Actin dynamics and the elasticity of cytoskeletal networks

The structural integrity of a cell depends on its cytoskeleton, which includes an actin network. This network is transient and depends upon the continual polymerization and depolymerization of actin. The degradation of an actin network, and a corresponding reduction in cell stiffness, can indicate the presence of disease. Numerical simulations will be invaluable for understanding the physics of these systems and the correlation between actin dynamics and elasticity. Here we develop a model that is capable of generating actin network structures. In particular, we develop a model of actin dynamics which considers the polymerization, depolymerization, nucleation, severing, and capping of actin filaments. The structures obtained are then fed directly into a mechanical model. This allows us to qualitatively assess the effects of changing various parameters associated with actin dynamics on the elasticity of the material

Cell cycle responses to Topoisomerase II inhibition: Molecular mechanisms and clinical implications.

DNA Topoisomerase IIA (Topo IIA) is an enzyme that alters the topological state of DNA and is essential for the separation of replicated sister chromatids and the integrity of cell division. Topo IIA dysfunction activates cell cycle checkpoints, resulting in arrest in either the G2-phase or metaphase of mitosis, ultimately triggering the abscission checkpoint if non-disjunction persists. These events, which directly or indirectly monitor the activity of Topo IIA, have become of major interest as many cancers have deficiencies in Topoisomerase checkpoints, leading to genome instability. Recent studies into how cells sense Topo IIA dysfunction and respond by regulating cell cycle progression demonstrate that the Topo IIA G2 checkpoint is distinct from the G2-DNA damage checkpoint. Likewise, in mitosis, the metaphase Topo IIA checkpoint is separate from the spindle assembly checkpoint. Here, we integrate mechanistic knowledge of Topo IIA checkpoints with the current understanding of how cells regulate progression through the cell cycle to accomplish faithful genome transmission and discuss the opportunities this offers for therapy

Virtual clinics in glaucoma care: face-to-face versus remote decision-making

BACKGROUND/AIMS: To examine the agreement in clinical decisions of glaucoma status made in a virtual glaucoma clinic with those made during a face-to-face consultation. METHODS: A trained nurse and technicians entered data prospectively for 204 patients into a proforma. A subsequent face-to-face clinical assessment was completed by either a glaucoma consultant or fellow. Proformas were reviewed remotely by one of two additional glaucoma consultants, and 12 months later, by the clinicians who had undertaken the original clinical examination. The interobserver and intraobserver decision-making agreements of virtual assessment versus standard care were calculated. RESULTS: We identified adverse disagreement between face-to-face and virtual review in 7/204 (3.4%, 95% CI 0.9% to 5.9%) patients, where virtual review failed to predict a need to accelerated follow-up identified in face-to-face review. Misclassification events were rare, occurring in 1.9% (95% CI 0.3% to 3.8%) of assessments. Interobserver κ (95% CI) showed only fair agreement (0.24 (0.04 to 0.43)); this improved to moderate agreement when only consultant decisions were compared against each other (κ=0.41 (0.16 to 0.65)). The intraobserver agreement κ (95% CI) for the consultant was 0.274 (0.073 to 0.476), and that for the fellow was 0.264 (0.031 to 0.497). CONCLUSIONS: The low rate of adverse misclassification, combined with the slowly progressive nature of most glaucoma, and the fact that patients will all be regularly reassessed, suggests that virtual clinics offer a safe, logistically viable option for selected patients with glaucoma

Analysis of similarities (ANOSIM) for 3‐way designs

Analysis of similarities (ANOSIM) is a robust non-parametric hypothesis-testing framework for differences in resemblances among groups of samples. To date, the generalisation and use of ANOSIM to analyse various 2-way nested and crossed designs with unordered or ordered factors has been described. This paper describes how the 2-way tests may be extended and modified for the analysis of 3-way designs, including the introduction of a different type of constrained permutation procedure for a design in which one factor is nested in another and crossed with a third. The construction of 3-way tests using the generalised statistic in various nested and crossed designs, with or without ordered factors, and with or without replication, is described. Applications of the new tests to ecological data are demonstrated using three marine examples. They are as follows: a study of changes in fish diet for fish of increasing size sampled in different locations at different times (a 3-way fully crossed design with ordered factors); a hierarchical spatial study of the fauna inhabiting kelp holdfasts (a 3- way fully nested design with unordered factors); and a study of infaunal macrobenthos in which sites within areas were resampled over a long time series (a design in which sites are nested in areas but crossed with years, both latter factors potentially being ordered). The magnitudes of the ANOSIM statistics provide information about relative effect sizes (accounting for other factors), which is often a focus for multifactorial designs. Though the described ANOSIM tests do not provide parallels for all the range of 3-way mixed-factor designs possible in ANOVA (and its multivariate semi-parametric counterpart PERMANOVA), it is seen that for nested factors these ANOSIM tests parallel the matching PERMANOVA random-effects models, and not their fixed-effects counterparts, thus allowing the same broader inference about the space from which these random factor levels are drawn

Analysis of similarities (ANOSIM) for 2‐way layouts using a generalised ANOSIM statistic, with comparative notes on Permutational Multivariate Analysis of Variance (PERMANOVA)

