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At least 127 records · Page 7Linked to original sources
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↗
PubMed2026

Human Systems Immunology in the Omics Era: Challenges, Methods, and Emerging Directions.

The human immune system is a highly complex, dynamic, and heterogeneous network shaped by genetic, environmental, and temporal influences. Advances in high-throughput omics technologies have transformed our ability to study this complexity directly and comprehensively in human cohorts. These developments have positioned systems immunology as a powerful framework for investigating coordinated immune responses, identifying regulatory mechanisms, and linking molecular patterns to clinical phenotypes. However, the analytical challenges inherent to large-scale, multimodal datasets-including batch effects, small sample sizes, high dimensionality, and substantial interindividual heterogeneity-require rigorous study design, robust statistical modeling, and thoughtful data analysis strategies. In this review, we summarize key technological foundations enabling modern human systems immunology, outline common analytical pitfalls and effective mitigation approaches, discuss data integration concepts, and highlight emerging opportunities in the field. Together, these technological and analytical advances are redefining how immune function is measured and interpreted in real-world human biology and hold significant promise for enhancing mechanistic insight, biomarker discovery, and precision medicine across immunological diseases and interventions.

Humans↗
PubMed1988

Associative neural network model for the generation of temporal patterns. Theory and application to central pattern generators.

Cyclic patterns of motor neuron activity are involved in the production of many rhythmic movements, such as walking, swimming, and scratching. These movements are controlled by neural circuits referred to as central pattern generators (CPGs). Some of these circuits function in the absence of both internal pacemakers and external feedback. We describe an associative neural network model whose dynamic behavior is similar to that of CPGs. The theory predicts the strength of all possible connections between pairs of neurons on the basis of the outputs of the CPG. It also allows the mean operating levels of the neurons to be deduced from the measured synaptic strengths between the pairs of neurons. We apply our theory to the CPG controlling escape swimming in the mollusk Tritonia diomedea. The basic rhythmic behavior is shown to be consistent with a simplified model that approximates neurons as threshold units and slow synaptic responses as elementary time delays. The model we describe may have relevance to other fixed action behaviors, as well as to the learning, recall, and recognition of temporally ordered information.

Animals↗
PubMed1992

An actin-associated protein present in the microtubule organizing center and the growth cones of PC-12 cells.

The pathfinding ability of the growth cone depends upon the integrity of a dynamic actin filament network. However, although a number of actin-binding proteins have been found in growth cones, it is not known how these proteins come to be concentrated there or how they might interact to produce these important actin filaments. In this report, an actin-associated protein recognized by the monoclonal antibody 2E4 is demonstrated to be present in PC-12 cells. In undifferentiated cells, this protein is present in an apparently inactive state in a perinuclear location that corresponds to that of the microtubule organizing center and not of the Golgi apparatus. Conversely, after NGF-induced differentiation, the antigen is found enriched in the neurite and growth cone and disappears from the perinuclear position. This disappearance is directly proportional to the length of the neurite. The antigen-antibody complex binds the ends of actin filaments in vitro in an ATP-sensitive manner, and the antibody stains the outermost edge of the actin filament ruffle in the leading edge of migrating fibroblasts. Hence, it is possibly involved in the membrane-associated polymerization of actin filaments such as that observed in growth cones.

Actins↗
PubMed2026

Urban stormwater infrastructure as a microplastic superhighway: a critical review of transport dynamics, modelling, and mitigation across pavements and drainage networks.

This review examines the transport, fate, modelling, and mitigation of Microplastics (MPs) in urban stormwater infrastructure, with emphasis on pavements, runoff pathways, micro-drainage, and macro-drainage systems. Following a systematic review approach, more than 1000 records were screened and approximately 50 core studies were retained when they addressed urban stormwater or drainage-related MP transport with adequate methodological reporting; marine-only studies and biological-effect studies without direct relevance to transport processes were excluded. The evidence shows that stormwater systems function not merely as passive conduits but as dynamic reactive transport systems with temporary storage, where particle mobilisation, sedimentation, resuspension, and temporary retention regulate MP export. Road surfaces, especially high-traffic areas, are major reservoirs of tyre wear, road-marking, atmospheric, and litter-derived particles that are rapidly mobilised during rainfall. Conventional grab sampling may underestimate MP loads, which in some cases exceed treated wastewater effluent loads by up to six-fold. Drainage structures such as manholes can immobilise up to 17.3% of near-neutrally buoyant particles, while biofouling and aggregation may shift buoyant polymers from wash-load to bedload. Mitigation systems, including permeable pavements, bioretention, wetlands, and technical inserts, can achieve high removal of coarse MPs, but performance declines for fine particles below 100 µm. The review highlights the need for standardised flow-proportional sampling, physically informed modelling, and treatment-train strategies targeting both surface sources and in-network storage.

