Inbreeding depression affects the growth of seedlings of an African timber species with a mixed mating reproductive system, Pericopsis elata (Harms) Meeuwen.

Selfing or mating between related individuals can lead to inbreeding depression (ID), which can influence the survival, growth and evolution of populations of tree species. As selective logging involves a decrease in the density of congeneric partners, it could lead to increasing biparental inbreeding or self-fertilization, exposing the population to higher ID. We assessed the influence of inbreeding on the growth of a commercial timber species, Pericopsis elata (Fabaceae), which produced about 54% of self-fertilized seedlings in a natural population of the Congo basin. We followed the survival and growth of 540 plants raised in a plantation along a gradient of plant density (0.07-15.9 plants per m2). Parentage analysis allowed us distinguishing selfed and outcrossed seedlings. The annual growth was higher for outcrossed than selfed plants, on average by 10.8% for diameter and 12.9% for height growth. Based on the difference in above ground biomass between selfed and outcrossed seedlings after 41 months, we estimated the level of ID at δ = 0.33, while a lifetime estimate of ID based on the proportions of selfed plants at seedling and adult stages led to δ = 0.7. The level of ID on growth rate did not change significantly with age but tended to vanish under high competition. Pericopsis elata is a particularly interesting model because inbreeding depression is partial, with about 26% of reproducing adults resulting from selfing, contrary to most tropical tree species where selfed individuals usually die before reaching adulthood. Hence, the risks of ID must be considered in the management and conservation of the species.

Density regulation amplifies environmentally induced population fluctuations.

Background: Density-dependent regulation is ubiquitous in population dynamics, and its potential interaction with environmental stochasticity complicates the characterization of the random component of population dynamics. Yet, this issue has not received attention commensurate with its relevance for descriptive and predictive modeling of population dynamics. Here we use a Bayesian modeling approach to investigate the contribution of density regulation to population variability in stochastic environments. Methods: We analytically derive a formula linking the stationary variance of population abundance/density under Gompertz regulation in a stochastic environment with constant variance to the environmental variance and the strength of density feedback, to investigate whether and how density regulation affects the stationary variance. We examine through simulations whether the relationship between stationary variance and density regulation inferred analytically under the Gompertz model carries over to the Ricker model, widely used in population dynamics modeling. Results: The analytical decomposition of the stationary variance under stochastic Gompertz dynamics implies higher variability for strongly regulated populations. Simulation results demonstrate that the pattern of increasing population variability with increasing density feedback found under the Gompertz model holds for the Ricker model as well, and is expected to be a general phenomenon with stochastic population models. We also analytically established and empirically validated that the square of the autoregressive parameter of the Gompertz model in AR(1) form represents the proportion of stationary variance due to density dependence. Discussion: Our results suggest that neither environmental stochasticity nor density regulation can alone explain the patterns of population variability in stochastic environments, as these two components of temporal variation interact, with a tendency for density regulation to amplify the magnitude of environmentally induced population fluctuations. This finding has far-reaching implications for population viability. It implies that intense intra-specific resource competition increases the risk of environment-driven population collapse at high density, making opportune harvesting a sensible practice for improving the resistance of managed populations such as fish stocks to environmental perturbations. The separation of density-dependent and density-independent processes will help improve population dynamics modeling, while providing a basis for evaluating the relative importance of these two categories of processes that remains a topic of long-standing controversy among ecologists.

Swift block-updating EM and pseudo-EM procedures for Bayesian shrinkage analysis of quantitative trait loci.

