Human operator dynamics in manual tracking systems with auditory input

Response characteristics of human operators in manual pursuit tracking with auditory input are investigated. The human operator hears in his left ear a sound whose frequency varies in proportion to an external random signal. At the same time, he hears in his right ear another sound whose frequency varies in proportion to the angle of a control lever of a potentiometer. The operator controls the angle of the lever so that the frequencies of the sounds in both ears remain as close as possible. The dynamics of the human operator is studied by assuming a manmachine system whose input is the external signal and whose output is the voltage of the potentiometer. A learning identification method proposed by one of the authors is used to calculate the weighting function of the man-machine system, which is displayed on a CRT screen in real time. During the tracking task, the skin potential activity (SPA) is measured as an index of arousal of the operator.     

Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making

Classic reinforcement learning (RL) theories cannot explain human behavior in the absence of external reward or when the environment changes. Here, we employ a deep sequential decision-making paradigm with sparse reward and abrupt environmental changes. To explain the behavior of human participants in these environments, we show that RL theories need to include surprise and novelty, each with a distinct role. While novelty drives exploration before the first encounter of a reward, surprise increases the rate of learning of a world-model as well as of model-free action-values. Even though the world-model is available for model-based RL, we find that human decisions are dominated by model-free action choices. The world-model is only marginally used for planning, but it is important to detect surprising events. Our theory predicts human action choices with high probability and allows us to dissociate surprise, novelty, and reward in EEG signals.     

Simulation of bone adaptive remodeling using a stochastic process as loading history

In this simulation study for bone adaptive remodeling, loading conditions are described as stochastic processes to catch the unpredictable characteristics of daily physical activities, which are observed to be closely related with bone adaptive remodeling. This will not only eliminate the necessity of arbitrary choices for loading conditions, but also generate greater flexibility for simulations of bone adaptive remodeling. The sensitivity of simulation outcomes to the parameters in the simulation algorithm was examined by applying stochastic loading conditions on finite element models of simplified spine structures. In this way, the limitations induced by simplifying loading conditions into constant or cyclic loads can be avoided and, potentially, more clinical observations could be accommodated when more comprehensive finite element models are available.     

Biodegradation of nitrobenzene by a sequential anaerobic-aerobic process

Nitrobenzene was completely degraded by mixed cultures using a sequential anaerobic-aerobic treatment process. Under anaerobic conditions in a fixed-bed column aniline was formed from nitrobenzene through gratuitous reduction by cells of sewage sludge. This reaction was accelerated by the addition of glucose. Complete mineralization of aniline was accomplished by subsequent aerobic treatment using activated sludge as inoculum. The maximum degradation rate of nitrobenzene (4.5 mM) in the two-stage system was 552 mg l^–1d^–1, referring to 154 mg of nitrobenzene per gram of glucose. In a second experimental phase glucose as cosubstrate and H-donor was replaced by synthetic waste containing ethanol, methanol, isopropanol and acetone. Again, nitrobenzene (1.9 mM) was completely degraded (maximum degradation rate of 237 mg l^–d^–1, referring to 251 mg per gram of solvents). The major advantage of the described two-stage process is that the reduction of nitrobenzene by anaerobic pretreatment drastically reduces emission by stripping during aerobic treatment.Abbreviations HRT hydraulic retention time - OD₅₄₆ optical density at 546 nm     

Analysis of membrane fusion as a two-state sequential process: evaluation of the stalk model

We propose a model that accounts for the time courses of PEG-induced fusion of membrane vesicles of varying lipid compositions and sizes. The model assumes that fusion proceeds from an initial, aggregated vesicle state ((A) membrane contact) through two sequential intermediate states (I(1) and I(2)) and then on to a fusion pore state (FP). Using this model, we interpreted data on the fusion of seven different vesicle systems. We found that the initial aggregated state involved no lipid or content mixing but did produce leakage. The final state (FP) was not leaky. Lipid mixing normally dominated the first intermediate state (I(1)), but content mixing signal was also observed in this state for most systems. The second intermediate state (I(2)) exhibited both lipid and content mixing signals and leakage, and was sometimes the only leaky state. In some systems, the first and second intermediates were indistinguishable and converted directly to the FP state. Having also tested a parallel, two-intermediate model subject to different assumptions about the nature of the intermediates, we conclude that a sequential, two-intermediate model is the simplest model sufficient to describe PEG-mediated fusion in all vesicle systems studied. We conclude as well that a fusion intermediate "state" should not be thought of as a fixed structure (e.g., "stalk" or "transmembrane contact") of uniform properties. Rather, a fusion "state" describes an ensemble of similar structures that can have different mechanical properties. Thus, a "state" can have varying probabilities of having a given functional property such as content mixing, lipid mixing, or leakage. Our data show that the content mixing signal may occur through two processes, one correlated and one not correlated with leakage. Finally, we consider the implications of our results in terms of the "modified stalk" hypothesis for the mechanism of lipid pore formation. We conclude that our results not only support this hypothesis but also provide a means of analyzing fusion time courses so as to test it and gauge the mechanism of action of fusion proteins in the context of the lipidic hypothesis of fusion.     

