Spatially modulated ablation driven by chaotic attractors in human lung epithelial cancer cells.

A significant modification in photoinduced energy transfer in cancer cells is reported by the assistance of a dynamic modulation of the beam size of laser irradiation. Human lung epithelial cancer cells in monolayer form were studied. In contrast to the quantum and thermal ablation effect promoted by a standard focused Gaussian beam, a spatially modulated beam can caused around 15% of decrease in the ablation threshold and formation of a ring-shaped distribution of the photothermal transfer effect. Optical irradiation was conducted in A549 cells by a 532 nm single-beam emerging from a Nd:YVO4 system. Ablation effects derived from spatially modulated convergent waves were controlled by an electrically focus-tunable lens. The proposed chaotic behavior of the spatial modulation followed an Arneodo chaotic oscillator. Fractional dynamic thermal transport was analyzed in order to describe photoenergy in propagation through the samples. Immediate applications of chaos theory for developing phototechnology devices driving biological functions or phototherapy treatments can be considered.

Metastasis Models: Thermodynamics and Complexity.

The thermodynamic formalism of nonequilibrium systems together with the theory of complex systems and systems biology offer an appropriate theoretical framework to explain the complexity observed at the macroscopic level in physiological phenomena. In turn, they allow the establishment of an appropriate conceptual and operational framework to address the study of phenomena such as the emergence and evolution of cancer.This chapter is organized as follows: In Subheading 1, an integrated vision of these disciplines is offered for the characterization of the emergence and evolution of cancer, seen as a nonlinear dynamic system, temporally and spatially self-organized out of thermodynamic equilibrium. The development of the various mathematical models and different techniques and approaches used in the characterization of cancer metastasis is presented in Subheading 2. Subheading 3 is devoted to the time course of cancer metastasis, with particular emphasis on the epithelial-mesenchymal transition (EMT henceforth) as well as chronotherapeutic treatments. In Subheading 4, models of the spatial evolution of cancer metastasis are presented. Finally, in Subheading 5, some conclusions and remarks are presented.

The Effect of Auditory Stimulation on the Nonlinear Dynamics of Heart Rate: The Impact of Emotional Valence and Arousal.

Background: Although it is known that sound exposure evokes changes in autonomic activity, the effects of noise and music on the nonlinear behavior of heart rate fluctuations remain poorly understood and controversial. This study aims to assess the influence of sound subjective emotional valence and arousal on the nonlinear characteristics of the autonomic nervous system during passive listening. Methods: In this study, 42 subjects listened to four sounds: (1) white noise, (2) road traffic noise, (3) excitatory music, and (4) a lullaby. The experiment consisted of two consecutive sessions: 5 minutes of rest, followed by 5 minutes of listening. RR intervals were recorded during both sessions. The following linear and nonlinear heart rate variability (HRV) indices were computed: Standard deviation of NN (SDNN), The root mean square of successive differences between normal heartbeats (RMSSD), F, high frequency (HF), approximate entropy (ApEn) and sample entropy (SampEn), correlation dimension (D2), Poincaré plot indices (SD1, SD2), fractal scaling exponents (alpha1, alpha2), and recurrence plot indices (mean line length [Lmean], maximum line length [Lmax], determinism [DET], laminarity [LAM], maximal vertical length [Vmax], trapping time [TT], Shannon entropy of line length distribution [ShanEn]). Results: Excitatory music was associated with a significant decrease in SDNN (from 47.3 ± 3.59 to 38.31 ± 3.16, P < 0.01), RMSSD (from 51.07 ± 4.75 to 42.53 ± 3.9, P < 0.05), HF (from 1516.26 ± 245.74 to 884.07 ± 183.44, P < 0.001), and low frequency (LF; from 973.33 ± 176.09 to 760.28 ± 150.35, P < 0.05). Excitatory music exposure induced significant increases in DET (P < 0.01), SD1 (P < 0.05), and SD2 (P < 0.05), but changes in detrended fluctuation analysis (DFA), SampEn, and D2 were nonsignificant. Traffic noise, white noise, and the lullaby did not cause significant changes in the measures of HRV. Conclusion: Presentation of excitatory music that evokes strong negative emotions elicits a prominent decrease in respiratory sinus arrhythmia. Poincaré plot and recurrence plot measures possess high sensitivity to excitatory music. Contrary to previous studies, we did not find the effects of relaxing music on HRV.

