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PubMed1994

The UNOS OPTN waiting list: 1988 to 1994. United Network for Organ Sharing. Organ Procurement and Transplantation Network.

1. On November 30, 1994, the combined waiting list contained 37,138 registrations. This number represents an 11.4% annual increase in registrations from 33,352 registrations as of December 31, 1993. This percentage of annual increase in registrations is the lowest since 1988. Kidney registrations account for 73.2% of all registrations. It should be noted that the number of registrations increases every year, but the percentage of annual increase tended to decline. 2. In general, the median waiting times for kidney, kidney-pancreas, liver, heart, and lung registrants have increased since 1988. For pancreas registrants, the median waiting times tended to decrease. Overall, heart-lung registrants have the longest median waiting time, whereas liver registrants have the shortest median waiting time, among all registrants on the combined waiting list. Registrants waiting for a kidney-pancreas transplant have a shorter median waiting time than registrants waiting for a kidney or pancreas transplant alone. 3. Kidney, liver, heart, and lung registrants who were listed in 1992 with blood type O waited longer than those with other blood types. Of kidney-pancreas and pancreas registrants, those who had previous transplants waited longer than those who did not. Blacks on the kidney, kidney-pancreas, liver, and heart waiting lists waited longer than patients of other races. Median waiting times varied among different age groups, for different organs. 4. The reported deaths on the kidney waiting list increased each year since 1988, but the percentage of deaths among all kidney registrants in a selected year remained within a range of 3.3-3.8%. Similarly, the number of deaths on the liver, heart, and lung waiting lists increased yearly, whereas the percentage of deaths on these waiting lists remained static over the years. For liver, heart, and lung, the percentage rates of death were 8.5%, 13.7%, and 11.5%, respectively. 5. Among registrants removed from the waiting lists for transplantation, about 58% of the removals were for kidney transplants, 20% for liver transplants, and 16% for heart transplants. Kidney-pancreas and liver transplant rates were higher than those of other organs. Kidney-pancreas transplant rates were higher than those of kidney or pancreas alone. Heart transplant rates were lower than those of kidney-pancreas and liver, but higher than those of other organs. Note that the kidney-pancreas and liver transplant rates have declined over the years.

Adolescent↗
PubMed2002

Scaling of differentiation in networks: nervous systems, organisms, ant colonies, ecosystems, businesses, universities, cities, electronic circuits, and Legos.

Nodes in networks are often of different types, and in this sense networks are differentiated. Here we examine the relationship between network differentiation and network size in networks under economic or natural selective pressure, such as electronic circuits (networks of electronic components), Legos (networks of Lego pieces), businesses (networks of employees), universities (networks of faculty), organisms (networks of cells), ant colonies (networks of ants), and nervous systems (networks of neurons). For each of these we find that (i) differentiation increases with network size, and (ii) the relationship is consistent with a power law. These results are explained by a hypothesis that, because nodes are costly to build and maintain in such "selected networks", network size is optimized, and from this the power-law relationship may be derived. The scaling exponent depends on the particular kind of network, and is determined by the degree to which nodes are used in a combinatorial fashion to carry out network-level functions. We find that networks under natural selection (organisms, ant colonies, and nervous systems) have much higher combinatorial abilities than the networks for which human ingenuity is involved (electronic circuits, Legos, businesses, and universities). A distinct but related optimization hypothesis may be used to explain scaling of differentiation in competitive networks (networks where the nodes themselves, rather than the entire network, are under selective pressure) such as ecosystems (networks of organisms).

Animals↗
PubMed2005

Investigating the fault tolerance of neural networks.

Particular levels of partial fault tolerance (PFT) in feedforward artificial neural networks of a given size can be obtained by redundancy (replicating a smaller normally trained network), by design (training specifically to increase PFT), and by a combination of the two (replicating a smaller PFT-trained network). This letter investigates the method of achieving the highest PFT per network size (total number of units and connections) for classification problems. It concludes that for non-toy problems, there exists a normally trained network of optimal size that produces the smallest fully fault-tolerant network when replicated. In addition, it shows that for particular network sizes, the best level of PFT is achieved by training a network of that size for fault tolerance. The results and discussion demonstrate how the outcome depends on the levels of saturation of the network nodes when classifying data points. With simple training tasks, where the complexity of the problem and the size of the network are well within the ability of the training method, the hidden-layer nodes operate close to their saturation points, and classification is clean. Under such circumstances, replicating the smallest normally trained correct network yields the highest PFT for any given network size. For hard training tasks (difficult classification problems or network sizes close to the minimum), normal training obtains networks that do not operate close to their saturation points, and outputs are not as close to their targets. In this case, training a larger network for fault tolerance yields better PFT than replicating a smaller, normally trained network. However, since fault-tolerant training on its own produces networks that operate closer to their linear areas than normal training, replicating normally trained networks ultimately leads to better PFT than replicating fault-tolerant networks of the same initial size.

