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PubMed1992

Reorganization of basement membrane matrices by cellular traction promotes the formation of cellular networks in vitro.

Vascular endothelial cells that are cultured on layers of gelled basement membrane matrix organize rapidly into networks of cords or tubelike structures. Although this phenomenon is a potential model for angiogenesis in vivo, we questioned whether basement membrane matrix directs the differentiation of endothelial cells in a specific manner. In this study, we have examined factors that influence the formation of cellular networks in vitro in an attempt to define a basic mechanism for this process. We found that endothelial cells, fibroblasts, smooth muscle cells, and cells of the murine Leydig cell line TM3 formed networks on basement membrane matrix in much the same fashion. Light and electron microscopy, combined with time-lapse videomicroscopy, revealed that cells organized on a tesselated network of aligned basement membrane matrix that was generated by tension forces of cellular traction. Cellular elongation and progressive motility across the surface of the gel were restricted to tracks of aligned matrix and did not occur until the tracks appeared. The formation of cellular networks on basement membrane matrix was inhibited by reducing the thickness of the matrix, by including native type I collagen in the matrix, or by disrupting cytoskeletal microfilaments and microtubules. Cell division was not required for network formation. Bovine aortic endothelial cells that formed networks did not simultaneously transcribe mRNA for type I collagen, a protein synthesized by endothelial cells that form tubes spontaneously in vitro. Moreover, levels of mRNA for fibronectin and SPARC (Secreted Protein that is Acidic and Rich in Cysteine) in network-forming cells were similar to levels seen in endothelial cells that did not form networks. Endothelial cells and TM3 cells that were plated on highly malleable gels of native type I collagen also formed cords and aligned matrix fibers into linear tracks that resembled those generated on basement membrane matrix, although the structures were not as well-defined. Our observations suggest that the mechanochemical properties of extracellular matrices are able to translate the forces of cellular traction into templates that direct the formation of complex cellular patterns.

Animals↗

PubMedDate not supplied

Characterizing the drug-injecting networks of cocaine and heroin injectors in Montreal.

BACKGROUND: There is little understanding about how the social networks of cocaine injectors are different from those of heroin users and about how such differences are associated with injection risk behaviours. Therefore, the objective of this study was to compare drug-injecting network characteristics of cocaine and heroin injectors believed to be associated with a risk of bloodborne infections. METHODS: Active injection drug users (IDUs) were recruited between April 2004 and January 2005 from three syringe exchange and two methadone treatment programs in Montreal, Canada. Characteristics of each participant and of up to 10 social network members (IDU and non-IDU) with whom frequent contact had occurred in the past month were elicited using a structured, interviewer-administered questionnaire. The current analysis focussed on the drug-injecting network members. Logistic regression was used to examine network characteristics in relation to cocaine and heroin injection. RESULTS: Of 282 study subjects, 81% used cocaine and 19% used heroin as their primary injected drug in the past 6 months. Compared to heroin injectors, participants who injected cocaine had lower odds of knowing their network members for a longer time (OR=0.92, 0.85-0.99), were more likely to report a larger IDU network (OR=1.64, 1.18-2.29) and have IDU partners who had a history of attending shooting galleries (OR=2.42, 1.05-5.56). INTERPRETATION: This study identified high-risk network-related factors associated with bloodborne infections in cocaine injectors. Prevention efforts may benefit from tailoring interventions according to type of drug used, with particular attention to the drug injecting-network of IDUs.

Adult↗

PubMed1990

A neural network simulation of simultaneous single-unit activity recorded from the dragonfly ganglia.