INTRODUCTION: Virtually all existing expectation-maximization (EM) algorithms for quantitative trait locus (QTL) mapping overlook the covariance structure of genetic effects, even though this information can help enhance the robustness of model-based inferences. RESULTS: Here, we propose fast EM and pseudo-EM-based procedures for Bayesian shrinkage analysis of QTLs, designed to accommodate the posterior covariance structure of genetic effects through a block-updating scheme. That is, updating all genetic effects simultaneously through many cycles of iterations. CONCLUSION: Simulation results based on computer-generated and real-world marker data demonstrated the ability of our method to swiftly produce sensible results regarding the phenotype-to-genotype association. Our new method provides a robust and remarkably fast alternative to full Bayesian estimation in high-dimensional models where the computational burden associated with Markov chain Monte Carlo simulation is often unwieldy. The R code used to fit the model to the data is provided in the online supplementary material.

Fine-mapping the immunodominant antibody epitopes on consensus sequence-based HIV-1 envelope trimer vaccine candidates.

The HIV-1 envelope glycoprotein (Env) trimer is the key target for vaccines aimed at inducing neutralizing antibodies (NAbs) against HIV-1. The clinical candidate immunogen ConM SOSIP.v7 is a stabilized native-like HIV-1 Env trimer based on an artificial consensus sequence of all HIV-1 isolates in group M. In preclinical studies ConM SOSIP.v7 trimers induced strong autologous NAb responses in non-human primates (NHPs). To fine-map these responses, we isolated monoclonal antibodies (mAbs) from six cynomolgus macaques that were immunized three times with ConM SOSIP.v7 protein and boosted twice with the closely related ConSOSL.UFO.664 immunogen. A total of 40 ConM and/or ConS-specific mAbs were isolated, of which 18 were retrieved after the three ConM SOSIP.v7 immunizations and 22 after the two immunizations with ConSOSL.UFO.664. 22 mAbs (55%) neutralized the ConM and/or ConS virus. Cross-neutralization of ConS virus by approximately one-third of the mAbs was seen prior to ConSOSL.UFO.664 immunization, albeit with modest potency. Neutralizing antibodies predominantly targeted the V1 and V2 regions of the immunogens, with an apparent extension towards the V3 region. Thus, the V1V2V3 region is immunodominant in the potent NAb response elicited by two consensus sequence native-like HIV-1 Env immunogens. Immunization with these soluble consensus Env proteins also elicited non-neutralizing mAbs targeting the trimer base. These results inform the use and improvement of consensus-based trimer immunogens in combinatorial vaccine strategies.

A multispecies perspective on ecological impacts of climatic forcing.

1. In the prevailing context of concerns over climate change and its potential impacts on ecosystems, evaluating ecological consequences of climatic forcing has become a critical issue. 2. Historical data on the abundance of organisms have been extensively used to characterize the ecological effects of climatic forcing through specific weather and/or climatic variables, with most of the studies confined to single population models. 3. However, population responses to environmental fluctuations typically depend upon positive and negative feedbacks induced by interactions with other species. It is therefore important to integrate the insights gained from single population approaches into a multispecies perspective. 4. Here we combine the hierarchical Bayesian modelling approach with the state-space formulation to extend the scope of previously proposed models of population dynamics under climatic forcing to multi-species systems. 5. We use our model to analyse long-term macro-moth (Lepidoptera) community data from the Rothamsted Insect Survey network in the UK, using winter rainfall and winter temperature as environmental covariates. 6. The effects of the two weather variables were consistent across species, being negative for winter rainfall and positive for winter temperature. The two weather variables jointly explained 15-40% of the total environmental variation affecting the dynamics of individual species, and could explain up to 90% of covariances in species dynamics. 7. The contribution of interspecific interactions to community-level variation was found to be weak compared to the contributions of environmental forcing and intraspecific interactions.

Integrating the niche and neutral perspectives on community structure and dynamics.

