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The hippocampus and associated cholinergic inputs have important roles in spatial memory in rodents. Muscarinic acetylcholine receptors (mAChRs) are involved in the communication of cholinergic signals and regulate spatial memory. They have been found to impact the memory encoding process, but the effect on memory retrieval is controversial. Previous studies report that scopolamine (a non-selective antagonist of mAChR) induces cognitive deficits on animals, resulting in impaired memory encoding, but the effect on memory retrieval is less certain. We tested the effects of blocking mAChRs on hippocampal network activity and neural ensembles that had previously encoded spatial information. The activity of hundreds of neurons in mouse hippocampal CA1 was recorded using calcium imaging with a miniaturised fluorescent microscope and properties of place cells and neuronal ensemble behaviour in a linear track environment were observed. We found that the decoding accuracy and the stability of spatial representation revealed by hippocampal neural ensemble were significantly reduced after the administration of scopolamine. Several other parameters, including neural firing rate, total number of active neurons, place cell number and spatial information content were affected. Similar results were also observed in a simulated hippocampal network model. This study enhances the understanding of the function of mAChRs on spatial memory impairment.

Neurological disorders (NDs), such as amyotrophic lateral sclerosis (ALS), Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and schizophrenia, represent a complex and multifaceted health challenge that affects millions of people around the world. Growing evidence suggests that disrupted neuronal calcium signalling contributes to the pathophysiology of NDs. Additionally, calcium functions as a ubiquitous second messenger involved in diverse cellular processes, from synaptic activity to intercellular communication, making it a potential therapeutic target. Recently, the development of the miniature fluorescence microscope (miniscope) enabled simultaneous recording of the spatiotemporal calcium activity from large neuronal ensembles in unrestrained animals, providing a novel method for studying NDs. In this review, we discuss the abnormalities observed in calcium signalling and its potential as a therapeutic target for NDs. Additionally, we highlight recent studies that utilise miniscope technology to investigate the alterations in calcium dynamics associated with NDs.

The design and production of augmented reality (AR) waveguide combiners face considerable challenges due to the intricate nature of conventional fabrication techniques and the need for high precision. To overcome these obstacles, the field requires rapid prototyping methods that enable researchers and engineers to swiftly explore various designs and configurations, thereby accelerating the development process. Here, we have developed a cost-effective method for fabricating liquid geometric waveguide combiners for AR applications using silicone oil as the medium, leveraging the capabilities of Polyjet 3D printing. During the design phase, we optimized the structure of the waveguide combiner to facilitate easier fabrication. Our proposed method simplifies the production process by removing the need for complicated steps like dicing, layer bonding, and polishing, which are usually involved in traditional manufacturing techniques. We conducted optical simulations and developed a prototype using our patented fabrication method, successfully demonstrating its feasibility for rapid prototyping. Dechuan Sun and colleagues describe a rapid prototyping method for geometric augmented reality waveguides using 3D printing. The approach simplifies fabrication and enables faster exploration of optical designs.

The hippocampus is a complex structure that has a major role in learning and memory. It also integrates information from multisensory modalities, supporting a comprehensive cognitive map for both spatial and non-spatial information. Previous studies have been limited to real-time spatial decoding, typically using electrodes. However, decoding hippocampal non-spatial information in real time has not been previously described. Here, we have constructed a real-time optical decoder driven by the calcium activity of large neuronal ensembles to decode spatial, visual, and auditory information effectively. Using advanced machine learning techniques, our rapid end-to-end decoding achieves high accuracy and provides a multisensory modality detection method. This method enables the real-time investigation of hippocampal neural coding and allows for direct neural communication with animals and patients affected by functional impairments. The ability to decode multimodal sensory inputs in real time thus forms the basis for an all-optical brain-computer interface. Sun and colleagues report real-time hippocampal calcium imaging for jointly capturing spatial, visual, and auditory information. Using machine learning techniques, they demonstrate a rapid and accurate all-optical brain-computer interface for decoding mice’s hippocampus activity.

A rocket engine for space propulsion usually has a nozzle of a large exit area ratio. The nozzle efficiency is greatly affected by the nozzle contour. This paper analysed the effect of the constant capacity ratio in Rao’s method through the design process of an apogee engine. The calculation results show that increasing the heat capacity ratio can produce an expansion contour of smaller expansion angle and exit area ratio. A simple modification of Rao’s method based on thermally perfect gas assumption was made and verified to be more effective. The expansion contour designed by this method has much thinner expansion section and higher performance. For the space engine, a new extension contour type for the end section of the nozzle is proposed. The extension curve bent outward with increasing expansion angle increases the vacuum specific impulse obviously.