Techniques are described that allow the use of multiple neuron spike data in a computational neural network architecture. The network architecture was devised to match the number of actual neurons from which data were obtained. The network was successfully trained to accurately predict the multiple neuron spike trains. Simultaneous spike histories of 44 neurons were modeled by a network architecture consisting of 44 input units, 88 hidden units with recurrent connections and 44 output units. The activation function of each unit was determined by data unique to a single neuron. These data were coupled with an analog gradient that preserved both the exact spiking times and the relative spiking tendency of each neuron. The input activation values were compared to network output target values calculated to occur 5 msec forward in the composite spiking records of all neurons. Following 2000 training cycles with the gradient data, the average error of each unit in the network was 0.0016. Discrete output values for each network unit were correlated with those of all other units. These correlations were comparable to those done using the actual neuron data. Both correlations reveal a functional connectivity pattern among the units and neurons. These connectivity patterns indicate that the networks may synthesize patterns of activity needed for biological function; in this case, flight patterns carried out in the mesothoracic ganglion of the dragonfly. This model represents, to the best of our knowledge, the first computer based network simulation using actual experimental neural data obtained from a large number of spontaneously active cells in a small intact ganglion.

Action Potentials↗

PubMed1996

[Current status and future prospect of organ transplant network in Japan].

Transplant network plays a indispensable role in the organ transplantation, which consists of the registration of patients, collection of donor information, recipient selection and shipping of the organs, retrieval of transplant data and their analysis. Since 1977 the cadaveric kidney transplantation have had been done in the kidney transplant network, which was run by National Sakura Hospital as the transplant center and 14 subcenters. As the number of cadaveric kidney transplantation, however, was decreased year by year since 1989, there raised some problems, such as allocation policy of kidney grafts, lack of standardized HLA examination and review system, management within each administrative district and so on. Based upon the report of ad hoc committee of the Multiorgan Transplant Network, there substituted the new kidney transplant network system, Japan Kidney Transplant Network for the former kidney transplant network since 1995. The new system is operated not only by transplant surgeon, but by transplant coordinators, nephrologists, doctors in charge of dialysis therapy, representatives of kidney bank and other organizations/associations, and others. After the legislation of the transplant act, the network will be reorganized into the network system for multiorgan sharing. To establish the multiorgan sharing network, uniform protocol of multiorgan procurement, insurance of informed consent, propagation of donor cards for multiorgan donation, registration for waiting lists, allocation system for other organs than kidney, and education of transplant coordinator for multiorgan procurement are required.

Forecasting↗

PubMed2002

Community structure in social and biological networks.

A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.

Algorithms↗

PubMed2026

Screening of biomarkers related to lung adenocarcinoma based on construction of ceRNA regulation network.

BACKGROUND: Lung adenocarcinoma (LUAD) is a common malignant tumor with a poor prognosis and limited effective therapeutic targets. The underlying molecular regulatory mechanisms driving its progression remain largely unclear. The study objectives were to build a circRNA-miRNA-mRNA ceRNA regulation network of LUAD and to identify miRNAs and mRNAs significantly related to the prognosis . METHODS: The gene expression data and GSE101684 were downloaded from the UCSC Xene and NCBI-GEO databases, respectively. The differentially expressed RNAs (DEcircRNAs, DEmiRNAs, and DEmRNAs; DERs) were obtained by the Limma package in R. Then, the differential LUAD-related genes were identified, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the differential LUAD-related genes were analyzed. Moreover, the circRNA-miRNA-mRNA ceRNA network of LUAD was built. The Kaplan-Meier (K-M) survival curve analysis of ceRNA network nodes was performed. In addition, the proliferation-related ceRNA network was built. RESULTS: A total of 382 DEcircRNAs, 1907 DEmRNAs and 156 DEmiRNAs were acquired. A total of 245 differential LUAD-related genes were acquired, which were significantly associated with 189 GO biological processes (BP) and 17 KEGG pathways. Moreover, the ceRNA network of LUAD was built. The K-M survival curve analysis of ceRNA network nodes revealed that a total of 2 miRNAs (hsa-miR-96-5p and hsa-miR-125b-2-3p) and 22 mRNAs (CGNL1, CTHRC1, TK1, etc) were significantly related to the prognosis. mRNAs were significantly enriched in 92 GO BPs (such as cell division, cell adhesion) and 9 KEGG pathways (such as cell cycle, HTLV-1 infection). In addition, the proliferation-related ceRNA network was built. CONCLUSION: This research built a ceRNA regulation network of LUAD and is of great significance for identifying biomarkers related to the prognosis in LUAD.

