Methods for averaging time - intensity curves

Although there has been increased interest in the use of time-intensity(T-I) measures in sensory studies, there are weaknesses in existingmethods for the analysis and presentation of T-I data. Thispaper reviews the methods for averaging time—intensitycurves, pointing out their limitations. A new method is proposed,in which normalization of individual curves is carried out forboth intensity and time, and the resulting mean curve is unique.The method accommodates intensity plateaus, non-zero endpointsand non-monotonic curves. The main parameters of the mean curvesare averages of the corresponding parameters of the individualcurves.     

POTENCY OF SWEETNESS OF ASPARTAME, D-TRYPTOPHAN AND THAUMATIN EVALUATED BY SINGLE VALUE AND TIME-INTENSITY MEASUREMENTS

Perceived sweetness of sucrose, aspartame, D-tryptophan and thaumatin in a sour, citric acid background was analyzed in terms of the potency of these compounds relative to sucrose-water combinations. Potencies of the sweeteners were determined from (1) maximum intensity using single value and time-intensity (T-I) measurements and (2) average intensity calculated as the ratio of area under the T-I curve and total perceived time. Stevens' law was applied to sweet responses, either in static or dynamic conditions. It was found that the exponent of the concentration-response function reflected the relative capacity of a compound to sweeten a given food and stressed differences of potency among sweeteners. Aspartame, D-tryptophan and thaumatin exhibited a decrease in sweetness potency relative to sucrose as sweetness increased from 10 to 100% of the full scale of response. Across the entire sweetness range, thaumatin showed the greatest potency but its long persistence time led to differentiate this intense sweetener from the other sweeteners evaluated.     

Largemouth bass (Micropterus salmoides) switch feeding modalities in response to sensory deprivation

Animals use a suite of sensory modalities to precisely locate and capture prey. While numerous studies have examined the effects of sensory deprivation on the behaviors leading to prey capture and while it is generally believed that information in the pre-strike period determines the way fish capture prey, this study is the first to examine the contribution of sensory information to jaw kinematics during capture. Largemouth bass were filmed using high-speed videography while capturing live mosquitofish. Bass were examined intact, with visual deprivation under infrared light, and with lateral line deprivation following treatment with cobalt chloride. Deprived of visual cues, this visual ram-feeding predator switches towards suction-based feeding to successfully capture prey. They approach prey slowly but open their mouths more rapidly, which has been shown to result in greater buccal pressure, causing their prey to move a greater distance at a more rapid velocity as they are being drawn into the predators' mouths. Deprived of lateral line cues, bass have higher forward velocities during capture and capture prey earlier in the gape cycle. This study demonstrates that sensory pre-strike information directly affects the capture modality employed by fishes and that fish can modulate between ram and suction not only by adjusting the amount of ram by increasing or decreasing their movements, but also by actively increasing the amount of suction used. These results suggest that the ability to modulate feeding behavior may allow animals to not only exploit a broader breadth of prey items, but also to be capable of doing so in a wider variety of environments.     

Quantification of sensory information transmission using timeseries decorrelation techniques

To estimate the information transmitted across a neuronal sensory system one has to deal with serial dependence among consecutive samples of the stimulus and the response signal. Common methods usually require a huge amount of data, or are restricted to Gaussian stimuli. Here, we describe stimulus and response as stochastic processes, i.e. as sequences of random variables, in the same coordinate system. Stimulus-response pairs of these random variables must not be considered independently because otherwise the transinformation is overestimated. To account for the linear fraction of the serial dependence, we present two decorrelation techniques based on coordinate transformation. They provide a representation of the processes with uncorrelated random variables and yield a more precise estimate of the transinformation.     

Thermodynamic Costs of Information Processing in Sensory Adaptation

Biological sensory systems react to changes in their surroundings. They are characterized by fast response and slow adaptation to varying environmental cues. Insofar as sensory adaptive systems map environmental changes to changes of their internal degrees of freedom, they can be regarded as computational devices manipulating information. Landauer established that information is ultimately physical, and its manipulation subject to the entropic and energetic bounds of thermodynamics. Thus the fundamental costs of biological sensory adaptation can be elucidated by tracking how the information the system has about its environment is altered. These bounds are particularly relevant for small organisms, which unlike everyday computers, operate at very low energies. In this paper, we establish a general framework for the thermodynamics of information processing in sensing. With it, we quantify how during sensory adaptation information about the past is erased, while information about the present is gathered. This process produces entropy larger than the amount of old information erased and has an energetic cost bounded by the amount of new information written to memory. We apply these principles to the E. coli''s chemotaxis pathway during binary ligand concentration changes. In this regime, we quantify the amount of information stored by each methyl group and show that receptors consume energy in the range of the information-theoretic minimum. Our work provides a basis for further inquiries into more complex phenomena, such as gradient sensing and frequency response.     

