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PubMed1990

Dynamics of a class of immune networks. II. Oscillatory activity of cellular and humoral components.

Simulations show that for a certain range of its free parameters, a model of the immune network including both cellular and humoral components is capable of self-sustained oscillations. The model also possesses a number of fixed points which appear to be unstable. These results, taken together, suggest the hypothesis that the immune network may be able to sustain a non-degenerate diversity of active clones which are actively connected to each other only on condition that the activities of these clones are oscillatory.

Animals↗
PubMed1990

Complex dynamics and noise in simple neural networks with delayed mixed feedback.

This paper briefly reviews the role of mixed feedback, neural delays, and neural noise in the genesis of complex oscillations in neurological feedback systems. The results are concretely discussed within the context of recurrent inhibition in the mammalian hippocampus, and a hybrid version of the pupil light reflex with externally imposed electronic feedback.

Animals↗
PubMed2024

Regulating IL-2 Immune Signaling Function Via A Core Allosteric Structural Network.

Human interleukin-2 (IL-2) is a crucial cytokine for T cell regulation, with therapeutic potential in cancer and autoimmune diseases. However, IL-2's pleiotropic effects across different immune cell types often lead to toxicity and limited efficacy. Previous efforts to enhance IL-2's therapeutic profile have focused on modifying its receptor binding sites. Yet, the underlying dynamics and intramolecular networks contributing to IL-2 receptor recognition remain unexplored. This study presents a detailed characterization of IL-2 dynamics compared to two engineered IL-2 mutants, "superkines" S15 and S1, which exhibit biased signaling towards effector T cells. Using NMR spectroscopy and molecular dynamics simulations, we demonstrate significant variations in core dynamic pathways and conformational exchange rates across these three IL-2 variants. We identify distinct allosteric networks and minor state conformations in the superkines, despite their structural similarity to wild-type IL-2. Furthermore, we rationally design a mutation (L56A) in the S1 superkine's core network, which partially reverts its dynamics, receptor binding affinity, and T cell signaling behavior towards that of wild-type IL-2. Our results reveal that IL-2 superkine core dynamics play a critical role in their enhanced receptor binding and function, suggesting that modulating IL-2 dynamics and core allostery represents an untapped approach for designing immunotherapies with improved immune cell selectivity profiles.

Interleukin-2↗
PubMed1978

Self-stimulation in the rat: quantitative characteristics of the reward pathway.

Quantitative characteristics of the neural pathway that carries the reinforcing signal in electrical self-stimulation of the brain were established by finding which combinations of stimulation parameters give the same performance in a runway. The reward for each run was a train of evenly spaced monophasic cathodal pulses from a monopolar electrode. With train duration and pulse frequency held constant, the required current was a hyperbolic function of pulse duration, with chronaxie c approximately 1.5 msec. With pulse duration held constant, the required strength of the train (the charge delivered per second) was a hyperbolic function of train duration, with chronaxie C approximately 500 msec. To a first approximation, the values of c and C were independent of the choice either of train duration and pulse frequency or of pulse duration, respectively. Hence, the current intensity required by any choice of train duration, pulse frequency, and pulse duration dependent on only two basic parameters, c and C, and one quantity, Qi, the required impulse charge. These may reflect, respectively, current integration by directly excited neurons; temporal integration of neural activity by synaptic processes in a neural network; and the peak of the impulse response of the network, assuming that the network has linear dynamics and that the reward depends on the peak of the output of the network.

Animals↗
PubMed1986

Associative recognition and storage in a model network of physiological neurons.

