Submovement control processes in discrete aiming as a function of space-time constraints.
Ontology highlight
ABSTRACT:
SUBMITTER: Hsieh TY
PROVIDER: S-EPMC5744918 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Bayesian statistics provides a framework for the integration of dynamic models with incomplete data to enable inference of model parameters and unobserved aspects of the system under study. An important class of dynamic models is discrete state space, continuous-time Markov processes (DCTMPs). Simulated via the Doob-Gillespie algorithm, these have been used to model systems ranging from chemistry to ecology to epidemiology. A new type of proposal, termed 'model-based proposal' (MBP), is developed for the efficient implementation of Bayesian inference in DCTMPs using Markov chain Monte Carlo (MCMC). This new method, which in principle can be applied to any DCTMP, is compared (using simple epidemiological SIS and SIR models as easy to follow exemplars) to a standard MCMC approach and a recently proposed particle MCMC (PMCMC) technique. When measurements are made on a single-state variable (e.g. the number of infected individuals in a population during an epidemic), model-based proposal MCMC (MBP-MCMC) is marginally faster than PMCMC (by a factor of 2-8 for the tests performed), and significantly faster than the standard MCMC scheme (by a factor of 400 at least). However, when model complexity increases and measurements are made on more than one state variable (e.g. simultaneously on the number of infected individuals in spatially separated subpopulations), MBP-MCMC is significantly faster than PMCMC (more than 100-fold for just four subpopulations) and this difference becomes increasingly large.
| S-EPMC4590508 | biostudies-other
Project description:The lawful continuous linear relation between movement time and task difficulty (i.e., index of difficulty; ID) in a goal-directed rapid aiming task (Fitts' law) has been recently challenged in reciprocal performance. Specifically, a discontinuity was observed at critical ID and was attributed to a transition between two distinct dynamic regimes that occurs with increasing difficulty. In the present paper, we show that such a discontinuity is also present in discrete aiming when ID is manipulated via target width (experiment 1) but not via target distance (experiment 2). Fitts' law's discontinuity appears, therefore, to be a suitable indicator of the underlying functional adaptations of the neuro-muscular-skeletal system to task properties/requirements, independently of reciprocal or discrete nature of the task. These findings open new perspectives to the study of dynamic regimes involved in discrete aiming and sensori-motor mechanisms underlying the speed-accuracy trade-off.
| S-EPMC3399829 | biostudies-literature
Project description:Cryoelectron microscope tomography (cryoEM) and a fluorescence loss in photobleaching (FLIP) assay were used to characterize progression of the terminal stages of Caulobacter crescentus cell division. Tomographic cryoEM images of the cell division site show separate constrictive processes closing first the inner membrane (IM) and then the outer membrane (OM) in a manner distinctly different from that of septum-forming bacteria. FLIP experiments had previously shown cytoplasmic compartmentalization (when cytoplasmic proteins can no longer diffuse between the two nascent progeny cell compartments) occurring 18 min before daughter cell separation in a 135-min cell cycle so the two constrictive processes are separated in both time and space. In the very latest stages of both IM and OM constriction, short membrane tether structures are observed. The smallest observed pre-fission tethers were 60 nm in diameter for both the inner and outer membranes. Here, we also used FLIP experiments to show that both membrane-bound and periplasmic fluorescent proteins diffuse freely through the FtsZ ring during most of the constriction procession.
| S-EPMC1251605 | biostudies-literature
Project description:Many ecosystems experience strong temporal variability in environmental conditions; yet, a clear picture of how niche and neutral processes operate to determine community assembly in temporally variable systems remains elusive. In this study, we constructed neutral metacommunity models to assess the relative importance of neutral processes in a spatially and temporally variable ecosystem. We analyzed macroinvertebrate community data spanning multiple seasons and years from 20 sites in a Sonoran Desert river network in Arizona. The model goodness-of-fit was used to infer the importance of neutral processes. Averaging over eight stream flow conditions across three years, we found that neutral processes were more important in perennial streams than in non-perennial streams (intermittent and ephemeral streams). Averaging across perennial and non-perennial streams, we found that neutral processes were more important during very high flow and in low flow periods; whereas, at very low flows, the relative importance of neutral processes varied greatly. These findings were robust to the choice of model parameter values. Our study suggested that the net effect of disturbance on the relative importance of niche and neutral processes in community assembly varies non-monotonically with the severity of disturbance. In contrast to the prevailing view that disturbance promotes niche processes, we found that neutral processes could become more important when the severity of disturbance is beyond a certain threshold such that all organisms are adversely affected regardless of their biological traits and strategies.
| S-EPMC5423606 | biostudies-literature
Project description:Recently, the nature of protein structure space has been widely discussed in the literature. The traditional discrete view of protein universe as a set of separate folds has been criticized in the light of growing evidence that almost any arrangement of secondary structures is possible and the whole protein space can be traversed through a path of similar structures. Here we argue that the discrete and continuous descriptions are not mutually exclusive, but complementary: the space is largely discrete in evolutionary sense, but continuous geometrically when purely structural similarities are quantified. Evolutionary connections are mainly confined to separate structural prototypes corresponding to folds as islands of structural stability, with few remaining traceable links between the islands. However, for a geometric similarity measure, it is usually possible to find a reasonable cutoff that yields paths connecting any two structures through intermediates.
