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We study the properties of a plasma sheath containing cold positive ions, secondary electrons, and primary electrons with a Cairns distribution (a non-thermal velocity distribution). We derive the generalized Bohm criterion and Bohm speed, the new floating potential at the wall, and the new critical secondary electron emission coefficient. We show that these properties of the plasma sheath depend significantly on the α-parameter in the non-thermal α-distribution, and so they are generally different from those of the plasma sheath if the primary electrons were assumed to be a Maxwellian distribution.

The idea of Chandrasekhar’s conditions for the equilibrium and stability of stars is revisited with a new universal three-parameter non-Maxwell distribution. We derive the maximum radiation pressures in the non-Maxwell distribution for a gas star and a centrally condensed star, and thus, we generalize Chandrasekhar’s conditions in a Maxwellian sense. By numerical analyses, we find that the non-Maxwellian distribution usually reduces the maximum radiation pressures in both gas stars and centrally condensed stars as compared with cases where the gas is assumed to have a Maxwellian distribution.

The internal brain dynamics that link sensation and action are arguably better studied during natural animal behaviors. Here, we report on a novel volume imaging and 3D tracking technique that monitors whole brain neural activity in freely swimming larval zebrafish (Danio rerio). We demonstrated the capability of our system through functional imaging of neural activity during visually evoked and prey capture behaviors in larval zebrafish. How do neurons in the brain process information from the senses and drive complex behaviors? This question has fascinated neuroscientists for many years. It is currently not possible to record the electrical activities of all of the 100 billion neurons in a human brain. Yet, in the last decade, it has become possible to genetically engineer some neurons in animals to produce fluorescence reporters that change their brightness in response to brain activity and then monitor them under a microscope. In small animals such as zebrafish larvae, this method makes it possible to monitor the activities of all the neurons in the brain if the animal’s head is held still. However, many behaviors – for example, catching prey – require movement, and no existing technique could image brain activity in enough detail if the animal’s head was moving. Cong, Wang, Chai, Hang et al. have now made progress towards this goal by developing a new technique to image neural activity across the whole brain of a zebrafish larva as it swims freely in a small water-filled chamber. The technique uses high-speed cameras and computer software to track the movements of the fish in three dimensions, and then automatically moves the chamber under the microscope such that the animal’s brain is constantly kept in focus. The newly developed microscope can capture changes in neural activity across a large volume all at the same time. It is then further adapted to overcome problems caused by sudden or swift movements, which would normally result in motion blur. With this microscope set up, Cong et al. were able to capture, for the first time, activity from all the neurons in a zebrafish larva’s brain as it pursued and caught its prey. This technique provides a new window into how brain activity changes when animals are behaving naturally. In the future, this technique could help link the activities of neurons to different behaviors in several popular model organisms including fish, worms and fruit flies.

A detailed understanding of the function of neural networks and how they are supported by a dynamic vascular system requires fast three-dimensional imaging in thick tissues. Here we present confocal light field microscopy, a method that enables fast volumetric imaging in the brain at depths of hundreds of micrometers. It uses a generalized confocal detection scheme that selectively collects fluorescent signals from the in-focus volume and provides optical sectioning capability to improve imaging resolution and sensitivity in thick tissues. We demonstrate recording of whole-brain calcium transients in freely swimming zebrafish larvae and observe behaviorally correlated activities in single neurons during prey capture. Furthermore, in the mouse brain, we detect neural activities at depths of up to 370 μm and track blood cells at 70 Hz over a volume of diameter 800 μm × thickness 150 μm and depth of up to 600 μm. A new imaging method enables deep imaging of neural networks.