Journal Article↗
PubMed2006

Hydrophobic, hydrophilic, and charged amino acid networks within protein.

The native three-dimensional structure of a single protein is determined by the physicochemical nature of its constituent amino acids. The 20 different types of amino acids, depending on their physicochemical properties, can be grouped into three major classes: hydrophobic, hydrophilic, and charged. The anatomy of the weighted and unweighted networks of hydrophobic, hydrophilic, and charged residues separately for a large number of proteins were studied. Results showed that the average degree of the hydrophobic networks has a significantly larger value than that of hydrophilic and charged networks. The average degree of the hydrophilic networks is slightly higher than that of the charged networks. The average strength of the nodes of hydrophobic networks is nearly equal to that of the charged network, whereas that of hydrophilic networks has a smaller value than that of hydrophobic and charged networks. The average strength for each of the three types of networks varies with its degree. The average strength of a node in a charged network increases more sharply than that of the hydrophobic and hydrophilic networks. Each of the three types of networks exhibits the "small-world" property. Our results further indicate that the all-amino-acids networks and hydrophobic networks are of assortative type. Although most of the hydrophilic and charged networks are of the assortative type, few others have the characteristics of disassortative mixing of the nodes. We have further observed that all-amino-acids networks and hydrophobic networks bear the signature of hierarchy, whereas the hydrophilic and charged networks do not have any hierarchical signature.

Amino Acids↗
PubMed2005

Intervention in a family of Boolean networks.

MOTIVATION: Intervention in a gene regulatory network is used to avoid undesirable states, such as those associated with a disease. Several types of intervention have been studied in the framework of a probabilistic Boolean network (PBN), which is a collection of Boolean networks in which the gene state vector transitions according to the rules of one of the constituent networks and where network choice is governed by a selection distribution. The theory of automatic control has been applied to find optimal strategies for manipulating external control variables that affect the transition probabilities to desirably affect dynamic evolution over a finite time horizon. In this paper we treat a case in which we lack the governing probability structure for Boolean network selection, so we simply have a family of Boolean networks, but where these networks possess a common attractor structure. This corresponds to the situation in which network construction is treated as an ill-posed inverse problem in which there are many Boolean networks created from the data under the constraint that they all possess attractor structures matching the data states, which are assumed to arise from sampling the steady state of the real biological network. RESULTS: Given a family of Boolean networks possessing a common attractor structure composed of singleton attractors, a control algorithm is derived by minimizing a composite finite-horizon cost function that is a weighted average over all the individual networks, the idea being that we desire a control policy that on average suits the networks because these are viewed as equivalent relative to the data. The weighting for each network at any time point is taken to be proportional to the instantaneous estimated probability of that network being the underlying network governing the state transition. The results are applied to a family of Boolean networks derived from gene-expression data collected in a study of metastatic melanoma, the intent being to devise a control strategy that reduces the WNT5A gene's action in affecting biological regulation. AVAILABILITY: The software is available on request. SUPPLEMENTARY INFORMATION: The supplementary Information is available at http://ee.tamu.edu/~edward/tree

Algorithms↗
PubMed2004

Clustering under the line graph transformation: application to reaction network.