Kinetic analysis of enzymatic chiral resolution by progress curve evaluation

The present study deals with kinetic modeling of enzyme-catalyzed reactions by integral progress curve analysis, and shows how to apply this technique to kinetic resolution of enantiomers. It is shown that kinetic parameters for both enantiomers and the enantioselectivity of the enzyme may be obtained from the progress curve measurement of a racemate only.A parameter estimation procedure has been established and it is shown that the covariance matrix of the obtained parameters is a useful statistical tool in the selection and verification of the model structure. Standard deviations calculated from this matrix have shown that progress curve analysis yields parameter values with high accuracies.Potential sources of systematic errors in (multiple) progress curve analysis are addressed in this article. Amongst these, the following needed to be dealt with: (1) the true initial substrate concentrations were obtained from the final amount of product experimentally measured (mass balancing); (2) systematic errors in the initial enzyme concentration were corrected by incorporating this variable in the fitting procedure as an extra parameter per curve; and (3) enzyme inactivation is included in the model and a first-order inactivation constant is determined.Experimental verification was carried out by continuous monitoring of the hydrolysis of ethyl 2-chloropropionate by carboxylesterase NP and the alpha-chymotrypsin-catalyzed hydrolysis of benzoylalanine mathyl ester in a pH-stat system. Kinetic parameter values were obtained with high accuracies and model predictions were in good agreement with independent measurements of enantiomeric excess values or literature data. (c) 1994 John Wiley & Sons, Inc.     

Prediction of circulatory equilibrium in response to changes in stressed blood volume

Accurate prediction of cardiac output (CO), left atrial pressure (PLA), and right atrial pressure (PRA) is a prerequisite for management of patients with compromised hemodynamics. In our previous study (Uemura et al. Am J Physiol Heart Circ Physiol 286: H2376-H2385, 2004), we demonstrated a circulatory equilibrium framework, which permits the prediction of CO, PLA, and PRA once the venous return surface and integrated CO curve are known. Inasmuch as we also showed that the surface can be estimated from single-point CO, PLA, and PRA measurements, we hypothesized that a similar single-point estimation of the CO curve would enable us to predict hemodynamics. In seven dogs, we measured the PLA-CO and PRA-CO relations and derived a standardized CO curve using the logarithmic function CO = SL[ln(PLA - 2.03) + 0.80] for the left heart and CO = SR[ln(PRA - 2.13) + 1.90] for the right heart, where SL and SR represent the preload sensitivity of CO, i.e., pumping ability, of the left and right heart, respectively. To estimate the integrated CO curve in each animal, we calculated SL and SR from single-point CO, PLA, and PRA measurements. Estimated and measured CO agreed reasonably well. In another eight dogs, we altered stressed blood volume (-8 to +8 ml/kg of reference volume) under normal and heart failure conditions and predicted the hemodynamics by intersecting the surface and the CO curve thus estimated. We could predict CO [y = 0.93x + 6.5, r2 = 0.96, standard error of estimate (SEE) = 7.5 ml.min(-1).kg(-1)], PLA (y = 0.90x + 0.5, r2= 0.93, SEE = 1.4 mmHg), and PRA (y = 0.87x + 0.4, r2= 0.91, SEE = 0.4 mmHg) reasonably well. In conclusion, single-point estimation of the integrated CO curve enables accurate prediction of hemodynamics in response to extensive changes in stressed blood volume.     

End of the chain? Rugosity and fine-scale bathymetry from existing underwater digital imagery using structure-from-motion (SfM) technology

The rugosity or complexity of the seafloor has been shown to be an important ecological parameter for fish, algae, and corals. Historically, rugosity has been measured either using simple and subjective manual methods such as ‘chain-and-tape’ or complicated and expensive geophysical methods. Here, we demonstrate the application of structure-from-motion (SfM) photogrammetry to generate high-resolution, three-dimensional bathymetric models of a fringing reef from existing underwater video collected to characterize the seafloor. SfM techniques are capable of achieving spatial resolution that can be orders of magnitude greater than large-scale lidar and sonar mapping of coral reef ecosystems. The resulting data provide finer-scale measurements of bathymetry and rugosity that are more applicable to ecological studies of coral reefs than provided by the more expensive and time-consuming geophysical methods. Utilizing SfM techniques for characterizing the benthic habitat proved to be more effective and quantitatively powerful than conventional methods and thus might portend the end of the ‘chain-and-tape’ method for measuring benthic complexity.     

