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A Proposal Regarding COVID-19 Programs Enabling Intensive Epidemiological Studies.
This enables the parallel top-down fabrication of Si nanowires and nanoribbons in a single DUV lithography step as a rapid and inexpensive alternative to conventional e-beam techniques.
Retinal ganglion cells (RGCs) represent an attractive target in vision restoration strategies, because they undergo little degeneration compared to other retinal neurons. Here we investigated the temporal and spatial resolution in adult photoreceptor-degenerated (rd10) mouse retinas, where RGCs have been transduced with the optogenetic actuator channelrhodopsin-2 (ChR2).

The RGC spiking activity was recorded continuously with a CMOS-based microelectrode array during a variety of photostimulation protocols. The temporal resolution was assessed through Gaussian white noise stimuli and evaluated using a linear-nonlinear-Poisson model. Spatial sensitivity was assessed upon photostimulation with single rectangular pulses stepped across the retina and upon stimulation with alternating gratings of different spatial frequencies. Spatial sensitivity was estimated using logistic regression or by evaluating the spiking activity of independent RGCs.

The temporal resolution after photostimulation displayed an about ten times faster kinetics as compared to physiological filters in wild-type RGCs. The optimal spatial resolution estimated using the logistic regression model was 10 µm and 87 µm based on the population response. These values correspond to an equivalent acuity of 1.7 or 0.2 cycles per degree, which is better than expected from the size of RGCs' optogenetic receptive fields.

The high temporal and spatial resolution obtained by photostimulation of optogenetically transduced RGCs indicate that high acuity vision restoration may be obtained by photostimulation of appropriately modified RGCs in photoreceptor-degenerated retinas.
The high temporal and spatial resolution obtained by photostimulation of optogenetically transduced RGCs indicate that high acuity vision restoration may be obtained by photostimulation of appropriately modified RGCs in photoreceptor-degenerated retinas.In this work, a graphene (GR)/MoS2/GR selector was proposed based on first principle calculations. First, MoS2 was chosen as the resistive switching layer due to its high carrier mobility and was doped with nine kinds of dopants. Semiconductor characteristics were still maintained with P, Si, and Ti doping, while the others showed semimetallic properties. U0126 price Then, heterostructures were built between metal GR and MoS·X (X = S, P, Si, Ti), and the conductivities of MoS·Si and MoS·Ti were obviously improved with the GR electrode through analysis of the impurity orbital contribution to the band energy. The plane average electrostatic potential and the charge density difference show that the Schottky barrier height and width of the GR/MoS·Si interface were the smallest and that the intensity of the built-in electric field was better than that of GR/MoS2 and GR/MoS·Ti. Finally, GR/MoS·X(X = S, Si, Ti)/GR selectors were proposed, and the electronic transmission shows that the ON-state current (I on) and nonlinear coefficient of the GR/MoS·Si/GR selector were increased by two and three orders of magnitude, respectively, and the threshold voltage (V th) was reduced by approximately 1 V, which can better suppress the leakage current in a one-selector one-RRAM cross array. This work may be instructive and valuable for the design and optimization of GR/MoS2/GR selectors.Nitrobenzene compounds are highly toxic pollutants with good stability, and they have a major negative impact on both human health and the ecological environment. Herein, it was found for the first time that fluorescent DNA-silver nanoclusters (DNA-AgNCs) can catalyze the reduction of toxic and harmful nitro compounds into less toxic amino compounds with excellent tolerance to high temperature and organic solvents. In this study, the reduction of p-nitrophenol (4-NP) as a model was systematically investigated, followed by expending the substrate to disclose the versatility of this reaction. This report not only expanded the conditions for utilizing catalytic reduction conditions of DNA-AgNCs as an efficient catalyst in the control of hazardous chemicals but also widened the substrate range of DNA-AgNCs reduction, providing a new angle for the application of noble metal nanoclusters.
Motor imagery electroencephalography (EEG) decoding is a vital technology for the brain-computer interface (BCI) systems and has been widely studied in recent years. However, the original EEG signals usually contain a lot of class-independent information, and the existing motor imagery EEG decoding methods are easily interfered by this irrelevant information, which greatly limits the decoding accuracy of these methods.

To overcome the interference of the class-independent information, a motor imagery EEG decoding method based on feature separation is proposed in this paper. Furthermore, a feature separation network based on adversarial learning (FSNAL) is designed for the feature separation of the original EEG samples. First, the class-related features and class-independent features are separated by the proposed FSNAL framework, and then motor imagery EEG decoding is performed only according to the class-related features to avoid the adverse effects of class-independent features.

To validate the effectiveness of the proposed motor imagery EEG decoding method, we conduct some experiments on two public EEG datasets (the BCI competition IV 2a and 2b datasets). The experimental results comparison between our method and some state-of-the-art methods demonstrates that our motor imagery EEG decoding method outperforms all the compared methods on the two experimental datasets.

Our motor imagery EEG decoding method can alleviate the interference of class-independent features, and it has great application potential for improving the performance of motor imagery BCI systems in the near future.
Our motor imagery EEG decoding method can alleviate the interference of class-independent features, and it has great application potential for improving the performance of motor imagery BCI systems in the near future.
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