Dynamics of efferent regulation of muscular contraction. Determination of transients: Efferent stimulation frequency-muscle length

Transients of the change in the length of the soleus, gastrocnemius, and plantaris muscles of the hind limb of the cat during gradual changes in the frequency of distributed efferent stimulation were recorded under isotonic conditions. It is shown that the dynamics of the investigated system is essentially nonlinear: The values of the time constants of the transients depended on the amplitude and sign of the input actions. With transition from shortening to lengthening of the muscle the time constants decreased abruptly, with increase of the absolute value of the amplitude of the transient they also decreased. According to the results of many experiments in the range of small values of the amplitudes of transients (up to 35% of the maximum amplitude), the values of the time constants for the soleus muscle were 1.15±0.09 sec for shortening and 0.40±0.07 sec for lengthening and for the gastrocnemius and plantaris muscles respectively 0.51±0.08 and 0.21±0.04 sec. These values are closely related to the corresponding values of the time constants of the external load-muscle length transients investigated earlier. The functional significance of nonlinear dynamic properties of muscular contraction is discussed.A. A. Bogomolets Institute of Physiology, Academy of Sciences of the Ukrainian SSR, Kiev. Translated from Neirofiziologiya, Vol. 17, No. 4, pp. 468–475, July–August, 1985.     

Bayesian semiparametric nonlinear mixed-effects joint models for data with skewness, missing responses, and measurement errors in covariates

Summary It is a common practice to analyze complex longitudinal data using semiparametric nonlinear mixed-effects (SNLME) models with a normal distribution. Normality assumption of model errors may unrealistically obscure important features of subject variations. To partially explain between- and within-subject variations, covariates are usually introduced in such models, but some covariates may often be measured with substantial errors. Moreover, the responses may be missing and the missingness may be nonignorable. Inferential procedures can be complicated dramatically when data with skewness, missing values, and measurement error are observed. In the literature, there has been considerable interest in accommodating either skewness, incompleteness or covariate measurement error in such models, but there has been relatively little study concerning all three features simultaneously. In this article, our objective is to address the simultaneous impact of skewness, missingness, and covariate measurement error by jointly modeling the response and covariate processes based on a flexible Bayesian SNLME model. The method is illustrated using a real AIDS data set to compare potential models with various scenarios and different distribution specifications.     

Wiener analysis of spike transmission by identified molluscan neurons

A mathematical model of spike train transmission by identified molluscan neurons was obtained by Wiener analysis. Poisson-distributed sequences of near-threshold stimuli were used as input trains for model construction and testing. Assuming that the error of describing responses of the synapse-neuron system purely by mean outflow frequency is 100%, addition of a linear component to the equation of the model reduces this error to 25%, and addition of a term allowing for nonlinear properties of the system reduces it to 16%. Comparison of the standard error of predicted responses of the model to testing stimulus trains and of responses of a real neuron to these same trains showed that the prediction error with allowance for nonlinear properties does not exceed 21%. Choice of adequate criteria for comparing model and experimental results is discussed.A. A. Bogomolets Institute of Physiology, Academy of Sciences of the Ukrainian SSR, Kiev. Translated from Neirofiziologiya, Vol. 16, No. 1, pp. 49–54, January–February, 1984.     

Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification

Background

Each lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating them to the lung diseases.

Objective and Methods

In this study, we presented a paradigm of an exhaled aerosol test that addresses the above two challenges and is promising to detect the site and severity of lung diseases. This paradigm consists of two steps: image feature extraction using sub-regional fractal analysis and data classification using a support vector machine (SVM). Numerical experiments were conducted to evaluate the feasibility of the breath test in four asthmatic lung models. A high-fidelity image-CFD approach was employed to compute the exhaled aerosol patterns under different disease conditions.

Findings

By employing the 10-fold cross-validation method, we achieved 100% classification accuracy among four asthmatic models using an ideal 108-sample dataset and 99.1% accuracy using a more realistic 324-sample dataset. The fractal-SVM classifier has been shown to be robust, highly sensitive to structural variations, and inherently suitable for investigating aerosol-disease correlations.

Conclusion

For the first time, this study quantitatively linked the exhaled aerosol patterns with their underlying diseases and set the stage for the development of a computer-aided diagnostic system for non-invasive detection of obstructive respiratory diseases.     

