Bayesian Nonparametric Estimation of the Probability of Discovering New Species

We consider the problem of evaluating the probability of discoveringa certain number of new species in a new sample of populationunits, conditional on the number of species recorded in a basicsample. We use a Bayesian nonparametric approach. The differentspecies proportions are assumed to be random and the observationsfrom the population exchangeable. We provide a Bayesian estimator,under quadratic loss, for the probability of discovering newspecies which can be compared with well-known frequentist estimators.The results we obtain are illustrated through a numerical exampleand an application to a genomic dataset concerning the discoveryof new genes by sequencing additional single-read sequencesof cdna fragments.     

Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error

Summary : We propose a semiparametric Bayesian method for handling measurement error in nutritional epidemiological data. Our goal is to estimate nonparametrically the form of association between a disease and exposure variable while the true values of the exposure are never observed. Motivated by nutritional epidemiological data, we consider the setting where a surrogate covariate is recorded in the primary data, and a calibration data set contains information on the surrogate variable and repeated measurements of an unbiased instrumental variable of the true exposure. We develop a flexible Bayesian method where not only is the relationship between the disease and exposure variable treated semiparametrically, but also the relationship between the surrogate and the true exposure is modeled semiparametrically. The two nonparametric functions are modeled simultaneously via B‐splines. In addition, we model the distribution of the exposure variable as a Dirichlet process mixture of normal distributions, thus making its modeling essentially nonparametric and placing this work into the context of functional measurement error modeling. We apply our method to the NIH‐AARP Diet and Health Study and examine its performance in a simulation study.     

A Semiparametric Estimator of the Crossing Point in the Two‐Sample Linear Shift Function: Application to Crossing Lifetime Distributions

Let X and Y be two random variables with continuous distribution functions F and G. Consider two independent observations X₁, … , X_m from F and Y₁, … , Y_n from G. Moreover, suppose there exists a unique x* such that F(x) > G(x) for x < x* and F(x) < G(x) for x > x* or vice versa. A semiparametric model with a linear shift function (Doksum, 1974) that is equivalent to a location‐scale model (Hsieh, 1995) will be assumed and an empirical process approach (Hsieh, 1995) is used to estimate the parameters of the shift function. Then, the estimated shift function is set to zero, and the solution is defined to be an estimate of the crossing‐point x*. An approximate confidence band of the linear shift function at the crossing‐point x* is also presented, which is inverted to yield an approximate confidence interval for the crossing‐point. Finally, the lifetime of guinea pigs in days observed in a treatment‐control experiment in Bjerkedal (1960) is used to demonstrate our procedure for estimating the crossing‐point. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Addressing Central Nervous System (CNS) Penetration in Drug Discovery: Basics and Implications of the Evolving New Concept

Despite enormous efforts, achieving a safe and efficacious concentration profile in the brain remains one of the big challenges in central nervous system (CNS) drug discovery and development. Although there are multiple reasons, many failures are due to underestimating the complexity of the brain, also in terms of pharmacokinetics (PK). To this day, PK support of CNS drug discovery heavily relies on improving the blood–brain barrier (BBB) permeability in vitro and/or the brain/plasma ratio (K_p) in vivo, even though neither parameter can be reliably linked to pharmacodynamic (PD) and efficacy readouts. While increasing BBB permeability may shorten the onset of drug action, an increase in the total amount in brain may not necessarily increase the relevant drug concentration at the pharmacological target. Since the traditional K_p ratio is based on a crude homogenization of brain tissue, it ignores the compartmentalization of the brain and an increase favors non‐specific binding to brain lipids rather than free drug levels. To better link exposure/PK to efficacy/PD and to delineate key parameters, an integrated approach to CNS drug discovery is emerging which distinguishes total from unbound brain concentrations. As the complex nature of the brain requires different compartments to be considered when trying to understand and improve new compounds, several complementary parameters need to be measured in vitro and in vivo, and integrated into a coherent model of brain penetration and distribution. The new paradigm thus concentrates on finding drug candidates with the right balance between free fraction in plasma and brain, and between rate and extent of CNS penetration. Integrating this data into a coherent model of CNS distribution which can be linked to efficacy will allow it to design compounds with an optimal mix in physicochemical, pharmacologic, and pharmacokinetic properties, ultimately mitigating the risk for failures in the clinic.     

A sampling optimization analysis of soil‐bugs diversity (Crustacea,Isopoda, Oniscidea)

Biological diversity analysis is among the most informative approaches to describe communities and regional species compositions. Soil ecosystems include large numbers of invertebrates, among which soil bugs (Crustacea, Isopoda, Oniscidea) play significant ecological roles. The aim of this study was to provide advices to optimize the sampling effort, to efficiently monitor the diversity of this taxon, to analyze its seasonal patterns of species composition, and ultimately to understand better the coexistence of so many species over a relatively small area. Terrestrial isopods were collected at the Natural Reserve “Saline di Trapani e Paceco” (Italy), using pitfall traps monthly monitored over 2 years. We analyzed parameters of α‐ and β‐diversity and calculated a number of indexes and measures to disentangle diversity patterns. We also used various approaches to analyze changes in biodiversity over time, such as distributions of species abundances and accumulation and rarefaction curves. As concerns species richness and total abundance of individuals, spring resulted the best season to monitor Isopoda, to reduce sampling efforts, and to save resources without losing information, while in both years abundances were maximum between summer and autumn. This suggests that evaluations of β‐diversity are maximized if samples are first collected during the spring and then between summer and autumn. Sampling during these coupled seasons allows to collect a number of species close to the γ‐diversity (24 species) of the area. Finally, our results show that seasonal shifts in community composition (i.e., dynamic fluctuations in species abundances during the four seasons) may minimize competitive interactions, contribute to stabilize total abundances, and allow the coexistence of phylogenetically close species within the ecosystem.     

