The identification of building structural systems. II. The nonlinear case

This paper models a building structure as a nonlinear feedback system and studies the effects of such a system model on the structural response to strong ground shaking. Nonlinear kernels arising in the identification procedure have been investigated and an error analysis presented. Applications of the Weiner method in studying the response of a reinforced concrete structure to strong ground shaking have been illustrated. The nature of the second order kernels has been displayed and the nonlinear contribution to the response at the roof level, during strong ground shaking, has been determined

Activity of the Heat Shock Protein 90 Inhibitor Ganetespib in Melanoma

Heat shock protein 90 (HSP90) is involved in the regulation of diverse biological processes such as cell signaling, proliferation and survival, and has been recently recognized as a potential target for cancer therapy. Ganetespib is a potent ATP competitive inhibitor of HSP90. Ganetespib downregulated the expression of multiple signal transducing molecules including EGFR, IGF-1R, c-Met, Akt, B-RAF and C-RAF, resulting in pronounced decrease in phosphorylation of Akt and Erk1/2 in a panel of five cutaneous melanoma cell lines including those harboring B-RAF and N-RAS mutations. Ganetespib exhibited potent antiproliferative activity on all five of these cell lines, with IC50 values between 37.5 and 84 nM. Importantly, Ganetespib is active on B-RAF mutated melanoma cells that have acquired resistance to B-RAF inhibition. Ganetespib induced apoptosis and cell cycle arrest at G1 and/or G2/M phase. Ganetespib induced cell cycle arrest was accompanied by altered expression of cyclin-dependent kinase inhibitor (CDKI) p21Cip1 and p27Kip1, cyclins B1, D1 and E, and/or cyclin-dependent kinases 1, 2 and 4. HSP90 is functionally important for melanoma cells and HSP90 inhibitors such as ganetespib could potentially be effective therapeutics for melanoma with various genetic mutations and acquired resistance to B-RAF inhibition

Volterra Series Truncation and Kernel Estimation of Nonlinear Systems in the Frequency Domain

The Volterra series model is a direct generalisation of the linear convolution integral and is capable of displaying the intrinsic features of a nonlinear system in a simple and easy to apply way. Nonlinear system analysis using Volterra series is normally based on the analysis of its frequency-domain kernels and a truncated description. But the estimation of Volterra kernels and the truncation of Volterra series are coupled with each other. In this paper, a novel complex-valued orthogonal least squares algorithm is developed. The new algorithm provides a powerful tool to determine which terms should be included in the Volterra series expansion and to estimate the kernels and thus solves the two problems all together. The estimated results are compared with those determined using the analytical expressions of the kernels to validate the method. To further evaluate the effectiveness of the method, the physical parameters of the system are also extracted from the measured kernels. Simulation studies demonstrates that the new approach not only can truncate the Volterra series expansion and estimate the kernels of a weakly nonlinear system, but also can indicate the applicability of the Volterra series analysis in a severely nonlinear system case

Classification videos reveal the visual information driving complex real-world speeded decisions

Humans can rapidly discriminate complex scenarios as they unfold in real time, for example during law enforcement or, more prosaically, driving and sport. Such decision-making improves with experience, as new sources of information are exploited. For example, sports experts are able to predict the outcome of their opponent’s next action (e.g. a tennis stroke) based on kinematic cues “read” from preparatory body movements. Here, we explore the use of psychophysical classification-image techniques to reveal how participants interpret complex scenarios. We used sport as a test case, filming tennis players serving and hitting ground strokes, each with two possible directions. These videos were presented to novices and club-level amateurs, running from 0.8 seconds before to 0.2 seconds after racquet-ball contact. During practice, participants anticipated shot direction under a time limit targeting 90% accuracy. Participants then viewed videos through Gaussian windows ("bubbles") placed at random in the temporal, spatial or spatiotemporal domains. Comparing bubbles from correct and incorrect trials revealed how information from different regions contributed toward a correct response. Temporally, only later frames of the videos supported accurate responding (from ~0.05 seconds before ball contact to 0.1+ seconds afterwards). Spatially, information was accrued from the ball’s trajectory and from the opponent’s head. Spatiotemporal bubbles again highlighted ball trajectory information, but seemed susceptible to an attentional cuing artefact, which may caution against their wider use. Overall, bubbles proved effective in revealing regions of information accrual, and could thus be applied to help understand choice behavior in a range of ecologically valid situations

