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PubMed1981

Organization of the neurofilamentous network.

The neurofilamentous network of the normal rabbit brain (lateral vestibular nucleus) and of biopsies of human patients (cerebral cortex, sural nerve) was investigated electron microscopically. Thin sections of samples prepared by standard techniques and unfixed spreads of freshly isolated perikarya were utilized. The neurofilaments are assembled into a three-dimensional network associated with the axolemma, microtubules, mitochondria and polyribosomes. The elements of this network demonstrate helicity at several levels of organization. It is proposed that they are in a dynamic state of equilibrium between ordered lattice and open network paracrystalline states. Reversible phase transitions in the subunit proteins of the neurofilaments may lead to coiling and uncoiling of the filaments and induce alterations in the network structure of the neuroplasm. Giant axonal swellings in biopsies of the sural nerve are interpreted as accumulations of cytoskeletal elements in the absence of the orienting effect of microtubules. In cortical neurons of patients with Alzheimer's disease parts of the neurofilamentous network are in altered paracrystalline states; virus-like particles occur within this modified network. These concepts of cytoskeletal organization - network, helicity, phase transitions, and paracrystallinity - are useful for the interpretation of pathological alterations of the cytoskeleton and for an understanding of cytoskeletal organization in general.

Alzheimer Disease↗
PubMed1982

Storage of temporal pattern sequence in a network.

Learning of single patterns and a temporal pattern sequence in a network when the coupling coefficients between the network elements change their values according to a definite coupling function is described. In contrast to technical systems (e.g. film, tape) where temporal sequences are often encoded in the storage location, the network stores information only by changing the values of the coupling coefficients. A network of 100 elements was stimulated on an UNIVAC 1100/80 computer. Eight single patterns and a sequence of these patterns were offered at the input of the network. After the learning process the network reproduces every stored pattern as an output signal when only parts of it are fed in. The activity, that is the sum of all output signals, is regulated by an external control signal. By setting that control signal to a suitable value the network is able to reproduce the stored pattern sequence starting from any arbitrary pattern. Lowering the external control signal during that process causes the network to hold the last presented pattern until the external control signal is changed again. It is speculated that the coupling function implemented in the stimulation may be analogous to a characteristic describing the chemical process of cooperative binding.

Computers↗
PubMed1984

A hierarchical neural network model for associative memory.

A hierarchical neural network model with feedback interconnections, which has the function of associative memory and the ability to recognize patterns, is proposed. The model consists of a hierarchical multi-layered network to which efferent connections are added, so as to make positive feedback loops in pairs with afferent connections. The cell-layer at the initial stage of the network is the input layer which receives the stimulus input and at the same time works as an output layer for associative recall. The deepest layer is the output layer for pattern-recognition. Pattern-recognition is performed hierarchically by integrating information by converging afferent paths in the network. For the purpose of associative recall, the integrated information is again distributed to lower-order cells by diverging efferent paths. These two operations progress simultaneously in the network. If a fragment of a training pattern is presented to the network which has completed its self-organization, the entire pattern will gradually be recalled in the initial layer. If a stimulus consisting of a number of training patterns superposed is presented, one pattern gradually becomes predominant in the recalled output after competition between the patterns, and the others disappear. At about the same time when the recalled pattern reaches a steady state in the initial layer, in the deepest layer of the network, a response is elicited from the cell corresponding to the category of the finally-recalled pattern. Once a steady state has been reached, the response of the network is automatically extinguished by inhibitory signals from a steadiness-detecting cell.(ABSTRACT TRUNCATED AT 250 WORDS)

Association Learning↗
PubMed1980

Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.

