Amine molecular cages as supramolecular fluorescent explosive sensors: a computational perspective

We investigate using a computational approach the physical and chemical processes underlying the application of organic (macro)molecules as fluorescence quenching sensors for explosives sensing. We concentrate on the use of amine molecular cages to sense nitroaromatic analytes, such as picric acid and 2,4-dinitrophenol, through fluorescence quenching. Our observations for this model system hold for many related systems. We consider the different possible mechanisms of fluorescence quenching: Förster resonance energy transfer, Dexter energy transfer and photoinduced electron transfer, and show that in the case of our model system, the fluorescence quenching is driven by the latter and involves stable supramolecular sensor–analyte host–guest complexes. Furthermore, we demonstrate that the experimentally observed selectivity of amine molecular cages for different explosives can be explained by the stability of these host–guest complexes and discuss how this is related to the geometry of the binding site in the sensor. Finally, we discuss what our observations mean for explosive sensing by fluorescence quenching in general and how this can help in future rational design of new supramolecular detection systems

Direct and indirect effects of rotavirus vaccination: Comparing predictions from transmission dynamic models

Early observations from countries that have introduced rotavirus vaccination suggest that there may be indirect protection for unvaccinated individuals, but it is unclear whether these benefits will extend to the long term. Transmission dynamic models have attempted to quantify the indirect protection that might be expected from rotavirus vaccination in developed countries, but results have varied. To better understand the magnitude and sources of variability in model projections, we undertook a comparative analysis of transmission dynamic models for rotavirus. We fit five models to reported rotavirus gastroenteritis (RVGE) data from England and Wales, and evaluated outcomes for short- and long-term vaccination effects. All of our models reproduced the important features of rotavirus epidemics in England and Wales. Models predicted that during the initial year after vaccine introduction, incidence of severe RVGE would be reduced 1.8-2.9 times more than expected from the direct effects of the vaccine alone (28-50% at 90% coverage), but over a 5-year period following vaccine introduction severe RVGE would be reduced only by 1.1-1.7 times more than expected from the direct effects (54-90% at 90% coverage). Projections for the long-term reduction of severe RVGE ranged from a 55% reduction at full coverage to elimination with at least 80% coverage. Our models predicted short-term reductions in the incidence of RVGE that exceeded estimates of the direct effects, consistent with observations from the United States and other countries. Some of the models predicted that the short-term indirect benefits may be offset by a partial shifting of the burden of RVGE to older unvaccinated individuals. Nonetheless, even when such a shift occurs, the overall reduction in severe RVGE is considerable. Discrepancies among model predictions reflect uncertainties about age variation in the risk and reporting of RVGE, and the duration of natural and vaccine-induced immunity, highlighting important questions for future research

The importance of passive integrated transponder (PIT) tags for measuring life-history traits of sea turtles

This is the final version. Available from the publisher via the DOI in this record.Capture-mark-recapture studies rely on the identification of individuals through time, using markers or tags, which are assumed to be retained. This assumption, however, may be violated, having implications for population models. In sea turtles, individual identification is typically based on external flipper tags, which can be combined with internal passive integrated transponder (PIT) tags. Despite the extensive use of flipper tags, few studies have modelled tag loss using continuous functions. Using a 26-year dataset for sympatrically nesting green (Chelonia mydas) and loggerhead (Caretta caretta) turtles, this study aims to assess how PIT tag use increases the accuracy of estimates of life-history traits. The addition of PIT tags improved female identification: between 2000 and 2017, 53% of green turtles and 29% of loggerhead turtles were identified from PIT tags alone. We found flipper and PIT tag losses were best described by decreasing logistic curves with lower asymptotes. Excluding PIT tags from our dataset led to underestimation of flipper tag loss, reproductive periodicity, reproductive longevity and annual survival, and overestimation of female abundance and recruitment for both species. This shows the importance of PIT tags in improving the accuracy of estimates of life-history traits. Thus, estimates where tag loss has not been corrected for should be interpreted with caution and could bias IUCN Red List assessments. As such, long-term population monitoring programmes should aim to estimate tag loss and assess the impact of loss on life-history estimates, to provide robust estimates without which population models and stock assessments cannot be derived accuratel

