Identification of sequence changes in myosin II that adjust muscle contraction velocity.

The speed of muscle contraction is related to body size; muscles in larger species contract at slower rates. Since contraction speed is a property of the myosin isoform expressed in a muscle, we investigated how sequence changes in a range of muscle myosin II isoforms enable this slower rate of muscle contraction. We considered 798 sequences from 13 mammalian myosin II isoforms to identify any adaptation to increasing body mass. We identified a correlation between body mass and sequence divergence for the motor domain of the 4 major adult myosin II isoforms (ß/Type I, IIa, IIb, and IIx), suggesting that these isoforms have adapted to increasing body mass. In contrast, the non-muscle and developmental isoforms show no correlation of sequence divergence with body mass. Analysis of the motor domain sequence of ß-myosin (predominant myosin in Type I/slow and cardiac muscle) from 67 mammals from 2 distinct clades identifies 16 sites, out of 800, associated with body mass (padj < 0.05) but not with the clade (padj > 0.05). Both clades change the same small set of amino acids, in the same order from small to large mammals, suggesting a limited number of ways in which contraction velocity can be successfully manipulated. To test this relationship, the 9 sites that differ between human and rat were mutated in the human ß-myosin to match the rat sequence. Biochemical analysis revealed that the rat-human ß-myosin chimera functioned like the native rat myosin with a 2-fold increase in both motility and in the rate of ADP release from the actin-myosin crossbridge (the step that limits contraction velocity). Thus, these sequence changes indicate adaptation of ß-myosin as species mass increased to enable a reduced contraction velocity and heart rate.

Computational aspects of N-mixture models.

The N-mixture model is widely used to estimate the abundance of a population in the presence of unknown detection probability from only a set of counts subject to spatial and temporal replication (Royle, 2004, Biometrics 60, 105-115). We explain and exploit the equivalence of N-mixture and multivariate Poisson and negative-binomial models, which provides powerful new approaches for fitting these models. We show that particularly when detection probability and the number of sampling occasions are small, infinite estimates of abundance can arise. We propose a sample covariance as a diagnostic for this event, and demonstrate its good performance in the Poisson case. Infinite estimates may be missed in practice, due to numerical optimization procedures terminating at arbitrarily large values. It is shown that the use of a bound, K, for an infinite summation in the N-mixture likelihood can result in underestimation of abundance, so that default values of K in computer packages should be avoided. Instead we propose a simple automatic way to choose K. The methods are illustrated by analysis of data on Hermann's tortoise Testudo hermanni.

Modified Glasgow Prognostic Score (mGPS) for Prognostication of Adult Oncology Patients With Palliative Intent in a Regional Victorian Hospital, Australia.

BACKGROUND: Accurate prognostication is essential in caring for palliative patients. Various prognostication tools have been validated in many settings in the past few years. Biomarkers of inflammation (albumin and C-reactive protein) are combined to calculate the modified Glasgow prognostic score (mGPS), which has been found to be a simple prognostic tool in this population. OBJECTIVE: This retrospective cohort study was to evaluate mGPS as a prognostication tool for cancer patients admitted to an acute hospital in regional Australia. METHODS: Adult cancer patients admitted to an acute Australian regional hospital during 2017 who had albumin and C-reactive protein (CRP) tested were included. The mGPS was calculated based on their admission values and discharge values. Based on their score (0-2), groups were compared using univariate and multivariate Cox regression analysis for prognostication. Kaplan-Meier survival plots and median survival time from admission and discharge were constructed. RESULTS: A total of 170 patient records were reviewed of which 95 had admission and discharge mGPS scores available for analysis. Of those, 86 had died at the time of data analysis. The median survival for admission mGPS 0, 1, 2 was 168,156 and 74 days. For discharge mGPS 0, 1, 2 medians were 168,119 and 70 days. On multi variate analysis admission mGPS 2 showed Hazard ratio of 2.29 (95% CI 1.16-4.56, p -0.02) and discharge mGPS 2 of 2.07 (95% CI 0.95-4.50, p value 0.07). CONCLUSIONS: In this study, mGPS was able to differentiate cancer patients into various prognostic groups. Further studies in regional acute hospitals could validate this prospectively with a multi-center larger sample size.

Prognosis prediction with two calculations of Palliative Prognostic Index: further prospective validation in hospice cancer patients with multicentre study.