Elucidating the mechanisms underlying the assembly and dynamics of ecological communities is a fundamental goal of ecology. Two conceptual approaches have emerged in this respect: the niche-assembly view and the neutral perspective. The debate as to which approach best explains the biodiversity patterns observed in nature is becoming outdated, as ecologists increasingly agree on the existence of a niche-neutral continuum of community dynamical behaviors. However, attempts to make the continuum idea operational and measurable remain sparse. Here, we propose a model-based approach to achieving this. The proposed methodology consists of separating out fluctuations in species abundances into niche-mediated and stochastic factors, linking the niche configuration to community dynamics through competition, and adding demographic stochasticity. This results in a comprehensive framework including neutrality and strict niche segregation as extreme cases. We develop an index of departure from neutral drift as a surrogate for community position on the niche-neutral continuum. We evaluate the performance of our modeling approach with simulated data, and subsequently use the model to analyze rodent web-trapping data from a real-world system. The model fitting is carried out with a Bayesian approach using Markov chain Monte Carlo simulation methods.

Validation of a combined ELISA to detect IgG, IgA and IgM antibody responses to SARS-CoV-2 in mild or moderate non-hospitalised patients.

BACKGROUND: Frequently SARS-CoV-2 results in mild or moderate disease with potentially lower concentrations of antibodies compared to those that are hospitalised. Here, we validated an ELISA using SARS-CoV-2 trimeric spike glycoprotein, with targeted detection of IgG, IgA and IgM (IgGAM) using serum and dried blood spots (DBS) from adults with mild or moderate disease. METHODS: Targeting the SARS-CoV-2 trimeric spike, a combined anti-IgG, IgA and IgM serology ELISA assay was developed using 62 PCR-confirmed non-hospitalised, mild or moderate COVID-19 samples, ≥14 days post symptom onset and 624 COVID-19 negative samples. The assay was validated using 73 PCR-confirmed non-hospitalised, mild or moderate COVID-19 samples, ≥14 days post symptom onset and 359 COVID-19 negative serum samples with an additional 81 DBSs. The assay was further validated in 226 PCR-confirmed non-hospitalised, mild or moderate COVID-19 samples, ≥14 days post symptom onset and 426 COVID-19 negative clinical samples. RESULTS: A sensitivity and specificity of 98.6% (95% CI, 92.6-100.0), 98.3% (95% CI, 96.4-99.4), respectively, was observed following validation of the SARS-CoV-2 ELISA. No cross-reactivities with endemic coronaviruses or other human viruses were observed, and no change in results were recorded for interfering substances. The assay was stable at temperature extremes and components were stable for 15 days once opened. A matrix comparison showed DBS to correlate with serum results. Clinical validation of the assay reported a sensitivity of 94.7% (95% CI, 90.9-97.2%) and a specificity of 98.4% (95% CI, 96.6-99.3%). CONCLUSIONS: The human anti-IgGAM SARS-CoV-2 ELISA provides accurate and sensitive detection of SARS-CoV-2 antibodies in non-hospitalised adults with mild or moderate disease. The use of dried blood spots makes the assay accessible to the wider community.

On the setting of environmental noise and the performance of population dynamical models.

BACKGROUND: Environmental noise is ubiquitous in population growth processes, with a well acknowledged potential to affect populations regardless of their sizes. It therefore deserves consideration in population dynamics modelling. The usual approach to incorporating noise into population dynamical models is to make some model parameter(s) (typically the growth rate, the carrying capacity, or both) stochastic and responsive to environment fluctuations. It is however still unclear whether including noise in one or/and another parameter makes a difference to the model performance. Here we investigated this issue with a focus on model fit and predictive accuracy. To do this, we developed three population dynamical models of the Ricker type with the noise included in the growth rate (Model 1), in the carrying capacity (Model 2), and in both (Model 3). We generated several population time series under each model, and used a Bayesian approach to fit the three models to the simulated data. We then compared the model performances in fitting to the data and in forecasting future observations. RESULTS: When the mean intrinsic growth rate, r, in the data was low, the three models had roughly comparable performances, irrespective of the true model and the level of noise. As r increased, Models 1 performed best on data generated from it, and Model 3 tended to perform best on data generated from either Models 2 or Model 3. Model 2 was uniformly outcompeted by the other two models, regardless of the true model and the level of noise. The correlation between the deviance information criterion (DIC) and the mean square error (MSE) used respectively as measure of fit and predictive accuracy was broadly positive. CONCLUSION: Our results suggested that the way environmental noise is incorporated into a population dynamical model may profoundly affect its performance. Overall, we found that including noise in one or/and another parameter does not matter as long as the mean intrinsic growth rate, r, is low. As r increased, however, the three models performed differently. Models 1 and 3 broadly outperformed Model 2, the first having the advantage of being simple and more computationally tractable. A comforting result emerging from our analysis is the broad positive correlation between MSEs and DICs, suggesting that the latter may also be informative about the predictive performance of a model.