Adaptation and selective information transmission in the cricket auditory neuron AN2

Sensory systems adapt their neural code to changes in the sensory environment, often on multiple time scales. Here, we report a new form of adaptation in a first-order auditory interneuron (AN2) of crickets. We characterize the response of the AN2 neuron to amplitude-modulated sound stimuli and find that adaptation shifts the stimulus-response curves toward higher stimulus intensities, with a time constant of 1.5 s for adaptation and recovery. The spike responses were thus reduced for low-intensity sounds. We then address the question whether adaptation leads to an improvement of the signal's representation and compare the experimental results with the predictions of two competing hypotheses: infomax, which predicts that information conveyed about the entire signal range should be maximized, and selective coding, which predicts that "foreground" signals should be enhanced while "background" signals should be selectively suppressed. We test how adaptation changes the input-response curve when presenting signals with two or three peaks in their amplitude distributions, for which selective coding and infomax predict conflicting changes. By means of Bayesian data analysis, we quantify the shifts of the measured response curves and also find a slight reduction of their slopes. These decreases in slopes are smaller, and the absolute response thresholds are higher than those predicted by infomax. Most remarkably, and in contrast to the infomax principle, adaptation actually reduces the amount of encoded information when considering the whole range of input signals. The response curve changes are also not consistent with the selective coding hypothesis, because the amount of information conveyed about the loudest part of the signal does not increase as predicted but remains nearly constant. Less information is transmitted about signals with lower intensity.     

Quantifying nonlinear anisotropic elastic material properties of biological tissue by use of membrane inflation

Determination of material parameters for soft tissue frequently involves regression of material parameters for nonlinear, anisotropic constitutive models against experimental data from heterogeneous tests. Here, parameter estimation based on membrane inflation is considered. A four parameter nonlinear, anisotropic hyperelastic strain energy function was used to model the material, in which the parameters are cast in terms of key response features. The experiment was simulated using finite element (FE) analysis in order to predict the experimental measurements of pressure versus profile strain. Material parameter regression was automated using inverse FE analysis; parameter values were updated by use of both local and global techniques, and the ability of these techniques to efficiently converge to a best case was examined. This approach provides a framework in which additional experimental data, including surface strain measurements or local structural information, may be incorporated in order to quantify heterogeneous nonlinear material properties.     

Analysis of Slow (Theta) Oscillations as a Potential Temporal Reference Frame for Information Coding in Sensory Cortices

While sensory neurons carry behaviorally relevant information in responses that often extend over hundreds of milliseconds, the key units of neural information likely consist of much shorter and temporally precise spike patterns. The mechanisms and temporal reference frames by which sensory networks partition responses into these shorter units of information remain unknown. One hypothesis holds that slow oscillations provide a network-intrinsic reference to temporally partitioned spike trains without exploiting the millisecond-precise alignment of spikes to sensory stimuli. We tested this hypothesis on neural responses recorded in visual and auditory cortices of macaque monkeys in response to natural stimuli. Comparing different schemes for response partitioning revealed that theta band oscillations provide a temporal reference that permits extracting significantly more information than can be obtained from spike counts, and sometimes almost as much information as obtained by partitioning spike trains using precisely stimulus-locked time bins. We further tested the robustness of these partitioning schemes to temporal uncertainty in the decoding process and to noise in the sensory input. This revealed that partitioning using an oscillatory reference provides greater robustness than partitioning using precisely stimulus-locked time bins. Overall, these results provide a computational proof of concept for the hypothesis that slow rhythmic network activity may serve as internal reference frame for information coding in sensory cortices and they foster the notion that slow oscillations serve as key elements for the computations underlying perception.     