A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by "learning without a teacher", and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname "neocognitron". After completion of self-organization, the network has a structure similar to the hierarchy model of the visual nervous system proposed by Hubel and Wiesel. The network consists of an input layer (photoreceptor array) followed by a cascade connection of a number of modular structures, each of which is composed of two layers of cells connected in a cascade. The first layer of each module consists of "S-cells", which show characteristics similar to simple cells or lower order hypercomplex cells, and the second layer consists of "C-cells" similar to complex cells or higher order hypercomplex cells. The afferent synapses to each S-cell have plasticity and are modifiable. The network has an ability of unsupervised learning: We do not need any "teacher" during the process of self-organization, and it is only needed to present a set of stimulus patterns repeatedly to the input layer of the network. The network has been simulated on a digital computer. After repetitive presentation of a set of stimulus patterns, each stimulus pattern has become to elicit an output only from one of the C-cells of the last layer, and conversely, this C-cell has become selectively responsive only to that stimulus pattern. That is, none of the C-cells of the last layer responds to more than one stimulus pattern. The response of the C-cells of the last layer is not affected by the pattern's position at all. Neither is it affected by a small change in shape nor in size of the stimulus pattern.

Cognition↗
PubMed1988

Recognition of general patterns using neural networks.

The Hopfield model of neural network stores memory in its symmetric synaptic connections and can only learn to recognize sets of nearly "orthogonal" patterns. A new algorithm is put forth to permit the recognition of general ("non-orthogonal") patterns. The algorithm specifies the construction of the new network's memory matrix Tij, which is, in general, asymmetrical and contains the Hopfield neural network (Hopfield 1982) as a special case. We find further that in addition to this new algorithm for general pattern recognition, there exists in fact a large class of Tij memory matrices which permit the recognition of non-orthogonal patterns. The general form of this class of Tij memory matrix is presented, and the projection matrix neural network (Personnaz et al. 1985) is found as a special case of this general form. This general form of memory matrix extends the library of memory matrices which allow a neural network to recognize non-orthogonal patterns. A neural network which followed this general form of memory matrix was modeled on a computer and successfully recognized a set of non-orthogonal patterns. The new network also showed a tolerance for altered and incomplete data. Through this new method, general patterns may be taught to the neural network.

Algorithms↗
PubMed1984

The structure and function of social support networks in families with handicapped children.

Three structural characteristics of social support networks (size, density, and boundary density) and two relationship characteristics (reciprocity and dimensionality) were assessed in a sample of 56 families with a child with spina bifida and 53 matched comparison families. The results indicate that social networks of families with handicapped children tended to be smaller than the networks of comparison families, particularly with regard to mothers' total and friendship networks. The networks of families with handicapped children were more dense as well. As predicted, there was greater boundary density in spousal networks in families with handicapped children. Differences in network reciprocity were inconclusive while the results indicate that families with handicapped children tended to rely more heavily upon multidimensional network contacts than did comparison families. The results are discussed in light of our understanding of stress, social support, and the social ecological context of families.

Adolescent↗
PubMed1986

Social networks and adult social identities: profiles and correlates of support and rejection.

The growth of interest in social networks has underscored the need for integrative analyses of network dimensions. A three-pronged strategy to achieve this objective was pursued in a study of 261 married women nurses. First, network variables related to important adult social identities were differentiated. Factor analyses revealed four network interaction factors: work support, work rejection, general support, and general rejection. An additional factor analysis differentiated work associates from nonwork network members. Second, cluster analysis of these factor scores identified five different network profiles. The profiles differed in the importance of the woman's work to her nonwork ties as well as the frequency and pattern of different positive and negative interactions. Third, individuals with these contrasting network profiles differed on levels of life satisfaction and psychological symptomatology. The discussion emphasizes social network differences within the broader context of the ecology of human development.

Adaptation, Psychological↗
PubMed1999

Establishing radiologic image transmission via a transmission control protocol/Internet protocol network between two teaching hospitals in Houston.

The technical and management considerations necessary for the establishment of a network link between computed tomography (CT) and magnetic resonance imaging (MRI) networks of two geographically separated teaching hospitals are presented. The University of Texas Medical School at Houston Department of Radiology provides radiology residency training at its primary teaching hospital and at a second county-run hospital located approximately 12 miles away. A direct network link between the two hospitals was desired to permit timely consultative services to residents and professional colleagues. The network link was established by integrating the county hospital free-standing imaging network into the network infrastructure of the Medical School and the main teaching hospital. Technical issues involved in the integration were reassignment of internet protocol (IP) addresses, determination of data transmission protocol compatibilities, proof of connectivity and image transmission, transmission speeds and network loading, and management of the new network. These issues were resolved in a planned stepwise fashion and despite the fact that the system has a rate-limiting T1 segment between the county hospital and the teaching hospital the transmission speed was deemed suitable. The project has proven successful and can provide a guide for planning similar projects elsewhere. It has in fact made possible several new services for the teaching and research activities of the department's faculty and residents, which were not envisaged before the implementation of this connection.