Microplastics↗
PubMed1990

Dynamics of F-actin and F-actin/filamin networks as studied by photon correlation spectroscopy.

Photon correlation spectroscopy was used to study both F-actin and F-actin/filamin networks in solution. The measured autocorrelation functions were analyzed with the inverse Laplace transform CONTIN. The resulting frequency distributions consist of maximal five relatively narrow peaks. This rather unexpected finding disagrees with the frequency spectra calculated for an entangled rigid rod model. For this model, we expect spectra consisting of a single broad peak. Factors like flexibility, deviation from an experimental length distribution, interference with chemical reactions, and translation-rotation coupling that would influence the profile of the frequency distribution could be excluded by comparison of the data with model calculations or qualitative estimates. We conclude that our data for F-actin are consistent with the dynamics of an infinite network stable on the time scale of the observed modes. Further support for this interpretation is provided by a comparison with frequency spectra of actin/filamin networks. These appear to be very similar in shape. The main peak of these spectra that corresponds to the slow motions shifts to lower frequencies with increasing cross-link density. It appears that higher frequency modes of the gel are more efficiently damped with progressive cross-link density, resulting in a predominance of the slow motions in the spectra. This behavior has been already found in other systems and seems to be a general feature of cross-linked systems.

Actins↗
PubMedDate not supplied

Mechanisms underlying somatosensory cortical dynamics: II. In vitro studies.

The response of the sensorimotor cortical slice to repetitive, single-site afferent drive is mapped using both evoked potential and metabolic mapping [2-deoxyglucose (2DG)] methods. Systematic changes (increases or decreases) in the evoked potential occur during repetitive 3-5 Hz stimulation. These resemble the changes in SI neuron response observed in the in vivo studies of the preceding companion article; they occur rapidly, recover within 1 min and are reproducible if stimulus parameters remain unchanged. Place, timing, and intensity of repetitive stimulation influence the amplitude and form of the response alterations observed at a given cortical locus. The neuron populations that exhibit different response modifications to the same repetitive stimulus are distributed nonrandomly in the slice: neurons occupying column-shaped aggregates undergo a common response alteration (either an increase or decrease) during repetitive stimulation, with sharp boundaries separating neighboring aggregates distinguishable on the basis of their dynamic behaviors. The distribution of stimulus-evoked 2DG uptake in the slice is "columnar," the dimensions of the 2DG columns corresponding to those mapped with neurophysiological methods. Taken together, the findings support the concept that repetitive stimulation causes the intrinsic network of somatosensory cortex to modify dynamically the network's response to extrinsic excitatory drive so that the local differences in the pattern of extrinsic excitatory drive to neighboring cortical columns are enhanced.

Afferent Pathways↗
PubMed1974

Dynamic single unit simulation of a realistic cerebellar network model. II. Purkinje cell activity within the basic circuit and modified by inhibitory systems.

In continuation of earlier computer simulation studies 15 of the feedback inhibition exercised by the Golgi cells of the cerebellum, an attempt is made at modeling the behavior of Purkinje and basket cells under somewhat more complex functional circumstances. The simulation study is based on a realistic network model (with respect to numerical and metrical parameters, and consisting of more than 3 X 10(4) units) of the cat cerebellar cortex, derived from a recent quantitative histological and stereological analysis 9-12. As an input two identical pairs of foci of incoming mossy fiber activity are applied, separated from one another both spatially and temporally. The stimulation results showed that the assumption of a rigidly preaddressed wiring in the parallel fiber--Purkinje cell synaptic system (i.e. that parallel fibers would systematically select for contact Purkinje cells standing in register) would hardly be realistic because such a solution would offer no advantage whatever over a randomly connected synaptic system. The results would favor the contention that dendritic geometry of the Purkinje cells has a crucial significance in the sense of the concepts developed by W. Rall. The stimulation gives some important hints for the ways in which the specific inhibitory interneuron systems (Golgi and basket cells) add refinement to the operations of the network, particularly in securing integration of the influences of parallel fibers thrown into action by mossy input to spatially and temporally separated foci, especially at the level of Purkinje cells positioned in interfocal areas. Reducing the length of the parallel fibers to one-half of what was suggested by the stereological studies causes a serious breakdown of interfocal neighborhood interactions.

Action Potentials↗
PubMed1990

Simulation study on dynamics transition in neuronal activity during sleep cycle by using asynchronous and symmetry neural network model.