Activin stimulates proliferation of rat ovarian thecal-interstitial cells

There is growing evidence that the function of ovarian theca-interstitial (T-I) cells may be modulated by paracrine actions of activin, inhibin, and follistatin. Furthermore, either dysregulation, dysfunction, or both, of these peptides may play a role in conditions associated with T-I hyperplasia, such as polycystic ovary syndrome (PCOS) and hyperthecosis. This study was designed to evaluate the role of activin, inhibin, and follistatin in the modulation of T-I cell proliferation. Interaction of these peptides with insulin-like growth factor-I (IGF-I), a known stimulator of T-I cell proliferation, was also assessed. Purified rat T-I cells were cultured for 48 h in chemically defined media and with or without activin (3-30 ng/ml), inhibin (3-30 ng/ml), follistatin (100 ng/ml), and/or IGF-I (10 nM). T-I cell proliferation was assessed using radiolabeled thymidine incorporation assay. Activin alone stimulated proliferation of T-I cells in a dose-dependent fashion (by up to 320% above control; P < 0.001), whereas inhibin alone or follistatin alone had no significant effect. Inhibin had also no effect on activin-induced proliferation. Follistatin significantly reduced the stimulatory effects of activin and decreased proliferation by up to 46% (P < 0.01) below the level attained in the presence of activin alone. IGF-I (10 nM), at a dose producing a near-maximal effect, increased proliferation by 175% above control (P < 0.001); insulin (10 nM) increased proliferation by 52% above control (P < 0.03). A combination of IGF-I (10 nM) and activin (30 ng/ml) resulted in a 1090% increase of proliferation above control (P < 0.001); this stimulatory effect was significantly greater than that achieved in the presence of either activin alone or IGF-I alone (P < 0.001). Similarly, a combination of insulin (10 nM) and activin (30 ng/ml) increased proliferation by 506% above control levels. Flow cytometry evaluation revealed that activin increased the proportion of actively dividing cells (in S or G2/M phase of the cell cycle) by 42% (P < 0.02), whereas IGF-I had no effect on the proportion of actively dividing cells. The present findings indicate that an activin-follistatin system may be involved in the regulation of the size of ovarian thecal-stromal compartment. In view of the synergy between activin and IGF-I, and the difference in the effects on the cell cycle distribution, stimulation of T-I proliferation by these agents is likely to be mediated via separate transduction pathways. Excess activin or insufficient follistatin may contribute to T-I hyperplasia.     

Methods for comparison of parameters from longitudinal rhythmometric models with multiple components

Multiple components linear least-squares methods have been proposed for the detection of periodic components in nonsinusoidal longitudinal time series. However, a proper test for comparison of parameters obtained from this method for two or more time series is not yet available. Accordingly, we propose two methods, one parametric and one nonparametric, to compare parameters from rhythmometric models with multiple components. The parametric method is based on techniques commonly and generally employed in linear regression analysis. The comparison of parameters among two or more time series is accomplished by the use of so-called dummy variables. The nonparametric method is based on bootstrap techniques. This approach basically tests if the difference in any given parameter obtained by fitting a model with the same periods to two different longitudinal time series differs from zero. This method calculates a confidence interval for the difference in the tested parameter. If this interval does not contain zero, it can be concluded that the parameters obtained from the two time series are different with high probability. An estimation of the p-value for the corresponding test can also be calculated. By the use of similar bootstrap techniques, confidence intervals can also be obtained for any parameter derived from the multiple component fit of several periods to nonsinusoidal longitudinal time series, including the orthophase (peak time), bathyphase (trough time), and global amplitude (difference between the maximum and the minimum) of the fitted model waveform. These methods represent a valuable tool for the comparison of rhythm parameters obtained by multiple component analysis, and they render this approach as a generally applicable one for waveform representation and detection of periodicities in nonsinusoidal, sparse, and noisy longitudinal time series sampled with either equidistant or unequidistant observations.     

Study of single sensillum responses to pheromone in the light-brown apple moth, Epiphyas postvittana, using an averaging technique

ABSTRACT. A technique is described for averaging the electrophysiological responses of single olfactory sensilla when repeatedly stimulated with the same odour. This enables measurements of electrophysiological responses to be interpreted in a way not normally possible because of the highly variable nature of the individual responses of sensory cells. When the antennal receptors of males of Epiphyas postvittana are stimulated with a single pheromone component, it is shown that the responses contain sufficient information to detect readily both the onset and removal of a stimulus and also a doubling or halving in the concentration of the pheromone. Significant changes in the spontaneous activity of cells following stimulation are also described.     

A comparative study of cells in inflammation,EAE and MS using biomedical literature data mining

Summary Biomedical literature and database annotations, available in electronic forms, contain a vast amount of knowledge resulting from global research. Users, attempting to utilize the current state-of-the-art research results are frequently overwhelmed by the volume of such information, making it difficult and time-consuming to locate the relevant knowledge. Literature mining, data mining, and domain specific knowledge integration techniques can be effectively used to provide a user-centric view of the information in a real-world biological problem setting. Bioinformatics tools that are based on real-world problems can provide varying levels of information content, bridging the gap between biomedical and bioinformatics research. We have developed a user-centric bioinformatics research tool, called BioMap, that can provide a customized, adaptive view of the information and knowledge space. BioMap was validated by using inflammatory diseases as a problem domain to identify and elucidate the associations among cells and cellular components involved in multiple sclerosis (MS) and its animal model, experimental allergic encephalomyelitis (EAE). The BioMap system was able to demonstrate the associations between cells directly excavated from biomedical literature for inflammation, EAE and MS. These association graphs followed the scale-free network behavior (average γ = 2.1) that are commonly found in biological networks.     

