Temporal lobe epilepsy (TLE) is one of the most common drug-resistant forms of epilepsy in adults and usually originates in the hippocampal formations. However, both the network mechanisms that support the seizure spread and the exact directions of ictal propagation remain largely unknown. Here we report the dissection of ictal propagation in the hippocampal–entorhinal cortex (HP–EC) structures using optogenetic methods in multiple brain regions of a kainic acid-induced model of TLE in VGAT-ChR2 transgenic mice. We perform highly temporally precise cross-area analyses of epileptic neuronal networks and find a feed-forward propagation pathway of ictal discharges from the dentate gyrus/hilus (DGH) to the medial entorhinal cortex, instead of a re-entrant loop. We also demonstrate that activating DGH GABAergic interneurons can significantly inhibit the spread of ictal seizures and largely rescue behavioural deficits in kainate-exposed animals. These findings may shed light on future therapeutic treatments of TLE.

Animal behavior usually has a hierarchical structure and dynamics. Therefore, to understand how the neural system coordinates with behaviors, neuroscientists need a quantitative description of the hierarchical dynamics of different behaviors. However, the recent end-to-end machine-learning-based methods for behavior analysis mostly focus on recognizing behavioral identities on a static timescale or based on limited observations. These approaches usually lose rich dynamic information on cross-scale behaviors. Here, inspired by the natural structure of animal behaviors, we address this challenge by proposing a parallel and multi-layered framework to learn the hierarchical dynamics and generate an objective metric to map the behavior into the feature space. In addition, we characterize the animal 3D kinematics with our low-cost and efficient multi-view 3D animal motion-capture system. Finally, we demonstrate that this framework can monitor spontaneous behavior and automatically identify the behavioral phenotypes of the transgenic animal disease model. The extensive experiment results suggest that our framework has a wide range of applications, including animal disease model phenotyping and the relationships modeling between the neural circuits and behavior.

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Graphical Abstract AbstractRecent advances in optogenetics have established a precisely timed and cell-specific methodology for understanding the functions of brain circuits and the mechanisms underlying neuropsychiatric disorders. However, the fabrication of optrodes, a key functional element in optogenetics, remains a great challenge. Here, we report reliable and efficient fabrication strategies for chronically implantable optrode arrays. To improve the performance of the fabricated optrode arrays, surfaces of the recording sites were modified using optimized electrochemical processes. We have also demonstrated the feasibility of using the fabricated optrode arrays to detect seizures in multiple brain regions and inhibit ictal propagation in vivo. Furthermore, the results of the histology study imply that the electrodeposition of composite conducting polymers notably alleviated the inflammatory response and improved neuronal survival at the implant/neural-tissue interface. In summary, we provide reliable and efficient strategies for the fabrication and modification of customized optrode arrays that can fulfill the requirements of in vivo optogenetic applications.

This paper proposes a temporal fuzzy reasoning spiking neural P system with real numbers (rTFRSN P system) and its corresponding fault diagnosis method called FDTSNP to diagnose faults in a power system. The introduction of the rTFRSN P system is to make full use of the temporal order information of alarm messages so as to model candidate fault sections. The presentation of the reasoning algorithm within the framework of an rTFRSN P system tries to obtain confidence levels of candidate faulty sections. Thus, FDTSNP offers an intuitive illustration based on a strictly mathematical expression and a good ability to handle incomplete and uncertain alarm messages with temporal order information. The effectiveness of FDTSNP is verified in various fault cases including single and multiple fault situations with/without incomplete and uncertain alarm messages. Experimental results show that FDTSNP is better than several methods reported in the literature, in terms of the correctness of diagnosis results.

The physiological chirality of extracellular environments is substantially affected by pathological diseases. However, how this stereochemical variation drives host immunity remains poorly understood. Here, it is reported that pathology‐mimetic M‐nanofibrils—but not physiology‐mimetic P‐nanofibrils—act as a defense mechanism that helps to restore tissue homeostasis by manipulating immunological response. Quantitative multi‐omics in vivo and in vitro shows that M‐nanofibrils significantly inhibit inflammation and promote tissue regeneration by upregulating M2 macrophage polarization and downstream immune signaling compared with P‐nanofibrils. Molecular analysis and theoretical simulation demonstrate that M‐chirality displays higher stereo‐affinity to cellular binding, which induces higher cellular contractile stress and activates mechanosensitive ion channel PIEZOl to conduct Ca2+ influx. In turn, the nuclear transfer of STAT is biased by Ca2+ influx to promote M2 polarization. These findings underscore the structural mechanisms of disease, providing design basis for immunotherapy with bionic functional materials.

Energy-efficient train speed profile optimization problem in urban rail transit systems has attracted much attention in recent years because of the requirement of reducing operation cost and protecting the environment. Traditional methods on this problem mainly focused on formulating kinematical equations to derive the speed profile and calculate the energy consumption, which caused the possible errors due to some assumptions used in the empirical equations. To fill this gap, according to the actual speed and energy data collected from the real-world urban rail system, this paper proposes a data-driven model and integrated heuristic algorithm based on machine learning to determine the optimal speed profile with minimum energy consumption. Firstly, a data-driven optimization model (DDOM) is proposed to describe the relationship between energy consumption and discrete speed profile processed from actual data. Then, two typical machine learning algorithms, random forest regression (RFR) algorithm and support vector machine regression (SVR) algorithm, are used to identify the importance degree of velocity in the different positions of profile and calculate the traction energy consumption. Results show that the calculation average error is less than 0.1 kwh, and the energy consumption can be reduced by about 2.84% in a case study of Beijing Changping Line.

Huiying Tan and Xiaotao Li contributed equally to this study.