Portable analysis tools help solve networking problems.
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Biomedical Engineering↗
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A spring network model for the analysis of load transfer and tissue reactions in intra-medullary fixation.
OBJECTIVE: A spring network can be used to represent the load transfer from a prosthetic stem into its surrounding bone. The study seeks to test the hypothesis that clinical patterns of bone remodelling can be simulated using a feedback that modifies the properties of the network depending on the load transfer. DESIGN: A mathematical model is used to simulate the initial properties of the linear system and its subsequent remodelling behaviour. BACKGROUND: A stable and pain-free transfer of physiological forces is essential for a clinically successful arthroplasty. Following surgery, bone remodelling and osteolysis can modify this load transfer. METHODS: The combined effect of all factors that influence prosthesis-bone load transfer are summarised in the properties of 'inter-link' springs that connect springs representing the prosthesis and bone in the linear network. It is on these inter-links that a remodelling feedback operates, and their properties can be varied with time in response to deformation or force values. RESULTS: Reducing inter-link stiffness leads to a broad distribution of load transfer, whilst an iso-elastic stem concentrates this transfer through the proximal and distal portions of a prosthesis. Physiological patterns of bone resorption and osteolysis become apparent in a time-series analysis of the feedback in the linear system. Specifically, osseo-integration requires a fixation of sufficient stiffness otherwise loosening will occur. Simulated osteolysis following osseo-integration loosens the implant from a distal to a proximal direction. CONCLUSIONS: Complex physiological bone remodelling patterns can emerge from a simple feedback within a linear system. Relevance. Implant loosening is presented here as an adverse response of a stable dynamic system caused by mechanical or biological stimuli.
Arthroplasty, Replacement, Hip↗
Bioreaction network topology and metabolic flux ratio analysis by biosynthetic fractional 13C labeling and two-dimensional NMR spectroscopy.
Biosynthetically directed fractional 13C labeling of the proteinogenic amino acids is achieved by feeding a mixture of uniformly 13C-labeled and unlabeled carbon source compounds into a bioreaction network. Analysis of the resulting labeling pattern enables both a comprehensive characterization of the network topology and the determination of metabolic flux ratios. Attractive features with regard to routine applications are (i) an inherently small demand for 13C-labeled source compounds and (ii) the high sensitivity of two-dimensional [13C,1H]-correlation nuclear magnetic resonance spectroscopy for analysis of 13C-labeling patterns. A user-friendly program, FCAL, is available to allow rapid data analysis. This novel approach, which recently also has been employed in conjunction with metabolic flux balancing to obtain reliable estimates of in vivo fluxes, enables efficient support of metabolic engineering and biotechnology process design.
Amino Acids↗
Prediction of stone disease by discriminant analysis and artificial neural networks in genetic polymorphisms: a new method.
OBJECTIVE: To use information from genetic polymorphisms and from patients (drinking/exercise habits) to identify their association with stone disease, the main analytical and predictive tools being discriminant analysis (DA) and artificial neural networks (ANNs). PATIENTS, SUBJECTS AND METHODS: Urinary stone disease is common in Taiwan; the formation of calcium oxalate stone is reportedly associated with genetic polymorphisms but there are many of these. Genotyping requires many individuals and markers because of the complexity of gene-gene and gene-environmental factor interactions. With the development of artificial intelligence, data-mining tools like ANNs can be used to derive more from patient data in predicting disease. Thus we compared 151 patients with calcium oxalate stones and 105 healthy controls for the presence of four genetic polymorphisms; cytochrome p450c17, E-cadherin, urokinase and vascular endothelial growth factor (VEGF). Information about environmental factors, e.g. water, milk and coffee consumption, and outdoor activities, was also collected. Stepwise DA and ANNs were used as classification methods to obtain an effective discriminant model. RESULTS: With only the genetic variables, DA successfully classified 64% of the participants, but when all related factors (gene and environmental factors) were considered simultaneously, stepwise DA was successful in classifying 74%. The results for DA were best when six variables (sex, VEGF, stone number, coffee, milk, outdoor activities), found by iterative selection, were used. The ANN successfully classified 89% of participants and was better than DA when considering all factors in the model. A sensitivity analysis of the input parameters for ANN was conducted after the ANN program was trained; the most important inputs affecting stone disease were genetic (VEGF), while the second and third were water and milk consumption. CONCLUSIONS: While data-mining tools such as DA and ANN both provide accurate results for assessing genetic markers of calcium stone disease, the ANN provides a better prediction than the DA, especially when considering all (genetic and environmental) related factors simultaneously. This model provides a new way to study stone disease in combination with genetic polymorphisms and environmental factors.
