High-performance confocal system for microscopic or endoscopic applications

We designed a high-performance confocal system that can be easily adapted to an existing light microscope or coupled with an endoscope for remote imaging. The system employs spatially and temporally patterned illumination produced by one of several mechanisms, including a micromirror array video projection device driven by a computer video source or a microlens array scanned by a piezo actuator in the microscope illumination path. A series of subsampled "component" video images are acquired from a solid-state video camera. Confocal images are digitally reconstructed using "virtual pinhole" synthetic aperture techniques applied to the collection of component images. Unlike conventional confocal techniques that raster scan a single detector and illumination point, our system samples multiple locations in parallel, with particular advantages for monitoring fast dynamic processes. We compared methods of patterned illumination and confocal image reconstruction by characterizing the point spread function, contrast, and intensity of imaged objects. Sample 3D reconstructions include a diatom and a Golgi-stained nerve cell collected in transmission.     

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion

Multi-color immunofluorescence microscopy to detect specific molecules in the cell membrane can be coupled with parallel plate flow chamber assays to investigate mechanisms governing cell adhesion under dynamic flow conditions. For instance, cancer cells labeled with multiple fluorophores can be perfused over a potentially reactive substrate to model mechanisms of cancer metastasis. However, multi-channel single camera systems and color cameras exhibit shortcomings in image acquisition for real-time live cell analysis. To overcome these limitations, we used a dual camera emission splitting system to simultaneously capture real-time image sequences of fluorescently labeled cells in the flow chamber. Dual camera emission splitting systems filter defined wavelength ranges into two monochrome CCD cameras, thereby simultaneously capturing two spatially identical but fluorophore-specific images. Subsequently, psuedocolored one-channel images are combined into a single real-time merged sequence that can reveal multiple target molecules on cells moving rapidly across a region of interest.     

Multiple attractors in stage-structured population models with birth pulses

In most models of population dynamics, increases in population due to birth are assumed to be time-independent, but many species reproduce only during a single period of the year. A single species stage-structured model with density-dependent maturation rate and birth pulse is formulated. Using the discrete dynamical system determined by its Poincaré map, we report a detailed study of the various dynamics, including (a) existence and stability of nonnegative equilibria, (b) nonunique dynamics, meaning that several attractors coexist, (c) basins of attraction (defined as the set of the initial conditions leading to a certain type of attractor), (d) supertransients, and (e) chaotic attractors. The occurrence of these complex dynamic behaviour is related to the fact that minor changes in parameter or initial values can strikingly change the dynamic behaviours of system. Further, it is shown that periodic birth pulse, in effect, provides a natural period or cyclicity that allows multiple oscillatory solutions in the continuous dynamical systems.     

植被系统中植物与环境因子相互作用的动态模拟

本文在综合考虑现有植被发展动态模型的基础上提出包含植被,环境因子,人类或其它外力干扰三者间相互作用情况下的植被系统一般动态模型,并从控制论的角度阐述了植被与环境因子间的耦合,反馈作用。最后以线性逼近和微分拟合的方法求取模型的局部解,进而作出外延预测。实例分析表明,模型预测对实际观测有较好的跟踪性能。     

Basins of attraction: population dynamics with two stable 4-cycles

We use the concepts of composite maps, basins of attraction, basin switching, and saddle fly-by's to make the ecological hypothesis of the existence of multiple attractors more accessible to experimental scrutiny. Specifically, in a periodically forced insect population growth model we identify multiple attractors, namely, two locally stable 4-cycles. Using the model-predicted basins of attraction, we examine data time series from a Tribolium experiment for evidence of the multiple attractors. We conclude that the multiple attractor hypothesis together with demographic stochasticity accounts for the experimental observations.     

Distinguishing the noise and attractor strength of coordinated limb movements using recurrence analysis

The variability of coupled rhythmic limb movements is assumed to be a consequence of the strength of a movement’s attractor dynamic and a constant stochastic noise process that continuously perturbs the movement system away from this dynamic. Recently, it has been suggested that the nonlinear technique of recurrence analysis can be used to index the effects of noise and attractor strength on movement variability. To test this, three experiments were conducted in which the attractor strength of bimanual wrist-pendulum movements (using coordination mode, movement frequency and detuning), as well as the magnitude of stochastic perturbations affecting the variability of these movements (using a temporally fluctuating visual metronome) was manipulated. The results of these experiments demonstrate that recurrence analysis can index parametric changes in the attractor strength of coupled rhythmic limb movements and the magnitude of metronome induced stochastic perturbations independently. The results of Experiments 1 and 2 also support the claim that differences between the variability of inphase and antiphase coordination, and between slow and fast movement frequencies are due to differences in attractor strength. In contrast to the standard assumption that the noise that characterizes interlimb coordination remains constant for different magnitudes of detuning (Δ ω) the results of Experiment 3 suggest that the magnitude of noise increases with increases in |Δ ω|.     

