Abodehella, A New Genus of Tetrataxid Foraminifera from the Late Permian

A new genus, Abadehella comprising three new species (tarazi, coniformis, and biconvexa) is proposed and described. So far as the morphologic features are concerned, the genus is considered to be a specialized genus in the family Tetrataxidae. This characteristic genus is found from the basal part of the Abadeh Formation in central Iran, the lowest member of the Zewan Formation in Kashmir, the Palaeofusulina limestone in Malaysia, Lepidolina multiseptata limestones in Cambodia and northeast Japan, the Takauchi and Reichelina-Colaniella limestones of the Maizuru belt, and the Lepidolina kumaensis zone of Shikoku in Japan. The stratigraphic occurrence is limited to the Late Permian and the genus is considered to be useful for international correlation

Deep Adversarial Reinforcement Learning With Noise Compensation by Autoencoder

We present a new adversarial learning method for deep reinforcement learning (DRL). Based on this method, robust internal representation in a deep Q-network (DQN) was introduced by applying adversarial noise to disturb the DQN policy; however, it was compensated for by the autoencoder network. In particular, we proposed the use of a new type of adversarial noise: it encourages the policy to choose the worst action leading to the worst outcome at each state. When the proposed method, called deep Q-W-network regularized with an autoencoder (DQWAE), was applied to seven different games in an Atari 2600, the results were convincing. DQWAE exhibited greater robustness against the random/adversarial noise added to the input and accelerated the learning process more than the baseline DQN. When applied to a realistic automatic driving simulation, the proposed DRL method was found to be effective at rendering the acquired policy robust against random/adversarial noise

Empirical Bayesian significance measure of neuronal spike response

Background: Functional connectivity analyses of multiple neurons provide a powerful bottom-up approach to reveal functions of local neuronal circuits by using simultaneous recording of neuronal activity. A statistical methodology, generalized linear modeling (GLM) of the spike response function, is one of the most promising methodologies to reduce false link discoveries arising from pseudo-correlation based on common inputs. Although recent advancement of fluorescent imaging techniques has increased the number of simultaneously recoded neurons up to the hundreds or thousands, the amount of information per pair of neurons has not correspondingly increased, partly because of the instruments' limitations, and partly because the number of neuron pairs increase in a quadratic manner. Consequently, the estimation of GLM suffers from large statistical uncertainty caused by the shortage in effective information. Results: In this study, we propose a new combination of GLM and empirical Bayesian testing for the estimation of spike response functions that enables both conservative false discovery control and powerful functional connectivity detection. We compared our proposed method's performance with those of sparse estimation of GLM and classical Granger causality testing. Our method achieved high detection performance of functional connectivity with conservative estimation of false discovery rate and q values in case of information shortage due to short observation time. We also showed that empirical Bayesian testing on arbitrary statistics in place of likelihood-ratio statistics reduce the computational cost without decreasing the detection performance. When our proposed method was applied to a functional multi-neuron calcium imaging dataset from the rat hippocampal region, we found significant functional connections that are possibly mediated by AMPA and NMDA receptors. Conclusions: The proposed empirical Bayesian testing framework with GLM is promising especially when the amount of information per a neuron pair is small because of growing size of observed network

Subtle modulation of ongoing calcium dynamics in astrocytic microdomains by sensory inputs.

Astrocytes communicate with neurons through their processes. In vitro experiments have demonstrated that astrocytic processes exhibit calcium activity both spontaneously and in response to external stimuli; however, it has not been fully determined whether and how astrocytic subcellular domains respond to sensory input in vivo. We visualized the calcium signals in astrocytes in the primary visual cortex of awake, head‐fixed mice. Bias‐free analyses of two‐photon imaging data revealed that calcium activity prevailed in astrocytic subcellular domains, was coordinated with variable spot‐like patterns, and was dominantly spontaneous. Indeed, visual stimuli did not affect the frequency of calcium domain activity, but it increased the domain size, whereas tetrodotoxin reduced the sizes of spontaneous calcium domains and abolished their visual responses. The "evoked" domain activity exhibited no apparent orientation tuning and was distributed unevenly within the cell, constituting multiple active hotspots that were often also recruited in spontaneous activity. The hotspots existed dominantly in the somata and endfeet of astrocytes. Thus, the patterns of astrocytic calcium dynamics are intrinsically constrained and are subject to minor but significant modulation by sensory input

Beat-frequency-resolved two-dimensional electronic spectroscopy: disentangling vibrational coherences in artificial fluorescent proteins with sub-10-fs visible laser pulses

We perform a beat-frequency-resolved analysis for two-dimensional electronic spectroscopy using a high-speed and stable 2D electronic spectrometer and few-cycle visible laser pulses to disentangle the vibrational coherences in an artificial fluorescent protein. We develop a highly stable ultrashort light source that generates 5.3-fs visible pulses with a pulse energy of 4.7 uJ at a repetition rate of 10 kHz using multi-plate pulse compression and laser filamentation in a gas cell. The above-5.3-fs laser pulses together with a high-speed multichannel detector enable us to measure a series of 2D electronic spectra, which are resolved in terms of beat frequency related to vibrational coherence. We successfully extract the discrete vibrational peaks behind the inhomogeneous broadening in the absorption spectra and the vibrational quantum beats of the excited electronic state behind the strong stationary signal in the typical 2D electronic spectra