Covert tracking: a combined ERP and fixational eye movement study

Attention can be directed to particular spatial locations, or to objects that appear at anticipated points in time. While most work has focused on spatial or temporal attention in isolation, we investigated covert tracking of smoothly moving objects, which requires continuous coordination of both. We tested two propositions about the neural and cognitive basis of this operation: first that covert tracking is a right hemisphere function, and second that pre-motor components of the oculomotor system are responsible for driving covert spatial attention during tracking. We simultaneously recorded event related potentials (ERPs) and eye position while participants covertly tracked dots that moved leftward or rightward at 12 or 20°/s. ERPs were sensitive to the direction of target motion. Topographic development in the leftward motion was a mirror image of the rightward motion, suggesting that both hemispheres contribute equally to covert tracking. Small shifts in eye position were also lateralized according to the direction of target motion, implying covert activation of the oculomotor system. The data addresses two outstanding questions about the nature of visuospatial tracking. First, covert tracking is reliant upon a symmetrical frontoparietal attentional system, rather than being right lateralized. Second, this same system controls both pursuit eye movements and covert tracking.     

The protein arrestin as a regulator of the phototransduction process and as a factor of pathogenesis of the eye diseases

In the first part of the paper, the results of the investigation of the rhodopsin arrestin interaction are presented. The results were mainly obtained with the technique of the selective labelling of the rhodopsin and arrestin SH-groups and the rhodopsin limited proteolysis. These results are discussed in the frame of the latest data on the three-dimensional structure of arrestin. In the second part of the paper, results of the antigenic properties of arrestin (S-antigen) and its role in the pathogenesis of the retina diseases are summarized. The data on the role of the autoimmune processes in the pathogenesis of diabetic retinopathy are presented. We have also described the results of the use of the elaborated technique of the immune diagnostics in the prognosis of the diabetic retinopathy and retinopathy of the premature babies.     

Micro sequential injection system as the interfacing device for process analytical applications

It is an important and desirable capability to be able to control the quality and quantity of biological product by maintaining and adjusting bioreactor performance throughout its production duration. Amino acids are the building blocks of proteins. Scientists will need to ensure sufficient supply of amino acids as the substrates in the bioreactors as well as to control the excess level of undesirable free amino acid byproducts to maintain an optimum growth environment for cell culture. We have developed a compact and robust sample preparation platform capable of interfacing with analytical instruments to achieve bioreactor amino acids monitoring. We demonstrated the feasibility of this concept by incorporating an automatic amino acid sample preparation protocol to a micro sequential injection (μSI) system connected to an ultra‐performance liquid chromatography system for real‐time, at‐line amino acid separation, and quantitation. The μSI system was configured into a “platform‐like” sample preparation system that is able to accommodate future wet chemistry‐type sample preparations. Its real‐time amino acid results can be readily available to bioprocess scientists for quick decision making and design of their next experiment. Potential automatic feedback control mechanisms can be established through trigger events based on predetermined analytical signal thresholds so the system can communicate with facility infrastructure to control bioreactors in near real‐time fashion. The proposed μSI system described in this paper can be widely used as an automatic sample preparation system connected to the front‐end of analytical instruments to enable process analytical technology applications. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:607–613, 2015     

Crossmodal visual input for odor tracking during fly flight

Flies generate robust and high-performance olfactory and visual behaviors. Adult fruit flies can distinguish small differences in odor concentration across antennae separated by less than 1 mm [1], and a single olfactory sensory neuron is sufficient for near-normal gradient tracking in larvae [2]. During flight a male housefly chasing a female executes a corrective turn within 40 ms after a course deviation by its target [3]. The challenges imposed by flying apparently benefit from the tight integration of unimodal sensory cues. Crossmodal interactions reduce the discrimination threshold for unimodal memory retrieval by enhancing stimulus salience [4], and dynamic crossmodal processing is required for odor search during free flight because animals fail to locate an odor source in the absence of rich visual feedback [5]. The visual requirements for odor localization are unknown. We tethered a hungry fly in a magnetic field, allowing it to yaw freely, presented odor plumes, and examined how visual cues influence odor tracking. We show that flies are unable to use a small-field object or landmark to assist plume tracking, whereas odor activates wide-field optomotor course control to enable accurate orientation toward an attractive food odor.     

