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Graphical abstract, Over recent years, there has been a rapid development of membrane-mimetic systems to encapsulate and stabilize planar segments of phospholipid bilayers in solution. One such system has been the use of amphipathic copolymers to solubilize lipid bilayers into nanodiscs. The attractiveness of this system, in part, stems from the capability of these polymers to solubilize membrane proteins directly from the host cell membrane. The assumption has been that the native lipid annulus remains intact, with nanodiscs providing a snapshot of the lipid environment. Recent studies have provided evidence that phospholipids can exchange from the nanodiscs with either lipids at interfaces, or with other nanodiscs in bulk solution. Here we investigate kinetics of lipid exchange between three recently studied polymer-stabilized nanodiscs and supported lipid bilayers at the silicon-water interface. We show that lipid and polymer exchange occurs in all nanodiscs tested, although the rate and extent differs between different nanodisc types. Furthermore, we observe adsorption of nanodiscs to the supported lipid bilayer for one nanodisc system which used a polymer made using reversible addition-fragmentation chain transfer polymerization. These results have important implications in applications of polymer-stabilized nanodiscs, such as in the fabrication of solid-supported films containing membrane proteins.

Objective: To study the impact of multiphase quality improvement efforts to enhance appropriate use of chemical and mechanical venous thromboembolism (VTE) prophylaxis (VTEP) on the rate of hospital-acquired VTE and determine whether efforts have been associated with increased bleeding complications. Patients and Methods: All adult inpatients discharged between January 1, 2005, and December 31, 2015, were included in the study. Retrospective interrupted time series analysis compared VTEP performance, VTE outcomes, and unintended consequences (derived from linked administrative and clinical data) across 5 improvement phases: baseline (January 1, 2005-December 31, 2006), paper order set phase (January 1, 2007-February 9, 2009), electronic order set phase (February 10, 2009-December 16, 2009), active reminder phase (December 17, 2009-May 31, 2012), and maintenance phase (June 1, 2012-September 30, 2015). Results: Guideline VTEP plan adherence at the end of the study period (including documenting contraindications) reached 88.8% (654,138 of 736,384 patient days). Delivery of pharmacological VTEP increased from 43.9% (49,155 of 111,906 patients) to 60.8% (75,784 of 124,676 patients); delivery of mechanical or pharmacological VTEP increased less (65.0% [431,791 of 664,087 patient days] to 67.4% [496,625 of 736,384 patient days]). Mean VTE rates decreased from 4.6 per 1000 hospitalizations (21.7 VTEs per month) at baseline to 4.3 per 1000 hospitalizations (18.0 VTEs per month) during the maintenance phase (P

Echinococcus granulosus sensu lato is a globally prevalent zoonotic parasitic cestode leading to cystic echinococcosis (CE) in both humans and sheep with both medical and financial impacts, whose reduction requires the application of a One Health approach to its control. Regarding the animal health component of this approach, lack of accurate and practical diagnostics in livestock impedes the assessment of disease burden and the implementation and evaluation of control strategies. We use of a Bayesian Latent Class Analysis (LCA) model to estimate ovine CE prevalence in sheep samples from the Río Negro province of Argentina accounting for uncertainty in the diagnostics. We use model outputs to evaluate the performance of a novel recombinant B8/2 antigen B subunit (rEgAgB8/2) indirect enzyme-linked immunosorbent assay (ELISA) for detecting E. granulosus in sheep. Necropsy (as a partial gold standard), western blot (WB) and ELISA diagnostic data were collected from 79 sheep within two Río Negro slaughterhouses, and used to estimate individual infection status (assigned as a latent variable within the model). Using the model outputs, the performance of the novel ELISA at both individual and flock levels was evaluated, respectively, using a receiver operating characteristic (ROC) curve, and simulating a range of sample sizes and prevalence levels within hypothetical flocks. The estimated (mean) prevalence of ovine CE was 27.5% (95%Bayesian credible interval (95%BCI): 13.8%–58.9%) within the sample population. At the individual level, the ELISA had a mean sensitivity and specificity of 55% (95%BCI: 46%–68%) and 68% (95%BCI: 63%–92%), respectively, at an optimal optical density (OD) threshold of 0.378. At the flock level, the ELISA had an 80% probability of correctly classifying infection at an optimal cut-off threshold of 0.496. These results suggest that the novel ELISA could play a useful role as a flock-level diagnostic for CE surveillance in the region, supplementing surveillance activities in the human population and thus strengthening a One Health approach. Importantly, selection of ELISA cut-off threshold values must be tailored according to the epidemiological situation., Highlights • Development of a Bayesian LCA model for ovine cystic echinococcosis (CE) in Argentina. • Evaluation of diagnostic performance for a novel rEgAgB8/2 indirect ELISA. • Identification of optimal ELISA thresholds for use as a flock-level surveillance tool. • Improvement of CE surveillance in sheep will help strengthen the One Health approach.

