No abstract

Binding of odorants to olfactory receptors (ORs) elicits downstream chemical and neural signals, which are further processed to odor perception in the brain. Recently, Mainland and colleagues have measured more than 500 pairs of odorant-OR interaction by a high-throughput screening assay method, opening a new avenue to understanding the principles of human odor coding. Here, using a recently developed minimal model for OR activation kinetics, we characterize the statistics of OR activation by odorants in terms of three empirical parameters: the half-maximum effective concentration EC50, the efficacy, and the basal activity. While the data size of odorants is still limited, the statistics offer meaningful information on the breadth and optimality of the tuning of human ORs to odorants, and allow us to relate the three parameters with the microscopic rate constants and binding affinities that define the OR activation kinetics. Despite the stochastic nature of the response expected at individual OR-odorant level, we assess that the confluence of signals in a neuron released from the multitude of ORs is effectively free of noise and deterministic with respect to changes in odorant concentration. Thus, setting a threshold to the fraction of activated OR copy number for neural spiking binarizes the electrophysiological signal of olfactory sensory neuron, thereby making an information theoretic approach a viable tool in studying the principles of odor perception.

Open-site paddle wheels, comprised of two transition metals bridged with four carboxylate ions, have been widely used for constructing metal−organic frameworks with large surface area and high binding energy sites. Using firstprinciples density functional theory calculations, we have investigated atomic and electronic structures of various 3d transition metal paddle wheels before and after metal exposure and their hydrogen adsorption properties at open metal sites. Notably, the hydrogen adsorption is impeded by covalent metal−metal bonds in early transition metal paddle wheels from Sc to Cr and by the strong ferromagnetic coupling of diatomic Mn and Fe in the paddle wheel configurations. A significantly enhanced H 2 adsorption is predicted in the nonmagnetic Co 2 and Zn 2 paddle wheel with the binding energy of ∼0.2 eV per H 2 . We also propose the use of two-dimensional Co 2 and Zn 2 paddle wheel frameworks that could have strongly adsorbed dihydrogen up to 1.35 wt % for noncryogenic hydrogen storage applications.

Psychometric functions (PFs) quantify how external stimuli affect behavior, and they play an important role in building models of sensory and cognitive processes. Adaptive stimulus-selection methods seek to select stimuli that are maximally informative about the PF given data observed so far in an experiment and thereby reduce the number of trials required to estimate the PF. Here we develop new adaptive stimulus-selection methods for flexible PF models in tasks with two or more alternatives. We model the PF with a multinomial logistic regression mixture model that incorporates realistic aspects of psychophysical behavior, including lapses and multiple alternatives for the response. We propose an information-theoretic criterion for stimulus selection and develop computationally efficient methods for inference and stimulus selection based on adaptive Markov-chain Monte Carlo sampling. We apply these methods to data from macaque monkeys performing a multi-alternative motion-discrimination task and show in simulated experiments that our method can achieve a substantial speed-up over random designs. These advances will reduce the amount of data needed to build accurate models of multi-alternative PFs and can be extended to high-dimensional PFs that would be infeasible to characterize with standard methods.