OBJECTIVES: In palliative care settings, predicting prognosis is important for patients and clinicians. The Palliative Prognostic Index (PPI), a prognostic tool calculated using clinical indices alone has been validated within cancer population. This study was to further test the discriminatory ability of the PPI (ie, its ability to determine whether a subject will live more or less than a certain amount of time) in a larger sample but with a palliative care context and to compare predictions at two different points in time. METHODS: Multicentre, prospective, observational study in 10 inpatient hospices in the UK. The PPI score was calculated on the day of admission (PPI1) and again once on days 3-5 of inpatient stay (PPI2). Patients were followed up for 6 weeks or until death, whichever was earlier. RESULTS: Of the 1164 patients included in the study, 962 had both scores available. The results from PPI2 showed improved sensitivity, specificity, positive predictive value and negative predictive value compared with PPI1. For PPI1versus PPI2, area under receiver operator character curve (ROC) for <21 days were 0.73 versus 0.82 and for ≥42 days prediction 0.72 versus 0.80. The median survival days for patients with PPI1 ≤4, 4.5-6 and >6 were 38 (31 to 44), 17 (14 to 19) and 5 (4 to 7). CONCLUSION: This study showed improved discriminatory ability using the PPI score calculated between day 3and day5 of admission compared with that calculated on admission. This study further validated PPI as a prognostic tool within a palliative care population and showed recording at two time points improved accuracy.

Computational methods for yeast prion curing curves.

If the chemical guanidine hydrochloride is added to a dividing culture of yeast cells in which some of the protein Sup35p is in its prion form, the proportion of cells that carry replicating units of the prion, termed propagons, decreases gradually over time. Stochastic models to describe this process of 'curing' have been developed in earlier work. The present paper investigates the use of numerical methods of Laplace transform inversion to calculate curing curves and contrasts this with an alternative, more direct, approach that involves numerical integration. Transform inversion is found to provide a much more efficient computational approach that allows different models to be investigated with minimal programming effort. The method is used to investigate the robustness of the curing curve to changes in the assumed distribution of cell generation times. Matlab code is available for carrying out the calculations.

The power of monitoring: optimizing survey designs to detect occupancy changes in a rare amphibian population.

Biodiversity conservation requires reliable species assessments and rigorously designed surveys. However, determining the survey effort required to reliably detect population change can be challenging for rare, cryptic and elusive species. We used a tropical bromeliad-dwelling frog as a model system to explore a cost-effective sampling design that optimizes the chances of detecting a population decline. Relatively few sampling visits were needed to estimate occupancy and detectability with good precision, and to detect a 30% change in occupancy with 80% power. Detectability was influenced by observer expertise, which therefore also had an effect on the sampling design - less experienced observers require more sampling visits to detect the species. Even when the sampling design provides precise parameter estimates, only moderate to large changes in occupancy will be detected with reliable power. Detecting a population change of 15% or less requires a large number of sites to be surveyed, which might be unachievable for range-restricted species occurring at relatively few sites. Unless there is high initial occupancy, rare and cryptic species will be particularly challenging when it comes to detecting small population changes. This may be a particular issue for long-term monitoring of amphibians which often display low detectability and wide natural fluctuations.

Efficient occupancy model-fitting for extensive citizen-science data.

Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species' range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists.

Accuracy of prognosis prediction by PPI in hospice inpatients with cancer: a multi-centre prospective study.

The Palliative Prognostic Index (PPI) is a prognostication tool for palliative care patients based on clinical indices developed in Japan and further validated by one study in the UK. The aim of this study was to test its prediction accuracy in a large inpatient hospice sample. The admitting doctor in three inpatient hospices calculated the PPI score on admission. Two hundred and sixty-two patients were included in this study. Based on the PPI score, three subgroups were identified. Group 1 corresponded to patients with PPI ≤4 and the median survival of 53 days (95% CI 40 to 80 days). Group 2 corresponded to those with PPI >4 and ≤6 and the median survival 15 days (95% CI 12 to 26 days) and Group 3 corresponded to patients with PPI >6 and the median survival of 5 days (95% CI 3 to 7 days). In this study, PPI was able to identify patients' likelihood of dying within 3 weeks with a sensitivity of 64% and specificity of 83%. It was able to identify a 6-week survival chance with a sensitivity of 62% and specificity of 86%. A one-unit increase in PPI score was estimated to increase the hazard for death by a factor of 1.33 (95% CI 1.26 to 1.40), based on fitting a stratified Cox proportional hazards model. The authors conclude that PPI can be used to predict prognosis for patients with advanced cancer.