We have found that single neuronal activities in different regions in the brain commonly exhibit the distinct dynamics transition during sleep-waking cycle in cats. Especially, power spectral densities of single neuronal activities change their profiles from the white to the 1/f along with sleep cycle from slow wave sleep (SWS) to paradoxical sleep (PS). Each region has different neural network structure and physiological function. This suggests a globally working mechanism may be underlying the dynamics transition we concern. Pharmacological studies have shown that a change in a wide-spread serotonergic input to these regions possibly causes the neuronal dynamics transition during sleep cycle. In this paper, based on these experimental results, an asynchronous and symmetry neural network model including inhibitory input, which represents the role of the serotonergic system, is utilized to examine the reality of our idea that the inhibitory input level varying during sleep cycle induce that transition. Simulation results show that the globally applied inhibitory input can control the dynamics of single neuronal state evolution in the artificial neural network: 1/f-like power spectral density profiles result under weak inhibition, which possibly corresponds to PS, and white profiles under strong inhibition, which possibly corresponds to SWS. An asynchronous neural network is known to change its state according to its energy function. The geometrical structure of network energy function is thought to vary along with the change in inhibitory level, which is expected to cause the dynamics transition of neuronal state evolution in the network model. These simulation results support the possibility that the serotonergic system is essential for the dynamics transition of single neuronal activities during sleep cycle.

Animals↗
PubMed1989

Associative memory neural network with low temporal spiking rates.

We describe a modified attractor neural network in which neuronal dynamics takes place on a time scale of the absolute refractory period but the mean temporal firing rate of any neuron in the network is lower by an arbitrary factor that characterizes the strength of the effective inhibition. It operates by encoding information on the excitatory neurons only and assuming the inhibitory neurons to be faster and to inhibit the excitatory ones by an effective postsynaptic potential that is expressed in terms of the activity of the excitatory neurons themselves. Retrieval is identified as a nonergodic behavior of the network whose consecutive states have a significantly enhanced activity rate for the neurons that should be active in a stored pattern and a reduced activity rate for the neurons that are inactive in the memorized pattern. In contrast to the Hopfield model the network operates away from fixed points and under the strong influence of noise. As a consequence, of the neurons that should be active in a pattern, only a small fraction is active in any given time cycle and those are randomly distributed, leading to reduced temporal rates. We argue that this model brings neural network models much closer to biological reality. We present the results of detailed analysis of the model as well as simulations.

Animals↗
PubMedDate not supplied

Microcomputer simulation as an aid in analyzing data and teaching the principles of glomerular dynamics.

This report describes a simple program, written in BASIC language, that closely emulates a previously published network thermodynamic model of glomerular dynamics. While the latter requires the SPICE 2 simulation program and a mainframe computer for its execution, the present program operates on any IBM-compatible microcomputer. It has equal utility as an aid in the interpretation of laboratory studies of glomerular dynamics and as a tool for teaching the intricacies of the control of glomerular function. The program is available in 'user friendly' format that obviates the need for any expertise in the use of computers.

Computer Simulation↗
PubMed1979

Spatial firing patterns of auditory neuron network modelling by computer simulation.

This communication examines, in digital computer simulated network, input signals and response patterns established at excitatory neurons' level i.e. the membrane potential of neuron soma. It is restricted to spatial patterns of the auditory neuron networks and time factor for nervous conduction and transmission is neglected compared with long maintained membrane potentials of neuron somas. The model analyzes the change in the spatial patterns of the membrane potential in the two dimensional networks of the auditory system. In order to evaluate the contribution of the various parameters, it is started that the simplest model has only one parameter, lateral inhibition. The other parameters are then added, one at a time, to successive models. The lateral inhibition is a necessary condition in the auditory nervous system if any sharpening of the response areas in the single neurons is to occur. A necessary condition for the validity of the model is that is should be applicable to the other senses such as vision and chemical patterns, taste. The threshold feature of auditory neurons aids in producing a sharpening in the neuron of the auditory relay nuclei. It does this clipping the spatial response patterns in one dimensional arrays of excitatory neurons. Recurrent inhibition seems a necessary condition in the sensory nervous system that any kinds of input signals are to be preserved over a wide range of stimulus intensity. In other words, this network has a wide dynamic range against any kinds of input signals. A simple self-recurrent negative feedback does not contribute to the sharpening, but more complex socalled averaged type does. A neuron network is capable of responding stably to stimuli with a wide range of intensity and with any kind of spatial patterns if there is a simple negative feedback mechanism. When there is no negative feedback, input signals soon disappear or saturate in the neuron network. Therefore, recurrent inhibition is the most important mechanism. Spontaneous activity appears to aid in the sharpening by providing a kind of contrast, that is by reducting the amount of activity in neurons adjacent to the excitatory area. Moreover, the effect of spontaneous activity in the model seems to make repples around the excitatory area and suggests that an introduction of activity at any stage of the networks, from whatever source for example reticulum formation and thalamus, might appreciably alter the response patterns at subsequent neuron network. This suggests that the mechanism of the consciousness that might be controlled by the thalamus and or reticular formation. These two dimensional neuron networks may be expanded to three dimensional neuron networks. The former might simulate the auditory nervous system while the latter might simulate the visual system.