Computer Systems↗
PubMed2003

The significance of social network in a geriatric assessment population.

BACKGROUND AND AIMS: There has long been speculation that socially-involved individuals suffer less illness, overcome sickness more readily, and experience better health outcomes than more socially isolated persons. This study describes social network characteristics, and determines whether social network affects health outcomes in the geriatric population. METHODS: Data from 1270 consecutive patients seen in the outpatient Geriatric Assessment Clinic (GAC) of the University of Nebraska Medical Center between August 1988 and May 1999 were analyzed. Social network was measured by the Social Network Index (SNI). RESULTS: Younger age, higher income and higher education were related to a broader social network. A larger social network was generally associated with lower caregiver burden, higher functional status and better cognition. Men with the smallest social networks were found to be at significantly higher risk of death than women with larger social networks (HR 4). After adjusting for age (HR 1.04, CI 1.03-1.06), income (HR 1.00, CI 1-1), education (HR, NS), IADL (HR 0.97, CI 0.95-0.99), CIRS (HR 1.08, CI 1.05-1.10) and informal service use (HR, NS), the risk of death for subjects based on SNI is only significant in the comparison between SNI III and SNI I (HR 0.67, CI 0.50-0.91). CONCLUSIONS: SNI I males are at significantly increased risk of death compared with all other groups. The average number of years from initial visit to death for SNI I males is only 2.8 years. These results are consistent with the literature, which suggests men are more affected by social isolation and have decreased survival compared with females. The influence of SNI on mortality in this frail, elderly group appears to be diluted by the presence of functional disability and a high degree of comorbid illness.

Activities of Daily Living↗
PubMed1999

[Modern network technologies for data communication in the hospital].

Modern network technologies provide the basic infrastructure for information and communication systems in the hospital today. The goal to build up a universal network infrastructure which meets the broadly varied requirements. Analysis of data communication in the hospital and the evaluation of established network technologies, as well as new developments, leads to the switching concept as the suitable solution. Two technologies play the major role in building enterprise networks: the Ethernet family, based on frame switching, and ATM, based on cell switching. Orientation on established standards is the key to free system choice and grants interoperability between systems from different manufacturers. However, the network design must meet the application and system architecture of the information technology in order to achieve successful mapping of the involved communication profiles. This means scaling of the bandwidth as needed, suitable segmenting of the network and resilient links. In a further step, policy-based networking takes advantages of prioritizing critical applications and therefore reduces interdependencies across the network.

Computer Systems↗
PubMed1998

Links between social network and quality of life: an epidemiologically representative study of psychotic patients in south London.

Quality of life has been found to be associated with social networks in patients with psychiatric disorders. We aimed to determine whether quality of life was related to social network size in group of severely mentally ill subjects living in the community. In a population-based, prospective controlled study of two sector mental health teams in South London, a random sample of representative 1-year prevalent cases of non-organic psychosis was identified. Patients were interviewed at baseline, and associations between quality of life and social network size were analyzed cross-sectionally. For average quality of life there was increase up to a certain level of social network size (about 20 social contacts). For the quality of life subscore on social relations there appeared to be an optimal middle level of network size (10-12), with lower subscores for smaller and larger networks. Multivariate analysis confirmed the associations between quality of life and social network size. In analyses of network subgroups the importance of confiding contacts was underlined.

Adolescent↗
PubMed2004

Molecular evolution in large genetic networks: does connectivity equal constraint?

