No abstract

We present PEAX, a novel feature-based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the user's perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of the sequential data, our accompanying visual query system enables interactive feedback-driven adjustments of the pattern search to adapt to the users' perceived similarity. Using an active learning sampling strategy, PEAX collects user-generated binary relevance feedback. This feedback is used to train a model for binary classification, to ultimately find other regions that exhibit patterns similar to the search target. We demonstrate PEAX's features through a case study in genomics and report on a user study with eight domain experts to assess the usability and usefulness of PEAX. Moreover, we evaluate the effectiveness of the learned feature representation for visual similarity search in two additional user studies. We find that our models retrieve significantly more similar patterns than other commonly used techniques.

During the first wave of an influenza pandemic prior to the availability of an effective vaccine, healthcare workers (HCWs) may be at particular risk of infection with the novel influenza strain. We conducted a cross-sectional study of the prevalence of antibody to pandemic influenza A (H1N1) 2009 (pH1N1) among HCWs in Hong Kong in February-March 2010 following the first pandemic wave. Sera collected from HCWs were tested for antibody to pH1N1 influenza virus by viral neutralisation (VN). We assessed factors associated with higher antibody titres, and we compared antibody titres in HCWs with those in a separate community study. In total we enrolled 703 HCWs. Among 599 HCWs who did not report receipt of pH1N1 vaccine, 12% had antibody titre ≥1:40 by VN. There were no significant differences in the age-specific proportions of unvaccinated HCWs with antibody titre ≥1:40 compared with the general community following the first wave of pH1N1. Under good adherence to infection control guidelines, potential occupational exposures in the hospital setting did not appear to be associated with any substantial excess risk of pH1N1 infection in HCWs. Most HCWs had low antibody titres following the first pandemic wave.

Reticulate evolution is thought to accelerate the process of evolution beyond simple genetic drift and selection, helping to rapidly generate novel hybrids with combinations of adaptive traits. However, the long-standing dogma that reticulate evolutionary processes are likewise advantageous for switching ecological niches, as in microbial pathogen host switch events, has not been explicitly tested. We use data from the influenza genome sequencing project and a phylogenetic heuristic approach to show that reassortment, a reticulate evolutionary mechanism, predominates over mutational drift in transmission between different host species. Moreover, as host evolutionary distance increases, reassortment is increasingly favored. We conclude that the greater the quantitative difference between ecological niches, the greater the importance of reticulate evolutionary processes in overcoming niche barriers.ecology | reticulate evolution | influenza | host switch | reassortment

Complex biotherapeutic modalities, such as antibody-drug conjugates (ADC), present significant challenges for the comprehensive bioanalytical characterization of their pharmacokinetics (PK) and catabolism in both preclinical and clinical settings. Thus, the bioanalytical strategy for ADCs must be designed to address the specific structural elements of the protein scaffold, linker, and warhead. A typical bioanalytical strategy for ADCs involves quantification of the Total ADC, Total IgG, and Free Warhead concentrations. Herein, we present bioanalytical characterization of the PK and catabolism of a novel ADC. MEDI3726 targets prostate-specific membrane antigen (PMSA) and is comprised of a humanized IgG1 antibody site-specifically conjugated to tesirine (SG3249). The MEDI3726 protein scaffold lacks interchain disulfide bonds and has an average drug to antibody ratio (DAR) of 2. Based on the structural characteristics of MEDI3726, an array of 4 bioanalytical assays detecting 6 different surrogate analyte classes representing at least 14 unique species was developed, validated, and employed in support of a first-in-human clinical trial (NCT02991911). MEDI3726 requires the combination of heavy-light chain structure and conjugated warhead to selectively deliver the warhead to the target cells. Therefore, both heavy-light chain dissociation and the deconjugation of the warhead will affect the activity of MEDI3726. The concentration− time profiles of subjects dosed with MEDI3726 revealed catabolism of the protein scaffold manifested by the more rapid clearance of the Active ADC, while exhibiting minimal deconjugation of the pyrrolobenzodiazepine (PBD) warhead (SG3199).