multiple comparisons or multiple testing has been viewed as a thorny issue in genetic association studies aiming to detect disease-associated genetic variants from a large number of genotyped variants. We alleviate the problem of multiple comparisons by proposing a hierarchical modeling approach that is fundamentally different from the existing methods. The proposed hierarchical models simultaneously fit as many variables as possible and shrink unimportant effects towards zero. Thus, the hierarchical models yield more efficient estimates of parameters than the traditional methods that analyze genetic variants separately, and also coherently address the multiple comparisons problem due to largely reducing the effective number of genetic effects and the number of statistically "significant" effects. We develop a method for computing the effective number of genetic effects in hierarchical generalized linear models, and propose a new adjustment for multiple comparisons, the hierarchical Bonferroni correction, based on the effective number of genetic effects. Our approach not only increases the power to detect disease-associated variants but also controls the Type I error. We illustrate and evaluate our method with real and simulated data sets from genetic association studies. The method has been implemented in our freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/).