Humans↗

PubMedDate not supplied

Discrete-event simulation of a wide-area health care network.

OBJECTIVE: Predict the behavior and estimate the telecommunication cost of a wide-area message store-and-forward network for health care providers that uses the telephone system. DESIGN: A tool with which to perform large-scale discrete-event simulations was developed. Network models for star and mesh topologies were constructed to analyze the differences in performances and telecommunication costs. The distribution of nodes in the network models approximates the distribution of physicians, hospitals, medical labs, and insurers in the Province of Saskatchewan, Canada. Modeling parameters were based on measurements taken from a prototype telephone network and a survey conducted at two medical clinics. Simulation studies were conducted for both topologies. RESULTS: For either topology, the telecommunication cost of a network in Saskatchewan is projected to be less than $100 (Canadian) per month per node. The estimated telecommunication cost of the star topology is approximately half that of the mesh. Simulations predict that a mean end-to-end message delivery time of two hours or less is achievable at this cost. A doubling of the data volume results in an increase of less than 50% in the mean end-to-end message transfer time. CONCLUSION: The simulation models provided an estimate of network performance and telecommunication cost in a specific Canadian province. At the expected operating point, network performance appeared to be relatively insensitive to increases in data volume. Similar results might be anticipated in other rural states and provinces in North America where a telephone-based network is desired.

Community Networks↗

PubMed2024

Comprehensive analysis of circRNA-miRNA-mRNA network related to angiogenesis in recurrent implantation failure.

BACKGROUND: Abnormal endometrial blood flow causes a decrease in endometrial receptivity and is considered a relatively independent risk factor for recurrent implantation failure (RIF). This study aimed to explore the potentially functional circRNA-miRNA-mRNA network in RIF, and further explore its mechanism. METHODS: Datasets were downloaded from the GEO database to identify differentially expressed circRNAs, miRNAs and mRNAs. The circRNA-miRNA-mRNA and PPI networks were constructed using Cytoscape 3.6.0 and the STRING database, the hub genes were identified with the cytoHubba plug-in, and a circRNA-miRNA-hub mRNA regulatory sub-network was constructed. Then, GO and KEGG pathway enrichment analyses of the hub genes were performed to comprehensively analyze the mechanism of hub mRNAs in RIF. Due to the results of circRNAs-miRNAs-hub mRNAs regulatory network, we verified the expression of circRNA_0001721, circRNA_0000714, miR-17-5p, miR-29b-3p, HIF1A and VEGFA in the RIF mouse model by qRT‒PCR and western blotting. RESULTS: We initially identified 175 DEmRNAs, 48 DEmiRNAs and 56 DEcircRNAs in RIF associated with angiogenesis and constructed a circRNA-miRNA‒mRNA network and PPI network. We further identified six hub genes in the acquired network. Based on these genes, functional enrichment analysis revealed that the HIF-1 signaling pathway plays a vital role in endometrial angiogenesis in RIF. In addition, the interaction networks of circRNA_0001721/miR-17-5p/HIF1A and the circRNA_0000714/miR-29b-3p/VEGFA axis were predicted. In the RIF mouse model, circRNA_0001721, circRNA_0000714, HIF1A and VEGFA were down-regulated, whereas miR-17-5p and miR-29b-3p were up-regulated according to qRT‒PCR and western blotting. CONCLUSION: This study revealed that the HIF-1 signaling pathway plays a vital role in endometrial angiogenesis in RIF. The circRNA_0001721/miR-17-5p/HIF1A and circRNA_0000714/miR-29b-3p/VEGFA axes might play a role in the pathogenesis of endometrial angiogenesis in RIF.

MicroRNAs↗

PubMed1995

A distributed, scalable, community care network architecture for wide-area electronic patient records: modeling and simulation.