Adult↗
Predicting risk of coronary artery disease from DNA microarray-based genotyping using neural networks and other statistical analysis tool.
This paper presents a novel approach for complex disease prediction that we have developed, exemplified by a study on risk of coronary artery disease (CAD). This multi-disciplinary approach straddles fields of microarray technology and genetics, neural networks (NN), data mining and machine learning, as well as traditional statistical analysis techniques, namely principal components analysis (PCA) and factor analysis (FA). A description of the biological background of the study is given, followed by a detailed description of how the problem has been modeled for analyses by neural networks and FA. A committee learning approach for NN has been used to improve generalization rates. We show that our NN approach is able to yield promising prediction results despite using only the most fundamental network structures. More interestingly, through the statistical analysis process, genes of similar biological functions have been clustered. In addition, a gene marker involved in breaking down lipids has been found to be the most correlated to CAD.
Algorithms↗
Network news. An exclusive analysis predicts that three basic models will emerge.
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Community Networks↗
A graph spectral analysis of the structural similarity network of protein chains.
We present a simple method for the analysis of large networks based on their graph spectral properties. One of the advantages of this method is that it uses a single numerical computation to identify subclusters in a connected graph, which can significantly simplify the complexity involved in analyzing large graphs. This is illustrated using a network of protein chains constructed on the basis of their structural similarities. The large-scale network properties and the cluster and subcluster organization of the protein chain network are presented. We summarize the results of structural and functional analyses of the nodes present in these clusters and elucidate the implications of structural similarity in the protein chain universe.
Cluster Analysis↗
[Discrimination of varieties of apple using near infrared spectra based on principal component analysis and artificial neural network model].
A new method for the discrimination of varieties of apple by means of near infrared spectroscopy (NIRS) was developed. First, principal component analysis (PCA) was used to compress thousands of spectral data into several variables and describe the body of spectra, the analysis suggested that the cumulate reliabilities of PC1 and PC2 (the first two principle components) were more than 98%, and the 2-dimentional plot was drawn with the scores of PC1 and PC2. It appeared to provide the best clustering of the varieties of apple. The loading plot was drawn with PC1 and PC2 through the whole wavelength region. The fingerprint spectra, which were sensitive to the variety of apple, were obtained from the loading plot. The fingerprint spectra were applied as ANN-BP inputs. Seventy five samples from three varieties were selected randomly, then they were used to build discrimination model. This model was used to predict the varieties of 15 unknown samples; the distinguishing rate of 100% was achieved. This model is reliable and practicable. So the present paper could offer a new approach to the fast discrimination of varieties of apple.
Malus↗
Network pharmacological and experimental validation of the mechanism of Chaihu Guizhi Ganjiang decoction regulating T helper cell 17/regulatory T cell balance to improve autoimmune hepatitis.