On-line monitoring of total sugar during kombucha fermentation process by near-infrared spectroscopy: Comparison of linear and non-linear multiple calibration methods.

Kombucha is widely recognized for its health benefits, and it facilitates high-quality transformation and utilization of tea during the fermentation process. Implementing on-line monitoring for the kombucha production process is crucial to promote the valuable utilization of low-quality tea residue. Near-infrared (NIR) spectroscopy, together with partial least squares (PLS), backpropagation neural network (BPANN), and their combination (PLS-BPANN), were utilized in this study to monitor the total sugar of kombucha. In all, 16 mathematical models were constructed and assessed. The results demonstrate that the PLS-BPANN model is superior to all others, with a determination coefficient (R2p) of 0.9437 and a root mean square error of prediction (RMSEP) of 0.8600 g/L and a good verification effect. The results suggest that NIR coupled with PLS-BPANN can be used as a non-destructive and on-line technique to monitor total sugar changes.

Nonlinear analysis of heart rate variability signals in meditative state: a review and perspective.

INTRODUCTION: In recent times, an upsurge in the investigation related to the effects of meditation in reconditioning various cardiovascular and psychological disorders is seen. In majority of these studies, heart rate variability (HRV) signal is used, probably for its ease of acquisition and low cost. Although understanding the dynamical complexity of HRV is not an easy task, the advances in nonlinear analysis has significantly helped in analyzing the impact of meditation of heart regulations. In this review, we intend to present the various nonlinear approaches, scientific findings and their limitations to develop deeper insights to carry out further research on this topic. RESULTS: Literature have shown that research focus on nonlinear domain is mainly concentrated on assessing predictability, fractality, and entropy-based dynamical complexity of HRV signal. Although there were some conflicting results, most of the studies observed a reduced dynamical complexity, reduced fractal dimension, and decimated long-range correlation behavior during meditation. However, techniques, such as multiscale entropy (MSE) and multifractal analysis (MFA) of HRV can be more effective in analyzing non-stationary HRV signal, which were hardly used in the existing research works on meditation. CONCLUSIONS: After going through the literature, it is realized that there is a requirement of a more rigorous research to get consistent and new findings about the changes in HRV dynamics due to the practice of meditation. The lack of adequate standard open access database is a concern in drawing statistically reliable results. Albeit, data augmentation technique is an alternative option to deal with this problem, data from adequate number of subjects can be more effective. Multiscale entropy analysis is scantily employed in studying the effect of meditation, which probably need more attention along with multifractal analysis. METHODS: Scientific databases, namely PubMed, Google Scholar, Web of Science, Scopus were searched to obtain the literature on "HRV analysis during meditation by nonlinear methods". Following an exclusion criteria, 26 articles were selected to carry out this scientific analysis.

Uncertainty quantification in the assessment of human exposure to pulsed or multi-frequency fields.

Objective:pulsed fields or waveforms with multi-frequency content have to be assessed with suitable methods. This paper deals with the uncertainty quantification associated to these methods.Approach:among all possible approaches, the weighted peak method (WPM) is widely employed in standards and guidelines, therefore, in this paper, we consider its implementation both in time domain and frequency domain. For the uncertainty quantification the polynomial chaos expansion theory is used. By means of a sensitivity analysis, for several standard waveforms, the parameters with more influence on the exposure index are identified and their sensitivity indices are quantified. The output of the sensitivity analysis is used to set up a parametric analysis with the aim of evaluating the uncertainty propagation of the analyzed methods and, finally, also several measured waveforms generated by a welding gun are tested.Main results:it is shown that the time domain implementation of the weighted peak method provides results in agreement with the basilar mechanisms of electromagnetic induction and electrostimulation. On the opposite, the WPM in frequency domain is found to be too sensitive to parameters that should not influence the exposure index because its weight function includes sharp variations of the phase centered on real zeros and poles. To overcome this issue, a new definition for the phase of the weight function in frequency domain is proposed.Significance:it is shown that the time domain implementation of the WPM is the more accurate and precise. The standard WPM in frequency domain has some issues that can be avoided with the proposed modification of the phase definition of the weight function. Finally, all the codes used in this paper are hosted on a GitHub and can be freely accessed athttps://github.com/giaccone/wpm_uncertainty.

