Ultraflexible electrodes for recording neural activity in the mouse spinal cord during motor behavior.

Implantable electrode arrays are powerful tools for directly interrogating neural circuitry in the brain, but implementing this technology in the spinal cord in behaving animals has been challenging due to the spinal cord's significant motion with respect to the vertebral column during behavior. Consequently, the individual and ensemble activity of spinal neurons processing motor commands remains poorly understood. Here, we demonstrate that custom ultraflexible 1-µm-thick polyimide nanoelectronic threads can conduct laminar recordings of many neuronal units within the lumbar spinal cord of unrestrained, freely moving mice. The extracellular action potentials have high signal-to-noise ratio, exhibit well-isolated feature clusters, and reveal diverse patterns of activity during locomotion. Furthermore, chronic recordings demonstrate the stable tracking of single units and their functional tuning over multiple days. This technology provides a path for elucidating how spinal circuits compute motor actions.

Effect of Exogenous and Endogenous Ectoine on Monascus Development, Metabolism, and Pigment Stability.

Monascus, a key player in fermented food production, is known for generating Monascus pigments (MPs) and monacolin K (MK), possessing bioactive properties. However, the limited stability of MPs and mycotoxin citrinin (CTN) constrain the Monascus industry. Extremolytes like ectoine, derived from bacteria, exhibit cytoprotective potential. Here, we investigated the impact of ectoine on Monascus purpureus ATCC 16365, emphasizing development and secondary metabolism. Exogenous 5 mM ectoine supplementation substantially increased the yields of MPs and MK (105%-150%) and reduced CTN production. Ectoine influenced mycelial growth, spore development, and gene expression in Monascus. Remarkably, ectoine biosynthesis was achieved in Monascus, showing comparable effects to exogenous addition. Notably, endogenous ectoine effectively enhanced the stability of MPs under diverse stress conditions. Our findings propose an innovative strategy for augmenting the production and stability of bioactive compounds while reducing CTN levels, advancing the Monascus industry.

Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement.

Enhancing the quality of low-light (LOL) images plays a very important role in many image processing and multimedia applications. In recent years, a variety of deep learning techniques have been developed to address this challenging task. A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene-level contextual information encapsulated in feature spaces, causing many unfavorable outcomes, e.g., details loss, color unsaturation, and artifacts. To address these issues, we develop a new context-sensitive decomposition network (CSDNet) architecture to exploit the scene-level contextual dependencies on spatial scales. More concretely, we build a two-stream estimation mechanism including reflectance and illumination estimation network. We design a novel context-sensitive decomposition connection to bridge the two-stream mechanism by incorporating the physical principle. The spatially varying illumination guidance is further constructed for achieving the edge-aware smoothness property of the illumination component. According to different training patterns, we construct CSDNet (paired supervision) and context-sensitive decomposition generative adversarial network (CSDGAN) (unpaired supervision) to fully evaluate our designed architecture. We test our method on seven testing benchmarks [including massachusetts institute of technology (MIT)-Adobe FiveK, LOL, ExDark, and naturalness preserved enhancement (NPE)] to conduct plenty of analytical and evaluated experiments. Thanks to our designed context-sensitive decomposition connection, we successfully realized excellent enhanced results (with sufficient details, vivid colors, and few noises), which fully indicates our superiority against existing state-of-the-art approaches. Finally, considering the practical needs for high efficiency, we develop a lightweight CSDNet (named LiteCSDNet) by reducing the number of channels. Furthermore, by sharing an encoder for these two components, we obtain a more lightweight version (SLiteCSDNet for short). SLiteCSDNet just contains 0.0301M parameters but achieves the almost same performance as CSDNet. Code is available at https://github.com/KarelZhang/CSDNet-CSDGAN.