Principal systems issues relative to computerizing patient medical records that are yet to be addressed in the scientific literature include (1) the characteristics of networks, i.e. bandwidth and capacity, and their impact on the performance of the system, (2) the architecture and the underlying algorithm of the system, (3) the location and migration of medical records, (4) scalability of the system, and (5) the nature of the performance variation under heavy and light use of the network. Key parameters that affect performance include the number of patients, doctors, frequency of patient visits, and the number of electronic queries and record entries initiated during a patient-doctor interaction episode. This paper presents AMPReD, a Distributed, Scalable, Community Care Network Architecture that aims to provide Real-Time Access to Geographically-Dispersed Patient Medical Records. The AMPReD model includes stationary hospitals and medical clinics, mobile clinics, migrating doctors as well as patients, the communications network, and the patient medical record database. AMPReD's goals include (1) the accurate modeling of the propagation of medical records and (2) providing real-time access to patient medical records from anywhere in the system. To achieve these goals, an asynchronous, distributed algorithm must be developed that achieves concurrent access of multiple, autonomous databases. AMPReD is modeled and simulated for a representative community care network on a network of workstations configured as a loosely-coupled parallel processor, for different parametric combinations of number of doctors, patients, and number of queries or record entries generated corresponding to every patient-doctor interaction episode. AMPReD defines and obtains key performance measures including the idle times of the doctors, patient waiting times, the access times of queries as functions of their sizes, and the growth of the databases. In addition, AMPReD also measures the deviation of the actual time required for a patient-doctor interaction episode from the scheduled interaction interval, as a function of the network load. For the representative system selected, performance measures indicate that the network, utilizing 1/2T1 links, and the database system poses no bottleneck to the system even where the number of doctors and patients within a 30 minute interval are chosen at 192 and 200 respectively. A T1 is a standard, digital, transmission link that is rated at 1.44Mbits/sec.

Algorithms↗

PubMed1997

Artificial neural networks improve the accuracy of cancer survival prediction.

BACKGROUND: The TNM staging system originated as a response to the need for an accurate, consistent, universal cancer outcome prediction system. Since the TNM staging system was introduced in the 1950s, new prognostic factors have been identified and new methods for integrating prognostic factors have been developed. This study compares the prediction accuracy of the TNM staging system with that of artificial neural network statistical models. METHODS: For 5-year survival of patients with breast or colorectal carcinoma, the authors compared the TNM staging system's predictive accuracy with that of artificial neural networks (ANN). The area under the receiver operating characteristic curve, as applied to an independent validation data set, was the measure of accuracy. RESULTS: For the American College of Surgeons' Patient Care Evaluation (PCE) data set, using only the TNM variables (tumor size, number of positive regional lymph nodes, and distant metastasis), the artificial neural network's predictions of the 5-year survival of patients with breast carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.720; ANN, 0.770; P < 0.001). For the National Cancer Institute's Surveillance, Epidemiology, and End Results breast carcinoma data set, using only the TNM variables, the artificial neural network's predictions of 10-year survival were significantly more accurate than those of the TNM staging system (TNM, 0.692; ANN, 0.730; P < 0.01). For the PCE colorectal data set, using only the TNM variables, the artificial neural network's predictions of the 5-year survival of patients with colorectal carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.737; ANN, 0.815; P < 0.001). Adding commonly collected demographic and anatomic variables to the TNM variables further increased the accuracy of the artificial neural network's predictions of breast carcinoma survival (0.784) and colorectal carcinoma survival (0.869). CONCLUSIONS: Artificial neural networks are significantly more accurate than the TNM staging system when both use the TNM prognostic factors alone. New prognostic factors can be added to artificial neural networks to increase prognostic accuracy further. These results are robust across different data sets and cancer sites.

Breast Neoplasms↗

PubMed1994

Neural network analysis of flow cytometric data for 40 marine phytoplankton species.

Flow cytometry data (time of flight, horizontal and vertical forward light scatter, 90 degrees light scatter, and "red" and "orange" integral fluorescence) were collected for laboratory cultures of 40 species of marine phytoplankton, from the following taxonomic classes, the Dinophyceae, Bacillariophyceae, Prymnesiophyceae, Cryptophyceae, and other flagellates. Single-hidden-layer "back-propagation" neural networks were trained to discriminate between species by recognising patterns in their flow cytometric signatures, and network performance was assessed using an independent test data set. Two approaches were adopted employing: (1) a hierarchy of small networks, the first identifying to which major taxonomic group a cell belonged, and then a network for that taxonomic group identified to species, and (2) a single large network. Discriminating some of the major taxonomic groups was successful but others less so. With networks for specific groups, cryptophyte species were all identified reliably (probability of correct classification always being > 0.75); in the other groups half of the species were identified reliably. With the large network, dinoflagellates, cryptomonads, and flagellates were identified almost as well as by networks specific for these groups. The application of neural computing techniques to identification of such a large number of species represents a significant advance from earlier studies, although further development is required.

