Concept: Transfer function
We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of “source” species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the “target” species) with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation.
: The Vicorder is a new brachial cuff-based device that estimates central blood pressure (cBP) using a brachial-to-aortic transfer function. The aim of this study was to evaluate cBP estimated by the Vicorder.
It has been established that vagal nerve stimulation (VNS) benefits patients and/or animals with heart failure. However, the impact of VNS on sympathetic nerve activity (SNA) remains unknown. In this study, we investigated how vagal afferent stimulation (AVNS) impacts baroreflex control of SNA. In 12 anesthetized Sprague-Dawley rats, we controlled the pressure in isolated bilateral carotid sinuses (CSP), and measured splanchnic SNA and arterial pressure (AP). Under a constant CSP, increasing the voltage of AVNS dose dependently decreased SNA and AP. The averaged maximal inhibition of SNA was -28.0 ± 10.3%. To evaluate the dynamic impacts of AVNS on SNA, we performed random AVNS using binary white noise sequences, and identified the transfer function from AVNS to SNA and that from SNA to AP. We also identified transfer functions of the native baroreflex from CSP to SNA (neural arc) and from SNA to AP (peripheral arc). The transfer function from AVNS to SNA strikingly resembled the baroreflex neural arc and the transfer functions of SNA to AP were indistinguishable whether we perturbed ANVS or CSP, indicating that they likely share common central and peripheral neural mechanisms. To examine the impact of AVNS on baroreflex, we changed CSP stepwise and measured SNA and AP responses with or without AVNS. AVNS resets the sigmoidal neural arc downward, but did not affect the linear peripheral arc. In conclusion, AVNS resets the baroreflex neural arc and induces sympathoinhibition in the same manner as the control of SNA and AP by the native baroreflex.
This study investigates modulation transfer function (MTF) in parallel beam (PB) and fan beam (FB) collimators using the Monte Carlo method with full width at half maximum (FWHM), square and circular-shaped holes, and scatter and penetration (S + P) components.
We investigate hyperthermal ion implantation (HyTII) as a means for substitutionally doping layered materials such as graphene. In particular, this systematic study characterizes the efficacy of substitutional N-doping of graphene using HyTII over an N(+) energy range of 25 eV to 100 eV. Scanning tunneling microscopy results establish the incorporation of N substituents into the graphene lattice during HyTII processing. We illustrate the differences in evolution of the characteristic Raman peaks following incremental doses of N(+). We use the ratios of the integrated D and D` peaks, I(D)/I(D`) to assess the N(+) energy-dependent doping efficacy, which shows a strong correlation with previously reported molecular dynamics (MD) simulation results and a peak doping efficiency regime ranging between approximately 30 eV to 50 eV. We also demonstrate the inherent monolayer depth control of the HyTII process, thereby establishing a unique advantage over other less-specific methods for doping. We achieve this by implementing twisted bilayer graphene (TBG), with one layer of isotopically-enriched (13)C and one layer of natural (12)C graphene, and modify only the top layer of the TBG sample. By assessing the effects of N-HyTII processing, we uncover dose-dependent shifts in the transfer characteristics consistent with electron doping and we find dose-dependent electronic localization that manifests in low-temperature magnetotransport measurements.
Growing demand of resources increases pressure on ecosystem services (ES) and biodiversity. Monetary valuation of ES is frequently seen as a decision-support tool by providing explicit values for unconsidered, non-market goods and services. Here we present global value transfer functions by using a meta-analytic framework for the synthesis of 194 case studies capturing 839 monetary values of ES. For 12 ES the variance of monetary values could be explained with a subset of 93 study- and site-specific variables by utilizing boosted regression trees. This provides the first global quantification of uncertainties and transferability of monetary valuations. Models explain from 18% (water provision) to 44% (food provision) of variance and provide statistically reliable extrapolations for 70% (water provision) to 91% (food provision) of the terrestrial earth surface. Although the application of different valuation methods is a source of uncertainty, we found evidence that assuming homogeneity of ecosystems is a major error in value transfer function models. Food provision is positively correlated with better life domains and variables indicating positive conditions for human well-being. Water provision and recreation service show that weak ownerships affect valuation of other common goods negatively (e.g. non-privately owned forests). Furthermore, we found support for the shifting baseline hypothesis in valuing climate regulation. Ecological conditions and societal vulnerability determine valuation of extreme event prevention. Valuation of habitat services is negatively correlated with indicators characterizing less favorable areas. Our analysis represents a stepping stone to establish a standardized integration of and reporting on uncertainties for reliable and valid benefit transfer as an important component for decision support.
