Icing: Large-scale inference of immunoglobulin clonotypes

Immunoglobulin (IG) clonotype identification is a fundamental open question in modern immunology. An accurate description of the IG repertoire is crucial to understand the variety within the immune system of an individual, potentially shedding light on the pathogenetic process. Intrinsic IG heterogeneity makes clonotype inference an extremely challenging task, both from a computational and a biological point of view. Here we present icing, a framework that allows to reconstruct clonal families also in case of highly mutated sequences. icing has a modular structure, and it is designed to be used with large next generation sequencing (NGS) datasets, a technology which allows the characterisation of large-scale IG repertoires. We extensively validated the framework with clustering performance metrics on the results in a simulated case. icing is implemented in Python, and it is publicly available under FreeBSD licence at https://github.com/slipguru/icing

Limited duration of vaccine poliovirus and other enterovirus excretion among human immunodeficiency virus infected children in Kenya

<p>Abstract</p> <p>Background</p> <p>Immunodeficient persons with persistent vaccine-related poliovirus infection may serve as a potential reservoir for reintroduction of polioviruses after wild poliovirus eradication, posing a risk of their further circulation in inadequately immunized populations.</p> <p>Methods</p> <p>To estimate the potential for vaccine-related poliovirus persistence among HIV-infected persons, we studied poliovirus excretion following vaccination among children at an orphanage in Kenya. For 12 months after national immunization days, we collected serial stool specimens from orphanage residents aged <5 years at enrollment and recorded their HIV status and demographic, clinical, immunological, and immunization data. To detect and characterize isolated polioviruses and non-polio enteroviruses (NPEV), we used viral culture, typing and intratypic differentiation of isolates by PCR, ELISA, and nucleic acid sequencing. Long-term persistence was defined as shedding for ≥ 6 months.</p> <p>Results</p> <p>Twenty-four children (15 HIV-infected, 9 HIV-uninfected) were enrolled, and 255 specimens (170 from HIV-infected, 85 from HIV-uninfected) were collected. All HIV-infected children had mildly or moderately symptomatic HIV-disease and moderate-to-severe immunosuppression. Fifteen participants shed vaccine-related polioviruses, and 22 shed NPEV at some point during the study period. Of 46 poliovirus-positive specimens, 31 were from HIV-infected, and 15 from HIV-uninfected children. No participant shed polioviruses for ≥ 6 months. Genomic sequencing of poliovirus isolates did not reveal any genetic evidence of long-term shedding. There was no long-term shedding of NPEV.</p> <p>Conclusion</p> <p>The results indicate that mildly to moderately symptomatic HIV-infected children retain the ability to clear enteroviruses, including vaccine-related poliovirus. Larger studies are needed to confirm and generalize these findings.</p

Thermodynamics-Based Models of Transcriptional Regulation by Enhancers: The Roles of Synergistic Activation, Cooperative Binding and Short-Range Repression

Quantitative models of cis-regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled, or heuristic approximations of the underlying regulatory mechanisms. We have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence, as a function of transcription factor concentrations and their DNA-binding specificities. It uses statistical thermodynamics theory to model not only protein-DNA interaction, but also the effect of DNA-bound activators and repressors on gene expression. In addition, the model incorporates mechanistic features such as synergistic effect of multiple activators, short range repression, and cooperativity in transcription factor-DNA binding, allowing us to systematically evaluate the significance of these features in the context of available expression data. Using this model on segmentation-related enhancers in Drosophila, we find that transcriptional synergy due to simultaneous action of multiple activators helps explain the data beyond what can be explained by cooperative DNA-binding alone. We find clear support for the phenomenon of short-range repression, where repressors do not directly interact with the basal transcriptional machinery. We also find that the binding sites contributing to an enhancer's function may not be conserved during evolution, and a noticeable fraction of these undergo lineage-specific changes. Our implementation of the model, called GEMSTAT, is the first publicly available program for simultaneously modeling the regulatory activities of a given set of sequences

The use of expert knowledge in the development of simulations for train driver training

The current study was undertaken to inform the development of simulations for improving train driver’s decision making under degraded track conditions. Trains are sophisticated heavy machinery and their performance is ever increasing resulting in the driving task becoming more complex and progressively dominated by cognitive and perceptual skills. A critical part of reducing the potential for train driver error and of increasing performance lies in the appropriate design of simulation training. In the current study a cognitive task analysis, using the critical decision method (CDM) was undertaken using a focus group research design. The process resulted in increased knowledge of expert train driver decision-making processes. Across four major incidents analyzed 11 decision points, 17 cues, 30 essential responsive actions and 45 possible errors where identified. The use of these results for supporting the design of simulation training and associated performance measures is discussed