The response properties of retinula cells in the flyCalliphora erythrocephala as a function of the wavelength and polarization properties of visible and ultraviolet light

Detailed functional response properties of the class (1–6) retinula cells in the insectCalliphora erythrocephela have been determined as a function of the wavelength of monochromatic light and polarization conditions over a six log unit range of light adaptation levels. These stimuli were produced as a small parallel beam that could combine various wavelengths in both the ultraviolet and visible regions whose polarization planes could be individually controlled. The wild type and white-eyed mutant were studied and shown to have no significant differences in their response properties. It was shown that previous tests using transient stimuli, large compared to the adaptation levels produced responses of high orders of nonlinearity and rapidly changing transient properties. In contrast, the use of white-noise tests did not cause changes in the basic cell properties as established by a given light adaptation condition. These tests accurately model the visual conditions of a wide and representative range of normal behavioral conditions and the model response data thus obtained can be directly correlated to important physiological tests for the same stabilized adaptation conditions. The simultaneous application of both visible and ultraviolet monochromatic light revealed a complex response interaction which changed the self response contribution of the ultraviolet light from that due only to ultraviolet light but had no effect on the visible light response. It also produced a mutual interaction. This effect was found to be the same for all members of the (1–6) class of cells when the light was unpolarized but varied for different members of the class for polarized light depending upon the relative angles of theE-vectors for the two types of light which in turn vary for different members of the six cell classes.     

Predicting human chronically paralyzed muscle force: a comparison of three mathematical models.

Chronic spinal cord injury (SCI) induces detrimental musculoskeletal adaptations that adversely affect health status, ranging from muscle paralysis and skin ulcerations to osteoporosis. SCI rehabilitative efforts may increasingly focus on preserving the integrity of paralyzed extremities to maximize health quality using electrical stimulation for isometric training and/or functional activities. Subject-specific mathematical muscle models could prove valuable for predicting the forces necessary to achieve therapeutic loading conditions in individuals with paralyzed limbs. Although numerous muscle models are available, three modeling approaches were chosen that can accommodate a variety of stimulation input patterns. To our knowledge, no direct comparisons between models using paralyzed muscle have been reported. The three models include 1) a simple second-order linear model with three parameters and 2) two six-parameter nonlinear models (a second-order nonlinear model and a Hill-derived nonlinear model). Soleus muscle forces from four individuals with complete, chronic SCI were used to optimize each model's parameters (using an increasing and decreasing frequency ramp) and to assess the models' predictive accuracies for constant and variable (doublet) stimulation trains at 5, 10, and 20 Hz in each individual. Despite the large differences in modeling approaches, the mean predicted force errors differed only moderately (8-15% error; P=0.0042), suggesting physiological force can be adequately represented by multiple mathematical constructs. The two nonlinear models predicted specific force characteristics better than the linear model in nearly all stimulation conditions, with minimal differences between the two nonlinear models. Either nonlinear mathematical model can provide reasonable force estimates; individual application needs may dictate the preferred modeling strategy.     

Tuned solutions in dynamic neural fields as building blocks for extended EEG models

The most prominent functional property of cortical neurons in sensory areas are their tuned receptive fields which provide specific responses of the neurons to external stimuli. Tuned neural firing indeed reflects the most basic and best worked out level of cognitive representations. Tuning properties can be dynamic on a short time-scale of fractions of a second. Such dynamic effects have been modeled by localised solutions (also called “bumps” or “peaks”) in dynamic neural fields. In the present work we develop an approximation method to reduce the dynamics of localised activation peaks in systems of n coupled nonlinear d-dimensional neural fields with transmission delays to a small set of delay differential equations for the peak amplitudes and widths only. The method considerably simplifies the analysis of peaked solutions as demonstrated for a two-dimensional example model of neural feature selectivity in the brain. The reduced equations describe the effective interaction between pools of local neurons of several (n) classes that participate in shaping the dynamic receptive field responses. To lowest order they resemble neural mass models as they often form the base of EEG-models. Thereby they provide a link between functional small-scale receptive field models and more coarse-grained EEG-models. More specifically, they connect the dynamics in feature-selective cortical microcircuits to the more abstract local elements used in coarse-grained models. However, beside amplitudes the reduced equations also reflect the sharpness of tuning of the activity in a d-dimensional feature space in response to localised stimuli.     