Cerebral autoregulation (CA) is an important vascular control mechanism responsible for relatively stable cerebral blood flow despite changes of systemic blood pressure (BP). Impaired CA may leave brain tissue unprotected against potentially harmful effects of BP fluctuations. It is generally accepted that CA is less effective or even inactive at frequencies >∼0.1 Hz. Without any physiological foundation, this concept is based on studies that quantified the coupling between BP and cerebral blood flow velocity (BFV) using transfer function analysis. This traditional analysis assumes stationary oscillations with constant amplitude and period, and may be unreliable or even invalid for analysis of nonstationary BP and BFV signals. In this study we propose a novel computational tool for CA assessment that is based on nonlinear dynamic theory without the assumption of stationary signals. Using this method, we studied BP and BFV recordings collected from 39 patients with chronic ischemic infarctions and 40 age-matched non-stroke subjects during baseline resting conditions. The active CA function in non-stroke subjects was associated with an advanced phase in BFV oscillations compared to BP oscillations at frequencies from ∼0.02 to 0.38 Hz. The phase shift was reduced in stroke patients even at > = 6 months after stroke, and the reduction was consistent at all tested frequencies and in both stroke and non-stroke hemispheres. These results provide strong evidence that CA may be active in a much wider frequency region than previously believed and that the altered multiscale CA in different vascular territories following stroke may have important clinical implications for post-stroke recovery. Moreover, the stroke effects on multiscale cerebral blood flow regulation could not be detected by transfer function analysis, suggesting that nonlinear approaches without the assumption of stationarity are more sensitive for the assessment of the coupling of nonstationary physiological signals.
While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinson’s disease and essential tremor, an opportunity for improving neuromodulation therapy remains in automatically adjusting therapy to continuously optimize patient outcomes. Practical issues associated with achieving this include the paucity of human data related to disease states, poorly validated estimators of patient state, and unknown dynamic mappings of optimal stimulation parameters based on estimated states. To overcome these challenges, we present an investigational platform including: an implanted sensing and stimulation device to collect data and run automated closed-loop algorithms; an external tool to prototype classifier and control-policy algorithms; and real-time telemetry to update the implanted device firmware and monitor its state. The prototyping system was demonstrated in a chronic large animal model studying hippocampal dynamics. We used the platform to find biomarkers of the observed states and transfer functions of different stimulation amplitudes. Data showed that moderate levels of stimulation suppress hippocampal beta activity, while high levels of stimulation produce seizure-like after-discharge activity. The biomarker and transfer function observations were mapped into classifier and control-policy algorithms, which were downloaded to the implanted device to continuously titrate stimulation amplitude for the desired network effect. The platform is designed to be a flexible prototyping tool and could be used to develop improved mechanistic models and automated closed-loop systems for a variety of neurological disorders.
The temporal modulation transfer function (TMTF) has been proposed to estimate the temporal resolution abilities of listeners with normal hearing and listeners with hearing loss. The TMTF data of patients would be useful for clinical diagnosis and for adjusting the hearing instruments at clinical and fitting sites. However, practical application is precluded by the long measurement time of the conventional method, which requires several measurement points. This article presents a new method to measure the TMTF that requires only two measurement points.
To compare the change in ocular higher-order wavefront aberrations (HOAs), visual acuity, and modulation transfer function (MTF) after lens extraction with intraocular lens (IOL) implantation in patients with subluxated lenses.