Stable isotope and trace element analyses with non-linear machine-learning data analysis improved coffee origin classification and marker selection.

BACKGROUND: This study investigated the geographical origin classification of green coffee beans from continental to country and regional levels. An innovative approach combined stable isotope and trace element analyses with non-linear machine learning data analysis to improve coffee origin classification and marker selection. Specialty green coffee beans sourced from three continents, eight countries, and 22 regions were analyzed by measuring five isotope ratios (δ13 C, δ15 N, δ18 O, δ2 H, and δ34 S) and 41 trace elements. Partial least squares discriminant analysis (PLS-DA) was applied to the integrated dataset for origin classification. RESULTS: Origins were predicted well at the country level and showed promise at the regional level, with discriminating marker selection at all levels. However, PLS-DA predicted origin poorly at the continental and Central American regional levels. Non-linear machine learning techniques improved predictions and enabled the identification of a higher number of origin markers, and those that were identified were more relevant. The best predictive accuracy was found using ensemble decision trees, random forest and extreme gradient boost, with accuracies of up to 0.94 and 0.89 for continental and Central American regional models, respectively. CONCLUSION: The potential for advanced machine learning models to improve origin classification and the identification of relevant origin markers was demonstrated. The decision-tree-based models were superior with their embedded variable identification features and visual interpretation. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Reflections on the fundamental nature of homeopathic potencies: Plasma, Langmuir Adsorption and Vitalism

There is a pressing need to develop methods and approaches that will identify the fundamental nature of homeopathic potencies. Aims: To bring together recent basic research on potencies, especially that using solvatochromic dyes, and to supplement these results with reliable observations made by Hahnemann and his contemporaries from the very beginnings of homeopathy, together with a detailed examination of the process of trituration and succussion coupled to dilution, in order to significantly limit the number of possible explanations as to the identity of potencies. Methodology: A mixture of lab based and literature studies such that as far as possible all verified and substantiated observations about homeopathic potencies have been examined. Results and Discussion: An understanding of the fundamental nature of homeopathic potencies that includes all known and accepted observations (in vitro, in vivoand clinical) is not realistic without embracing hypotheses involving the emergent properties of complex systems and in particular, vitalistic concepts. Using a vitalistic model it is possible to explain a wide range of seemingly unrelated phenomena -such as the polarising effect of potencies on solvatochromic dyes, the ability to use a range of materials such as water, lactose and cellulose as carriers of potencies, the administering of potencies by olfaction, the antidoting effect of camphor on potency action, the non-linear dependence of potency strength on volume as well as succussion level, the oscillatory behaviour of potencies and experimenter/observer/practitioner effects. Conclusion: A hypothesis in which homeopathic potencies can be seen as self-actuating and autonomous plasma generated by trituration and/or succussion and carried according to Langmuir adsorption models fits the known observations about potencies.

Chaos Adaptive Particle Swarm for Physical Exercise Health Assessment.