BACKGROUND: Many real networks can be understood as two complementary networks with two kind of nodes. This is the case of metabolic networks where the first network has chemical compounds as nodes and the second one has nodes as reactions. In general, the second network may be related to the first one by a technique called line graph transformation (i.e., edges in an initial network are transformed into nodes). Recently, the main topological properties of the metabolic networks have been properly described by means of a hierarchical model. While the chemical compound network has been classified as hierarchical network, a detailed study of the chemical reaction network had not been carried out. RESULTS: We have applied the line graph transformation to a hierarchical network and the degree-dependent clustering coefficient C(k) is calculated for the transformed network. C(k) indicates the probability that two nearest neighbours of a vertex of degree k are connected to each other. While C(k) follows the scaling law C(k) approximately k(-1.1) for the initial hierarchical network, C(k) scales weakly as k0.08 for the transformed network. This theoretical prediction was compared with the experimental data of chemical reactions from the KEGG database finding a good agreement. CONCLUSIONS: The weak scaling found for the transformed network indicates that the reaction network can be identified as a degree-independent clustering network. By using this result, the hierarchical classification of the reaction network is discussed.

Algorithms↗
PubMed2005

Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network.

BACKGROUND: Large-scale studies have revealed networks of various biological interaction types, such as protein-protein interaction, genetic interaction, transcriptional regulation, sequence homology, and expression correlation. Recurring patterns of interconnection, or 'network motifs', have revealed biological insights for networks containing either one or two types of interaction. RESULTS: To study more complex relationships involving multiple biological interaction types, we assembled an integrated Saccharomyces cerevisiae network in which nodes represent genes (or their protein products) and differently colored links represent the aforementioned five biological interaction types. We examined three- and four-node interconnection patterns containing multiple interaction types and found many enriched multi-color network motifs. Furthermore, we showed that most of the motifs form 'network themes' -- classes of higher-order recurring interconnection patterns that encompass multiple occurrences of network motifs. Network themes can be tied to specific biological phenomena and may represent more fundamental network design principles. Examples of network themes include a pair of protein complexes with many inter-complex genetic interactions -- the 'compensatory complexes' theme. Thematic maps -- networks rendered in terms of such themes -- can simplify an otherwise confusing tangle of biological relationships. We show this by mapping the S. cerevisiae network in terms of two specific network themes. CONCLUSION: Significantly enriched motifs in an integrated S. cerevisiae interaction network are often signatures of network themes, higher-order network structures that correspond to biological phenomena. Representing networks in terms of network themes provides a useful simplification of complex biological relationships.

Amino Acid Motifs↗
PubMed1978

The replication of kinetoplast DNA networks in Crithidia fasciculata.

Kinetoplast DNA from the mitochondria of Crithidia is in the form of a two-dimensional network of thousands of minicircles each containing about 2.5 kb, and a small number of maxicircles each containing about 40 kb. Fractionation of kinetoplast DNA by equilibrium centrifugation in a CsCl-propidium diiodide gradient resolves it into three types of networks. Form I networks band at high density and contain minicircles which are covalently closed; form II networks band at low density and contain minicircles which are nicked or gapped; and replicating networks band at intermediate density and contain some minicircles of each type. Form I networks contain about 5000 minicircles; form II networks contain about 11,000; and replicating networks contain an intermediate number. When cells are pulse-labeled with 3H-thymidine, radioactivity in mitochondrial DNA is preferentially incorporated into replicating networks, but after a chase it appears first in form II networks and finally in form I. Examination of replicating networks by electron microscopy in the presence of ethidium bromide reveals that minicircles in the central region of the network are twisted and therefore covalently closed, whereas those in the peripheral region are not twisted and therefore must be nicked or gapped. The pulse-label is incorporated into the nicked or gapped minicircles of the replicating networks. These results indicate that replication of form I networks begins in peripheral minicircles and that progeny minicircles remain nicked or gapped. As replication proceeds, the size of the network increases, and the peripheral zone of nicked or gapped minicircles enlarges. Finally, when all minicircles have replicated, the network, now form II is double the size of form I and contains only nicked or gapped minicircles. The final step in replication presumably includes both the cleavage of the network into two form I species and the covalent closure of all the minicircles.

Animals↗
PubMed2004

Network-based regulatory pathways analysis.

