Human Alzheimer’s disease (AD) brains and transgenic AD mouse models manifest hyperexcitability. This aberrant electrical activity is caused by synaptic dysfunction that represents the major pathophysiological correlate of cognitive decline. However, the underlying mechanism for this excessive excitability remains incompletely understood. To investigate the basis for the hyperactivity, we performed electrophysiological and immunofluorescence studies on hiPSC-derived cerebrocortical neuronal cultures and cerebral organoids bearing AD-related mutations in presenilin-1 or amyloid precursor protein vs. isogenic gene corrected controls. In the AD hiPSC-derived neurons/organoids, we found increased excitatory bursting activity, which could be explained in part by a decrease in neurite length. AD hiPSC-derived neurons also displayed increased sodium current density and increased excitatory and decreased inhibitory synaptic activity. Our findings establish hiPSC-derived AD neuronal cultures and organoids as a relevant model of early AD pathophysiology and provide mechanistic insight into the observed hyperexcitability.
Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors and drift. In this paper, we propose a multi-sensor hybrid method to solve this problem. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. Secondly, Cramér-Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Furthermore, proposed method is verified in 3D practical application scenarios. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications.
Electromagnetic (EM) waves are the most versatile and widely adopted physical layer technology for underground wireless communication networks (UWCNs). However, they are not suitable for underground communication especially when coils are buried in soil. Magneto-inductive (MI) communication is a promising technique for UWCNs; it is not affected by large propagation delays, multipath propagation, and fading. Furthermore, MI communication has lower transmitting power and smaller antenna size than EM. A near-field low-power communication device with an electrically small antenna for low-frequency UWCNs is proposed in this study. The device can be easily buried underground. The proposed device is analyzed in terms of basic communication metrics, i.e., magnetic field intensity, bandwidth, path losses, and received power. Comparison of these four metrics shows that the experimental data closely match those of the theoretical model, and the maximum communication distance of 28.5 m, can be achieved. Moreover, according to the experimental results of our device, we further propose the modified received power model. Results show that the device exhibits good performance and can be used as a stand-alone module in directional underground wireless communication.
National Materials Data Management and Service platform (NMDMS) is a materials data repository for the publication and sharing of heterogeneous materials scientific data and follows the FAIR principles: Findable, Accessible, Interoperable, and Reusable. To ensure data are ‘Interoperable, NMDMS uses a user-friendly semi-structured scientific data model, named dynamic container’, to define, exchange, and store heterogeneous scientific data. Then, a personalized yet standardized data submission subsystem, a rigorous project data review and publication subsystem, and a multi-granularity data query and retrieval subsystem collaboratively make data ‘Reusable’, ‘Findable’, and ‘Accessible’. Finally, China’s “National Key R&D Program: Material Genetic Engineering Key Special Project” has adopted NMDMS to publish and share its project data. There are 12,251,040 pieces of data published in NMDMS since 2018, under 87 categories and 1,912 user-defined schemas from 45 projects. The platform has been accessed 908875 times, and 2403,208 pieces of data have been downloaded. In short, NMDMS effectively accelerates the publication and sharing of material project data in China.
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