Graphical abstract, Highlights • ASR allows to explore the distant past in gene sequence space. • We provide a primer for non-experts to reconstruct the sequence of an extinct gene. • ASR, GWAS and fitness landscapes explore molecular evolution at different timescales., Whilst substantial research effort has been placed on understanding the interactions of plant proteins with their molecular partners, relatively few studies in plants - by contrast to work in other organisms - address how these interactions evolve. It is thought that ancestral proteins were more promiscuous than modern proteins and that specificity often evolved following gene duplication and subsequent functional refining. However, ancestral protein resurrection studies have found that some modern proteins have evolved de novo from ancestors lacking those functions. Intriguingly, the new interactions evolved as a consequence of just a few mutations and, as such, acquisition of new functions appears to be neither difficult nor rare, however, only a few of them are incorporated into biological processes before they are lost to subsequent mutations. Here, we detail the approach of ancestral sequence reconstruction (ASR), providing a primer to reconstruct the sequence of an ancestral gene. We will present case studies from a range of different eukaryotes before discussing the few instances where ancestral reconstructions have been used in plants. As ASR is used to dig into the remote evolutionary past, we will also present some alternative genetic approaches to investigate molecular evolution on shorter timescales. We argue that the study of plant secondary metabolism is particularly well suited for ancestral reconstruction studies. Indeed, its ancient evolutionary roots and highly diverse landscape provide an ideal context in which to address the focal issue around the emergence of evolutionary novelties and how this affects the chemical diversification of plant metabolism.

Understanding the sequence of biological and clinical events along the course of Alzheimer's disease provides insights into dementia pathophysiology and can help participant selection in clinical trials. Our objective is to train two data-driven computational models for sequencing these events, the Event Based Model (EBM) and discriminative-EBM (DEBM), on the basis of well-characterized research data, then validate the trained models on subjects from clinical cohorts characterized by less-structured data-acquisition protocols. Seven independent data cohorts were considered totalling 2389 cognitively normal (CN), 1424 mild cognitive impairment (MCI) and 743 Alzheimer's disease (AD) patients. The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set was used as training set for the constriction of disease models while a collection of multi-centric data cohorts was used as test set for validation. Cross-sectional information related to clinical, cognitive, imaging and cerebrospinal fluid (CSF) biomarkers was used. Event sequences obtained with EBM and DEBM showed differences in the ordering of single biomarkers but according to both the first biomarkers to become abnormal were those related to CSF, followed by cognitive scores, while structural imaging showed significant volumetric decreases at later stages of the disease progression. Staging of test set subjects based on sequences obtained with both models showed good linear correlation with the Mini Mental State Examination score (R2EBM = 0.866; R2DEBM = 0.906). In discriminant analyses, significant differences (p-value ≤ 0.05) between the staging of subjects from training and test sets were observed in both models. No significant difference between the staging of subjects from the training and test was observed (p-value > 0.05) when considering a subset composed by 562 subjects for which all biomarker families (cognitive, imaging and CSF) are available. Event sequence obtained with DEBM recapitulates the heuristic models in a data-driven fashion and is clinically plausible. We demonstrated inter-cohort transferability of two disease progression models and their robustness in detecting AD phases. This is an important step towards the adoption of data-driven statistical models into clinical domain., Highlights • Data-driven event sequences describe evolution of relevant biomarkers in AD. • Agreement between event sequences and heuristic AD progression models • Accuracy in classifying subjects from clinical cohorts up to 91% • Staging of subjects and MMSE scores of individuals show linear relation. • Transferability of AD progression models based on research data to clinical cohorts

RNA polymerase II (Pol II) surveils the genome, pausing as it encounters DNA lesions and base modifications and initiating signals for DNA repair among other important regulatory events. Recent work suggests that Pol II pauses at 5-carboxycytosine (5caC), an epigenetic modification of cytosine, because of a specific hydrogen bond between the carboxyl group of 5caC and a specific residue in fork loop 3 of Pol II. This hydrogen bond compromises productive NTP binding and slows down elongation. Apart from this specific interaction, the carboxyl group of 5caC can potentially interact with numerous charged residues in the cleft of Pol II. However, it is not clear how other interactions between Pol II and 5caC contribute to pausing. In this study, we use Markov state models (a type of kinetic network models) built from extensive molecular dynamics simulations to comprehensively study the impact of 5caC on Pol II translocation. We describe two translocation intermediates with specific interactions that prevent the template base from loading into the Pol II active site. In addition to the previously observed state with 5caC constrained by fork loop 3, we discovered a new intermediate state with a hydrogen bond between 5caC and fork loop 2. Surprisingly, we find that 5caC may curb translocation by suppressing kinking of the helix bordering the active site (the bridge helix) because its high flexibility is critical to translocation. Our work provides new insights into how epigenetic modifications of genomic DNA can modulate Pol II translocation, inducing pauses in transcription.

Highlights • Spatial model fit is adequate when network contacts are limited to small distances. • Spatial model fit is poor when network contacts exists over long distances. • Results for spatial models tested were fairly consistent for all contact networks. • Results show the importance of collecting high quality network information., Often, when modeling infectious disease spread, the complex network through which the disease propagates is approximated by simplified spatial information. Here, we simulate epidemic spread through various contact networks and fit spatial-based models in a Bayesian framework using Markov chain Monte Carlo methods. These spatial models are individual-level models which account for the spatio-temporal dynamics of infectious disease. The focus here is on choosing a spatial model which best predicts the true probabilities of infection, as well as determining under which conditions such spatial models fail. Spatial models tend to predict infection probability reasonably well when disease spread is propagated through contact networks in which contacts are only within a certain distance of each other. If contacts exist over long distances, the spatial models tend to perform worse when compared to the network model.