We consider a neural network model in which the single neurons are chosen to closely resemble known physiological properties. The neurons are assumed to be linked by synapses which change their strength according to Hebbian rules on a short time scale (100 ms). The dynamics of the network--the time evolution of the cell potentials and the synapses--is investigated by computer simulation. As in more abstract network models (Cooper 1973; Hopfield 1982; Kohonen 1984) it is found that the local dynamics of the cell potentials and the synaptic strengths result in global cooperative properties of the network and enable the network to process an incoming flux of information and to learn and store patterns associatively. A trained net can associate missing details of a pattern, can correct wrong details and can suppress noise in a pattern. The network can further abstract the prototype from a series of patterns with variations. A suitable coupling constant connecting the dynamics of the cell potentials with the synaptic strengths is derived by a mean field approximation. This coupling constant controls the neural sensitivity and thereby avoids both extremes of the network state, the state of permanent inactivity and the state of epileptic hyperactivity.

Animals↗
PubMed2024

Seasonal hydrological dynamics affected the diversity and assembly process of the antibiotic resistome in a canal network.

The significant threat of antibiotic resistance genes (ARGs) to aquatic environments health has been widely acknowledged. To date, several studies have focused on the distribution and diversity of ARGs in a single river while their profiles in complex river networks are largely known. Here, the spatiotemporal dynamics of ARG profiles in a canal network were examined using high-throughput quantitative PCR, and the underlying assembly processes and its main environmental influencing factors were elucidated using multiple statistical analyses. The results demonstrated significant seasonal dynamics with greater richness and relative abundance of ARGs observed during the dry season compared to the wet season. ARG profiles exhibited a pronounced distance-decay pattern in the dry season, whereas no such pattern was evident in the wet season. Null model analysis indicated that deterministic processes, in contrast to stochastic processes, had a significant impact on shaping the ARG profiles. Furthermore, it was found that Firmicutes and pH emerged as the foremost factors influencing these profiles. This study enhanced our comprehension of the variations in ARG profiles within canal networks, which may contribute to the design of efficient management approaches aimed at restraining the propagation of ARGs.

Seasons↗
PubMed1991

Transient expression of mouse hair keratins in transfected HeLa cells: interactions between "hard" and "soft" keratins.

Although it has been shown previously that an acidic (type I) "soft" keratin can interact with many basic (type II) "soft" keratins to form 10-nm intermediate filaments, it has been unclear whether "soft" keratins are compatible with the "hard" keratins typically found in hair and nail. To address this issue and to generate more structural information about hard keratins, we have isolated and sequenced a cDNA clone that encodes a mouse hair basic keratin (b4). Our sequence data revealed new information regarding the structural conservation of hard keratins as a group, being significantly different from soft keratins. Using expression vectors containing appropriate cDNA inserts, we studied the expression of this basic (b4) as well as an acidic (a1) mouse hair keratin in HeLa cells. The expression of these alien hair keratins in the transfected cells was surveyed using a panel of monoclonal and polyclonal antibodies. Our results indicated that the basic and acidic hair keratin readily incorporated into the existing endogenous soft keratin network of HeLa cells. Overproduction of hair keratin, however, occasionally led to the formation of cytoplasmic aggregates containing both hard and soft keratins. These data suggest that although small amounts of newly synthesized hair keratins can incorporate into the "scaffolding" of the preformed soft keratin filament network, possibly through dynamic subunit exchange, overproduction of hard keratins can lead to the partial collapse of the soft keratin network. These observations, along with the deduced amino acid sequence data, support and extend the concept that hard and soft keratins, although closely related, are divergent enough to justify their being divided into two separate subgroups.

Animals↗
PubMed1990

[Cognitive processes and neuronal networks].

It is clear that computers are but a poor brain models: the nervous system has many "processors" (neurons) in parallel, whereas von Neuman's machines work sequentially on a single processor. In complex systems, emergent properties cannot be inferred from the behaviour of single elements. Anthills display collective "meaningful" moves, while each ant seems to obey local interactions only. Likewise, large parallel networks of processing elements elicit emergent properties. Like brains, some of them are self-organizing systems. In large parallel processing networks, each unit performs an elementary computation: adding inputs from other units. Large nets display surprising spontaneous computational abilities: associative memories, classes, generalizations may be seen as emergent properties of the network. Symbols are dynamical entities, whose handing is driven by local interactions of activation/inhibition of related representations. In such models, representations (memories) are distributed in the whole network, as stable configurations. Indeed, the basic properties of representation in connectionist models seem closer to human mental objects than the classic Artificial Intelligence concepts. Connectionist models have been used in many fields, namely simulations of real neural networks, pattern recognition and artificial vision, speech recognition, language understanding and knowledge representation, problem solving... Connectionist models have been thus used in neurobiology as well as cognition. One basic structure seems indeed able to account for a range of cognitive functions, from perception to problem solving and high level cognitive tasks. Nevertheless studies about "pathological" networks are yet rare, still an open field... We explore some of these fields.

