response dynamics
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Gustatory Cortex (GC), a structure deeply involved in the making of consumption decisions, presumably performs this function by integrating information about taste, experiences, and internal states related to the animal’s health, such as illness. Here, we investigated this assertion, examining whether illness is represented in GC activity, and how this representation impacts taste responses and behavior. We recorded GC single-neuron activity and local field potentials (LFP) from healthy rats and (the same) rats made ill ( via LiCl injection). We show (consistent with the extant literature) that the onset of illness-related behaviors arises contemporaneously with alterations in spontaneous 7-12Hz LFP power at ~11 min following injection. This process was accompanied by reductions in single-neuron taste response magnitudes and discriminability, and with enhancements in palatability-relatedness – a result reflecting the collapse of responses toward a simple “good-bad” code arising in a specific subset of GC neurons. Overall, our data show that a state (illness) that profoundly reduces consumption changes basic properties of the sensory cortical response to tastes, in a manner that can easily explain illness’ impact on consumption.

In a non-orthogonal multiple access (NOMA) environment, an Internet of Things (IoT) device achieves a high data rate by increasing its transmission power. However, excessively high transmission power can cause an energy outage of an IoT device and have a detrimental effect on the signal-to-interference-plus-noise ratio of neighbor IoT devices. In this paper, we propose a neighbor-aware NOMA scheme (NA-NOMA) where each IoT device determines whether to transmit data to the base station and the transmission power at each time epoch in a distributed manner with the consideration of its energy level and other devices’ transmission powers. To maximize the aggregated data rate of IoT devices while keeping an acceptable average energy outage probability, a constrained stochastic game model is formulated, and the solution of the model is obtained using a best response dynamics-based algorithm. Evaluation results show that NA-NOMA can increase the average data rate up to 22% compared with a probability-based scheme while providing a sufficiently low energy outage probability (e.g., 0.05).

By any objective measure, the United States has mishandled its response to the SARS-CoV-2 and COVID-19 outbreak, with 177,394 deaths and 5,745,721 cases. In the world, there have been some 796,330 deaths and 22,848,030 validated coronavirus cases (with 15,500,447 recovered). The real rates are thought to be 3x – 10x higher given the low access to tests. In this moment, there are multiple epidemics ongoing in the U.S., resulting in massive government and private industry expenditures, disrupted markets, and social roiling. In journalistic coverage and social image sets, the interactive call-response between public health officials and the general American public may be seen in the Summer of 2020 (in a time of phased shutdowns and re-openings and re-closings). This work offers an original content analysis of over 2,431 journalistic articles and 2,224 social images captured July 3, 2020 to understand where the call-response communications broke down and the message got lost at enormous social and personal cost. A sidebar offers an analysis of COVID-19 social memes.

Abstract We have studied the transient response dynamics of 87Rb atomic vapor buffered in 8 Torr Ne gas through an electromagnetically induced transparency configured in Λ-scheme. Experimentally, the temporal transmission spectra versus probe detuning by switching on and off the coupling one show complex structures. The transmitted probe light intensity drops to a minimum value when the coupling light turns off, showing a strong absorption. While at the moment of turning on the coupling light at a subsequent delayed time, the atomic medium shows a fast transient response. To account for the transient switching feature, in the time-dependent optical Bloch equation, we have to take the transverse relaxation dephasing process of atomic vapor into account, as well as the fluorescence relaxation along with the optical absorption. This work supplies a technique to quantify the transverse relaxation time scale and sensitively monitor its variation along the environment by observing the transient dynamics of coherent medium, which is helpful in characterizing the coherent feature of the atomic medium.

The result that says the sequence of iterations [Formula: see text] converges if [Formula: see text] is an increasing function has numerous applications in elementary economic analysis. I generalize this simple result to some mappings [Formula: see text]. The applications of the new result include the convergence of the best-response dynamics in the general version of the Crawford and Sobel model and in some versions of the Hotelling and Tiebout models.

This paper synthesizes a continuous, multivariable, finite-time-convergent, super-twisting attitude and rate controller for rotorcraft with the objective of providing desired handling qualities and robustness characteristics. A sliding manifold is defined in the system state space to represent ideal attitude and rate command response dynamics of relative degree one with respect to the command input. Subsequently, robust command tracking is achieved via the synthesis of a multivariable super-twisting flight controller, which renders the plant states convergent on to the defined sliding manifold in finite-time and in the presence of matched external disturbance input. To validate the efficacy of the controller, simulation results are presented based on a nonlinear, higher-order rotorcraft model operating in turbulence. True system convergence to the sliding manifold from an untrimmed state is shown to lie within the theoretically predicted finite-time convergence bound. Furthermore, simulations with a linear quadratic flight controller are also presented for performance comparison with the proposed super-twisting flight controller.

Three-quarters of compounds that enter clinical trials fail to make it to market due to safety or efficacy concerns. This statistic strongly suggests a need for better screening methods that result in improved translatability of compounds during the preclinical testing period. Patient-derived organoids have been touted as a promising 3D preclinical model system to impact the drug discovery pipeline, particularly in oncology. However, assessing drug efficacy in such models poses its own set of challenges, and traditional cell viability readouts fail to leverage some of the advantages that the organoid systems provide. Consequently, phenotypically evaluating complex 3D cell culture models remains difficult due to intra- and inter-patient organoid size differences, cellular heterogeneities, and temporal response dynamics. Here, we present an image-based high-content assay that provides object level information on 3D patient-derived tumor organoids without the need for vital dyes. Leveraging computer vision, we segment and define organoids as independent regions of interest and obtain morphometric and textural information per organoid. By acquiring brightfield images at different timepoints in a robust, non-destructive manner, we can track the dynamic response of individual organoids to various drugs. Furthermore, to simplify the analysis of the resulting large, complex data files, we developed a web-based data visualization tool, the Organoizer, that is available for public use. Our work demonstrates the feasibility and utility of using imaging, computer vision and machine learning to determine the vital status of individual patient-derived organoids without relying upon vital dyes, thus taking advantage of the characteristics offered by this preclinical model system.

Abstract Molecular neuromorphic devices are composed of a random and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). Such devices are expected to have the rudimentary ability of reservoir computing (RC), which utilizes signal response dynamics and a certain degree of network complexity. In this study, we performed RC using multiple signals collected from a SWNT/POM random network. The signals showed a nonlinear response with wide diversity originating from the network complexity. The performance of RC was evaluated for various tasks such as waveform reconstruction, a nonlinear autoregressive model, and memory capacity. The obtained results indicated its high capability as a nonlinear dynamical system, capable of information processing incorporated into edge computing in future technologies.