A method for adjusting allele frequencies in the case of microsatellite allele drop‐out

A new method is proposed to adjust allele frequencies when allelic drop‐out is common. This method assumes Hardy–Weinberg equilibrium (HWE), and treats the problematic alleles as a one‐locus two‐allele system with dominance. By assuming that the homozygote frequency of the ‘recessive’ allele is measured correctly, we can back calculate the allele frequency of the ‘dominant’ allele, and adjust the heterozygote frequency accordingly. The drawback is that multilocus genotypes cannot be constructed and tests that use deviations from Hardy–Weinberg such as tests for bottlenecks become impossible. An example is given where a large homozygote excess (F_IS = 0.44) is adjusted to a reasonable level (F_IS = 0.046). The effect of scoring error was set in relation to sampling error and while F_IS values can be seriously biased, F_ST values are not necessarily so, if scoring error and sample size are both low. As sample size increases, the effect of scoring error increases.     

A stage‐wise approach for the analysis of multi‐environment trials

Plant breeders and variety testing agencies routinely test candidate genotypes (crop varieties, lines, test hybrids) in multiple environments. Such multi‐environment trials can be efficiently analysed by mixed models. A single‐stage analysis models the entire observed data at the level of individual plots. This kind of analysis is usually considered as the gold standard. In practice, however, it is more convenient to use a two‐stage approach, in which experiments are first analysed per environment, yielding adjusted means per genotype, which are then summarised across environments in the second stage. Stage‐wise approaches suggested so far are approximate in that they cannot fully reproduce a single‐stage analysis, except in very simple cases, because the variance–covariance matrix of adjusted means from individual environments needs to be approximated by a diagonal matrix. This paper proposes a fully efficient stage‐wise method, which carries forward the full variance–covariance matrix of adjusted means from the individual environments to the analysis across the series of trials. Provided the variance components are known, this method can fully reproduce the results of a single‐stage analysis. Computations are made efficient by a diagonalisation of the residual variance–covariance matrix, which necessitates a corresponding linear transformation of both the first‐stage estimates (e.g. adjusted means and regression slopes for plot covariates) and the corresponding design matrices for fixed and random effects. We also exemplify the extension of the general approach to a three‐stage analysis. The method is illustrated using two datasets, one real and the other simulated. The proposed approach has close connections with meta‐analysis, where environments correspond to centres and genotypes to medical treatments. We therefore compare our theoretical results with recently published results from a meta‐analysis.     

Importance of geographic origin for invasion success: A case study of the North and Baltic Seas versus the Great Lakes–St. Lawrence River region

Recently, several studies indicated that species from the Ponto‐Caspian region may be evolutionarily predisposed to become nonindigenous species (NIS); however, origin of NIS established in different regions has rarely been compared to confirm these statements. More importantly, if species from certain area/s are proven to be better colonizers, management strategies to control transport vectors coming from those areas must be more stringent, as prevention of new introductions is a cheaper and more effective strategy than eradication or control of established NIS populations. To determine whether species evolved in certain areas have inherent advantages over other species in colonizing new habitats, we explored NIS established in the North and Baltic Seas and Great Lakes–St. Lawrence River regions—two areas intensively studied in concern to NIS, highly invaded by Ponto‐Caspian species and with different salinity patterns (marine vs. freshwater). We compared observed numbers of NIS in these two regions to expected numbers of NIS from major donor regions. The expected numbers were calculated based on the available species pool from donor regions, frequency of shipping transit, and an environmental match between donor and recipient regions. A total of 281 NIS established in the North and Baltic Seas and 188 in the Great Lakes–St. Lawrence River. Ponto‐Caspian taxa colonized both types of habitats, saltwater areas of the North and Baltic Seas and freshwater of the Great Lakes–St. Lawrence River, in much higher numbers than expected. Propagule pressure (i.e., number of introduced individuals or introduction effort) is of great importance for establishment success of NIS; however in our study, either shipping vector or environmental match between regions did not clarify the high numbers of Ponto‐Caspian taxa in our study areas. Although we cannot exclude the influence of other transport vectors, our findings suggest that the origin of the species plays an important role for the predisposition of successful invaders.     

A New Perspective on the Role of A‐Site Cations in Perovskite Solar Cells

As the race toward higher efficiency for inorganic/organic hybrid perovskite solar cells (PSCs) is becoming highly competitive, a design scheme to maximize carrier transport toward higher power efficiency has been urgently demanded. In this study, a hidden role of A‐site cations of PSCs in carrier transport, which has been largely neglected is unraveled, i.e., tuning the Fröhlich electron–phonon (e–ph) coupling of longitudinal optical (LO) phonon by A‐site cations. The key for steering Fröhlich polaron is to control the interaction strength and the number of proton (or lithium) coordination to halide ions. The coordination to I^? alleviates electron–phonon scattering by either decreasing the Born effective charge or absorbing the LO motion of I. This novel principle discloses low electron–phonon coupling in several promising organic cations including hydroxyl–ammonium cation (NH₃OH⁺), hydrazinium cation (NH₃NH₂⁺) and possibly Li⁺ solvating methylamine (Li⁺???NH₂CH₃), on a par with methyl–ammonium cations. A new perspective on the role of A‐site cations could help in improving power efficiency and accelerating the application of PSCs.