Artificial Intelligence↗
PubMedDate not supplied

[Changes in synaptic potentials and axonal delays as factors causing epileptic instability of neuronal networks].

Synaptic weights, axonal delays and excitability thresholds are three basic parameters influencing the function of neuronal network as a dynamic system. Changes of the general level of the signal flow, without changes in relation between excitatory and inhibitory connections can cause pathologic oscillations in the network. The conductivity in neuronal nets depends on electrochemical processes and may be influenced by the pH-status, ion concentrations, among other parameters. Also different pathological processes can change it. Even slight changes of axonal delays, as shown above, can cause in certain situations unstable, epileptic oscillations of the net.

Action Potentials↗
PubMed1986

Sequential state generation by model neural networks.

Sequential patterns of neural output activity form the basis of many biological processes, such as the cyclic pattern of outputs that control locomotion. I show how such sequences can be generated by a class of model neural networks that make defined sets of transitions between selected memory states. Sequence-generating networks depend upon the interplay between two sets of synaptic connections. One set acts to stabilize the network in its current memory state, while the second set, whose action is delayed in time, causes the network to make specified transitions between the memories. The dynamic properties of these networks are described in terms of motion along an energy surface. The performance of the networks, both with intact connections and with noisy or missing connections, is illustrated by numerical examples. In addition, I present a scheme for the recognition of externally generated sequences by these networks.

Artificial Intelligence↗
PubMed1979

Effect of boundaries on the response of a neural network.

The effect an abrupt boundary has upon the dynamical response of a neural network is investigated. The retina of the Limulus eye is used as a model system for studying this effect. A theoretical technique is presented for the quantitative prediction of the manner in which this neural network responds in the vicinity of its boundary. Corresponding experimental measurements of the response to moving stimuli by single optic neurons located near retinal boundaries are presented. Theory and experiment show detailed quantitative agreement.

Animals↗
PubMed1992

A neural network model rapidly learning gains and gating of reflexes necessary to adapt to an arm's dynamics.

Effects of dynamic coupling, gravity, inertia and the mechanical impedances of the segments of a multi-jointed arm are shown to be neutralizable through a reflex-like operating three layer static feedforward network. The network requires the proprioceptively mediated actual state variables (here angular velocity and position) of each arm segment. Added neural integrators (and/or differentiators) can make the network exhibit dynamic properties. Then, actual feedback is not necessary and the network can operate in a pure feedforward fashion. Feedforward of an additional load can easily be implemented into the network using "descendent gating", and a negative feedback control loop added to the feedforward control reduces errors due to external noise. A training, which combines a least squared error based simultaneous learning rule (LSQ-rule) with a "self-imitation algorithm" based on direct inverse modeling, enables the network to acquire the whole inverse dynamics, limb parameters included, during one short training movement. The considerations presented also hold for multi-jointed manipulators.

Animals↗
PubMed1992

Bayesian belief networks in quantitative histopathology.

Bayesian belief networks have a dynamic range and numeric response characteristics that make them uniquely suitable for descriptive classification schemes. Features showing considerable overlap of tolerance regions may be used, in a cumulative manner, to derive unequivocal classification decisions. The numeric response characteristics of Bayesian belief networks are analyzed, and their application as control modules in automated scene segmentation in histopathology is demonstrated.

