A model for control of breathing in mammals: coupling neural dynamics to peripheral gas exchange and transport

A new model for aspects of the control of respiration in mammals has been developed. The model integrates a reduced representation of the brainstem respiratory neural controller together with peripheral gas exchange and transport mechanisms. The neural controller consists of two components. One component represents the inspiratory oscillator in the pre-Bötzinger complex (pre-BötC) incorporating biophysical mechanisms for rhythm generation. The other component represents the ventral respiratory group (VRG), which is driven by the pre-BötC for generation of inspiratory (pre)motor output. The neural model was coupled to simplified models of the lungs incorporating oxygen and carbon dioxide transport. The simplified representation of the brainstem neural circuitry has regulation of both frequency and amplitude of respiration and is done in response to partial pressures of oxygen and carbon dioxide in the blood using proportional (P) and proportional plus integral (PI) controllers. We have studied the coupled system under open and closed loop control. We show that two breathing regimes can exist in the model. In one regime an increase in the inspiratory frequency is accompanied by an increase in amplitude. In the second regime an increase in frequency is accompanied by a decrease in amplitude. The dynamic response of the model to changes in the concentration of inspired O₂ or inspired CO₂ was compared qualitatively with experimental data reported in the physiological literature. We show that the dynamic response with a PI-controller fits the experimental data better but suggests that when high levels of CO₂ are inspired the respiratory system cannot reach steady state. Our model also predicts that there could be two possible mechanisms for apnea appearance when 100% O₂ is inspired following a period of 5% inspired O₂. This paper represents a novel attempt to link neural control and gas transport mechanisms, highlights important issues in amplitude and frequency control and sets the stage for more complete neurophysiological control models.     

When shared concept cells support associations: Theory of overlapping memory engrams

Assemblies of neurons, called concepts cells, encode acquired concepts in human Medial Temporal Lobe. Those concept cells that are shared between two assemblies have been hypothesized to encode associations between concepts. Here we test this hypothesis in a computational model of attractor neural networks. We find that for concepts encoded in sparse neural assemblies there is a minimal fraction c_min of neurons shared between assemblies below which associations cannot be reliably implemented; and a maximal fraction c_max of shared neurons above which single concepts can no longer be retrieved. In the presence of a periodically modulated background signal, such as hippocampal oscillations, recall takes the form of association chains reminiscent of those postulated by theories of free recall of words. Predictions of an iterative overlap-generating model match experimental data on the number of concepts to which a neuron responds.     

Discovering sparse control strategies in neural activity

Biological circuits such as neural or gene regulation networks use internal states to map sensory input to an adaptive repertoire of behavior. Characterizing this mapping is a major challenge for systems biology. Though experiments that probe internal states are developing rapidly, organismal complexity presents a fundamental obstacle given the many possible ways internal states could map to behavior. Using C. elegans as an example, we propose a protocol for systematic perturbation of neural states that limits experimental complexity and could eventually help characterize collective aspects of the neural-behavioral map. We consider experimentally motivated small perturbations—ones that are most likely to preserve natural dynamics and are closer to internal control mechanisms—to neural states and their impact on collective neural activity. Then, we connect such perturbations to the local information geometry of collective statistics, which can be fully characterized using pairwise perturbations. Applying the protocol to a minimal model of C. elegans neural activity, we find that collective neural statistics are most sensitive to a few principal perturbative modes. Dominant eigenvalues decay initially as a power law, unveiling a hierarchy that arises from variation in individual neural activity and pairwise interactions. Highest-ranking modes tend to be dominated by a few, “pivotal” neurons that account for most of the system’s sensitivity, suggesting a sparse mechanism of collective control.     

Genetic and Anatomical Basis of the Barrier Separating Wakefulness and Anesthetic-Induced Unresponsiveness

A robust, bistable switch regulates the fluctuations between wakefulness and natural sleep as well as those between wakefulness and anesthetic-induced unresponsiveness. We previously provided experimental evidence for the existence of a behavioral barrier to transitions between these states of arousal, which we call neural inertia. Here we show that neural inertia is controlled by processes that contribute to sleep homeostasis and requires four genes involved in electrical excitability: Sh, sss, na and unc79. Although loss of function mutations in these genes can increase or decrease sensitivity to anesthesia induction, surprisingly, they all collapse neural inertia. These effects are genetically selective: neural inertia is not perturbed by loss-of-function mutations in all genes required for the sleep/wake cycle. These effects are also anatomically selective: sss acts in different neurons to influence arousal-promoting and arousal-suppressing processes underlying neural inertia. Supporting the idea that anesthesia and sleep share some, but not all, genetic and anatomical arousal-regulating pathways, we demonstrate that increasing homeostatic sleep drive widens the neural inertial barrier. We propose that processes selectively contributing to sleep homeostasis and neural inertia may be impaired in pathophysiological conditions such as coma and persistent vegetative states.     

Timing as an intrinsic property of neural networks: evidence from in vivo and in vitro experiments

The discrimination and production of temporal patterns on the scale of hundreds of milliseconds are critical to sensory and motor processing. Indeed, most complex behaviours, such as speech comprehension and production, would be impossible in the absence of sophisticated timing mechanisms. Despite the importance of timing to human learning and cognition, little is known about the underlying mechanisms, in particular whether timing relies on specialized dedicated circuits and mechanisms or on general and intrinsic properties of neurons and neural circuits. Here, we review experimental data describing timing and interval-selective neurons in vivo and in vitro. We also review theoretical models of timing, focusing primarily on the state-dependent network model, which proposes that timing in the subsecond range relies on the inherent time-dependent properties of neurons and the active neural dynamics within recurrent circuits. Within this framework, time is naturally encoded in populations of neurons whose pattern of activity is dynamically changing in time. Together, we argue that current experimental and theoretical studies provide sufficient evidence to conclude that at least some forms of temporal processing reflect intrinsic computations based on local neural network dynamics.     

Intracellular Recording,Sensory Field Mapping,and Culturing Identified Neurons in the Leech,Hirudo medicinalis

The freshwater leech, Hirudo medicinalis, is a versatile model organism that has been used to address scientific questions in the fields of neurophysiology, neuroethology, and developmental biology. The goal of this report is to consolidate experimental techniques from the leech system into a single article that will be of use to physiologists with expertise in other nervous system preparations, or to biology students with little or no electrophysiology experience. We demonstrate how to dissect the leech for recording intracellularly from identified neural circuits in the ganglion. Next we show how individual cells of known function can be removed from the ganglion to be cultured in a Petri dish, and how to record from those neurons in culture. Then we demonstrate how to prepare a patch of innervated skin to be used for mapping sensory or motor fields. These leech preparations are still widely used to address basic electrical properties of neural networks, behavior, synaptogenesis, and development. They are also an appropriate training module for neuroscience or physiology teaching laboratories.     

Visualizing the lipid dynamics role in infrared neural stimulation using stimulated Raman scattering

Infrared neural stimulation (INS) uses pulsed infrared light to yield label-free neural stimulation with broad experimental and translational utility. Despite its robust demonstration, INS’s mechanistic and biophysical underpinnings have been the subject of debate for more than a decade. The role of lipid membrane thermodynamics appears to play an important role in how fast IR-mediated heating nonspecifically drives action potential generation. Direct observation of lipid membrane dynamics during INS remains to be shown in a live neural model system. We used hyperspectral stimulated Raman scattering microscopy to study biochemical signatures of high-speed vibrational dynamics underlying INS in a live neural cell culture model. The findings suggest that lipid bilayer structural changes occur during INS in vitro in NG108-15 neuroglioma cells. Lipid-specific signatures of cell stimulated Raman scattering spectra varied with stimulation energy and radiation exposure. The spectroscopic observations agree with high-speed ratiometric fluorescence imaging of a conventional lipophilic membrane structure reporter, 4-(2-(6-(dibutylamino)-2-naphthalenyl)ethenyl)-1-(3-sulfopropyl)pyridinium hydroxide. The findings support the hypothesis that INS causes changes in the lipid membrane of neural cells by changing the lipid membrane packing order. This work highlights the potential of hyperspectral stimulated Raman scattering as a method to safely study biophysical and biochemical dynamics in live cells.     

Live image profiling of neural crest lineages in zebrafish transgenic lines

Zebrafish transgenic lines are important experimental tools for lineage tracing and imaging studies. It is crucial to precisely characterize the cell lineages labeled in transgenic lines to understand their limitations and thus properly interpret the data obtained from their use; only then can we confidently select a line appropriate for our particular research objectives. Here we profiled the cell lineages labeled in the closely related neural crest transgenic lines Tg(foxd3:GFP), Tg(sox10:eGFP) and Tg(sox10:mRFP). These fish were crossed to generate embryos, in which foxd3 and sox10 transgenic neural crest labeling could be directly compared at the cellular level using live confocal imaging. We have identified key differences in the cell lineages labeled in each line during early neural crest development and demonstrated that the most anterior cranial neural crest cells initially migrating out of neural tube at the level of forebrain and anterior midbrain express sox10:eGFP and sox10:mRFP, but not foxd3:GFP. This differential profile was robustly maintained in the different-tiating progeny of the neural crest lineages until 3.5dpf. Our data will enable researchers to make an informed choice in selecting transgenic lines for future neural crest research.     

基于BP神经网络与ETM+遥感数据的盐城滨海自然湿地覆被分类

高效而精确的湿地遥感分类是大范围湿地资源动态监测与管理的必要保障。本研究使用ETM 遥感数据,借助Matlab神经网络工具箱,构建了基于BP神经网络的滨海湿地覆被分类模型,并将其应用于江苏盐城沿海湿地珍禽国家级自然保护区的核心区的自然湿地覆被分类研究中。本研究选择3、4、7、8波段作为输入层变量,单隐藏层设为10个节点,输出层变量对应待划分的8种覆被类型,构建三层式BP神经网络滨海湿地覆被分类模型。结果显示,BP分类总精度为85.91%,Kappa系数为0.8328,与最小距离法和极大似然法的分类总精度相比,分别提高了7.99%和6.08%,Kappa系数也相比提高。研究结果表明,BP神经网络分类法是一种较为有效的湿地遥感影像分类技术,能够提高分类精度。     

Experiments and ANFIS modelling for the biodegradation of penicillin-G wastewater using anaerobic hybrid reactor

The performance of an anaerobic hybrid reactor (AHR) for treating penicillin-G wastewater was investigated at the ambient temperatures of 30-35 °C for 245 days in three phases. The experimental data were analysed by adopting an adaptive network-based fuzzy inference system (ANFIS) model, which combines the merits of both fuzzy systems and neural network technology. The statistical quality of the ANFIS model was significant due to its high correlation coefficient R² between experimental and simulated COD values. The R² was found to be 0.9718, 0.9268 and 0.9796 for the I, II and III phases, respectively. Furthermore, one to one correlation among the simulated and observed values was also observed. The results showed the proposed ANFIS model was well performed in predicting the performance of AHR.     

Neural Crest Cells Isolated from the Bone Marrow of Transgenic Mice Express JCV T-Antigen

JC virus (JCV), a common human polyomavirus, is the etiological agent of the demyelinating disease, progressive multifocal leukoencephalopathy (PML). In addition to its role in PML, studies have demonstrated the transforming ability of the JCV early protein, T-antigen, and its association with some human cancers. JCV infection occurs in childhood and latent virus is thought to be maintained within the bone marrow, which harbors cells of hematopoietic and non-hematopoietic lineages. Here we show that non-hematopoietic mesenchymal stem cells (MSCs) isolated from the bone marrow of JCV T-antigen transgenic mice give rise to JCV T-antigen positive cells when cultured under neural conditions. JCV T-antigen positive cells exhibited neural crest characteristics and demonstrated p75, SOX-10 and nestin positivity. When cultured in conditions typical for mesenchymal cells, a population of T-antigen negative cells, which did not express neural crest markers arose from the MSCs. JCV T-antigen positive cells could be cultured long-term while maintaining their neural crest characteristics. When these cells were induced to differentiate into neural crest derivatives, JCV T-antigen was downregulated in cells differentiating into bone and maintained in glial cells expressing GFAP and S100. We conclude that JCV T-antigen can be stably expressed within a fraction of bone marrow cells differentiating along the neural crest/glial lineage when cultured in vitro. These findings identify a cell population within the bone marrow permissible for JCV early gene expression suggesting the possibility that these cells could support persistent viral infection and thus provide clues toward understanding the role of the bone marrow in JCV latency and reactivation. Further, our data provides an excellent experimental model system for studying the cell-type specificity of JCV T-antigen expression, the role of bone marrow-derived stem cells in the pathogenesis of JCV-related diseases and the opportunities for the use of this model in development of therapeutic strategies.     

Prediction of removal efficiency of Lanaset Red G on walnut husk using artificial neural network model

An artificial neural network (ANN) model was used to predict removal efficiency of Lanaset Red (LR) G on walnut husk (WH). This adsorbent was characterized by FTIR-ATR. Effects of particle size, adsorbent dose, initial pH value, dye concentration, and contact time were investigated to optimize sorption process. Operating variables were used as the inputs to the constructed neural network to predict the dye uptake at any time as an output. Commonly used pseudo second-order model was fitted to the experimental data to compare with ANN model. According to error analyses and determination of coefficients, ANN was the more appropriate model to describe this sorption process. Results of ANN indicated that pH was the most efficient parameter (43%), followed by initial dye concentration (40%) for sorption of LR G on WH.     

Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze

Neuropsychological tasks used in primates to investigate mechanisms of learning and memory are typically visually guided cognitive tasks. We have developed visual cognitive tasks for rats using the Floor Projection Maze^1,2 that are optimized for visual abilities of rats permitting stronger comparisons of experimental findings with other species.In order to investigate neural correlates of learning and memory, we have integrated electrophysiological recordings into fully automated cognitive tasks on the Floor Projection Maze^1,2. Behavioral software interfaced with an animal tracking system allows monitoring of the animal''s behavior with precise control of image presentation and reward contingencies for better trained animals. Integration with an in vivo electrophysiological recording system enables examination of behavioral correlates of neural activity at selected epochs of a given cognitive task.We describe protocols for a model system that combines automated visual presentation of information to rodents and intracranial reward with electrophysiological approaches. Our model system offers a sophisticated set of tools as a framework for other cognitive tasks to better isolate and identify specific mechanisms contributing to particular cognitive processes.     

Deep learning allows genome-scale prediction of Michaelis constants from structural features

The Michaelis constant K_M describes the affinity of an enzyme for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of K_M are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme–substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts K_M values for natural enzyme–substrate combinations using machine and deep learning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and on a deep numerical representation of the enzyme’s amino acid sequence. We provide genome-scale K_M predictions for 47 model organisms, which can be used to approximately relate metabolite concentrations to cellular physiology and to aid in the parameterization of kinetic models of cellular metabolism.

To understand the action of an enzyme, we need to know its affinity for its substrates, quantified by Michaelis constants, but these are difficult to measure experimentally. This study shows that a deep learning model that can predict them from structural features of the enzyme and substrate, providing KM predictions for all enzymes across 47 model organisms.     

On the Number of Neurons and Time Scale of Integration Underlying the Formation of Percepts in the Brain

All of our perceptual experiences arise from the activity of neural populations. Here we study the formation of such percepts under the assumption that they emerge from a linear readout, i.e., a weighted sum of the neurons’ firing rates. We show that this assumption constrains the trial-to-trial covariance structure of neural activities and animal behavior. The predicted covariance structure depends on the readout parameters, and in particular on the temporal integration window w and typical number of neurons K used in the formation of the percept. Using these predictions, we show how to infer the readout parameters from joint measurements of a subject’s behavior and neural activities. We consider three such scenarios: (1) recordings from the complete neural population, (2) recordings of neuronal sub-ensembles whose size exceeds K, and (3) recordings of neuronal sub-ensembles that are smaller than K. Using theoretical arguments and artificially generated data, we show that the first two scenarios allow us to recover the typical spatial and temporal scales of the readout. In the third scenario, we show that the readout parameters can only be recovered by making additional assumptions about the structure of the full population activity. Our work provides the first thorough interpretation of (feed-forward) percept formation from a population of sensory neurons. We discuss applications to experimental recordings in classic sensory decision-making tasks, which will hopefully provide new insights into the nature of perceptual integration.     

MeCP2 Dependent Heterochromatin Reorganization during Neural Differentiation of a Novel Mecp2-Deficient Embryonic Stem Cell Reporter Line

The X-linked Mecp2 is a known interpreter of epigenetic information and mutated in Rett syndrome, a complex neurological disease. MeCP2 recruits HDAC complexes to chromatin thereby modulating gene expression and, importantly regulates higher order heterochromatin structure. To address the effects of MeCP2 deficiency on heterochromatin organization during neural differentiation, we developed a versatile model for stem cell in vitro differentiation. Therefore, we modified murine Mecp2 deficient (Mecp2 ^−/y) embryonic stem cells to generate cells exhibiting green fluorescent protein expression upon neural differentiation. Subsequently, we quantitatively analyzed heterochromatin organization during neural differentiation in wild type and in Mecp2 deficient cells. We found that MeCP2 protein levels increase significantly during neural differentiation and accumulate at constitutive heterochromatin. Statistical analysis of Mecp2 wild type neurons revealed a significant clustering of heterochromatin per nuclei with progressing differentiation. In contrast we found Mecp2 deficient neurons and astroglia cells to be significantly impaired in heterochromatin reorganization. Our results (i) introduce a new and manageable cellular model to study the molecular effects of Mecp2 deficiency, and (ii) support the view of MeCP2 as a central protein in heterochromatin architecture in maturating cells, possibly involved in stabilizing their differentiated state.     

Conditional Genetic Transsynaptic Tracing in the Embryonic Mouse Brain

Anatomical path tracing is of pivotal importance to decipher the relationship between brain and behavior. Unraveling the formation of neural circuits during embryonic maturation of the brain however is technically challenging because most transsynaptic tracing methods developed to date depend on stereotaxic tracer injection. To overcome this problem, we developed a binary genetic strategy for conditional genetic transsynaptic tracing in the mouse brain. Towards this end we generated two complementary knock-in mouse strains to selectively express the bidirectional transsynaptic tracer barley lectin (BL) and the retrograde transsynaptic tracer Tetanus Toxin fragment C from the ROSA26 locus after Cre-mediated recombination. Cell-specific tracer production in these mice is genetically encoded and does not depend on mechanical tracer injection. Therefore our experimental approach is suitable to study neural circuit formation in the embryonic murine brain. Furthermore, because tracer transfer across synapses depends on synaptic activity, these mouse strains can be used to analyze the communication between genetically defined neuronal populations during brain development at a single cell resolution. Here we provide a detailed protocol for transsynaptic tracing in mouse embryos using the novel recombinant ROSA26 alleles. We have utilized this experimental technique in order to delineate the neural circuitry underlying maturation of the reproductive axis in the developing female mouse brain.     

Magnetic resonance imaging of a microvascular-interstitium model on a microfluidic device

We report a microvascular-interstitium model on microfluidic devices to study leakage of drugs from blood vessels under in vivo-like flow conditions. We employed magnetic resonance imaging to demonstrate the compatibility of the model for experimental animals and humans. We observed transport of two types of different molecular-weight contrast agents into the model interstitium. The ratio of the transport rates of agents agreed with the ratio calculated from diffusion coefficients of the agents. We expect that the model will be useful for the estimation and evaluation of leakage of many kinds of agents in vivo.     

Progenitor-derived Oligodendrocyte Culture System from Human Fetal Brain

Differentiation of human neural progenitors into neuronal and glial cell types offers a model to study and compare molecular regulation of neural cell lineage development. In vitro expansion of neural progenitors from fetal CNS tissue has been well characterized. Despite the identification and isolation of glial progenitors from adult human sub-cortical white matter and development of various culture conditions to direct differentiation of fetal neural progenitors into myelin producing oligodendrocytes, acquiring sufficient human oligodendrocytes for in vitro experimentation remains difficult. Differentiation of galactocerebroside⁺ (GalC) and O4⁺ oligodendrocyte precursor or progenitor cells (OPC) from neural precursor cells has been reported using second trimester fetal brain. However, these cells do not proliferate in the absence of support cells including astrocytes and neurons, and are lost quickly over time in culture. The need remains for a culture system to produce cells of the oligodendrocyte lineage suitable for in vitro experimentation.Culture of primary human oligodendrocytes could, for example, be a useful model to study the pathogenesis of neurotropic infectious agents like the human polyomavirus, JCV, that in vivo infects those cells. These cultured cells could also provide models of other demyelinating diseases of the central nervous system (CNS). Primary, human fetal brain-derived, multipotential neural progenitor cells proliferate in vitro while maintaining the capacity to differentiate into neurons (progenitor-derived neurons, PDN) and astrocytes (progenitor-derived astrocytes, PDA) This study shows that neural progenitors can be induced to differentiate through many of the stages of oligodendrocytic lineage development (progenitor-derived oligodendrocytes, PDO). We culture neural progenitor cells in DMEM-F12 serum-free media supplemented with basic fibroblast growth factor (bFGF), platelet derived growth factor (PDGF-AA), Sonic hedgehog (Shh), neurotrophic factor 3 (NT-3), N-2 and triiodothyronine (T3). The cultured cells are passaged at 2.5e6 cells per 75cm flasks approximately every seven days. Using these conditions, the majority of the cells in culture maintain a morphology characterized by few processes and express markers of pre-oligodendrocyte cells, such as A2B5 and O-4. When we remove the four growth factors (GF) (bFGF, PDGF-AA, Shh, NT-3) and add conditioned media from PDN, the cells start to acquire more processes and express markers specific of oligodendrocyte differentiation, such as GalC and myelin basic protein (MBP). We performed phenotypic characterization using multicolor flow cytometry to identify unique markers of oligodendrocyte.