Background Malaria is the most deadly parasitic infectious disease. Existing drug treatments have limited efficacy in malaria elimination, and the complex pathogenesis of the disease is not fully understood. Detecting novel malaria-associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for anti-malaria drugs. Methods In this study, we developed a network-based approach to predict malaria-associated genes. We constructed a cross-species network to integrate human-human, parasite-parasite and human-parasite protein interactions. Then we extended the random walk algorithm on this network, and used known malaria genes as the seeds to find novel candidate genes for malaria. Results We validated our algorithms using 77 known malaria genes: 14 human genes and 63 parasite genes were ranked averagely within top 2% and top 4%, respectively among human and parasite genomes. We also evaluated our method for predicting novel malaria genes using a set of 27 genes with literature supporting evidence. Our approach ranked 12 genes within top 1% and 24 genes within top 5%. In addition, we demonstrated that top-ranked candied genes were enriched for drug targets, and identified commonalities underlying top-ranked malaria genes through pathway analysis. In summary, the candidate malaria-associated genes predicted by our data-driven approach have the potential to guide genetics-based anti-malaria drug discovery. PMID:26099491
Alvarez, Mariano J.; Shen, Yao; Giorgi, Federico M.; Lachmann, Alexander; Ding, B. Belinda; Ye, B. Hilda; Califano, Andrea
Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, VIPER (Virtual Inference of Protein-activity by Enriched Regulon analysis), for the accurate assessment of protein activity from gene expression data. We use VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all TCGA samples. In addition to accurately inferring aberrant protein activity induced by established mutations, we also identify a significant fraction of tumors with aberrant activity of druggable oncoproteins—despite a lack of mutations, and vice-versa. In vitro assays confirmed that VIPER-inferred protein activity outperforms mutational analysis in predicting sensitivity to targeted inhibitors. PMID:27322546
Siu, Theodore; Vivar, Miguel; Shinbrot, Troy
We present a neural network model that uses a genetic algorithm to identify spatial patterns. We show that the model both learns and reproduces common visual patterns and optical illusions. Surprisingly, we find that the illusions generated are a direct consequence of the network architecture used. We discuss the implications of our results and the insights that we gain on how humans fall for optical illusions
Mizrachi, Eshchar; Verbeke, Lieven; Christie, Nanette; Fierro, Ana C; Mansfield, Shawn D; Davis, Mark F; Gjersing, Erica; Tuskan, Gerald A; Van Montagu, Marc; Van de Peer, Yves; Marchal, Kathleen; Myburg, Alexander A
As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trait information (e.g., transcript, protein, and metabolite variation) in populations that vary in complex traits, has proven effective for dissecting the genetics and biology of such traits. We have applied a network-based data integration (NBDI) method for a systems-level analysis of genes, processes and pathways underlying biomass and bioenergy-related traits using a segregating Eucalyptus hybrid population. We show that the integrative approach can link biologically meaningful sets of genes to complex traits and at the same time reveal the molecular basis of trait variation. Gene sets identified for related woody biomass traits were found to share regulatory loci, cluster in network neighborhoods, and exhibit enrichment for molecular functions such as xylan metabolism and cell wall development. These findings offer a framework for identifying the molecular underpinnings of complex biomass and bioprocessing-related traits. A more thorough understanding of the molecular basis of plant biomass traits should provide additional opportunities for the establishment of a sustainable bio-based economy.
Mizrachi, Eshchar; Verbeke, Lieven; Christie, Nanette; ...