Accuracy of prognosis prediction by PPI in hospice inpatients with cancer: a multi-centre prospective study.

The Palliative Prognostic Index (PPI) is a prognostication tool for palliative care patients based on clinical indices developed in Japan and further validated by one study in the UK. The aim of this study was to test its prediction accuracy in a large inpatient hospice sample. The admitting doctor in three inpatient hospices calculated the PPI score on admission. Two hundred and sixty-two patients were included in this study. Based on the PPI score, three subgroups were identified. Group 1 corresponded to patients with PPI ≤4 and the median survival of 53 days (95% CI 40 to 80 days). Group 2 corresponded to those with PPI >4 and ≤6 and the median survival 15 days (95% CI 12 to 26 days) and Group 3 corresponded to patients with PPI >6 and the median survival of 5 days (95% CI 3 to 7 days). In this study, PPI was able to identify patients' likelihood of dying within 3 weeks with a sensitivity of 64% and specificity of 83%. It was able to identify a 6-week survival chance with a sensitivity of 62% and specificity of 86%. A one-unit increase in PPI score was estimated to increase the hazard for death by a factor of 1.33 (95% CI 1.26 to 1.40), based on fitting a stratified Cox proportional hazards model. The authors conclude that PPI can be used to predict prognosis for patients with advanced cancer.

The number and transmission of [PSI] prion seeds (Propagons) in the yeast Saccharomyces cerevisiae.

BACKGROUND: Yeast (Saccharomyces cerevisiae) prions are efficiently propagated and the on-going generation and transmission of prion seeds (propagons) to daughter cells during cell division ensures a high degree of mitotic stability. The reversible inhibition of the molecular chaperone Hsp104p by guanidine hydrochloride (GdnHCl) results in cell division-dependent elimination of yeast prions due to a block in propagon generation and the subsequent dilution out of propagons by cell division. PRINCIPAL FINDINGS: Analysing the kinetics of the GdnHCl-induced elimination of the yeast [PSI+] prion has allowed us to develop novel statistical models that aid our understanding of prion propagation in yeast cells. Here we describe the application of a new stochastic model that allows us to estimate more accurately the mean number of propagons in a [PSI+] cell. To achieve this accuracy we also experimentally determine key cell reproduction parameters and show that the presence of the [PSI+] prion has no impact on these key processes. Additionally, we experimentally determine the proportion of propagons transmitted to a daughter cell and show this reflects the relative cell volume of mother and daughter cells at cell division. CONCLUSIONS: While propagon generation is an ATP-driven process, the partition of propagons to daughter cells occurs by passive transfer via the distribution of cytoplasm. Furthermore, our new estimates of n(0), the number of propagons per cell (500-1000), are some five times higher than our previous estimates and this has important implications for our understanding of the inheritance of the [PSI+] and the spontaneous formation of prion-free cells.

Cell division is essential for elimination of the yeast [PSI+] prion by guanidine hydrochloride.

Guanidine hydrochloride (Gdn.HCl) blocks the propagation of yeast prions by inhibiting Hsp104, a molecular chaperone that is absolutely required for yeast prion propagation. We had previously proposed that ongoing cell division is required for Gdn.HCl-induced loss of the [PSI+] prion. Subsequently, Wu et al.[Wu Y, Greene LE, Masison DC, Eisenberg E (2005) Proc Natl Acad Sci USA 102:12789-12794] claimed to show that Gdn.HCl can eliminate the [PSI+] prion from alpha-factor-arrested cells leading them to propose that in Gdn.HCl-treated cells the prion aggregates are degraded by an Hsp104-independent mechanism. Here we demonstrate that the results of Wu et al. can be explained by an unusually high rate of alpha-factor-induced cell death in the [PSI+] strain (780-1D) used in their studies. What appeared to be no growth in their experiments was actually no increase in total cell number in a dividing culture through a counterbalancing level of cell death. Using media-exchange experiments, we provide further support for our original proposal that elimination of the [PSI+] prion by Gdn.HCl requires ongoing cell division and that prions are not destroyed during or after the evident curing phase.