VPN: Learning Video-Pose Embedding for Activities of Daily Living

In this paper, we focus on the spatio-temporal aspect of recognizing Activities of Daily Living (ADL). ADL have two specific properties (i) subtle spatio-temporal patterns and (ii) similar visual patterns varying with time. Therefore, ADL may look very similar and often necessitate to look at their fine-grained details to distinguish them. Because the recent spatio-temporal 3D ConvNets are too rigid to capture the subtle visual patterns across an action, we propose a novel Video-Pose Network: VPN. The 2 key components of this VPN are a spatial embedding and an attention network. The spatial embedding projects the 3D poses and RGB cues in a common semantic space. This enables the action recognition framework to learn better spatio-temporal features exploiting both modalities. In order to discriminate similar actions, the attention network provides two functionalities - (i) an end-to-end learnable pose backbone exploiting the topology of human body, and (ii) a coupler to provide joint spatio-temporal attention weights across a video. Experiments show that VPN outperforms the state-of-the-art results for action classification on a large scale human activity dataset: NTU-RGB+D 120, its subset NTU-RGB+D 60, a real-world challenging human activity dataset: Toyota Smarthome and a small scale human-object interaction dataset Northwestern UCLA.Comment: Accepted in ECCV 202

Development and evaluation of a diagnostic cytokine-release assay for Mycobacterium suricattae infection in meerkats (Suricata suricatta)

CITATION: Clarke, C., et al. 2017. Development and evaluation of a diagnostic cytokine-release assay for mycobacterium suricattae infection in meerkats (Suricata suricatta). BMC Veterinary Research, 13:2, doi:10.1186/s12917-016-0927-x.The original publication is available at http://bmcvetres.biomedcentral.comBackground: Sensitive diagnostic tools are necessary for the detection of Mycobacterium suricattae infection in meerkats (Suricata suricatta) in order to more clearly understand the epidemiology of tuberculosis and the ecological consequences of the disease in this species. We therefore aimed to develop a cytokine release assay to measure antigen-specific cell-mediated immune responses of meerkats. Results: Enzyme-linked immunosorbent assays (ELISAs) were evaluated for the detection of interferon-gamma (IFN-γ) and IFN-γ inducible protein 10 (IP-10) in meerkat plasma. An IP-10 ELISA was selected to measure the release of this cytokine in whole blood in response to Bovigam® PC-HP Stimulating Antigen, a commercial peptide pool of M. bovis antigens. Using this protocol, captive meerkats with no known M. suricattae exposure (n = 10) were tested and results were used to define a diagnostic cut off value (mean plus 2 standard deviations). This IP-10 release assay (IPRA) was then evaluated in free-living meerkats with known M. suricattae exposure, categorized as having either a low, moderate or high risk of infection with this pathogen. In each category, respectively, 24.7%, 27.3% and 82.4% of animals tested IPRA-positive. The odds of an animal testing positive was 14.0 times greater for animals with a high risk of M. suricattae infection compared to animals with a low risk. Conclusion: These results support the use of this assay as a measure of M. suricattae exposure in meerkat populations. Ongoing longitudinal studies aim to evaluate the value of the IPRA as a diagnostic test of M. suricattae infection in individual animals.http://bmcvetres.biomedcentral.com/articles/10.1186/s12917-016-0927-xPublisher's versio

Novel statistical approaches for non-normal censored immunological data: analysis of cytokine and gene expression data

Background: For several immune-mediated diseases, immunological analysis will become more complex in the future with datasets in which cytokine and gene expression data play a major role. These data have certain characteristics that require sophisticated statistical analysis such as strategies for non-normal distribution and censoring. Additionally, complex and multiple immunological relationships need to be adjusted for potential confounding and interaction effects. Objective: We aimed to introduce and apply different methods for statistical analysis of non-normal censored cytokine and gene expression data. Furthermore, we assessed the performance and accuracy of a novel regression approach in order to allow adjusting for covariates and potential confounding. Methods: For non-normally distributed censored data traditional means such as the Kaplan-Meier method or the generalized Wilcoxon test are described. In order to adjust for covariates the novel approach named Tobit regression on ranks was introduced. Its performance and accuracy for analysis of non-normal censored cytokine/gene expression data was evaluated by a simulation study and a statistical experiment applying permutation and bootstrapping. Results: If adjustment for covariates is not necessary traditional statistical methods are adequate for non-normal censored data. Comparable with these and appropriate if additional adjustment is required, Tobit regression on ranks is a valid method. Its power, type-I error rate and accuracy were comparable to the classical Tobit regression. Conclusion: Non-normally distributed censored immunological data require appropriate statistical methods. Tobit regression on ranks meets these requirements and can be used for adjustment for covariates and potential confounding in large and complex immunological datasets

A novel approach to simulate gene-environment interactions in complex diseases

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study