Particle crowd algorithmic rule is a mayor examination hotspot in the authentic optimization algorithmic rule respond. Based on the PSO algorithmic rule to make optimal the RBFNN example, an amended order of nonlinear adaptable laziness power supported on the contest of population variegation is intended to extend the fixedness of population unlikeness performance and hunt capabilities to preclude the algorithmic rule from dripping into a topical extreme point prematurely, thereby further improving the prophecy correctness. Simulation experience shows that the amended PSO-RBFNN standard has open advantageous in the fixedness and sharp convergency of the prognosis proceed. In fashion to reprove the justness of reverse kinematics of robots with composite make and supercilious degrees of liberty, an amended adaptative suffix abound optimization (IAPSO) is spoken. First, the motoric equality of the 6-DOF strength-example avaricious robot design is established by the amended DH (Denavit-Hartenberg) argument course; second, on the base of the existent morsel abound algorithmic rule, the population Manhattan ceremoniousness is interested to lead the maneuver condition of the population in aqiqiy measure. And bound the adaptative lore substitute accordingly to the dissimilar maneuver possession and then adopt distinct site and hurry update modes; lastly, the fitness province with handicap substitute is present to trial the honest-prick and extended course transposition of the robot mold, and the delusion is not joint product major than 0.005 rad. The feint inference shows that the established kinematics shape is chasten, and the amended algorithmic program captures into recital the nicety, uniqueness, and velocity of the inverted resolution of the existent PSO algorithmic program, as well as higher deliverance truths. We conduct an experiment on the Brazilian jiu-jitsu. The results have clearly shown the advantage of our method.

Nonlinear visuoauditory integration in the mouse superior colliculus.

Sensory information from different modalities is processed in parallel, and then integrated in associative brain areas to improve object identification and the interpretation of sensory experiences. The Superior Colliculus (SC) is a midbrain structure that plays a critical role in integrating visual, auditory, and somatosensory input to assess saliency and promote action. Although the response properties of the individual SC neurons to visuoauditory stimuli have been characterized, little is known about the spatial and temporal dynamics of the integration at the population level. Here we recorded the response properties of SC neurons to spatially restricted visual and auditory stimuli using large-scale electrophysiology. We then created a general, population-level model that explains the spatial, temporal, and intensity requirements of stimuli needed for sensory integration. We found that the mouse SC contains topographically organized visual and auditory neurons that exhibit nonlinear multisensory integration. We show that nonlinear integration depends on properties of auditory but not visual stimuli. We also find that a heuristically derived nonlinear modulation function reveals conditions required for sensory integration that are consistent with previously proposed models of sensory integration such as spatial matching and the principle of inverse effectiveness.

Robust multi-input multi-output adaptive fuzzy terminal sliding mode control of deep brain stimulation in Parkinson's disease: a simulation study.

Deep brain stimulation (DBS) has become an effective therapeutic solution for Parkinson's disease (PD). Adaptive closed-loop DBS can be used to minimize stimulation-induced side effects by automatically determining the stimulation parameters based on the PD dynamics. In this paper, by modeling the interaction between the neurons in populations of the thalamic, the network-level modulation of thalamic is represented in a standard canonical form as a multi-input multi-output (MIMO) nonlinear first-order system with uncertainty and external disturbances. A class of fast and robust MIMO adaptive fuzzy terminal sliding mode control (AFTSMC) has been presented for control of membrane potential of thalamic neuron populations through continuous adaptive DBS current applied to the thalamus. A fuzzy logic system (FLS) is used to estimate the unknown nonlinear dynamics of the model, and the weights of FLS are adjusted online to guarantee the convergence of FLS parameters to optimal values. The simulation results show that the proposed AFTSMC not only significantly produces lower tracking errors in comparison with the classical adaptive fuzzy sliding mode control (AFSMC), but also makes more robust and reliable outputs. The results suggest that the proposed AFTSMC provides a more robust and smooth control input which is highly desirable for hardware design and implementation.

Cochlear outer hair cell electromotility enhances organ of Corti motion on a cycle-by-cycle basis at high frequencies in vivo.

Mammalian hearing depends on an amplification process involving prestin, a voltage-sensitive motor protein that enables cochlear outer hair cells (OHCs) to change length and generate force. However, it has been questioned whether this prestin-based somatic electromotility can operate fast enough in vivo to amplify cochlear vibrations at the high frequencies that mammals hear. In this study, we measured sound-evoked vibrations from within the living mouse cochlea and found that the top and bottom of the OHCs move in opposite directions at frequencies exceeding 20 kHz, consistent with fast somatic length changes. These motions are physiologically vulnerable, depend on prestin, and dominate the cochlea's vibratory response to high-frequency sound. This dominance was observed despite mechanisms that clearly low-pass filter the in vivo electromotile response. Low-pass filtering therefore does not critically limit the OHC's ability to move the organ of Corti on a cycle-by-cycle basis. Our data argue that electromotility serves as the primary high-frequency amplifying mechanism within the mammalian cochlea.