Multidimensional landscapes of regulatory genes in neuronal phenotypes at whole-brain levels in the vertebrate remain elusive. We generated single-cell transcriptomes of ~67,000 region- and neurotransmitter/neuromodulator-identifiable cells from larval zebrafish brains. Hierarchical clustering based on effector gene profiles (‘terminal features’) distinguished major brain cell types. Sister clusters at hierarchical termini displayed similar terminal features. It was further verified by a population-level statistical method. Intriguingly, glutamatergic/GABAergic sister clusters mostly expressed distinct transcription factor (TF) profiles (‘convergent pattern’), whereas neuromodulator-type sister clusters predominantly expressed the same TF profiles (‘matched pattern’). Interestingly, glutamatergic/GABAergic clusters with similar TF profiles could also display different terminal features (‘divergent pattern’). It led us to identify a library of RNA-binding proteins that differentially marked divergent pair clusters, suggesting the post-transcriptional regulation of neuron diversification. Thus, our findings reveal multidimensional landscapes of transcriptional and post-transcriptional regulators in whole-brain neuronal phenotypes in the zebrafish brain. The brain harbors an astounding diversity of interconnected cells. Each cell contains the same basic set of genetic instructions, but only a fraction of the genome is used in each cell to assemble proteins. This selective gene expression gives rise to each cell’s characteristic properties, such as their shape and location, or whether they can activate or inhibit neighbouring cells. How these defining features are encoded on a genetic level and selectively activated in cells to produce such diversity in the brain is not fully understood. One way to study gene expression in single cells involves profiling the transcriptome, the full range of intermediary RNA molecules a cell produces from its genes to make proteins. Zhang et al. used transcriptome profiling to better understand how thousands of regulatory genes encoding regulatory proteins called transcription factors create different types of neurons in the zebrafish brain, which is similar to but much simpler than the human brain. To do so, they analysed transcriptome data extracted from cell populations located in specific brain regions and displaying different properties. Zhang et al. identified distinct clusters of neurons in the larval zebrafish brain. Mathematical models then analysed the transcriptome profiles of these neuronal clusters with characteristic features. They revealed that neurons with similar characteristics did not necessarily share the same transcription factors. In other words, distinct sets of transcription factors gave rise to the same types of cells. Zhang et al. described this observation as a ‘convergent’ pattern. On the contrary, some neurons with dissimilar features expressed the same sorts of transcription factors, suggesting a ‘divergent’ developmental pattern also exists. In summary, this work sheds light on variable gene expression patterns akin to design principles that shape neuronal diversity in the brain. It gives a new appreciation of how neuronal subtypes result from a complex set of regulatory factors controlling gene expression.

By a natural nonextensive generalization of the conservation of energy in the q-kinetic theory, we study the nonextensivity and the power-law distributions for the many-body systems with the self-gravitating long-range interactions. It is shown that the power-law distributions describe the long-range nature of the interactions and the non-local correlations within the self-gravitating system with the inhomogeneous velocity dispersion. A relation is established between the nonextensive parameter q[ne]1 and the measurable quantities of the self-gravitating system: the velocity dispersion and the mass density. Correspondingly, the nonextensive parameter q can be uniquely determined from the microscopic dynamical equation and thus the physical interpretation of q different from unity can be clearly presented. We derive a nonlinear differential equation for the radial density dependence of the self-gravitating system with the inhomogeneous velocity dispersion, which can correctly describe the density distribution for the dark matter in the above physical situation. We also apply this q-kinetic approach to analyze the nonextensivity of self-gravitating collisionless systems and self-gravitating gaseous dynamical systems, giving the power-law distributions the clear physical meaning.

Anesthetics are commonly used in various medical procedures. Accumulating evidence suggests that early-life anesthetics exposure in infants and children affects brain development, causing psychiatric and neurological disorders. However, the underlying mechanisms are poorly understood. Using zebrafish larvae as a model, we found that the proliferation and migration of oligodendrocyte progenitor cells (OPCs) were severely impaired by the exposure of midazolam (MDZ), an anesthetic widely used in pediatric surgery and intensive care medicine, leading to a reduction of oligodendroglial lineage cell in the dorsal spinal cord. This defect was mimicked by the bath application of translocator protein (TSPO) agonists and partially rescued by genetic downregulation of TSPO. Cell transplantation experiments showed that requirement of TSPO for MDZ-induced oligodendroglial lineage cell defects is cell-autonomous. Furthermore, transmission electron microscopy and in vivo electrophysiological recording experiments demonstrated that MDZ exposure caused axon hypomyelination and action potential propagation retardation, resulting in delayed behavior initiation. Thus, our findings reveal that MDZ affects oligodendroglial lineage cell development and myelination in young animals, raising the care about its clinic use in infants and children.