Genetic networks show a broad-tailed distribution of the number of interaction partners per protein, which is consistent with a power-law. It has been proposed that such broad-tailed distributions are observed because they confer robustness against mutations to the network. We evaluate this hypothesis for two genetic networks, that of the E. coli core intermediary metabolism and that of the yeast protein-interaction network. Specifically, we test the hypothesis through one of its key predictions: highly connected proteins should be more important to the cell and, thus, subject to more severe selective and evolutionary constraints. We find, however, that no correlation between highly connected proteins and evolutionary rate exists in the E. coli metabolic network and that there is only a weak correlation in the yeast protein-interaction network. Furthermore, we show that the observed correlation is function-specific within the protein-interaction network: only genes involved in the cell cycle and transcription show significant correlations. Our work sheds light on conflicting results by previous researchers by comparing data from multiple types of protein-interaction datasets and by using a closely related species as a reference taxon. The finding that highly connected proteins can tolerate just as many amino acid substitutions as other proteins leads us to conclude that power-laws in cellular networks do not reflect selection for mutational robustness.

Energy Metabolism↗
PubMed2003

Role of ventilation scintigraphy in diagnosis of acute pulmonary embolism: an evaluation using artificial neural networks.

The purpose of this study was to assess the value of the ventilation study in the diagnosis of acute pulmonary embolism using a new automated method. Either perfusion scintigrams alone or two different combinations of ventilation/perfusion scintigrams were used as the only source of information regarding pulmonary embolism. A completely automated method based on computerised image processing and artificial neural networks was used for the interpretation. Three artificial neural networks were trained for the diagnosis of pulmonary embolism. Each network was trained with 18 automatically obtained features. Three different sets of features originating from three sets of scintigrams were used. One network was trained using features obtained from each set of perfusion scintigrams, including six projections. The second network was trained using features from each set of (joint) ventilation and perfusion studies in six projections. A third network was trained using features from the perfusion study in six projections combined with a single ventilation image from the posterior view. A total of 1,087 scintigrams from patients with suspected pulmonary embolism were used for network training. The test group consisted of 102 patients who had undergone both scintigraphy and pulmonary angiography. Performances in the test group were measured as area under the receiver operation characteristic curve. The performance of the neural network in interpreting perfusion scintigrams alone was 0.79 (95% confidence limits 0.71-0.86). When one ventilation image (posterior view) was added to the perfusion study, the performance was 0.84 (0.77-0.90). This increase was statistically significant ( P=0.022). The performance increased to 0.87 (0.81-0.93) when all perfusion and ventilation images were used, and the increase in performance from 0.79 to 0.87 was also statistically significant ( P=0.016). The automated method presented here for the interpretation of lung scintigrams shows a significant increase in performance when one or all ventilation images are added to the six perfusion images. Thus, the ventilation study has a significant role in the diagnosis of acute lung embolism.

Adolescent↗
PubMed1998

A neural network study of precollicular saccadic averaging.

Saccadic averaging is the phenomenon that two simultaneously presented retinal inputs result in a saccade with an endpoint located on an intermediate position between the two stimuli. Recordings from neurons in the deeper layers of the superior colliculus have revealed neural correlates of saccade averaging, indicating that it takes place at this level or upstream. Recently, we proposed a neural network for internal feedback in saccades. This neural network model is different from other models in that it suggests the possibility that averaging takes place in a stage upstream of the colliculus. The network consists of output units representing the neural map of the deeper layers of the superior colliculus and hidden layers imitating areas in the posterior parietal cortex. The deeper layers of the superior colliculus represent the motor error of a desired saccade, e.g. an eye movement to a visual target. In this article we show that averaging is an emergent property of the proposed network. When two retinal targets with different intensities are simultaneously presented to the network, the activity in the output layer represents a single motor error with a weighted average value. Our goal is to understand the mechanism of weighted averaging in this neural network. It appears that averaging in the model is caused by the linear dependence of the net input, received by the hidden units, on retinal error, independent of its retinal coding format. For nonnormalized retinal error inputs, also the nonlinearity between the net input and the activity of the hidden units plays a role in the averaging process. The averaging properties of the model are in agreement with physiological experiments if the hypothetical retinal error input map is normalized. The neural network predicts that if this normalization is overruled by electrical stimulation, averaging still takes place. However, in this case--as a consequence of the feedback task--the location of the resulting saccade depends on the initial eye position and the total intensity/current applied at the two locations. This could be a way to verify the neural network model. If the assumptions for the model are valid, a physiological implication of this paper is that averaging of saccades takes place upstream of the superior colliculus.