OBJECTIVE: To elucidate the therapeutic efficacy and mechanism of action of Chaihu Guizhi Ganjiang decoction (, CGGD) in autoimmune hepatitis. METHODS: CGGD components and potential target genes were extracted from previously published databases. The autoimmune hepatitis (AIH)-related regulatory genes were obtained from the DisGeNET database. Intersections were taken, and enrichment analyses were performed on the extracted data. Concanavalin A (ConA)-induced AIH model mice were treated with CGGD via gavage. The results of network pharmacological analysis were experimentally validated. RESULTS: Network pharmacology revealed 228 genes at the intersection of AIH and CGGD. Kyoto Encyclopedia of Genes and Genomes analysis revealed that CGGD primarily regulates the phosphoinositide 3-kinase (PI3K)/ protein kinase B (AKT) signaling pathway and cellular metabolism in AIH. Gene Ontology enrichment analysis revealed that CGGD modulates inflammation through transcription factor-mediated signaling pathways. As predicted, CGGD attenuated ConA-induced AIH in a dose-dependent manner by activating the PI3K/AKT signaling pathway. Histopathological assessment confirmed the protective effects of CGGD against ConA-induced AIH. Further investigation revealed that CGGD regulated the T helper cell 17 (Th17)/regulatory T cell (Treg) balance by modulating the PI3K/Akt/ nuclear factor kappa-B (NF-κB) pathway. CONCLUSIONS: This study demonstrated the therapeutic effect of CGGD on AIH through a combination of network pharmacological prediction and experimental validation. Its mechanism of action involves PI3K/Akt/ NF-κB-mediated regulation of Th17/Treg cells.
Animals↗
Adult-age donors offer acceptable long-term survival to pediatric heart transplant recipients: an analysis of the United Network of Organ Sharing database.
OBJECTIVES: A critical shortage of donor organs has caused many centers to use less restrictive donor criteria, including the use of adult-age donors for pediatric recipients. The purpose of this study is (1) to describe the supply of pediatric (0-18 years) heart donors, (2) to explore the relationship between donor age and long-term survival, and (3) to define threshold age ranges associated with decreased long-term survival. METHODS: The United Network of Organ Sharing provided deidentified patient-level data. Primary analysis focused on 1887 heart transplant recipients aged 9 to 18 years undergoing transplantation from October 1, 1987, to September 25, 2005. Kaplan-Meier analysis and log-rank tests were used in time-to-event analysis. Receiver operating characteristic curves and stratum-specific likelihood ratios were generated to compare survival at various donor age thresholds. RESULTS: The number of pediatric donors decreased (P < .001) over the study period, particularly from 1993 (n = 640) through 2004 (n = 432). Among recipients aged 9 to 18 years, univariate analysis demonstrated a statistically significant (P < .001) inverse relationship between donor age and survival. Stratum-specific likelihood ratio analysis generated 3 strata for donor age: the low-risk, intermediate-risk, and high-risk groups consisted of donors aged 13 years or younger (n = 611, 32.41%), 14 to 51 years (n = 1258, 66.7%), and 52 years and older (n = 16, 0.85%), respectively. In the low-risk, intermediate-risk, and high-risk groups median survival was 4069 days (11.1 years), 3495 days (9.57 years), and 1197 days (3.28 years), respectively. CONCLUSIONS: Although donors aged 13 years or less offer pediatric recipients the best chance for achieving long-term survival, donors aged 14 to 51 years offer good outcomes to pediatric recipients. Consideration should be given to expanded use of well-selected adult-age donors for pediatric recipients.
Adolescent↗
Neural networks for visual field analysis: how do they compare with other algorithms?
PURPOSE: To compare the performance of a neural network in identifying visual field defects with the performance of other available algorithms. METHODS: A feed-forward neural network with a single hidden layer was trained to recognize visual field defects previously collected in a longitudinal follow-up glaucoma study, and then tested on fields taken from the same study but not used in the training. The receiver operating characteristics of the network then were compared with the previously determined performance of other algorithms on the same data set. RESULTS: At a specificity greater than 90%, the neural network was more sensitive than any of the available algorithms (although only the global indices were available for comparison, as the cluster and cross-meridional algorithms did not achieve such high specificity at their current settings). At a lower specificity (80-85%), the neural network was unable to attain the high sensitivity of the cluster or cross-meridional algorithms; in fact, the cluster algorithm from the Low-Tension Glaucoma study was significantly more sensitive. CONCLUSION: The receiver operating characteristics of a feed-forward neural network designed to detect visual field defects were explored. At a very high specificity (90-95%) a neural network performed better than the global indices. However, at a lower specificity (78%-88%), the neural network performed worse than cluster and cross-meridional algorithms.
Algorithms↗
[GeneBee-NET: An Internet based server for biopolymer structure analysis].