The Impact of the Declaration of the State of Emergency on the Spread of COVID-19: A Modeling Analysis.

When encountering the outbreak and early spreading of COVID-19, the Government of Japan imposed gradually upgraded restriction policies and declared the state of emergency in April 2020 for the first time. To evaluate the efficacy of the countering strategies in different periods, we constructed a SEIADR (susceptible-exposed-infected-asymptomatic-documented-recovered) model to simulate the cases and determined corresponding spreading coefficients. The effective reproduction number R t was obtained to evaluate the measures controlling the COVID-19 conducted by the Government of Japan during different stages. It was found that the strict containing strategies during the state of emergency period drastically inhibit the COVID-19 trend. R t was decreased to 1.1123 and 0.8911 in stages 4 and 5 (a state of emergency in April and May 2020) from 3.5736, 2.0126, 3.0672 in the previous three stages when the containing strategies were weak. The state of emergency was declared again in view of the second wave of massive infections in January 2021. We estimated the cumulative infected cases and additional days to contain the COVID-19 transmission for the second state of emergency using this model. R t was 1.028 which illustrated that the strategies were less effective than the previous state of emergency. Finally, the overall infected population was predicted using combined isolation and testing intensity; the effectiveness and the expected peak time were evaluated. If using the optimized control strategies in the current stage, the spread of COVID-19 in Japan could be controlled within 30 days. The total confirmed cases should reduce to less than 4.2 × 105 by April 2021. This model study suggested stricter isolating measures may be required to shorten the period of the state of emergency.

Disengagement of Motor Cortex during Long-Term Learning Tracks the Performance Level of Learned Movements.

Not all movements require the motor cortex for execution. Intriguingly, dependence on motor cortex of a given movement is not fixed, but instead can dynamically change over the course of long-term learning. For instance, rodent forelimb movements that initially require motor cortex can become independent of the motor cortex after an extended period of training. However, it remains unclear whether long-term neural changes rendering the motor cortex dispensable are a simple function of the training length. To address this issue, we trained mice (both male and female) to perform two distinct forelimb movements, forward versus downward reaches with a joystick, concomitantly over several weeks, and then compared the involvement of the motor cortex between the two movements. Most mice achieved different levels of motor performance between the two movements after long-term training. Of the two movements, the one that achieved higher trial-to-trial consistency (i.e., consistent-direction movement) was significantly less affected by inactivation of motor cortex than the other (i.e., variable-direction movement). Two-photon calcium imaging of motor cortical neurons revealed that the consistent-direction movement activates fewer neurons, producing weaker and less consistent population activity than the variable-direction movement. Together, the motor cortex was less engaged and less necessary for learned movements that achieved higher levels of consistency. Thus, the long-term reorganization of neural circuits that frees the motor cortex from the learned movement is not a mere function of training length. Rather, this reorganization tracks the level of motor performance that the animal achieves during training.SIGNIFICANCE STATEMENT Long-term training of a movement reshapes motor circuits, disengaging motor cortex potentially for automatized execution of the learned movement. Acquiring new motor skills often involves learning of multiple movements (e.g., forehand and backhand strokes when learning tennis), but different movements do not always improve at the same time nor reach the same level of proficiency. Here we showed that the involvement of motor cortex after long-term training differs between similar yet distinct movements that reached different levels of expertise. Motor cortex was less engaged and less necessary for the more proficient movement. Thus, disengagement of motor cortex is not a simple function of training time, but instead tracks the level of expertise of a learned movement.

Theoretical Performance of Nonlinear Mixed-Effect Models Incorporating Saliva as an Alternative Sampling Matrix for Therapeutic Drug Monitoring in Pediatrics: A Simulation Study.