Planning and analyzing adaptive group sequential survival trials

The use of adaptive methods has experienced increasing interest in the current literature on group sequential designs. Group sequential analysis in survival trials usually apply the error spending function approach due to the unpredictable amount of information available in an interim analysis. An alternative way is to apply adaptive methods where additionally the maximum amount of information and other designing parameters can be changed based on the information available at the interim stage. In this paper, it is shown how the inverse normal method can be used within a survival design using the log-rank test for comparing two survival functions. This method allows for many kinds of design modifications. In case of no modifications, the inverse normal method coincides with the commonly used analysis strategy. It is straightforward to specify effect estimates. Confidence intervals for the hazard ratio that can be calculated at each stage of the trial and intervals that can only be computed by the end of the trial are proposed. The latter also enables the calculation of median unbiased estimates. Overall p-values can be defined analogously. Properties of the analyses techniques are investigated and compared with alternative approaches. It is shown that the proposed analysis technique might help to rescue an underpowered study and opens the way to other types of changes in design. The proposed technique is implemented in the software ADDPLAN Adaptive Design, Plans and Analyses (http://www.addplan.com).     

Exact confidence bounds following adaptive group sequential tests

Summary .  We provide a method for obtaining confidence intervals, point estimates, and p-values for the primary effect size parameter at the end of a two-arm group sequential clinical trial in which adaptive changes have been implemented along the way. The method is based on applying the adaptive hypothesis testing procedure of Müller and Schäfer (2001, Biometrics 57, 886–891) to a sequence of dual tests derived from the stage-wise adjusted confidence interval of Tsiatis, Rosner, and Mehta (1984, Biometrics 40, 797–803). In the nonadaptive setting this confidence interval is known to provide exact coverage. In the adaptive setting exact coverage is guaranteed provided the adaptation takes place at the penultimate stage. In general, however, all that can be claimed theoretically is that the coverage is guaranteed to be conservative. Nevertheless, extensive simulation experiments, supported by an empirical characterization of the conditional error function, demonstrate convincingly that for all practical purposes the coverage is exact and the point estimate is median unbiased. No procedure has previously been available for producing confidence intervals and point estimates with these desirable properties in an adaptive group sequential setting. The methodology is illustrated by an application to a clinical trial of deep brain stimulation for Parkinson's disease.     

Improvement of design of a surgical interface using an eye tracking device

Background

Surgical interfaces are used for helping surgeons in interpretation and quantification of the patient information, and for the presentation of an integrated workflow where all available data are combined to enable optimal treatments. Human factors research provides a systematic approach to design user interfaces with safety, accuracy, satisfaction and comfort. One of the human factors research called user-centered design approach is used to develop a surgical interface for kidney tumor cryoablation. An eye tracking device is used to obtain the best configuration of the developed surgical interface.

Methods

Surgical interface for kidney tumor cryoablation has been developed considering the four phases of user-centered design approach, which are analysis, design, implementation and deployment. Possible configurations of the surgical interface, which comprise various combinations of menu-based command controls, visual display of multi-modal medical images, 2D and 3D models of the surgical environment, graphical or tabulated information, visual alerts, etc., has been developed. Experiments of a simulated cryoablation of a tumor task have been performed with surgeons to evaluate the proposed surgical interface. Fixation durations and number of fixations at informative regions of the surgical interface have been analyzed, and these data are used to modify the surgical interface.

Results

Eye movement data has shown that participants concentrated their attention on informative regions more when the number of displayed Computer Tomography (CT) images has been reduced. Additionally, the time required to complete the kidney tumor cryoablation task by the participants had been decreased with the reduced number of CT images. Furthermore, the fixation durations obtained after the revision of the surgical interface are very close to what is observed in visual search and natural scene perception studies suggesting more efficient and comfortable interaction with the surgical interface. The National Aeronautics and Space Administration Task Load Index (NASA-TLX) and Short Post-Assessment Situational Awareness (SPASA) questionnaire results have shown that overall mental workload of surgeons related with surgical interface has been low as it has been aimed, and overall situational awareness scores of surgeons have been considerably high.