Flow Cytometry↗

PubMed1995

An artificial neural network approach to ERP classification.

Artificial neural network-based approaches were developed to classify event-related potential (ERP) waveforms. The networks utilized scalp-recorded ERP returns from six electrode sites. These ERPs were evoked as one individual responded to a series of auditorily presented object names while viewing various objects on a computer screen. The ERPs at the electrode sites were classified as a match decision or a no-match decision. A three-layer backpropagation neural network model was selected to formulate a global and a local classification approach. The backpropagation network in the global approach was designed to operate on a single ERP response which was the average of the ERP responses generated at the six electrode sites. The local ERP classification system consisted of six three-layer backpropagation networks. Each network was designed to operate on the ERPs generated at a single electrode site. A small data base consisting of eight match and eight no-match ERP responses was used to train and test the networks in a variety of ways. The results obtained clearly show that the neural network-based classifiers are able to discriminate with a high degree of accuracy between match and no-match conditions in ERP waveforms.

Brain↗

PubMed1993

Artificial neural networks as adjuncts for assessing medical students' problem solving performances on computer-based simulations.

Artificial neural networks were trained by supervised learning to recognize the test selection patterns associated with students' successful solutions to seven immunology computer-based simulations. New test selection patterns evaluated by the trained neural network were correctly classified as successful or unsuccessful solutions to the problem > 90% of the time. The examination of the neural networks output weights after each test selection revealed a progressive and selective increase for the relevant problem suggesting that a successful solution is represented by the neural network as the accumulation of relevant tests. Unsuccessful problem solutions were classified by the neural network software into two patterns of students performance. The first pattern was characterized by low neural network output weights for all seven problems reflecting extensive searching and lack of recognition of relevant information. In the second pattern, the output weights from the neural network were biased toward one of the remaining six incorrect problems suggesting that the student misrepresented the current problem as an instance of a previous problem. Finally, neural network analysis could detect cases where the students switched hypotheses during the problem solving exercises.

California↗

PubMed1996

Neural network-based detection of esophageal intubation in anesthetized patients.

OBJECTIVE: To test whether a neural network-based method could differentiate between tracheal and esophageal intubation in anesthetized patients by recognizing breathing circuit pressure and flow waveform patterns. METHODS: Tracheal tubes were placed in the trachea and in the esophagus of adult patients undergoing elective operations. After ensuring for proper oxygenation, ventilator settings were changed to 5 ml/kg tidal volume (VT) and 15 cpm and circuit pressure and flow were recorded for 15 seconds. Then, the breathing circuit was switched to the tube placed in the esophagus, and signals were recorded for an additional 15-second period. During off-line analysis, individual waveforms were separated. Tracheal breaths were labeled with a score of 1 while esophageal "breaths" were labeled with -1. A neural network was defined to learn to associate waveforms to their corresponding scores. Data from 54% of the patients were used to train the neural network. Data from the remaining subjects were used for testing. RESULTS: Forty-six patients were studied. Neural network training was achieved with 100 tracheal and 94 esophageal waveforms from 25 patients. Neural network performance was tested on 84 tracheal and 76 esophageal waveforms from 21 subjects. The neural network assigned scores of 0.99 +/- 0.05 (mean +/- SD) to tracheal waveforms and -0.99 +/- 0.03 to esophageal waveforms. The difference between mean esophageal and tracheal scores was -1.99 with a 99.999% confidence range of -2.01 to -1.96. Any arbitrary cutoff threshold, ranging between -0.76 and 0.7, separated tracheal and esophageal score regions, yielding no false positive or negative results. CONCLUSION: A neural network differentiated consistently tracheal from esophageal intubation when the ventilation test mode was used. The ventilation mode employed is feasible in most adult patients undergoing elective procedures under general anesthesia. Further research is required to train neural networks to recognize esophageal intubation in different age groups and when different ventilation modes are applied.