A multispectral camera records image data in various wavelengths across the electromagnetic spectrum to acquire additional information that a conventional camera fails to capture. With the advent of high-resolution image sensors and color filter technologies, multispectral imagers in the visible wavelengths have become popular with increasing commercial viability in the last decade. However, multispectral imaging in longwave infrared (LWIR, 8–14 μm) is still an emerging area due to the limited availability of optical materials, filter technologies, and high-resolution sensors. Images from LWIR multispectral cameras can capture emission spectra of objects to extract additional information that a human eye fails to capture and thus have important applications in precision agriculture, forestry, medicine, and object identification. In this work, we experimentally demonstrate an LWIR multispectral image sensor with three wavelength bands using optical elements made of an aluminum (Al)-based plasmonic filter array sandwiched in germanium (Ge). To realize the multispectral sensor, the filter arrays are then integrated into a three-dimensional (3D) printed wheel stacked on a low-resolution monochrome thermal sensor. Our prototype device is calibrated using a blackbody and its thermal output has been enhanced with computer vision methods. By applying a state-of-the-art deep learning method, we have also reconstructed multispectral images to a better spatial resolution. Scientifically, our work demonstrates a versatile spectral thermography technique for detecting target signatures in the LWIR range and other advanced spectral analyses.

We propose a new technique to fabricate flexible-near-field Argument-Reality (AR) display using modular-molds. A near-eye flexible-AR-display is fabricated based on parameters extracted from simulations. Our AR-display successfully reconstructed images and videos from a light-engine. It opens a new approach to fabricate flexible-near-field AR display with good physical stress and collision-resilience.

Dynamical systems exhibit transitions between ordered and disordered states and \"criticality\" occurs when the system lies at the borderline between these states at which the input is neither strongly damped nor excessively amplified. Impairments in brain function such as dementia or epilepsy could arise from failure of adaptive criticality, and deviation from criticality may be a potential biomarker for cognition-related neurological and psychiatric impairments. Miniscope wide-field calcium imaging of several hundred hippocampal CA1 neurons in freely-behaving mice was studied during rest, a cognitive task of novel object recognition (NOR), and novel object recognition following scopolamine administration that greatly impairs spatial memory encoding. We find that while hippocampal networks exhibit characteristics of a near-critical system at rest, the network activity shifts significantly closer to a critical state when the mice engaged in the NOR task. The dynamics shift away from criticality with impairment of novel object performance due to scopolamine-induced memory impairment. These results support the concept that hippocampal neural networks move closer to criticality when successfully processing increased cognitive load, taking advantage of maximal dynamical range, information content, and transmission that occur in critical regimes.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Figures and revisions to text, mainly discussions.

The human retina contains a complex arrangement of photoreceptors that convert light into visual information. Conventional image sensors mimic the trichromacy of the retina using periodic filter mosaics responsive to three primary colors. However, this is, at best, an approximation, as an actual retina exhibits a quasi-random spatial distribution of light-sensitive rod and cone photoreceptors, where the ratio of rods to cones and their concentrations vary across the retina. Hence, the periodic mosaics are limited to accurately simulate the properties of the eye. Here, we present an image sensor with similar distribution, spacing, ratios and spectral characteristics of an actual foveal mosaic for emulating eye-like sampling and mimicking color blindness. To perform image reconstruction, we use a fully convolutional U-Net neural network adopting the concept of receptive fields in the retinal circuitry. Our research will enable the development of digital twin of a retina to further understand color vision deficiencies.

The complexities of fabrication techniques and the demand for high precision have posed significant challenges in the mass production of augmented reality (AR) waveguide combiners. Leveraging the capabilities of Polyjet 3D printing techniques, we have developed a cost-effective method for fabricating liquid geometric waveguide combiners for AR applications, using silicone oil as the medium. During the design phase, we optimized the structure of the waveguide combiner to facilitate easier fabrication. Our proposed method simplifies the production process by removing the need for complicated steps like dicing, layer bonding, and polishing, which are usually involved in traditional manufacturing techniques. We conducted optical simulations and developed a prototype using our patented fabrication method, which successfully demonstrated the integration of virtual images with the real-world environment, thereby confirming its feasibility and potential for cost-effective mass production.