Aggregation error in nonlinear ecological models

Conditions are presented for accurately representing the dynamics of several components of a nonlinear ecological system by a single state variable. For mass-balance ecosystem models, only two conditions exist that permit such aggregation without introducing error into the model. Under the first condition, components can be lumped if their environmental losses (e.g. respiration or migration) are characterized by identical linear functions. Under the second condition, the components must maintain constant proportionality throughout their transient behavior. If these conditions are violated, it is not possible to aggregate without error. However, it is shown that aggregation error can still be minimized. The theorems developed here extend related results derived for the general theory of systems and have the advantage of generality, being applicable to nonlinear models of considerable complexity.     

Mathematical models of human paralyzed muscle after long-term training

Spinal cord injury (SCI) results in major musculoskeletal adaptations, including muscle atrophy, faster contractile properties, increased fatigability, and bone loss. The use of functional electrical stimulation (FES) provides a method to prevent paralyzed muscle adaptations in order to sustain force-generating capacity. Mathematical muscle models may be able to predict optimal activation strategies during FES, however muscle properties further adapt with long-term training. The purpose of this study was to compare the accuracy of three muscle models, one linear and two nonlinear, for predicting paralyzed soleus muscle force after exposure to long-term FES training. Further, we contrasted the findings between the trained and untrained limbs. The three models' parameters were best fit to a single force train in the trained soleus muscle (N=4). Nine additional force trains (test trains) were predicted for each subject using the developed models. Model errors between predicted and experimental force trains were determined, including specific muscle force properties. The mean overall error was greatest for the linear model (15.8%) and least for the nonlinear Hill Huxley type model (7.8%). No significant error differences were observed between the trained versus untrained limbs, although model parameter values were significantly altered with training. This study confirmed that nonlinear models most accurately predict both trained and untrained paralyzed muscle force properties. Moreover, the optimized model parameter values were responsive to the relative physiological state of the paralyzed muscle (trained versus untrained). These findings are relevant for the design and control of neuro-prosthetic devices for those with SCI.     

Crossover inhibition in the retina: circuitry that compensates for nonlinear rectifying synaptic transmission

In the mammalian retina, complementary ON and OFF visual streams are formed at the bipolar cell dendrites, then carried to amacrine and ganglion cells via nonlinear excitatory synapses from bipolar cells. Bipolar, amacrine and ganglion cells also receive a nonlinear inhibitory input from amacrine cells. The most common form of such inhibition crosses over from the opposite visual stream: Amacrine cells carry ON inhibition to the OFF cells and carry OFF inhibition to the ON cells (”crossover inhibition”). Although these synapses are predominantly nonlinear, linear signal processing is required for computing many properties of the visual world such as average intensity across a receptive field. Linear signaling is also necessary for maintaining the distinction between brightness and contrast. It has long been known that a subset of retinal outputs provide exactly this sort of linear representation of the world; we show here that rectifying (nonlinear) synaptic currents, when combined thorough crossover inhibition can generate this linear signaling. Using simple mathematical models we show that for a large set of cases, repeated rounds of synaptic rectification without crossover inhibition can destroy information carried by those synapses. A similar circuit motif is employed in the electronics industry to compensate for transistor nonlinearities in analog circuits.     

Distinct Functional Roles of Cardiac Mitochondrial Subpopulations Revealed by a 3D Simulation Model

Experimental characterization of two cardiac mitochondrial subpopulations, namely, subsarcolemmal mitochondria (SSM) and interfibrillar mitochondria (IFM), has been hampered by technical difficulties, and an alternative approach is eagerly awaited. We previously developed a three-dimensional computational cardiomyocyte model that integrates electrophysiology, metabolism, and mechanics with subcellular structure. In this study, we further developed our model to include intracellular oxygen diffusion, and determined whether mitochondrial localization or intrinsic properties cause functional variations. For this purpose, we created two models: one with equal SSM and IFM properties and one with IFM having higher activity levels. Using these two models to compare the SSM and IFM responses of [Ca²⁺], tricarboxylic acid cycle activity, [NADH], and mitochondrial inner membrane potential to abrupt changes in pacing frequency (0.25–2 Hz), we found that the reported functional differences between these subpopulations appear to be mostly related to local [Ca²⁺] heterogeneity, and variations in intrinsic properties only serve to augment these differences. We also examined the effect of hypoxia on mitochondrial function. Under normoxic conditions, intracellular oxygen is much higher throughout the cell than the half-saturation concentration for oxidative phosphorylation. However, under limited oxygen supply, oxygen is mostly exhausted in SSM, leaving the core region in an anoxic condition. Reflecting this heterogeneous oxygen environment, the inner membrane potential continues to decrease in IFM, whereas it is maintained to nearly normal levels in SSM, thereby ensuring ATP supply to this region. Our simulation results provide clues to understanding the origin of functional variations in two cardiac mitochondrial subpopulations and their differential roles in maintaining cardiomyocyte function as a whole.     