BACKGROUND: Historically, pharmacokinetic (PK) studies and therapeutic drug monitoring (TDM) have relied on plasma as a sampling matrix. Noninvasive sampling matrices, such as saliva, can reduce the burden on pediatric patients. The variable plasma-saliva relationship can be quantified using population PK models (nonlinear mixed-effect models). However, criteria regarding acceptable levels of variability in such models remain unclear. In this simulation study, the authors aimed to propose a saliva TDM evaluation framework and evaluate model requirements in the context of TDM, with gentamicin and lamotrigine as model compounds. METHODS: Two population pharmacokinetic models for gentamicin in neonates and lamotrigine in pediatrics were extended with a saliva compartment including a delay constant (kSALIVA), a saliva:plasma ratio, and between-subject variability (BSV) on both parameters. Subjects were simulated using a realistic covariate distribution. Bayesian maximum a posteriori TDM was applied to assess the performance of an increasing number of TDM saliva samples and varying levels of BSV and residual variability. Saliva TDM performance was compared with plasma TDM performance. The framework was applied to a known voriconazole saliva model as a case study. RESULTS: TDM performed using saliva resulted in higher target attainment than no TDM, and a residual proportional error <25% on saliva observations led to saliva TDM performance comparable with plasma TDM. BSV on kSALIVA did not affect performance, whereas increasing BSV on saliva:plasma ratios by >25% for gentamicin and >50% for lamotrigine reduced performance. The simulated target attainment for voriconazole saliva TDM was >90%. CONCLUSIONS: Saliva as an alternative matrix for noninvasive TDM is possible using nonlinear mixed-effect models combined with Bayesian optimization. This article provides a workflow to explore TDM performance for compounds measured in saliva and can be used for evaluation during model building.

Chaos in memory function of sleep: A nonlinear dynamical analysis in thalamocortical study.

Studying the dynamical behaviors of neuronal models may help in better understanding of real nervous system. In addition, it can help researchers to understand some specific phenomena in neuronal system. The thalamocortical network is made of neurons in the thalamus and cortex. In it, the memory function is consolidated in sleep by creating up and down state oscillations (1 Hz) and fast (13-17 Hz) - slow (8-12 Hz) spindles. Recently, a nonlinear biological model for up-down oscillations and fast-slow spindles of the thalamocortical network has been proposed. In this research, the power spectral for the fast-slow spindle of the model is extracted. Dynamical properties of the model, such as the bifurcation diagrams, and attractors are investigated. The results show that the variation of the synaptic power between the excitatory neurons of the cortex and the reticular neurons in the thalamus changes the spindles' activity. According to previous experimental findings, it is an essential rule for consolidating the memory function during sleep. It is also pointed out that when the fast-slow spindles of the brain increase, the dynamics of the thalamocortical system tend to chaos.

LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network.

An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID-19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development. LUNAR can well integrate various topological structure information in heterogeneous networks, and skillfully combine attention mechanisms to reflect the importance of neighborhood information of different types of nodes, improving the interpretability of the model. The area under the curve(AUC) of the model is 0.949 and the accurate recall curve (AUPR) is 0.866 using 10-fold cross-validation. These two performance indexes show that the model has superior predictive performance. Besides, some of the drugs screened out by our model have appeared in some clinical studies to further illustrate the effectiveness of the model.

Spectrally specific temporal analyses of spike-train responses to complex sounds: A unifying framework.

Significant scientific and translational questions remain in auditory neuroscience surrounding the neural correlates of perception. Relating perceptual and neural data collected from humans can be useful; however, human-based neural data are typically limited to evoked far-field responses, which lack anatomical and physiological specificity. Laboratory-controlled preclinical animal models offer the advantage of comparing single-unit and evoked responses from the same animals. This ability provides opportunities to develop invaluable insight into proper interpretations of evoked responses, which benefits both basic-science studies of neural mechanisms and translational applications, e.g., diagnostic development. However, these comparisons have been limited by a disconnect between the types of spectrotemporal analyses used with single-unit spike trains and evoked responses, which results because these response types are fundamentally different (point-process versus continuous-valued signals) even though the responses themselves are related. Here, we describe a unifying framework to study temporal coding of complex sounds that allows spike-train and evoked-response data to be analyzed and compared using the same advanced signal-processing techniques. The framework uses a set of peristimulus-time histograms computed from single-unit spike trains in response to polarity-alternating stimuli to allow advanced spectral analyses of both slow (envelope) and rapid (temporal fine structure) response components. Demonstrated benefits include: (1) novel spectrally specific temporal-coding measures that are less confounded by distortions due to hair-cell transduction, synaptic rectification, and neural stochasticity compared to previous metrics, e.g., the correlogram peak-height, (2) spectrally specific analyses of spike-train modulation coding (magnitude and phase), which can be directly compared to modern perceptually based models of speech intelligibility (e.g., that depend on modulation filter banks), and (3) superior spectral resolution in analyzing the neural representation of nonstationary sounds, such as speech and music. This unifying framework significantly expands the potential of preclinical animal models to advance our understanding of the physiological correlates of perceptual deficits in real-world listening following sensorineural hearing loss.