A neural-network system for control of eye movements: basic mechanisms

This paper presents a neural-network-based system that can generate and control movements of the eyes. It was inspired by a number of experimental observations on the saccadic and gaze systems of monkeys and cats. Because of the generality of the approach undertaken, the system can be regarded as a demonstration of how parallel distributed processing principles, namely learning and attractor dynamics, can be integrated with experimental findings, as well as a biologically inspired controller for a dexterous robotic orientation device. The system is composed of three parts: a dynamic motor map, a push-pull circuitry, and a plant. The dynamics of the motor map is generated by a multi-layer network that was trained to compute a bidimensional temporal-spatial transformation. Simulation results indicate (1) that the system is able to reproduce some of the properties observed in the biological system at the neural and movement levels and (2) that the dynamics of the motor map remains stereotyped even when the motor map is subject to abnormal stimulation patterns. The latter result emphasizes the role of the topographic projection that connects the motor map to the push-pull circuitry in determining the features of the resulting movements.     

Statistical analysis of multipath neural systems

Peripheral sensory and motor systems may be characterized by models consisting of multiple parallel convergent pathways, each described by the same set of equations, but having different parameter values in each path. Such models, although deterministic, are best analyzed using a statistical approach, which is illustrated here by analysis of several simple multipath models composed of linear dynamic elements and static non-linear elements. Relationships between instantaneous means of signals at different points in such systems are used to show that a multipath system can exhibit behaviour which would not be expected from observations of individual pathways. Mechanisms for linearization of static non-linearities are briefly described. Important implications for neurophysiologists are discussed.     

A synthetic three-color scaffold for monitoring genetic regulation and noise

Background  

Current methods for analyzing the dynamics of natural regulatory networks, and quantifying synthetic circuit function, are limited by the lack of well-characterized genetic measurement tools. Fluorescent reporters have been used to measure dynamic gene expression, but recent attempts to monitor multiple genes simultaneously in single cells have not focused on independent, isolated measurements. Multiple reporters can be used to observe interactions between natural genes, or to facilitate the 'debugging' of biologically engineered genetic networks. Using three distinguishable reporter genes in a single cell can reveal information not obtainable from only one or two reporters. One application of multiple reporters is the use of genetic noise to reveal regulatory connections between genes. Experiments in both natural and synthetic systems would benefit from a well-characterized platform for expressing multiple reporter genes and synthetic network components.     

An efficient multivariate approach for estimating preference when individual observations are dependent

1. We discuss aspects of resource selection based on observing a given vector of resource variables for different individuals at discrete time steps. A new technique for estimating preference of habitat characteristics, applicable when there are multiple individual observations, is proposed. 2. We first show how to estimate preference on the population and individual level when only a single site- or resource component is observed. A variance component model based on normal scores in used to estimate mean preference for the population as well as the heterogeneity among individuals defined by the intra-class correlation. 3. Next, a general technique is proposed for time series of observations of a vector with several components, correcting for the effect of correlations between these. The preference of each single component is analyzed under the assumption of arbitrarily complex selection of the other components. This approach is based on the theory for conditional distributions in the multi-normal model. 4. The method is demonstrated using a data set of radio-tagged dispersing juvenile goshawks and their site characteristics, and can be used as a general tool in resource or habitat selection analysis.     

Dynamic resource allocation in disaster response: tradeoffs in wildfire suppression

Challenges associated with the allocation of limited resources to mitigate the impact of natural disasters inspire fundamentally new theoretical questions for dynamic decision making in coupled human and natural systems. Wildfires are one of several types of disaster phenomena, including oil spills and disease epidemics, where (1) the disaster evolves on the same timescale as the response effort, and (2) delays in response can lead to increased disaster severity and thus greater demand for resources. We introduce a minimal stochastic process to represent wildfire progression that nonetheless accurately captures the heavy tailed statistical distribution of fire sizes observed in nature. We then couple this model for fire spread to a series of response models that isolate fundamental tradeoffs both in the strength and timing of response and also in division of limited resources across multiple competing suppression efforts. Using this framework, we compute optimal strategies for decision making scenarios that arise in fire response policy.     