The photosynthesis-light curve: A simple analog model

An analog model of the photosynthesis-light curve is presented and applied to published data. Previous equations have been constructed with parameters having geometric rather than biologic definition. The present study defines each parameter as an analog of a hypothetical photosynthetic system. The model is constructed of two stages, a light stage composed of photosynthetic factories, N, with probability P of intercepting a given amount of light, and a dark stage approximated by the Michaelis-Menten equation. An index of the speed of the dark stage, relative to the potential speed of the light stage is provided. The model describes a family of photosynthesis-light curves and can be applied to data to interpret changes in the photosynthetic system from changes in the shape of the curve.     

Parameter estimation in surface exchange models using nonlinear inversion: how many parameters can we estimate and which measurements are most useful?

Models of mass and energy exchanges between the biosphere and the atmosphere generally contain a nonlinear dependence between fluxes and model parameters, and thus estimation of these parameters from measurements in a heterogeneous landscape depends on the scale of the observations. The scale‐dependence of a typical surface‐exchange model (the CSIRO Biospheric Model, CBM) is examined using the diurnal variation of hourly fluxes of CO₂, latent heat, sensible heat and soil heat. The fluxes were measured using micrometeorological techniques over six sites in a grazing/pasture system in SE Australia during a period of three weeks in 1995. Nonlinear parameter inversion was used to determine model parameters. Analysis of the covariance of the estimates of the parameters and the unexplained residuals of the model showed that a maximum of three or four parameters could be determined independently from the observations for all six sites. Estimates of a key model parameter, j_max, the mean of maximum potential electron transport rate of all leaves within the canopy, was best determined by the measurements of net CO₂ flux at all sites examined. Measurements of ground heat flux provide little information about any of the model parameters in CBM. Because of nonlinearities in the surface exchange model, calculated fluxes will be in error if parameters for the component vegetation types are simply averaged in proportion to their areal fraction. The magnitude of these errors was examined for CBM using a hypothetical land surface consisting of two surface types, each with different parameter values. Predictions of net CO₂, latent heat and ground heat fluxes using a linear combination of model parameters for the two surface types were quite similar with those found using optimal estimates of the parameters for the landscape, but were significantly poorer for sensible heat fluxes.     

How behavioral constraints may determine optimal sensory representations

The sensory-triggered activity of a neuron is typically characterized in terms of a tuning curve, which describes the neuron's average response as a function of a parameter that characterizes a physical stimulus. What determines the shapes of tuning curves in a neuronal population? Previous theoretical studies and related experiments suggest that many response characteristics of sensory neurons are optimal for encoding stimulus-related information. This notion, however, does not explain the two general types of tuning profiles that are commonly observed: unimodal and monotonic. Here I quantify the efficacy of a set of tuning curves according to the possible downstream motor responses that can be constructed from them. Curves that are optimal in this sense may have monotonic or nonmonotonic profiles, where the proportion of monotonic curves and the optimal tuning-curve width depend on the general properties of the target downstream functions. This dependence explains intriguing features of visual cells that are sensitive to binocular disparity and of neurons tuned to echo delay in bats. The numerical results suggest that optimal sensory tuning curves are shaped not only by stimulus statistics and signal-to-noise properties but also according to their impact on downstream neural circuits and, ultimately, on behavior.     

Muon spin relaxation studies of iron(II) spin crossover complexes

A series of model iron(II) spin crossover complexes have been investigated by temperature dependent muon spin relaxation (μSR) techniques at ISIS, UK. The thermally induced spin crossover in these materials could be monitored by following the initial asymmetry parameter, a₀, in zero-field. We established that the behavior of a₀ correlates well with the shape of the spin crossover curve derived from magnetic susceptibility measurements, whether hysteretic, smooth, or abrupt. In addition, the longitudinal field dependence of a₀ not only provides information on the nature of the muonic species but also on their interactions and respective localization in the crystal lattice. Useful insights to the electronic structure and dynamic phenomena of these model spin crossover complexes can be derived.