MOTIVATION: A useful approach to unraveling and understanding complex biological networks is to decompose networks into basic functional and structural units. Recent application of convex analysis to metabolic networks leads to the development of network-based metabolic pathway analysis and the decomposition of metabolic networks into metabolic extreme pathways that are true functional units of metabolic systems. Metabolic extreme pathways are derived from limited knowledge of the metabolic networks, but provide an integrated predictive description of metabolic networks. It is important to extend the concept of network-based metabolic pathways to genetic networks and develop mathematical procedures for network-based regulatory pathway analysis. RESULTS: We have established Kirchhoff's first law in genetic networks and introduced a concept of gene flows using matrix decomposition method. The Kirchhoff's first law provides the theoretical foundations for mathematical framework for development defining network-based regulatory pathways, and applying convex analysis in decomposing the genetic networks into regulatory extreme pathways. We presented a new approach to characterize the extreme pathway and developed a new algorithm for identifying a set of extreme pathways. Convex analysis and extreme pathway structure provide a unified framework for functional and structural analysis of metabolic and genetic networks, which will increase our ability to analyze, interpret and predict the function of metabolic and genetic networks. The proposed models for network-based regulatory pathway analysis have been applied to apoptosis regulatory network.

Algorithms↗
PubMed2005

Function of local networks in palliative care: a Dutch view.

BACKGROUND: Although network formation is considered an effective method of stimulating the integrated delivery of palliative care, scientific evidence on the usefulness of network formation is scarce. In 1998 the Ministry of Health of The Netherlands started a 5-year stimulation program on palliative care by founding and funding six regional Centres for the Development of Palliative Care. These centers were structured around pivotal organizations such as university hospitals and comprehensive cancer centers. As part of the stimulation program a locoregional network model was introduced within each center for the Development of Palliative Care to integrate palliative care services in the Dutch health care system. OBJECTIVE: We performed a study on network formation in the southwestern area of The Netherlands with 2.4 million inhabitants. The study aimed to answer the following questions: (1) how do networks in palliative care develop, which care providers participate and how do they function? (2) which are the achievements of the palliative care networks as perceived by their participants? (3) which are the success factors of the palliative care networks according to their participants and which factors predict the achievements? DESIGN: Between September 2000 and January 2004 eight local palliative care networks in the region of the Center for Development of Palliative Care-Rotterdam (southwestern area of The Netherlands) were closely followed to gain information on their characteristics and developmental course. MEASUREMENTS: At the start of the study semistructured interviews were held with the coordinators of the eight networks. The information from these interviews and from the network documents were used to constitute a questionnaire to assess the opinions and experiences of the network participants. RESULTS: According to the vast majority of responders, the most important reason to install the networks was the lack of integration between the existing local health care services. The networks were initiated to stimulate mutual collaboration, improve accessibility to health care services and increase the quality of these services. The most important achievements obtained by the palliative care networks were: increase in personal contacts between colleagues in a region, improved engagement and collaboration between participating organizations, enhanced insight in the health care provisions, joined initiatives for the development of new care products, and organization of patient-tailored care. Important success factors for the networks were deemed: fruitful mutual contacts, regular funding and the collective development of care products. By logistic regression analyses, the collective development of new care products and the organization of case discussions between caregivers from different health care services turned out to be the most important predictors for success of the palliative care networks. CONCLUSIONS: Projects that stimulate the communication between professionals appear to improve the mutual collaboration between individual participants and between the participating organizations, which consequently enhances the quality of palliative care.

Community Networks↗
PubMed1997

Rural health network development: public policy issues and state initiatives.

Rural health networks are a potential way for rural health care systems to improve access to care, reduce costs, and enhance quality of care. Networks provide a means for rural providers to contract with managed care organizations, develop their own managed care entities, share resources, and structure practice opportunities to support recruitment and retention of rural physicians and other health care professionals. The results of early network development initiatives indicate a need for state officials and others interested in encouraging network development to agree on common rural health network definitions, to identify clearly the goals of network development programs, and to document and analyze program outcomes. Future network development efforts need to be much more comprehensive if they are to have a significant impact on rural health care. This article analyzes public policy issues related to integrated rural health network development, discusses current efforts to encourage network development in rural areas, and suggests actions that states may take if they desire to support rural health network development. These actions include adopting a formal rural health network definition, providing networks with alternatives to certain regulatory requirements, and providing incentives such as matching grants, loans, or technical assistance. Without public sector support for networks, managed care options may continue to be unavailable in many less densely populated rural areas of the country, and locally controlled rural health networks are unlikely to develop as an alternative to the dominant pattern of managed care expansion by large urban entities. Implementation of Medicare reform legislation could provide significant incentives for the development of rural health networks, depending on the reimbursement provisions, financial solvency standards, and antitrust exemptions for provider-sponsored networks in the final legislation and federal regulations.