Animals↗
PubMed2007

Some data-driven reflections on priorities in AIDS network research.

Risk networks can transmit HIV or other infections; social networks can transmit social influence and thus help shape norms and behaviors. This primarily-theoretical paper starts with a review of network concepts, and then presents data from a New York network study to study patterns of sexual and injection linkages among IDUs and other drug users and nonusers, men who have sex with men, women who have sex with women, other men and other women in a high-risk community and the distribution of HIV, sex at group sex events, and health intravention behaviors in this network. It then discusses how risk network microstructures might influence HIV epidemics and urban vulnerability to epidemics; what social and other forces (such as "Big Events" like wars or ecological disasters) might shape networks and their associated norms, intraventions, practices and behaviors; and how network theory and research have and may continue to contribute to developing interventions against HIV epidemics.

Acquired Immunodeficiency Syndrome↗
PubMed1986

Topological structure of rat mesenteric microvessel networks.

Microvascular lengths, diameters, and flow directions were determined in all vessel segments (n = 1303) between bifurcations in three complete rat mesenteric microvessel networks (25 mm2 each) using intravital video- and photomicroscopy. The classification of vessel segments as arteriolar, venular, or av-segments (all segments connecting the arteriolar to the venular tree) was based on purely topological criteria. The topological structure of the networks was analyzed using the Horton-Strahler technique and a new generation scheme. Generation numbers were assigned to the vessel segments on the basis of the number of upstream (in the arteriolar tree) and downstream (in the venular tree) bifurcations. The mean generation number of the av-segments, a characteristic parameter of the generation scheme, reflects the topological structure of the network more accurately than Horton's branching ratio Rb. Both the arteriolar and venular tree of the mesenteric networks were found to be dichotomous branching structures which were neither strictly symmetric nor strictly asymmetric. The topological information obtained was compared to network models generated by different random branching algorithms. The result of this comparison suggests that the network structure changes at a certain generation level. Distal to this generation level, the mesenteric networks resemble a model network generated by random branching at any segment, while the proximal portion is similar to a model allowing random branching at terminal segments only.

Animals↗
PubMed1982

The impact of kin, friend and neighbor networks on infant feeding practices. Cuban, Puerto Rican and Anglo families in Florida.

This paper uses social networks as the major conceptual device to examine the impact of relatives, friends and neighbors on infant practices, Kim, friend and neighbor networks of Cuban, Puerto Rican and Anglo families in Dade County, Florida are described, noting inter-ethnic differences in network members who provide respondents with infant feeding advice and assistance. The geographical proximity of influential network members is shown to effect the impact that health care professionals have on mothers' decisions about feeding practices. Network influences on specific feeding practices also are discussed. Kin, friend and neighbor networks are shown to have a significant impact on decisions surrounding breastfeeding, bottle-feeding, use of sucrose supplements and the time to introduce solid foods into the baby's diet. Network member's advice and encouragement appears to contribute to a successful lactation experience. Applying the Fisher's exact test to a small subsample, however, does not yield a significant association between presence or absence of network support and lactation outcome.

Adult↗
PubMed1995

Using social network analysis to study patterns of drug use among urban drug users at high risk for HIV/AIDS.

Few studies have examined the current social relationships of injecting drug users. This paper examines the structural and relationship characteristics of the social networks of injecting drug users, and the relation of social network characteristics to the HIV infection risk behavior of frequency of injecting heroin and cocaine. The study sample was comprised of 293 inner city injecting drug users in Baltimore, Maryland. Most participants (89%) reported at least one family member in their social network, and 44% listed their mother or step-mother in their network. Presence of family members in personal social networks was not related to patterns of drug use examined here; however, those who reported a partner in their personal social network injected significantly less often than those who did not report a partner. Network density and size of drug subnetworks were positively associated with frequency of drug injection. The results of this study suggest that social network analysis may be a useful tool for understanding the social context of HIV/AIDS risk behaviors.

Acquired Immunodeficiency Syndrome↗