Inhomogeneous point-process entropy: an instantaneous measure of complexity in discrete systems

Measures of entropy have been widely used to characterize complexity, particularly in physiological dynamical systems modeled in discrete time. Current approaches associate these measures to finite single values within an observation window, thus not being able to characterize the system evolution at each moment in time. Here, we propose a new definition of approximate and sample entropy based on the inhomogeneous point-process theory. The discrete time series is modeled through probability density functions, which characterize and predict the time until the next event occurs as a function of the past history. Laguerre expansions of the Wiener-Volterra autoregressive terms account for the long-term nonlinear information. As the proposed measures of entropy are instantaneously defined through probability functions, the novel indices are able to provide instantaneous tracking of the system complexity. The new measures are tested on synthetic data, as well as on real data gathered from heartbeat dynamics of healthy subjects and patients with cardiac heart failure and gait recordings from short walks of young and elderly subjects. Results show that instantaneous complexity is able to effectively track the system dynamics and is not affected by statistical noise properties

Linear and Nonlinear Modeling of Cerebral Flow Autoregulation Using Principal Dynamic Modes

Cerebral Flow Autoregulation (CFA) is the dynamic process by which cerebral blood flow is maintained within physiologically acceptable bounds during fluctuations of cerebral perfusion pressure. The distinction is made with “static” flow autoregulation under steady-state conditions of perfusion pressure, described by the celebrated “autoregulatory curve” with a homeostatic plateau. This paper studies the dynamic CFA during changes in perfusion pressure, which attains critical clinical importance in patients with stroke, traumatic brain injury and neurodegenerative disease with a cerebrovascular component. Mathematical and computational models have been used to advance our quantitative understanding of dynamic CFA and to elucidate the underlying physiological mechanisms by analyzing the relation between beat-to-beat data of mean arterial blood pressure (viewed as input) and mean cerebral blood flow velocity(viewed as output) of a putative CFA system. Although previous studies have shown that the dynamic CFA process is nonlinear, most modeling studies to date have been linear. It has also been shown that blood CO2 tension affects the CFA process. This paper presents a nonlinear modeling methodology that includes the dynamic effects of CO2 tension (or its surrogate, end-tidal CO2) as a second input and quantifies CFA from short data-records of healthy human subjects by use of the modeling concept of Principal Dynamic Modes (PDMs). The PDMs improve the robustness of the obtained nonlinear models and facilitate their physiological interpretation. The results demonstrate the importance of including the CO2 input in the dynamic CFA study and the utility of nonlinear models under hypercapnic or hypocapnic conditions

A ‘spoon full of sugar’ helps the medicine go down: how a participant friendly version of a psychophysics task significantly improves task engagement, performance and data quality in a typical adult sample

Few would argue that the unique insights brought by studying the typical and atypical development of psychological processes are essential to building a comprehensive understanding of the brain. Often, however, the associated challenges of working with non-standard adult populations results in the more complex psychophysical paradigms being rejected as too complex. Recently we created a child (and clinical group) friendly implementation of one such technique – the reverse correlation Bubbles approach and noted an associated performance boost in adult participants. Here, we compare the administration of three different versions of this participant-friendly task in the same adult participants to empirically confirm that introducing elements in the experiment with the sole purpose of improving the participant experience, not only boost the participant’s engagement and motivation for the task but results in significantly improved objective task performance and stronger statistical results