A Bayesian Framework for Robust Quantitative Trait Locus Mapping and Outlier Detection.

We introduce a Bayesian framework for simultaneous feature selection and outlier detection in sparse high-dimensional regression models, with a focus on quantitative trait locus (QTL) mapping in experimental crosses. More specifically, we incorporate the robust mean shift outlier handling mechanism into the multiple QTL mapping regression model and apply LASSO regularization concurrently to the genetic effects and the mean-shift terms through the flexible extended Bayesian LASSO (EBL) prior structure, thereby combining QTL mapping and outlier detection into a single sparse model representation problem. The EBL priors on the mean-shift terms prevent outlying phenotypic values from distorting the genotype-phenotype association and allow their detection as cases with outstanding mean shift values following the LASSO shrinkage. Simulation results demonstrate the effectiveness of our new methodology at mapping QTLs in the presence of outlying phenotypic values and simultaneously identifying the potential outliers, while maintaining a comparable performance to the standard EBL on outlier-free data.

Intestinal epithelial NAIP/NLRC4 restricts systemic dissemination of the adapted pathogen Salmonella Typhimurium due to site-specific bacterial PAMP expression.

Inflammasomes can prevent systemic dissemination of enteropathogenic bacteria. As adapted pathogens including Salmonella Typhimurium (S. Tm) have evolved evasion strategies, it has remained unclear when and where inflammasomes restrict their dissemination. Bacterial population dynamics establish that the NAIP/NLRC4 inflammasome specifically restricts S. Tm migration from the gut to draining lymph nodes. This is solely attributable to NAIP/NLRC4 within intestinal epithelial cells (IECs), while S. Tm evades restriction by phagocyte NAIP/NLRC4. NLRP3 and Caspase-11 also fail to restrict S. Tm mucosa traversal, migration to lymph nodes, and systemic pathogen growth. The ability of IECs (not phagocytes) to mount a NAIP/NLRC4 defense in vivo is explained by particularly high NAIP/NLRC4 expression in IECs and the necessity for epithelium-invading S. Tm to express the NAIP1-6 ligands-flagella and type-III-secretion-system-1. Imaging reveals both ligands to be promptly downregulated following IEC-traversal. These results highlight the importance of intestinal epithelial NAIP/NLRC4 in blocking bacterial dissemination in vivo, and explain why this constitutes a uniquely evasion-proof defense against the adapted enteropathogen S. Tm.

What drives community dynamics?

The search for general mechanisms of community assembly is a major focus of community ecology. The common practice so far has been to examine alternative assembly theories using dichotomist approaches of the form neutrality versus niche, or compensatory dynamics versus environmental forcing. In reality, all these mechanisms will be operating, albeit with different strengths. While there have been different approaches to community structure and dynamics, including neutrality and niche differentiation, less work has gone into separating out the temporal variation in species abundances into relative contributions from different components. Here we use a refined statistical machinery to decompose temporal fluctuations in species abundances into contributions from environmental stochasticity and inter-/intraspecific interactions, to see which ones dominate. We apply the methodology to community data from a range of taxa. Our results show that communities are largely driven by environmental fluctuations, and that member populations are, to different extents, regulated through intraspecific interactions, the effects of interspecific interactions remaining broadly minor. By decomposing the temporal variation in this way, we have been able to show directly what has been previously inferred indirectly: compensatory dynamics are in fact largely outweighed by environmental forcing, and the latter tends to synchronize the population dynamics.