Antitrust Laws↗
PubMed1991

Size and connectivity as emergent properties of a developing immune network.

The development of the immune repertoire during neonatal life involves a strong selection process among different clones. The immune system is genetically capable of producing a much more diverse set of lymphocyte receptors than are expressed in the actual repertoire. By means of a model we investigate the hypothesis that repertoire selection is carried out during early life by the immune network. We develop a model network in which possibly hundreds of B cell clones proliferate and produce antibodies following stimulation. Stimulation is viewed as occurring through receptor crosslinking and is modeled via a log bell-shaped dose-response function. Through secretion of free antibody B cell clones can stimulate one another if their receptors have complementary shapes. Receptor shapes are modeled as binary strings and complementarity is evaluated by a string matching algorithm. The dynamic behavior of our model is typically oscillatory and for some parameters chaotic. In the case of two complementary B cell clones, the chaotic attractor has a number of features in common with the Lorenz attractor. The networks we model do not have a predetermined size or topology. Rather, we model the bone marrow as a source which generates novel clones. These novel clones can either be incorporated into the network or remain isolated, mimicking the non-network portion of the immune system. Clones are removed from the network if they fail to expand. We investigate the properties of the network as a function of P(match), the probability that two randomly selected immunoglobulins have complementary shapes. As the model networks evolve they develop a number of self-regulatory features. Most importantly, networks attain a specific equilibrium size and generate a characteristic amount of "natural" antibody. Because the network reaches an asymptotic size even though the bone marrow keeps supplying novel clones, clones must compete for presence in the network, i.e. repertoire selection takes place. Networks comprised of cells with multireactive receptors remain small, whereas networks consisting of cells with specific receptors become much larger. We find an inverse relationship between n, the number of clones in a network, and P(match), and a linear relationship between n and M, the rate at which novel clones are produced in the bone marrow. We present a simple phenomenological model for the number of clones in the network that accounts for the inverse relationship between n and P(match), and that can account for the relationship between n and M. Additionally, the phenomenological model suggests that there are two qualitatively different network equilibria.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗
PubMed1996

Effects of horizontal cell network architecture on signal spread in the turtle outer retina. Experiments and simulations.

In the Pseudemys turtle retina five functionally distinct, electrically coupled networks of horizontal cells distribute signals in the outer plexiform layer. These networks differ significantly in their architecture, as determined by intracellular labeling with Neurobiotin after physiological recording and identification. The density of H1 horizontal cells is highest, ranging around 1800 cells/mm2 at approximately 2.3 mm eccentricity. H1 horizontal cell somata are connected via 6-10 thin, short dendrites. The H1 horizontal cell axon terminal network is composed of thick axon terminals, forming a three-dimensional, sheath-like structure. Networks of coupled H2 and H3 horizontal cells have cell densities of around 210 cells/mm2 and 350 cells/mm2, respectively, at the same eccentricity of 2.3 mm. Cell bodies are connected with 6-12 long, thin dendrites. Here we report for the first time H4 horizontal cell networks. Cell density is approximately 970 cells/mm2 at 2 mm eccentricity, and cell bodies are connected with 6-10 thin, short dendrites. General properties of passive voltage spread were compared for three of these horizontal cell networks using NeuronC. Realistic network architectures were obtained by digitizing the intracellularly labeled networks, respectively. One network obtained from coupled H1 horizontal cell bodies, one from coupled H1 horizontal cell axon terminals, and one from H2 horizontal cells were simulated. These three realistic networks were compared with an artificial, electrically coupled regular triangular network. Passive signal spread in these networks strongly depended on the exact network architecture using otherwise identical parameters. Changes in coupling strength affected signal spread in these networks differently. As in the experimental situation, changes in synaptic conductance influenced signal spread. Some principal effects of extensively coupled horizontal cells on photoreceptor signal processing were simulated with one type of photoreceptor connected by telodendria, synapsing onto an underlying triangular network and receiving feedback synapses. Under certain conditions, spatial information is coded in single photoreceptors. This was also the case in the experimental situation. In the simulation, spatial filter adjustment for optimal spatial coding in photoreceptors can be achieved by changing coupling strength in the horizontal cell network.

Animals↗