The use of preserved tissue in finite element modelling of fresh ovine vertebral behaviour

The aim of this study was to investigate whether the predicted finite element (FE) stiffness of vertebral bone is altered when using images of preserved rather than fresh tissue to generate specimen-specific FE models. Fresh ovine vertebrae were used to represent embalmed (n = 3) and macerated dry-bone (n = 3) specimens and treated accordingly. Specimens were scanned pre- and post-treatment using micro-computed tomography. A constant threshold level derived from these images was used to calculate the respective bone volume fraction (BV/TV) from which the conversion factor validated for fresh tissue was used to determine material properties that were assigned to corresponding FE models. Results showed a definite change in the BV/TV between the fresh and the preserved bone. However, the changes in the predicted FE stiffness were not generally greater than the variations expected from assignment of loading and boundary conditions. In conclusion, images of preserved tissue can be used to generate FE models that are representative of fresh tissue with a tolerable level of error.     

Anti‐inflammatory and immune‐modulatory impacts of berberine on activation of autoreactive T cells in autoimmune inflammation

Autoreactive inflammatory CD4⁺ T cells, such as T helper (Th)1 and Th17 subtypes, have been found to associate with the pathogenesis of autoimmune disorders. On the other hand, CD4⁺ Foxp3⁺ T regulatory (Treg) cells are crucial for the immune tolerance and have a critical role in the suppression of the excessive immune and inflammatory response promoted by these Th cells. In contrast, dendritic cells (DCs) and macrophages are immune cells that through their inflammatory functions promote autoreactive T‐cell responses in autoimmune conditions. In recent years, there has been increasing attention to exploring effective immunomodulatory or anti‐inflammatory agents from the herbal collection of traditional medicine. Berberine, an isoquinoline alkaloid, is one of the main active ingredients extracted from medicinal herbs and has been shown to exert various biological and pharmacological effects that are suggested to be mainly attributed to its anti‐inflammatory and immunomodulatory properties. Several lines of experimental study have recently investigated the therapeutic potential of berberine for treating autoimmune conditions in animal models of human autoimmune diseases. Here, we aimed to seek mechanisms underlying immunomodulatory and anti‐inflammatory effects of berberine on autoreactive inflammatory responses in autoimmune conditions. Reported data reveal that berberine can directly suppress functions and differentiation of pro‐inflammatory Th1 and Th17 cells, and indirectly decrease Th cell‐mediated inflammation through modulating or suppressing other cells assisting autoreactive inflammation, such as Tregs, DCs and macrophages.     

Cadherin Expression,Vectorial Active Transport,and Metallothionein Isoform 3 Mediated EMT/MET Responses in Cultured Primary and Immortalized Human Proximal Tubule Cells

Background

Cultures of human proximal tubule cells have been widely utilized to study the role of EMT in renal disease. The goal of this study was to define the role of growth media composition on classic EMT responses, define the expression of E- and N-cadherin, and define the functional epitope of MT-3 that mediates MET in HK-2 cells.

Methods

Immunohistochemistry, microdissection, real-time PCR, western blotting, and ELISA were used to define the expression of E- and N-cadherin mRNA and protein in HK-2 and HPT cell cultures. Site-directed mutagenesis, stable transfection, measurement of transepithelial resistance and dome formation were used to define the unique amino acid sequence of MT-3 associated with MET in HK-2 cells.

Results

It was shown that both E- and N-cadherin mRNA and protein are expressed in the human renal proximal tubule. It was shown, based on the pattern of cadherin expression, connexin expression, vectorial active transport, and transepithelial resistance, that the HK-2 cell line has already undergone many of the early features associated with EMT. It was shown that the unique, six amino acid, C-terminal sequence of MT-3 is required for MT-3 to induce MET in HK-2 cells.

Conclusions

The results show that the HK-2 cell line can be an effective model to study later stages in the conversion of the renal epithelial cell to a mesenchymal cell. The HK-2 cell line, transfected with MT-3, may be an effective model to study the process of MET. The study implicates the unique C-terminal sequence of MT-3 in the conversion of HK-2 cells to display an enhanced epithelial phenotype.     