In the study of multivariate data, for example of change in ecological communities, ANOSIM is a robust non-parametric hypothesis-testing framework for differences in resemblances among groups of samples. RELATE is a non-parametric Mantel test of the hypothesis of no relationship between two resemblance matrices. Details are given of the explicit link between the RELATE statistic, a Spearman rank correlation (ρ) between corresponding elements in the two resemblance matrices, and the ANOSIM statistic R, a scaled contrast between the among- and within-group ranks. It is seen that R can equivalently be defined as the slope of the linear regression of ranked resemblances from observations against ranked distances among samples, the latter from a simple model matrix assigning the values 1 and 0 to between- and within-group distances, respectively. Re-defining this model matrix to represent ordered distances among groups leads naturally to a generalised ANOSIM statistic, RO, suitable for testing, for example, ordered factor levels in space or time, or an environmental or pollution gradient. Two variants of the generalised ANOSIM statistic are described, namely ROc where there are replicates within groups, and ROs where there are only single samples (no replicates) within groups, for which an ANOSIM test was not previously available. Three marine ecological examples using ANOSIM to analyse an ordered factor in one-way designs are provided. These are: (1) changes in macrofaunal composition with increasing distance from an oil rig; (2) differences in phytal meiofaunal community composition with increasing macroalgal complexity; and (3) changes in average community composition of free-living nematodes along a long-term heavy metal gradient. Incorporating knowledge of an ordering structure is seen to provide more focussed, and thus stronger, ANOSIM tests, but inevitably risks losing power if that prior knowledge is incorrect or inappropriate

A generalised analysis of similarities (ANOSIM) statistic for designs with ordered factors

In the study of multivariate data, for example of change in ecological communities, ANOSIM is a robust non-parametric hypothesis-testing framework for differences in resemblances among groups of samples. RELATE is a non-parametric Mantel test of the hypothesis of no relationship between two resemblance matrices. Details are given of the explicit link between the RELATE statistic, a Spearman rank correlation (ρ) between corresponding elements in the two resemblance matrices, and the ANOSIM statistic R, a scaled contrast between the among- and within-group ranks. It is seen that R can equivalently be defined as the slope of the linear regression of ranked resem�blances from observations against ranked distances among samples, the latter from a simple model matrix assigning the values 1 and 0 to between- and within-group distances, respectively. Re-defining this model matrix to represent ordered distances among groups leads naturally to a generalised ANOSIM statistic, RO, suitable for testing, for example, ordered factor levels in space or time, or an environmental or pollution gradient. Two variants of the generalised ANOSIM statistic are described, namely ROc where there are replicates within groups, and ROs where there are only single samples (no replicates) within groups, for which an ANOSIM test was not previously available. Three marine ecological examples using ANOSIM to analyse an ordered factor in one-way designs are provided. These are: (1) changes in macrofaunal composition with increasing distance from an oil rig; (2) differences in phytal meiofaunal community composition with increasing macroalgal complexity; and (3) changes in average community composition of free-living nematodes along a long-term heavy metal gradient. Incorporating knowledge of an ordering structure is seen to provide more focussed, and thus stronger, ANOSIM tests, but inevitably risks losing power if that prior knowledge is incorrect or inappropriat

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach

RepSeq-A database of amino acid repeats present in lower eukaryotic pathogens

BACKGROUND Amino acid repeat-containing proteins have a broad range of functions and their identification is of relevance to many experimental biologists. In human-infective protozoan parasites (such as the Kinetoplastid and Plasmodium species), they are implicated in immune evasion and have been shown to influence virulence and pathogenicity. RepSeq http://repseq.gugbe.com is a new database of amino acid repeat-containing proteins found in lower eukaryotic pathogens. The RepSeq database is accessed via a web-based application which also provides links to related online tools and databases for further analyses. RESULTS The RepSeq algorithm typically identifies more than 98% of repeat-containing proteins and is capable of identifying both perfect and mismatch repeats. The proportion of proteins that contain repeat elements varies greatly between different families and even species (3 - 35% of the total protein content). The most common motif type is the Sequence Repeat Region (SRR) - a repeated motif containing multiple different amino acid types. Proteins containing Single Amino Acid Repeats (SAARs) and Di-Peptide Repeats (DPRs) typically account for 0.5 - 1.0% of the total protein number. Notable exceptions are P. falciparum and D. discoideum, in which 33.67% and 34.28% respectively of the predicted proteomes consist of repeat-containing proteins. These numbers are due to large insertions of low complexity single and multi-codon repeat regions. CONCLUSION The RepSeq database provides a repository for repeat-containing proteins found in parasitic protozoa. The database allows for both individual and cross-species proteome analyses and also allows users to upload sequences of interest for analysis by the RepSeq algorithm. Identification of repeat-containing proteins provides researchers with a defined subset of proteins which can be analysed by expression profiling and functional characterisation, thereby facilitating study of pathogenicity and virulence factors in the parasitic protozoa. While primarily designed for kinetoplastid work, the RepSeq algorithm and database retain full functionality when used to analyse other species

Verification of Decision Making Software in an Autonomous Vehicle: An Industrial Case Study

Correctness of autonomous driving systems is crucial as\ua0incorrect behaviour may have catastrophic consequences. Many different\ua0hardware and software components (e.g. sensing, decision making, actuation,\ua0and control) interact to solve the autonomous driving task, leading to a level of complexity that brings new challenges for the formal verification\ua0community. Though formal verification has been used to prove\ua0correctness of software, there are significant challenges in transferring\ua0such techniques to an agile software development process and to ensure\ua0widespread industrial adoption. In the light of these challenges, the identification\ua0of appropriate formalisms, and consequently the right verification\ua0tools, has significant impact on addressing them. In this paper, we\ua0evaluate the application of different formal techniques from supervisory\ua0control theory, model checking, and deductive verification to verify existing\ua0decision and control software (in development) for an autonomous\ua0vehicle. We discuss how the verification objective differs with respect tothe choice of formalism and the level of formality that can be applied.\ua0Insights from the case study show a need for multiple formal methods to\ua0prove correctness, the difficulty to capture the right level of abstraction\ua0to model and specify the formal properties for the verification objectives