Control of Functional Electrical Stimulation Systems Using Simultaneous Pulse Width, Amplitude, and Frequency Modulations.

BACKGROUND: One significant challenge of developing a controller for functional electrical stimulation systems is the time-variant nonlinear dynamics of the neuromusculoskeletal system. In the conventional methods, the stimulation intensity is adjusted by a controller; however, the stimulation frequency is always constant. The previous studies have shown that the stimulation frequency is effective in fatigue formation. OBJECTIVES: A simultaneous modulation of the stimulation intensity and frequency is proposed to improve the joint controllability and muscle endurance. The presented control method determines pulse width (PW), amplitude, and frequency of the electrical stimulation signal, synchronously. Three different modulations are applied for control of the knee joint to show the superiority of the proposed modulation. METHODS: The stimulation intensity is controlled by the PW and pulse amplitude of the electrical signal using an adaptive fuzzy terminal sliding mode controller and a fuzzy logic controller, respectively. Also, a fuzzy logic controller is applied to adjust the stimulation frequency. The proposed method is utilized to control the knee joint movement using quadriceps femoris muscles for ten paraplegic subjects. RESULTS: Two different test protocols are defined to evaluate the presented method: A protocol for testing the controllability and another protocol for evaluating the produced muscle endurance. The average value of the root mean square of the tracking error was 6.4° for the proposed method which is 5.1° and 9.6° less than PW modulation and synchronous PW and amplitude modulation, respectively. The average time duration of the knee full extension was 96 sec for the proposed method which is 17 and 26 sec more than PW modulation and synchronous PW and amplitude modulation, respectively. CONCLUSIONS: The experimental results show that control performance and tracking ability of the joint reference trajectory are improved by using the simultaneous modulation of PW, amplitude, and frequency.

Z-scan method to measure the nonlinear optical behavior of cells for evaluating the cytotoxic effects of chemotherapy and hyperthermia treatments.

The effects of new treatments must be investigated in vitro before using clinically or in vivo. The aim of this study was to introduce the Z-scan technique as a fast, accurate, inexpensive, and safe in vitro method to distinguish the cytotoxic effects of various treatments. C6 and OLN-93 cell lines were prepared and treated with Temozolomide (TMZ), radiofrequency hyperthermia (HT), and chemo-hyperthermia (HT+TMZ). The cytotoxic effects of different treatments on both cell lines were evaluated using colony formation assay and Z-scan method. The results of colony assay showed that the surviving fraction (SF) of C6 cells treated with TMZ, HT, and HT + TMZ were significantly decreased compared to the control group. Whereas, hyperthermia treatment had no significant effect on the SF of OLN-93 cells. The results of Z-scan technique indicated that the control group of C6 cells had the negative nonlinear refractive index (n2). Whereas, the C6 cells treated with HT, TMZ, and HT + TMZ had the positive n2 index. The sign of n2 index in the control and HT groups of OLN-93 cells was positive but treatment of cells with TMZ and HT + TMZ changed the sign of it. Moreover, with increasing the cytotoxic effects of different treatments, the SF value of both cell lines decreased and the magnitude of n2 index increased. The results of Z-scan technique were completely in line with the results of colony assay. Therefore, Z-scan method could distinguish the cytotoxic effects of various treatments by examining the nonlinear optical properties of the samples.