A network server providing biopolymer structure databank retrieval as well as some other biocomputing procedures for Internet users is described. Its basic procedures consist in looking for sequence and 3D homologies (similarities). Found homologies are used for constructing multiple alignment, for predicting RNA and protein secondary structures as well as for constructing phylogenetic trees. Alongside traditional methods of sequence homology retrieval, a "matrix-free" (correlation) method is proposed. A similar procedure is used to locate protein 3D similarities. For novel procedures algorithm ideas and their possible applications are discussed. The service ideology is based on the interaction of server and client programs. The client program (GeneBee for IBM PC) can be used to form queries to the server as well as to manipulate a treatment result. In the absence of the client program the interaction with the server can be in the text mode. The E-mail and WWW addresses for the server are as follows: SERVE/INDY.GENEBEE.MSU.SU and WWW.GENEBEE.MSU.SU.
Amino Acid Sequence↗
Development of predictive models of laboratory animal growth using artificial neural networks.
Traditional regression analysis of body weight growth curves encounters problems when the data are extremely variable. While transformations are often employed to meet the criteria of the analysis, some transformations are inadequate for normalizing the data. Regression analysis also requires presuppositions regarding the model to be fit and the techniques to be used in the analysis. An alternative approach using artificial neural networks is presented which may be suitable for developing predictive models of growth. Neural networks are simulators of the processes that occur in the biological brain during the learning process. They are trained on the data, developing the necessary algorithms within their internal architecture, and produce a predictive model based on the learned facts. A dataset of Sprague-Dawley rat (Rattus norvegicus) weights is analyzed by both traditional regression analysis and neural network training. Predictions of body weight are made from both models. While both methods produce models that adequately predict the body weights, the neural network model is superior in that it combines accuracy and precision, being less influenced by longitudinal variability in the data. Thus, the neural network provides another tool for researchers to analyze growth curve data.
Algorithms↗
Visual field analysis using artificial neural networks.
There have been several reports on the application of artificial neural networks (ANNs) to visual field classification. While these have demonstrated that neural networks can be used with good results they have not explored the effects that the training set can have upon network performance nor emphasized the unique value of ANNs in visual field analysis. This paper considers the problem of differentiating normal and glaucomatous visual fields and explores different training set characteristics using field data collected from a Henson CFS2000 perimeter. Training set properties including size, balance between normals and glaucomas, extent of field loss and the spatial location of glaucomatous defects are explored. A multilayer network with 132 input nodes, 20 hidden layer nodes and 2 output nodes in trained using an error backpropagation algorithm. Both sensitivity and specificity are measured during testing. The results demonstrate that large random sets are better than small random sets since sensitivity improves with size and specificity is not adversely affected. The variability in performance also reduces as training set size increases. In addition, sets that are biased towards glaucoma examples are more sensitive and less specific, while sets biased with normal examples are more specific and less sensitive than balanced sets. Thus large training sets with class balance are generally desirable for good sensitivities and specificities. The actual glaucoma examples contained in the set are also important. A training set deficient in examples has no detrimental effect on sensitivity or specificity. The spatial distribution of defects is also crucial. Spatially biased sets are unable to recognize defects that occur in locations where no previous defect has been presented while more balanced sets lead to better performance. In conclusion the 'ideal' training set should contain many examples of early defects that represent the full range of locations where these defects may occur.
Algorithms↗
A neural network approach to the analysis and classification of human craniofacial growth.
Planning of treatment in the field of orthodontics and maxillo-facial surgery is largely dependent on the individual growth of a patient. In the present work, the growth of 43 orthodontically untreated children was analysed by means of lateral cephalograms taken at the ages of 7 and 15. For the description of craniofacial skeletal changes, the concept of tensor analysis and related methods have been applied. Thus the geometric and analytical shortcomings of conventional cephalometric methods have been avoided. Through the use of an artificial neural network, namely self-organizing neural maps, the resultant growth data were classified and the relationships of the various growth patterns were monitored. As a result of self-organization, the 43 children were topologically ordered on the emerging map according to their craniofacial size and shape changes during growth. As a new patient can be allocated on the map, this type of network provides a frame of reference for classifying and analysing previously unknown cases with respect to their growth pattern. If landmarks are used for the determination of growth, the morphometric methods applied as well as the subsequent visualization of the growth data by means of neural networks can be employed for the analysis and classification of growth-related skeletal changes in general.