Animals↗
PubMed2025

MNMO: discover driver genes from a multi-omics data based-multi-layer network.

MOTIVATION: Cancer as a public health problem is driven by genomic variations in "cancer driver" genes. The identification of driver genes is critical for the discovery of key biomarkers and the development of personalized therapy. RESULTS: We propose a prediction method MNMO: a multi-layer network model based on multi-omics data. MNMO firstly constructs a dynamically adjusted four-layer network composed of miRNAs and three kinds of genes with different features. Then three kinds of scores, i.e. control capacity, mutation score, and network score, are devised and calculated by harmonic mean to produce the integrated gene score. Experiments were performed on three kinds of real cancer data to compare the identification performance of method MNMO with that of six state-of-the-art ones. The results indicate that method MNMO presents the best identification performance under most circumstances. The genes prioritized by method MNMO not only have a better match to the benchmark ones than those identified by the other methods, but also are all associated with the development and progression of cancers. In addition, some extended versions of method MNMO can further achieve better performance on most evaluation metrics for some specific datasets. They may be more conducive to identifying tissue-specific genes, which has been verified through a number of experiments. AVAILABILITY AND IMPLEMENTATION: The source code and the R package "MNMO" are available at https://github.com/Zheng-D/MNMO. The dataset and code are archived at https://doi.org/10.5281/zenodo.14969986.

Humans↗
PubMed1992

The impact of IAIMS at Georgetown: strategies and outcomes.

Integration of multiple information systems of a medical center will change the way physicians work and practice medicine in the future. Several major steps must be taken by an institution to make this a reality. Since 1983, Georgetown has been engaged in an Integrated Academic Information Management System (IAIMS) project to bring together multiple sources of information that reside on different computers and database systems. Georgetown is developing a Biotechnology and Biomedical Knowledge Network that includes informational and clinical databases, scholar workstations, instruction on computer use, a campuswide network with local area network nodes, and a modular approach to systems integration. The IAIMS project, spearheaded by the medical library, has enabled a broad spectrum of health professionals to benefit directly from new, dynamic information services. The network is heavily used; in 1991, more than 2,100 individual users conducted more than 148,500 computer functions and more than 104,000 searches. There is economy of scale in high-volume use. Overall, the average search cost is $1.57; for high use databases the cost is $0.38, and for low use, it is $9.41. As described in this paper, IAIMS offers a cost-effective means of enhancing patient care by improving information services to physicians. At Georgetown, IAIMS has advanced the concept of integration, accelerated use of computers in education, increased user acceptance of advanced technologies, and established cost factors for providing information resources. While progress made in improving the transfer of medical information is impressive, it is clear that IAIMS requires several more years of support to achieve full implementation.

Academic Medical Centers↗
PubMed1987

Extending the quasi-steady state concept to analysis of metabolic networks.

A means is proposed for evaluating enzyme effectiveness in vivo via a simplified dynamic description of the metabolic reaction network within which the enzyme operates. The basis of the method is application of sensitivity analysis to a quasi-steady approximation of a complete dynamic model, and its implementation centers on interpreting the transient relations of selected intermediates following a perturbation to the system of interest: for many important situations such relations can be simply interpreted to give a useful global measure of enzyme effectiveness. This method is found to be successful for estimating phosphofructokinase and pyruvate kinase activity in the human red cell, and it appears promising as a basis for developing a means for detecting enzyme abnormalities caused by environmental or genetic factors. This method may also prove useful for comparative studies of glycolysis in different types of cells. The analysis presented is based on available models of red cell glycolysis, but the results are not highly sensitive to ambiguities in the system model. The approach suggested appears to provide an effective means for describing system dynamics and determining the behavior of an individual enzyme in an intact system by making a first-order allowance for interaction with the system as a whole. Requirements for success of this approach remain to be identified in detail, but effective time-scale separation is probably the key.

Erythrocytes↗