| S-EPMC3688466 | biostudies-literature
Project description:The discovery that RNA molecules can fold into complex structures and carry out diverse cellular roles has led to interest in developing tools for modeling RNA tertiary structure. While significant progress has been made in establishing that the RNA backbone is rotameric, few libraries of discrete conformations specifically for use in RNA modeling have been validated. Here, we present six libraries of discrete RNA conformations based on a simplified pseudo-torsional notation of the RNA backbone, comparable to phi and psi in the protein backbone. We evaluate the ability of each library to represent single nucleotide backbone conformations, and we show how individual library fragments can be assembled into dinucleotides that are consistent with established RNA backbone descriptors spanning from sugar to sugar. We then use each library to build all-atom models of 20 test folds, and we show how the composition of a fragment library can limit model quality. Despite the limitations inherent in using discretized libraries, we find that several hundred discrete fragments can rebuild RNA folds up to 174 nucleotides in length with atomic-level accuracy (<1.5 Å RMSD). We anticipate that the libraries presented here could easily be incorporated into RNA structural modeling, analysis, or refinement tools.
| S-EPMC3422622 | biostudies-literature
Project description:Hawkes processes are a form of self-exciting process that has been used in numerous applications, including neuroscience, seismology, and terrorism. While these self-exciting processes have a simple formulation, they can model incredibly complex phenomena. Traditionally Hawkes processes are a continuous-time process, however we enable these models to be applied to a wider range of problems by considering a discrete-time variant of Hawkes processes. We illustrate this through the novel coronavirus disease (COVID-19) as a substantive case study. While alternative models, such as compartmental and growth curve models, have been widely applied to the COVID-19 epidemic, the use of discrete-time Hawkes processes allows us to gain alternative insights. This paper evaluates the capability of discrete-time Hawkes processes by modelling daily mortality counts as distinct phases in the COVID-19 outbreak. We first consider the initial stage of exponential growth and the subsequent decline as preventative measures become effective. We then explore subsequent phases with more recent data. Various countries that have been adversely affected by the epidemic are considered, namely, Brazil, China, France, Germany, India, Italy, Spain, Sweden, the United Kingdom and the United States. These countries are all unique concerning the spread of the virus and their corresponding response measures. However, we find that this simple model is useful in accurately capturing the dynamics of the process, despite hidden interactions that are not directly modelled due to their complexity, and differences both within and between countries. The utility of this model is not confined to the current COVID-19 epidemic, rather this model could explain many other complex phenomena. It is of interest to have simple models that adequately describe these complex processes with unknown dynamics. As models become more complex, a simpler representation of the process can be desirable for the sake of parsimony.
| S-EPMC8034752 | biostudies-literature
Project description:Precision control of molecular activities and chemical reactions in live cells is a long-sought capability by life scientists. No existing technology can probe molecular targets in cells and simultaneously control the activities of only these targets at high spatial precision. We develop a real-time precision opto-control (RPOC) technology that detects a chemical-specific optical response from molecular targets during laser scanning and uses the optical signal to couple a separate laser to only interact with these molecules without affecting other sample locations. We demonstrate precision control of molecular states of a photochromic molecule in different regions of the cells. We also synthesize a photoswitchable compound and use it with RPOC to achieve site-specific inhibition of microtubule polymerization and control of organelle dynamics in live cells. RPOC can automatically detect and control biomolecular activities and chemical processes in dynamic living samples with submicron spatial accuracy, fast response time, and high chemical specificity.
| S-EPMC9329476 | biostudies-literature
Project description:A classic problem in population genetics is the characterization of discrete population structure in the presence of continuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignment methods may incorrectly ascribe differentiation due to continuous processes (e.g., geographic isolation by distance) to discrete processes, such as geographic, ecological, or reproductive barriers between populations. This reflects a shortcoming of current methods for inferring and visualizing population structure when applied to genetic data deriving from geographically distributed populations. Here, we present a statistical framework for the simultaneous inference of continuous and discrete patterns of population structure. The method estimates ancestry proportions for each sample from a set of two-dimensional population layers, and, within each layer, estimates a rate at which relatedness decays with distance. This thereby explicitly addresses the "clines versus clusters" problem in modeling population genetic variation, and remedies some of the overfitting to which nonspatial models are prone. The method produces useful descriptions of structure in genetic relatedness in situations where separated, geographically distributed populations interact, as after a range expansion or secondary contact. We demonstrate the utility of this approach using simulations and by applying it to empirical datasets of poplars and black bears in North America.
| S-EPMC6116973 | biostudies-literature
Project description:Discrete state-space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state-space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state-space models using discrete analogues of methods for continuous state-space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant.
| S-EPMC5031231 | biostudies-literature