Adolescent↗
Survival after heart transplantation is not diminished among recipients with uncomplicated diabetes mellitus: an analysis of the United Network of Organ Sharing database.
BACKGROUND: This study compares posttransplantation outcomes of survival and morbidity among recipients with and without diabetes mellitus (DM). METHODS AND RESULTS: The United Network of Organ Sharing (UNOS) provided deidentified patient-level data. Primary analysis focused on 20,412 first-time heart transplant recipients aged > or = 18 years who underwent transplantation between January 1, 1995, and December 31, 2005. To determine severity of DM, DM recipients were stratified by their aggregate number of diabetes-related complications (DRCs), including pretransplantation history of renal failure (serum creatinine = 2.5 mg/dL), peripheral vascular disease, cerebrovascular accident, and severe obesity (body mass index > or = 35 kg/m2). Kaplan-Meier analysis was performed to compare time to event. Although posttransplantation survival was significantly better (P<0.001) among patients without DM (median survival 10.1 years) than among those with DM (9.0 years), survival did not differ (P=0.08) between those without DM (10.1 years) and those with uncomplicated DM (0 DRCs; 9.3 years). Among those with DM, survival was worse with each additional DRC: 0 DRC, 9.3 years; 1 DRC, 6.7 years; and > or = 2 DRCs, 3.6 years. Although acute rejection and transplant coronary artery disease-free survival did not differ between groups, renal failure and severe infection-free survival were worse in those with DM and were inversely related to the number of DRCs. CONCLUSIONS: Posttransplantation survival among patients with uncomplicated DM was not significantly different than that among nondiabetics. However, when stratified by disease severity, recipients with more severe diabetes had significantly worse survival than nondiabetics. Therefore, although DM alone should not be a contraindication to heart transplantation, given the critical shortage of transplantable organs, maximal benefit may be achieved by exploring alternative treatment options in patients with severe DM. These include use of high-risk transplant lists and destination therapy.
Adult↗
Classification of premium and regular gasoline by gas chromatography/mass spectrometry, principal component analysis and artificial neural networks.
Detection and correct classification of gasoline is important for both arson and fuel spill investigation. Principal component analysis (PCA) was used to classify premium and regular gasolines from gas chromatography-mass spectrometry (GC-MS) spectral data obtained from gasoline sold in Canada over one calendar year. Depending upon the dataset used for training and tests, around 80-93% of the samples were correctly classified as either premium or regular gasoline using the Mahalanobis distances calculated from the principal components scores. Only 48-62% of the samples were correctly classified when the premium and regular gasoline samples were divided further into their winter/summer sub-groups. Artificial neural networks (ANNs) were trained to recognise premium and regular gasolines from the same GC-MS data. The best-performing ANN correctly identified all samples as either a premium or regular grade. Approximately 97% of the premium and regular samples were correctly classified according to their winter or summer sub-group.
Journal Article↗
Emotion recognition through facial expression analysis based on a neurofuzzy network.
Extracting and validating emotional cues through analysis of users' facial expressions is of high importance for improving the level of interaction in man machine communication systems. Extraction of appropriate facial features and consequent recognition of the user's emotional state that can be robust to facial expression variations among different users is the topic of this paper. Facial animation parameters (FAPs) defined according to the ISO MPEG-4 standard are extracted by a robust facial analysis system, accompanied by appropriate confidence measures of the estimation accuracy. A novel neurofuzzy system is then created, based on rules that have been defined through analysis of FAP variations both at the discrete emotional space, as well as in the 2D continuous activation-evaluation one. The neurofuzzy system allows for further learning and adaptation to specific users' facial expression characteristics, measured though FAP estimation in real life application of the system, using analysis by clustering of the obtained FAP values. Experimental studies with emotionally expressive datasets, generated in the EC IST ERMIS project indicate the good performance and potential of the developed technologies.
Adaptation, Psychological↗