Bayes Theorem↗
PubMed1991

Retinal microcirculation in patients with diabetes mellitus: dynamic and morphological analysis of perifoveal capillary network.

The new scanning laser technique allows one to quantify the retinal microcirculation. A digital image analysing system was used to study capillary blood flow velocities and morphological parameters of perifoveal intercapillary areas and foveal avascular zones in normal and diabetic subjects. Diabetic patients showed a significant reduction in capillary blood cell velocities in comparison with normal subjects. Perifoveal intercapillary areas and foveal avascular zones were significantly increased in all stages of diabetic retinopathy, and both parameters increased with progressing diabetic retinopathy. Significant changes in the perifoveal intercapillary areas were observed between normal subjects and patients with no retinopathy.

Adult↗
PubMed2026

Differential regulation of CYP46A1 in ischemic core and peri-infarct regions of male mouse brain after permanent middle cerebral artery occlusion.

Cholesterol 24-hydroxylase (CYP46A1) regulates brain cholesterol homeostasis and synaptic plasticity, playing a crucial role in ischemic stroke. Although previous studies have reported post-ischemic CYP46A1 upregulation, its spatiotemporal dynamics remain poorly defined. To elucidate these dynamics, we investigated the expression of CYP46A1 and other essential cholesterol homeostasis-related genes from 6 h to 3 days after permanent middle cerebral artery occlusion (pMCAO) in CB-17 mice. We utilized single-cell and single-nucleus transcriptomics, regional quantitative PCR, and high-resolution immunohistochemistry. CYP46A1 is predominantly expressed in neurons. Following ischemia, the cholesterol network exhibited a dynamic spatiotemporal divergence. Acutely (6 h post-ischemia), surviving regions transiently upregulated cell-autonomous cholesterol synthesis genes and CYP46A1. Subacutely (3 days), this response shifted toward a widespread upregulation of glia-dependent cholesterol transport genes and general CYP46A1 downregulation. At 24 h, CYP46A1 protein was substantially reduced in the necrotic core and superficial layer II/III of the peri-infarct cortex, but upregulated in deeper layer V, hippocampus, and lateral striatum. Notably, this localized upregulation spatially coincided with reactive microglial hypertrophy. These findings indicate that CYP46A1 is dynamically modulated in viable tissues following ischemic stress. This spatial divergence likely reflects a synergistic interaction between inflammatory propagation and neural circuit-mediated oxidative stress. Resolving these spatiotemporal profiles provides a rigorous foundation for evaluating CYP46A1 functionality and developing stage-specific therapeutic interventions.

Cholesterol 24-hydroxylase↗
PubMed1991

Correlations between frequencies of kin.

Recent years have seen the development of formal and microsimulation models of the structure and dynamics of kin networks. These models generally assume uncorrelated fertility within and across generations. Several sets of real data, however, show positive correlations between the frequencies of various categories of kin. This paper uses formal models to calculate the correlations that will exist between certain categories of kin even if mothers and daughters have independent fertility. Mechanisms by which fertility might be transmitted from mothers to their daughters are considered and the implications for kin correlations are evaluated.

Adolescent↗
PubMed1989

Circuit analysis of the oscillatory state in glycolysis.

The oscillatory state of glycolysis in yeast extracts has been analysed by methods known from electronic circuit studies. The time course of the reactions are calculated by the method of least squares from experimentally determined sets of the concentrations of most of the metabolites. The dynamics of the glycolytic network of reactions can then be represented in terms of flow versus driving force (current versus voltage in the corresponding electronic circuit). The analysis of the dynamics leads to the conclusion that glycolysis is switched on and off in a pulsed manner during the oscillatory state. The resulting pulsed flow cannot only be measured with glycolytic end products, like carbon dioxide or ethanol, but can also readily be demonstrated by diagrams of reaction rates of single enzymic steps even in the initial stages of the glycolytic sequence. An analytic method widely applied to electronic circuits also proved to be useful in the study of the dynamics of a complex enzymic network.

Data Interpretation, Statistical↗