Empirical evaluation of observation scale effects in community time series

Natural communities are highly complex and dynamic over time, with populations structured by numerous abiotic and biotic forces acting through direct and indirect pathways. Multispecies Autoregressive (MAR) modeling can be used to partition effects of variables that are interrelated and temporally autocorrelated in time series from natural systems. Here we address two main questions in applying MAR models to community time series. First, what is the effect of observation scale on interpretation of community dynamics? We used a 10‐year weekly planktonic time series from Lake Washington to construct multiple “biweekly” and “monthly” data sets, and compared resulting community interaction models. Direct abiotic effects and intraspecific autocorrelation were apparent using all data sets. Biotic interactions were more apparent using biweekly and monthly data, indicating that time lags longer than one week were necessary to detect numerical response to interspecific interactions. Second, we examined effects of dropping the winter months from our analyses to simulate the common practice of sampling only during the “growing season” in long‐term ecological studies. We found that biotic interactions remained similarly characterized in models using only non‐winter months, but that the importance of seasonal physical factors nearly disappeared in non‐winter models. Exclusion of winter data in sampling designs may therefore allow us to characterize biotic interactions, although it may not help us understand populations’ relationships to seasonal abiotic variables. The models supported many previous findings from experimental and qualitative investigations of Lake Washington community interactions, implying that MARs provided plausible characterizations of community dynamics, but some previously unconsidered relationships did emerge, such as the importance of cryptomonads and picoplankton for zooplankton growth. We conclude that explicit consideration of time lags in biotic response is necessary to understand relative importance of abiotic and biotic factors, and that sampling regime can therefore strongly influence our interpretations of community dynamics.     

Identifying environmental signals from population abundance data using multivariate time-series analysis

Individual organisms are affected by various natural and anthropogenic environmental factors throughout their life history. This is reflected in the way population abundance fluctuates. Consequently, observed population dynamics are often produced by the superimposition of multiple environmental signals. This complicates the analysis of population time-series. Here, a multivariate time-series method called maximum autocorrelation factor analysis (MAFA) was used to extract underlying signals from multiple population time series data. The extracted signals were compared with environmental variables that were suspected to affect the populations. Finally, a simple multiple regression analysis was applied to the same data set, and the results from the regression analysis were compared with those from MAFA. The extracted signals with MAFA were strongly associated with the environmental variables, suggesting that they represent environmental factors. On the other hand, with the multiple regression analysis, one of the important signals was not identifiable, revealing the shortcoming of the conventional approach. MAFA summarizes data based on their lag-one autocorrelation. This allows the identification of underlying signals with a small effect size on population abundance during the observation. It also uses multiple time series collected in parallel; this enables us to effectively analyze short time series. In this study, annual spawning adult counts of Chinook salmon at various locations within the Klamath Basin, California, were analyzed.     

Identification of regime shifts in time series using neighborhood statistics

Sudden and significant changes in biotic and abiotic variables have been observed across a variety of systems. The identification of such regime shifts in time series includes both model-fitting and statistical approaches. We introduce two methods that use state- or measurement-space neighborhood statistics to pick out regime shifts. Analysis of simulated and real data sets shows that these methods can be an effective means of identifying regime shifts for single variable as well as multivariable time series. In addition, these methods can be used on systems with non-equilibrium steady states. However, care must be taken in interpreting results as these methods do respond to changes in time series that are not consistent with the regime shift concept.     

All systems go: launching cell simulation fueled by integrated experimental biology data

Biological simulation serves to unify the basic elements of systems biology, namely, model selection, experimentation and model refinement. To select biochemical models for simulation, metabolome analysis can be performed using capillary electrophoresis or liquid chromatography coupled with mass spectrometry. In this manner, selected models can be elaborated with temporal/spatial gene and protein expression data obtained from model organisms such as Escherichia coli. The E. coli single gene deletion mutant library (KO collection) and His-tag/GFP-fusion single open reading frame clone expression library (ASKA) are powerful resources for this task. The integration of parallel experimental datasets into dynamic simulation tools forms the remaining challenge for the systematic analysis and elucidation of biological networks and holds promise for biotechnological applications.     

Evidence of alternate attractors from a whole-ecosystem regime shift experiment

Ecosystems sometimes shift between different states or dynamic regimes. Theory attributes these shifts to multiple ecosystem attractors. However, documenting multiple ecosystem attractors is difficult, particularly at spatial and temporal scales relevant to ecosystem management. We manipulated the fish community of a lake with the goal of causing trophic cascades and shifting the food web from a planktivore-dominated state to an alternate piscivore-dominated state. We evaluated evidence that the shifts in the fish community comprise alternate attractors using two complementary approaches. First, we calculated phase space trajectories to visualize the shift between attractors. Second, we computed generalized autoregressive conditional heteroskedasticity (GARCH) models and the Brock–Dechert–Scheinkman (BDS) test for linearity. The reconstructed phase space trajectories show the system departing a point attractor, entering a limit cycle, and then shifting to a new point attractor. The GARCH and BDS results indicate that linear explanations are not sufficient to explain the observed patterns. The results provide evidence for alternate attractors based on high-frequency time series of field measurements.