Conclusions

This preliminary study highlights the improvement of a developed surgical interface using eye tracking technology to obtain the best SI configuration. The results presented here reveal that visual surgical interface design prepared according to eye movement characteristics may lead to improved usability.

     

Pulse pressure variation tracking using sequential Monte Carlo methods

The pulse pressure variation (PPV) is a measure of the respiratory effect on the variation of systemic arterial blood pressure (ABP) in patients receiving full mechanical ventilation. It is a promising predictor of increases in cardiac output due to an infusion of fluid. We describe a novel automatic algorithm to estimate the PPV of ABP signals recorded under full respiratory support. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM), which is called a maximum a-posteriori adaptive marginalized particle filter (MAM-PF). MAM-PF estimates the state-space model parameters of the ABP signal continuously and its upper and lower envelopes are derived as a combination of those parameter estimates. Then, the continuous PPV values can be easily obtained based on those estimated envelopes. We report the assessment results of the proposed algorithm on real ABP signals.     

Gaia as a complex adaptive system

We define the Gaia system of life and its environment on Earth, review the status of the Gaia theory, introduce potentially relevant concepts from complexity theory, then try to apply them to Gaia. We consider whether Gaia is a complex adaptive system (CAS) in terms of its behaviour and suggest that the system is self-organizing but does not reside in a critical state. Gaia has supported abundant life for most of the last 3.8 Gyr. Large perturbations have occasionally suppressed life but the system has always recovered without losing the capacity for large-scale free energy capture and recycling of essential elements. To illustrate how complexity theory can help us understand the emergence of planetary-scale order, we present a simple cellular automata (CA) model of the imaginary planet Daisyworld. This exhibits emergent self-regulation as a consequence of feedback coupling between life and its environment. Local spatial interaction, which was absent from the original model, can destabilize the system by generating bifurcation regimes. Variation and natural selection tend to remove this instability. With mutation in the model system, it exhibits self-organizing adaptive behaviour in its response to forcing. We close by suggesting how artificial life ('Alife') techniques may enable more comprehensive feasibility tests of Gaia.     

Apparent two-state tendamistat folding is a sequential process along a defined route

The small all-beta-sheet protein tendamistat folds and unfolds rapidly in apparent two-state reactions. Kinetic measurements of two tendamistat variants under various solvent conditions reveal, however, that folding occurs in at least two sequential steps through a metastable obligatory intermediate. Depending on the solvent conditions either step can become rate limiting. The activation parameters indicate that the first step represents an enthalpic barrier whereas the second step is an entropic barrier at 25 degrees C. Our results suggest that initial non-specific collapse precedes formation of native secondary and tertiary structure in tendamistat folding. This points at a distinct route in tendamistat folding and indicates that partially folded metastable intermediates might play an important role in the mechanism of apparent two-state folding.     

Online nonlinear sequential Bayesian estimation of a biological wastewater treatment process

Online estimation of unknown state variables is a key component in the accurate modelling of biological wastewater treatment processes due to a lack of reliable online measurement systems. The extended Kalman filter (EKF) algorithm has been widely applied for wastewater treatment processes. However, the series approximations in the EKF algorithm are not valid, because biological wastewater treatment processes are highly nonlinear with a time-varying characteristic. This work proposes an alternative online estimation approach using the sequential Monte Carlo (SMC) methods for recursive online state estimation of a biological sequencing batch reactor for wastewater treatment. SMC is an algorithm that makes it possible to recursively construct the posterior probability density of the state variables, with respect to all available measurements, through a random exploration of the states by entities called ‘particle’. In this work, the simplified and modified Activated Sludge Model No. 3 with nonlinear biological kinetic models is used as a process model and formulated in a dynamic state-space model applied to the SMC method. The performance of the SMC method for online state estimation applied to a biological sequencing batch reactor with online and offline measured data is encouraging. The results indicate that the SMC method could emerge as a powerful tool for solving online state and parameter estimation problems without any model linearization or restrictive assumptions pertaining to the type of nonlinear models for biological wastewater treatment processes.