Anesthesia, General↗

PubMed2004

Performance analysis of an iSCSI-based unified storage network.

In this paper, we introduced a novel storage architecture "Unified Storage Network", which merges NAC(Network Attached Channel) and SAN(Storage Area Network), and provides the file I/O services as NAS devices and provides the block I/O services as SAN. To overcome the drawbacks from FC, we employ iSCSI to implement the USN(Unified Storage Network). To evaluate whether iSCSI is more suitable for implementing the USN, we analyze iSCSI protocol and compare it with FC protocol from several components of a network protocol which impact the performance of the network. From the analysis and comparison, we can conclude that the iSCSI is more suitable for implementing the storage network than the FC under condition of the wide-area network. At last, we designed two groups of experiments carefully.

Computer Communication Networks↗

PubMed2000

Self-organized dynamics in plastic neural networks: bistability and coherence.

In this paper, we study the combined dynamics of the neural activity and the synaptic efficiency changes in a fully connected network of biologically realistic neurons with simple synaptic plasticity dynamics including both potentiation and depression. Using a mean-field of technique, we analyzed the equilibrium states of neural networks with dynamic synaptic connections and found a class of bistable networks. For this class of networks, one of the stable equilibrium states shows strong connectivity and coherent responses to external input. In the other stable equilibrium, the network is loosely connected and responds non coherently to external input. Transitions between the two states can be achieved by positively or negatively correlated external inputs. Such networks can therefore switch between their phases according to the statistical properties of the external input. Non-coherent input can only "rcad" the state of the network, while a correlated one can change its state. We speculate that this property, specific for plastic neural networks, can give a clue to understand fully unsupervised learning models.

Animals↗

PubMed1996

Numerical bifurcation analysis of distance-dependent on-center off-surround shunting neural networks.

On-center off-surround shunting neural networks are often applied as models for content-address-able memory (CAM), the equilibria being the stored memories. One important demand of biological plausible CAMs is that they function under a broad range of parameters, since several parameters vary due to postnatal maturation or learning. Ellias, Cohen and Grossberg have put much effort into showing the stability properties of several configurations of on-center off-surround shunting neural networks. In this article we present numerical bifurcation analysis of distance-dependent on-center off-surround shunting neural networks with fixed external input. We varied four parameters that may be subject to postnatal maturation: the range of both excitatory and inhibitory connections and the strength of both inhibitory and excitatory connections. These analyses show that fold bifurcations occur in the equilibrium behavior of the network by variation of all four parameters. The most important result is that the number of activation peaks in the equilibrium behavior varies from one to many if the range of inhibitory connections is decreased. Moreover, under a broad range of the parameters the stability of the network is maintained. The examined network is implemented in an ART network, Exact ART, where it functions as the classification layer F2. The stability of the ART network with the F2-field in different dynamic regimes is maintained and the behavior is functional in Exact ART. Through a bifurcation the learning behavior of Exact ART may even change from forming local representations to forming distributed representations.

Cybernetics↗

PubMed2005

Learning-induced synchronization and plasticity of a developing neural network.

Learning-induced synchronization of a neural network at various developing stages is studied by computer simulations using a pulse-coupled neural network model in which the neuronal activity is simulated by a one-dimensional map. Two types of Hebbian plasticity rules are investigated and their differences are compared. For both models, our simulations show a logarithmic increase in the synchronous firing frequency of the network with the culturing time of the neural network. This result is consistent with recent experimental observations. To investigate how to control the synchronization behavior of a neural network after learning, we compare the occurrence of synchronization for four networks with different designed patterns under the influence of an external signal. The effect of such a signal on the network activity highly depends on the number of connections between neurons. We discuss the synaptic plasticity and enhancement effects for a random network after learning at various developing stages.

Algorithms↗