Contrasting IgG structures reveal extreme asymmetry and flexibility.

The crystal structure of IgG1 b12 represents the first visualization of an intact human IgG with a full-length hinge that has all domains ordered and visible. In comparison to intact murine antibodies and hinge-deletant human antibodies, b12 reveals extreme asymmetry, indicative of the extraordinary interdomain flexibility within an antibody. In addition, the structure provides an illustration of the human IgG1 hinge in its entirety and of asymmetry in the composition of the carbohydrate attached to each C(H)2 domain of the Fc. The two separate hinges assume different conformations in order to accommodate the vastly different placements of the two Fab domains relative to the Fc domain. Interestingly, only one of two possible intra-hinge disulfides is formed.

Monoglucosylated glycans in the secreted human complement component C3: implications for protein biosynthesis and structure.

The monoglucosylated oligomannose N-linked oligosaccharide (Glc(1)Man(9)GlcNAc(2)) is a retention signal for the calnexin-calreticulin quality control pathway in the endoplasmic reticulum. We report here the presence of such monoglucosylated N-glycans on the human complement serum glycoprotein C3. This finding represents the first report of monoglucosylated glycans on a human serum glycoprotein from non-diseased individuals. The presence of the glucose moiety in 5% of the human C3 glycoprotein suggests that this glycosylation site is sequestered within the protein and is consistent with previous studies identifying a cryptic conglutinin binding site on C3 that becomes exposed upon its conversion to iC3b.

Middle ear gas composition during nitrous oxide-oxygen ventilation.

The middle ear gas composition during 180 minutes ventilation with nitrous oxide-oxygen mixture was determined in 12 mongrel dogs. The mean relative concentration of N2O in the middle ear (ME) rose to 12,26.4 and 29.3% after 60, 120 and 180 minutes respectively. During this period, the relative concentration of N2 dropped from a mean of 83.2% in the air-ventilated dogs to 54.8%, without an essential change in the concentrations of O2 or CO2. The elimination of N2O from the ME during 30 minutes of postanesthetic ventilation with O2 was incomplete, an average of 11.4% N2O remaining in the ME. At the same time, the mean relative concentration of O2 reached 19%, higher than the O2 relative concentration normally present in the ME. The results indicate that gas diffusion may occur across the ME mucosa for N2O as well as for O2, producing selective changes in the middle ear gas composition.

Micromethod for determination of middle ear gas composition.

This is a microchromatographic method for simultaneous determinations of O2, N2, CO2 and N2O in gas samples of 40-100 microliters. A Packard 836 U gas chromatograph with a thermal conductivity detector and helium gas as carrier was used. The combination of Porapak and 5A molecular sieve column system was found adequate and is described in detail. The fidelity of this method was proved by a high constancy of the retention time, the linearity of the response and the reproducibility of results. The present method proved to be reliable for determination of all middle ear gases in man and experimental animals during general anesthesia with N2O.

The middle ear gas composition in air-ventilated dogs.

The middle ear gas composition has been examined in 5 air-ventilated dogs under sodium thiopentone anesthesia. The gas samples were obtained by transtympanic puncture and analysed by gas chromatography. The following mean +/- S.D. gas composition was obtained: N2 83.2 +/- 5.0; O2 12.1 +/- 2.2; and CO2 4.7 +/- 0.7.

A decision rule for quantitative trait locus detection under the extended Bayesian LASSO model.

Bayesian shrinkage analysis is arguably the state-of-the-art technique for large-scale multiple quantitative trait locus (QTL) mapping. However, when the shrinkage model does not involve indicator variables for marker inclusion, QTL detection remains heavily dependent on significance thresholds derived from phenotype permutation under the null hypothesis of no phenotype-to-genotype association. This approach is computationally intensive and more importantly, the hypothetical data generation at the heart of the permutation-based method violates the Bayesian philosophy. Here we propose a fully Bayesian decision rule for QTL detection under the recently introduced extended Bayesian LASSO for QTL mapping. Our new decision rule is free of any hypothetical data generation and relies on the well-established Bayes factors for evaluating the evidence for QTL presence at any locus. Simulation results demonstrate the remarkable performance of our decision rule. An application to real-world data is considered as well.