As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trait information (e.g., transcript, protein, and metabolite variation) in populations that vary in complex traits, has proven effective for dissecting the genetics and biology of such traits. We havemore » applied a network-based data integration (NBDI) method for a systems-level analysis of genes, processes and pathways underlying biomass and bioenergy-related traits using a segregating Eucalyptus hybrid population. We show that the integrative approach can link biologically meaningful sets of genes to complex traits and at the same time reveal the molecular basis of trait variation. Gene sets identified for related woody biomass traits were found to share regulatory loci, cluster in network neighborhoods, and exhibit enrichment for molecular functions such as xylan metabolism and cell wall development. These findings offer a framework for identifying the molecular underpinnings of complex biomass and bioprocessing-related traits. Furthermore, a more thorough understanding of the molecular basis of plant biomass traits should provide additional opportunities for the establishment of a sustainable bio-based economy.« less
Mizrachi, Eshchar; Christie, Nanette; Fierro, Ana C.; Mansfield, Shawn D.; Davis, Mark F.; Gjersing, Erica; Tuskan, Gerald A.; Van Montagu, Marc; Van de Peer, Yves; Marchal, Kathleen; Myburg, Alexander A.
As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trait information (e.g., transcript, protein, and metabolite variation) in populations that vary in complex traits, has proven effective for dissecting the genetics and biology of such traits. We have applied a network-based data integration (NBDI) method for a systems-level analysis of genes, processes and pathways underlying biomass and bioenergy-related traits using a segregating Eucalyptus hybrid population. We show that the integrative approach can link biologically meaningful sets of genes to complex traits and at the same time reveal the molecular basis of trait variation. Gene sets identified for related woody biomass traits were found to share regulatory loci, cluster in network neighborhoods, and exhibit enrichment for molecular functions such as xylan metabolism and cell wall development. These findings offer a framework for identifying the molecular underpinnings of complex biomass and bioprocessing-related traits. A more thorough understanding of the molecular basis of plant biomass traits should provide additional opportunities for the establishment of a sustainable bio-based economy. PMID:28096391
In this study, a novel approach via improved genetic algorithm (IGA)-based fuzzy observer is proposed to realise exponential optimal H∞ synchronisation and secure communication in multiple time-delay chaotic (MTDC) systems. First, an original message is inserted into the MTDC system. Then, a neural-network (NN) model is employed to approximate the MTDC system. Next, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. Based on this LDI state-space representation, this study proposes a delay-dependent exponential stability criterion derived in terms of Lyapunov's direct method, thus ensuring that the trajectories of the slave system approach those of the master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI). Due to GA's random global optimisation search capabilities, the lower and upper bounds of the search space can be set so that the GA will seek better fuzzy observer feedback gains, accelerating feedback gain-based synchronisation via the LMI-based approach. IGA, which exhibits better performance than traditional GA, is used to synthesise a fuzzy observer to not only realise the exponential synchronisation, but also achieve optimal H∞ performance by minimizing the disturbance attenuation level and recovering the transmitted message. Finally, a numerical example with simulations is given in order to demonstrate the effectiveness of our approach.
Rengel, David; Arribat, Sandrine; Maury, Pierre; Martin-Magniette, Marie-Laure; Hourlier, Thibaut; Laporte, Marion; Varès, Didier; Carrère, Sébastien; Grieu, Philippe; Balzergue, Sandrine; Gouzy, Jérôme
Identifying the connections between molecular and physiological processes underlying the diversity of drought stress responses in plants is key for basic and applied science. Drought stress response involves a large number of molecular pathways and subsequent physiological processes. Therefore, it constitutes an archetypical systems biology model. We first inferred a gene-phenotype network exploiting differences in drought responses of eight sunflower (Helianthus annuus) genotypes to two drought stress scenarios. Large transcriptomic data were obtained with the sunflower Affymetrix microarray, comprising 32423 probesets, and were associated to nine morpho-physiological traits (integrated transpired water, leaf transpiration rate, osmotic potential, relative water content, leaf mass per area, carbon isotope discrimination, plant height, number of leaves and collar diameter) using sPLS regression. Overall, we could associate the expression patterns of 1263 probesets to six phenotypic traits and identify if correlations were due to treatment, genotype and/or their interaction. We also identified genes whose expression is affected at moderate and/or intense drought stress together with genes whose expression variation could explain phenotypic and drought tolerance variability among our genetic material. We then used the network model to study phenotypic changes in less tractable agronomical conditions, i.e. sunflower hybrids subjected to different watering regimes in field trials. