Ionic composition and pH of the vacuolar sap in marine dinoflagellate Noctiluca

Ionic composition of the vacuolar sap of Noctiluca miliariswas as follows: [Na⁺] = 487.3 mM, [K⁺]=24.1 mM, [Ca²⁺]=6.6 mM,[Mg²⁺]=2.8 mM, [Cl^–]=500mM, [NH₄⁺]=15–25 mM, and[SO₄^2–]=undetectable. To measure the vacuolar pH of singleliving cells, a pH-sensitive glass microelectrode was used.The vacuolar pH value was 3.50 ±0.18. When the cellswere transferred from normal sea water into osmotically adjusted50% sea water for one day, the vacuolar ion concentrations remainedalmost constant. Upon immersing the cells in osmotically unadjustedsea water of various concentrations for one day, the observedincrements or decrements of the vacuolar ion concentrationscould be accounted for largely by the migration of water outof or into the cells. The intrinsic ionic composition of thevacuole seems to be constant against changes in ion concentrationsof the bathing medium. (Received October 20, 1975; )     

Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data

The brain performs various cognitive functions by learning the spatiotemporal salient features of the environment. This learning requires unsupervised segmentation of hierarchically organized spike sequences, but the underlying neural mechanism is only poorly understood. Here, we show that a recurrent gated network of neurons with dendrites can efficiently solve difficult segmentation tasks. In this model, multiplicative recurrent connections learn a context-dependent gating of dendro-somatic information transfers to minimize error in the prediction of somatic responses by the dendrites. Consequently, these connections filter the redundant input features represented by the dendrites but unnecessary in the given context. The model was tested on both synthetic and real neural data. In particular, the model was successful for segmenting multiple cell assemblies repeating in large-scale calcium imaging data containing thousands of cortical neurons. Our results suggest that recurrent gating of dendro-somatic signal transfers is crucial for cortical learning of context-dependent segmentation tasks.     

Learning shapes spontaneous activity itinerating over memorized states

Learning is a process that helps create neural dynamical systems so that an appropriate output pattern is generated for a given input. Often, such a memory is considered to be included in one of the attractors in neural dynamical systems, depending on the initial neural state specified by an input. Neither neural activities observed in the absence of inputs nor changes caused in the neural activity when an input is provided were studied extensively in the past. However, recent experimental studies have reported existence of structured spontaneous neural activity and its changes when an input is provided. With this background, we propose that memory recall occurs when the spontaneous neural activity changes to an appropriate output activity upon the application of an input, and this phenomenon is known as bifurcation in the dynamical systems theory. We introduce a reinforcement-learning-based layered neural network model with two synaptic time scales; in this network, I/O relations are successively memorized when the difference between the time scales is appropriate. After the learning process is complete, the neural dynamics are shaped so that it changes appropriately with each input. As the number of memorized patterns is increased, the generated spontaneous neural activity after learning shows itineration over the previously learned output patterns. This theoretical finding also shows remarkable agreement with recent experimental reports, where spontaneous neural activity in the visual cortex without stimuli itinerate over evoked patterns by previously applied signals. Our results suggest that itinerant spontaneous activity can be a natural outcome of successive learning of several patterns, and it facilitates bifurcation of the network when an input is provided.     

Distribution of a novel protein AgK114 expression in the normal tissues of adult mice: dual expression of AgK114 and growth hormone in anterior pituitary cells

The novel antigen K114 (AgK114) has been previously identified in normal hamster skin, and its expression has been up-regulated accompanying tissue damages of the skin, although there is no information on its biological functions. To determine the physiological role of AgK114, we prepared anti-mouse AgK114 monoclonal antibody and studied its tissue distribution in healthy adult mice by immunocytochemistry. A widespread and unique expression of AgK114 peptide was found in the selected organs of various systems (hair follicle cells and sebaceous gland of skin, ciliated epithelial cells of trachea and bronchial tube, striated portion of submandibular gland, distal convoluted tubule cells of kidney, ciliated epithelial cells of oviduct, medulla of adrenal gland and anterior lobe of pituitary gland). Interestingly, dual expression of AgK114 peptide and growth hormone in somatotrophs was found in anterior lobe of pituitary gland by double immunocytochemistry. AgK114 peptide was expressed widely in many regionally well-defined cellular systems in various peripheral tissues, suggesting that AgK114 peptide may have some roles of physiological functions in these organs. The data from our current study have provided a rationale for further studies of functional roles of AgK114 peptide in a variety of organs or tissues under physiological conditions.     

Early Archean planktonic mode of life: Implications from fluid dynamics of lenticular microfossils

Lenticular, and commonly flanged, microfossils in 3.0–3.4 Ga sedimentary deposits in Western Australia and South Africa are unusually large (20–80 μm across), robust, and widespread in space and time. To gain insight into the ecology of these organisms, we performed simulations of fluid dynamics of virtual cells mimicking lenticular forms of variable sizes, oblateness, flange presence, and flange thickness. Results demonstrate that (a) the flange reduces sedimentation velocity, (b) this flange function works more effectively in larger cells, and (c) modest oblateness lowers sedimentation rate. These observations support interpretations that the lenticular microbes were planktonic—a lifestyle that could have been advantageous in an early Earth harsh environment including violent volcanic activities, repeated asteroid impacts, and relatively high UV‐radiation. Although the robustness of these organisms could have provided additional protection on the early Earth, this architecture may have impeded a planktonic lifestyle by increasing cell density. However, our data suggest that this disadvantage could have been compensated by enlargement of cell volume, which could have enhanced the ability of the flange to slow sedimentation rate, especially if coupled with vacuolation. The results of this simulation study may help to explain the unique morphology and unusually large size of these Archean microfossils.     

Switching dynamics and the transient memory storage in a model enzyme network involving Ca2+/calmodulin-dependent protein kinase II in synapses

 The signal processing through a chain of phosphorylation-dephosphorylations mediated by a pair of enzymes, Ca²⁺/calmodulin-dependent protein kinase II and the associated phosphatase, is formulated as a non-autonomous dynamical system in the framework of non-autocatalytic, intraholoenzyme reaction dynamics. A classification of switching characteristics of the system is made in the parameter space comprising the three controllable system parameters: an input-pulse intensity and initial concentrations of the two associated enzymes. It is found that a region of parameter space exists termed the transition zone, that exhibits a quasi-switching behaviour characterized by a signal storage time being prolonged by more than several orders of magnitude (10⁴ times in certain cases) for the increase of two orders of magnitude in the input signal intensity. The effect of alterations of certain rate constants on the quasi-switching property is explored. It is numerically demonstrated that the Ca²⁺/calmodulin-dependent kinase II-related phosphatase is the most important key enzyme for regulating the signal storage time. Received: 25 April 1994/Accepted in revised form: 16 December 1994     

Factors influencing lifespan dependency on agricultural crops by brown bears

Landscape and Ecological Engineering - Investigating factors underlying human-wildlife conflicts in agricultural landscapes is important for both preventing crop damage and wildlife conservation....     

Aberrant Active cis-Regulatory Elements Associated with Downregulation of RET Finger Protein Overcome Chemoresistance in Glioblastoma

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Coordinated ciliary beating requires Odf2-mediated polarization of basal bodies via basal feet

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Highlights? Mice expressing truncated Odf2 cough and sneeze due to primary ciliary dyskinesia ? Full-length Odf2 is needed for the formation of basal body-associated basal feet ? In the absence of basal feet, basal bodies fail to align with planar polarity cues ? Polarization of basal bodies by Odf2 is required for coordinated ciliary beating     

Power-law inter-spike interval distributions infer a conditional maximization of entropy in cortical neurons

The brain is considered to use a relatively small amount of energy for its efficient information processing. Under a severe restriction on the energy consumption, the maximization of mutual information (MMI), which is adequate for designing artificial processing machines, may not suit for the brain. The MMI attempts to send information as accurate as possible and this usually requires a sufficient energy supply for establishing clearly discretized communication bands. Here, we derive an alternative hypothesis for neural code from the neuronal activities recorded juxtacellularly in the sensorimotor cortex of behaving rats. Our hypothesis states that in vivo cortical neurons maximize the entropy of neuronal firing under two constraints, one limiting the energy consumption (as assumed previously) and one restricting the uncertainty in output spike sequences at given firing rate. Thus, the conditional maximization of firing-rate entropy (CMFE) solves a tradeoff between the energy cost and noise in neuronal response. In short, the CMFE sends a rich variety of information through broader communication bands (i.e., widely distributed firing rates) at the cost of accuracy. We demonstrate that the CMFE is reflected in the long-tailed, typically power law, distributions of inter-spike intervals obtained for the majority of recorded neurons. In other words, the power-law tails are more consistent with the CMFE rather than the MMI. Thus, we propose the mathematical principle by which cortical neurons may represent information about synaptic input into their output spike trains.