A hierarchical bayesian approach to multi-trait clinical quantitative trait locus modeling.

Recent advances in high-throughput genotyping and transcript profiling technologies have enabled the inexpensive production of genome-wide dense marker maps in tandem with huge amounts of expression profiles. These large-scale data encompass valuable information about the genetic architecture of important phenotypic traits. Comprehensive models that combine molecular markers and gene transcript levels are increasingly advocated as an effective approach to dissecting the genetic architecture of complex phenotypic traits. The simultaneous utilization of marker and gene expression data to explain the variation in clinical quantitative trait, known as clinical quantitative trait locus (cQTL) mapping, poses challenges that are both conceptual and computational. Nonetheless, the hierarchical Bayesian (HB) modeling approach, in combination with modern computational tools such as Markov chain Monte Carlo (MCMC) simulation techniques, provides much versatility for cQTL analysis. Sillanpää and Noykova (2008) developed a HB model for single-trait cQTL analysis in inbred line cross-data using molecular markers, gene expressions, and marker-gene expression pairs. However, clinical traits generally relate to one another through environmental correlations and/or pleiotropy. A multi-trait approach can improve on the power to detect genetic effects and on their estimation precision. A multi-trait model also provides a framework for examining a number of biologically interesting hypotheses. In this paper we extend the HB cQTL model for inbred line crosses proposed by Sillanpää and Noykova to a multi-trait setting. We illustrate the implementation of our new model with simulated data, and evaluate the multi-trait model performance with regard to its single-trait counterpart. The data simulation process was based on the multi-trait cQTL model, assuming three traits with uncorrelated and correlated cQTL residuals, with the simulated data under uncorrelated cQTL residuals serving as our test set for comparing the performances of the multi-trait and single-trait models. The simulated data under correlated cQTL residuals were essentially used to assess how well our new model can estimate the cQTL residual covariance structure. The model fitting to the data was carried out by MCMC simulation through OpenBUGS. The multi-trait model outperformed its single-trait counterpart in identifying cQTLs, with a consistently lower false discovery rate. Moreover, the covariance matrix of cQTL residuals was typically estimated to an appreciable degree of precision under the multi-trait cQTL model, making our new model a promising approach to addressing a wide range of issues facing the analysis of correlated clinical traits.

Extended Bayesian LASSO for multiple quantitative trait loci mapping and unobserved phenotype prediction.

The Bayesian LASSO (BL) has been pointed out to be an effective approach to sparse model representation and successfully applied to quantitative trait loci (QTL) mapping and genomic breeding value (GBV) estimation using genome-wide dense sets of markers. However, the BL relies on a single parameter known as the regularization parameter to simultaneously control the overall model sparsity and the shrinkage of individual covariate effects. This may be idealistic when dealing with a large number of predictors whose effect sizes may differ by orders of magnitude. Here we propose the extended Bayesian LASSO (EBL) for QTL mapping and unobserved phenotype prediction, which introduces an additional level to the hierarchical specification of the BL to explicitly separate out these two model features. Compared to the adaptiveness of the BL, the EBL is "doubly adaptive" and thus, more robust to tuning. In simulations, the EBL outperformed the BL in regard to the accuracy of both effect size estimates and phenotypic value predictions, with comparable computational time. Moreover, the EBL proved to be less sensitive to tuning than the related Bayesian adaptive LASSO (BAL), which introduces locus-specific regularization parameters as well, but involves no mechanism for distinguishing between model sparsity and parameter shrinkage. Consequently, the EBL seems to point to a new direction for QTL mapping, phenotype prediction, and GBV estimation.