Predator dietary response to prey density variation and consequences for cestode transmission

The functional response of predators to prey density variations has previously been investigated in order to understand predation patterns. However, the consequences of functional response on parasite transmission remain largely unexplored. The rodents Microtus arvalis and Arvicola terrestris are the main prey of the red fox Vulpes vulpes in eastern France. These species are intermediate and definitive hosts of the cestode Echinococcus multilocularis. We explored the dietary and contamination responses of the red fox to variations in prey density. The dietary response differed between the two prey species: no response for M. arvalis and a type III-like (sigmoidal) response for A. terrestris that shows possible interference with M. arvalis. The fox contamination response followed a type II shape (asymptotic) for both species. We conclude that fox predation is species specific and E. multilocularis transmission is likely to be regulated by a complex combination of predation and immunologic factors. These results should provide a better understanding of the biological and ecological mechanisms involved in the transmission dynamics of trophically transmitted parasites when multiple hosts are involved. The relevance of the models of parasite transmission should be enhanced if non-linear patterns are taken into account.     

A Bayesian Approach to Joint Mixed‐Effects Models with a Skew‐Normal Distribution and Measurement Errors in Covariates

Summary In recent years, nonlinear mixed‐effects (NLME) models have been proposed for modeling complex longitudinal data. Covariates are usually introduced in the models to partially explain intersubject variations. However, one often assumes that both model random error and random effects are normally distributed, which may not always give reliable results if the data exhibit skewness. Moreover, some covariates such as CD4 cell count may be often measured with substantial errors. In this article, we address these issues simultaneously by jointly modeling the response and covariate processes using a Bayesian approach to NLME models with covariate measurement errors and a skew‐normal distribution. A real data example is offered to illustrate the methodologies by comparing various potential models with different distribution specifications. It is showed that the models with skew‐normality assumption may provide more reasonable results if the data exhibit skewness and the results may be important for HIV/AIDS studies in providing quantitative guidance to better understand the virologic responses to antiretroviral treatment.     

Dielectric Properties of Yeast Cells Expressed With the Motor Protein Prestin

We report on the linear and nonlinear dielectric properties of budding yeast (S. cerevisiae) cells, one strain of which has been genetically modified to express prestin. This motor protein plays a crucial role in the large electromotility exhibited by the outer hair cells of mammalian inner ears. Live cell suspensions exhibit enormous dielectric responses, which can be used to probe metabolic activity, membrane potential, and other properties. The aims of this study are: (1) to compare the dielectric responses of organisms expressing prestin from those of control specimens, and (2) ultimately to further develop dielectric response as a tool to study live cells, proteins, and lipids.     

Functional Organization of the Action Observation Network in Autism: A Graph Theory Approach

Background

The ability to recognize, understand and interpret other’s actions and emotions has been linked to the mirror system or action-observation-network (AON). Although variations in these abilities are prevalent in the neuro-typical population, persons diagnosed with autism spectrum disorders (ASD) have deficits in the social domain and exhibit alterations in this neural network.

Method

Here, we examined functional network properties of the AON using graph theory measures and region-to-region functional connectivity analyses of resting-state fMRI-data from adolescents and young adults with ASD and typical controls (TC).

Results

Overall, our graph theory analyses provided convergent evidence that the network integrity of the AON is altered in ASD, and that reductions in network efficiency relate to reductions in overall network density (i.e., decreased overall connection strength). Compared to TC, individuals with ASD showed significant reductions in network efficiency and increased shortest path lengths and centrality. Importantly, when adjusting for overall differences in network density between ASD and TC groups, participants with ASD continued to display reductions in network integrity, suggesting that also network-level organizational properties of the AON are altered in ASD.

Conclusion

While differences in empirical connectivity contributed to reductions in network integrity, graph theoretical analyses provided indications that also changes in the high-level network organization reduced integrity of the AON.     

Effects of network and dynamical model structure on species persistence in large model food webs

Four models of network structure are combined with models of bioenergetic dynamics to study the role of food web topology and nonlinear dynamics on species coexistence in complex ecological networks. Network models range from the highly structured niche model to loosely constrained energetically feasible random networks. Bioenergetic models differ in how they represent primary production, functional responses, and consumption by generalists. Network structure weakly influenced the ability of species to coexist. Species persistence is strongly affected by functional responses and generalists’ consumption rates but weakly affected by models and amounts of primary production. Despite these generalities, specific mechanisms that determine persistence under one dynamical regime, such as top-down control by consumers, may play an insignificant role under different dynamical conditions. Future research is needed to strengthen the weak empirical basis for various functional forms and parameter values that strongly influence whether species can coexist in complex food webs. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.