Mitophagy, a mitochondrial quality control process for eliminating dysfunctional mitochondria, can be induced by a response of dynamin-related protein 1 (Drp1) to a reduction in mitochondrial membrane potential (MMP) and mitochondrial division. However, the coordination between MMP and mitochondrial division for selecting the damaged portion of the mitochondrial network is less understood. Here, we found that MMP is reduced focally at a fission site by the Drp1 recruitment, which is initiated by the interaction of Drp1 with mitochondrial zinc transporter Zip1 and Zn 2+ entry through the Zip1-MCU complex. After division, healthy mitochondria restore MMP levels and participate in the fusion-fission cycle again, but mitochondria that fail to restore MMP undergo mitophagy. Thus, interfering with the interaction between Drp1 and Zip1 blocks the reduction of MMP and the subsequent mitophagic selection of damaged mitochondria. These results suggest that Drp1-dependent fission provides selective pressure for eliminating ''bad sectors'' in the mitochondrial network, serving as a mitochondrial quality surveillance system.

Tissue-clearing techniques have received great attention for volume imaging and for the potential to be applied in optical diagnosis. In principle, tissue clearing is achieved by reducing light scattering through a combination of lipid removal, size change, and matching of the refractive index (RI) between the imaging solution and the tissue. However, the contributions of these major factors in tissue clearing have not been systematically evaluated yet. In this study, we experimentally measured and mathematically calculated the contribution of these factors to the clearing of four organs (brain, liver, kidney, and lung). We found that these factors differentially influence the maximal clearing efficacy of tissues and the diffusivity of materials inside the tissue. We propose that these physical properties of organs can be utilized for the quality control (Q/C) process during tissue clearing, as well as for the monitoring of the pathological changes of tissues.

The human spinal cord forms well-organized neural circuits for environment sensing and motor behavior. The three-dimensional (3D) induction of the spinal cord-like tissue from human pluripotent stem cells has been reported, but they often do not mimic morphological features of neurulation and their maturity is limited. Here, we report an advanced 3D culture system for the production of human spinal cord-like organoids (hSCOs) suitable for the scaleup and quantitative studies. The hSCOs exhibited many aspects of spinal cord development, including neurulation-like tube-forming morphogenesis, differentiation of the major spinal cord neurons and glial cells, and mature synaptic functional activities. We further demonstrated that hSCOs platform allowed quantitative and systematic high-throughput examination of the potential risk of neural tube defects induced by antiepileptic drugs. Thus, hSCOs can be used for understanding human spinal cord development, disease modeling, and toxicology screening..

There is no simple cutoff beam-flow value, such as the well-quoted less than 60°, at which velocity measurements can be considered accurate. For follow-up imaging, beam-flow angle differences should be considered, and the same beam-flow angles should be used when possible. Follow-up imaging by different sonography machines is feasible.

Synchronized neural bursts are one of the most noticeable dynamic features of neural networks, being essential for various phenomena in neuroscience, yet their complex dynamics are not well understood. With extrinsic electrical and optical manipulations on cultured neural networks, we demonstrate that the regularity (or randomness) of burst sequences is in many cases determined by a (few) low-dimensional attractor(s) working under strong neural noise. Moreover, there is an optimal level of noise strength at which the regularity of the interburst interval sequence becomes maximal-a phenomenon of coherence resonance. The experimental observations are successfully reproduced through computer simulations on a well-established neural network model, suggesting that the same phenomena may occur in many in vivo as well as in vitro neural networks.