In the study of circadian rhythms, it has been a puzzle how a limited number of circadian clock genes can control diverse aspects of physiology. Here we investigate circadian gene expression genome-wide using larval zebrafish as a model system. We made use of a spatial gene expression atlas to investigate the expression of circadian genes in various tissues and cell types. Comparison of genome-wide circadian gene expression data between zebrafish and mouse revealed a nearly anti-phase relationship and allowed us to detect novel evolutionarily conserved circadian genes in vertebrates. We identified three groups of zebrafish genes with distinct responses to light entrainment: fast light-induced genes, slow light-induced genes, and dark-induced genes. Our computational analysis of the circadian gene regulatory network revealed several transcription factors (TFs) involved in diverse aspects of circadian physiology through transcriptional cascade. Of these, microphthalmia-associated transcription factor a (mitfa), a dark-induced TF, mediates a circadian rhythm of melanin synthesis, which may be involved in zebrafish's adaptation to daily light cycling. Our study describes a systematic method to discover previously unidentified TFs involved in circadian physiology in complex organisms.

The brain topology highly reflects the complex cognitive functions of the biological brain after million-years of evolution. Learning from these biological topologies is a smarter and easier way to achieve brain-like intelligence with features of efficiency, robustness, and flexibility. Here we proposed a brain topology-improved spiking neural network (BT-SNN) for efficient reinforcement learning. First, hundreds of biological topologies are generated and selected as subsets of the Allen mouse brain topology with the help of the Tanimoto hierarchical clustering algorithm, which has been widely used in analyzing key features of the brain connectome. Second, a few biological constraints are used to filter out three key topology candidates, including but not limited to the proportion of node functions (e.g., sensation, memory, and motor types) and network sparsity. Third, the network topology is integrated with the hybrid numerical solver-improved leaky-integrated and fire neurons. Fourth, the algorithm is then tuned with an evolutionary algorithm named adaptive random search instead of backpropagation to guide synaptic modifications without affecting raw key features of the topology. Fifth, under the test of four animal-survival-like RL tasks (i.e., dynamic controlling in Mujoco), the BT-SNN can achieve higher scores than not only counterpart SNN using random topology but also some classical ANNs (i.e., long-short-term memory and multi-layer perception). This result indicates that the research effort of incorporating biological topology and evolutionary learning rules has much in store for the future.

Detection of moving objects is an essential skill for animals to hunt prey, recognize conspecifics and avoid predators. The zebrafish, as a vertebrate model, primarily uses its elaborate visual system to distinguish moving objects against background scenes. The optic tectum (OT) receives and integrates inputs from various types of retinal ganglion cells (RGCs), including direction-selective (DS) RGCs and size-selective RGCs, and is required for both prey capture and predator avoidance. However, it remains largely unknown how motion information is processed within the OT. Here we performed in vivo whole-cell recording and calcium imaging to investigate the role of superficial interneurons (SINs), a specific type of optic tectal neurons, in motion detection of larval zebrafish. SINs mainly receive excitatory synaptic inputs, exhibit transient ON- or OFF-type of responses evoked by light flashes, and possess a large receptive field (RF). One fifth of SINs are DS and classified into two subsets with separate preferred directions. Furthermore, SINs show size-dependent responses to moving dots. They are efficiently activated by moving objects but not static ones, capable of showing sustained responses to moving objects and having less visual adaptation than periventricular neurons (PVNs), the principal tectal cells. Behaviorally, ablation of SINs impairs prey capture, which requires local motion detection, but not global looming-evoked escape. Finally, starvation enhances the gain of SINs' motion responses while maintaining their size tuning and DS. These results indicate that SINs serve as a motion detector for sensing and localizing sized moving objects in the visual field.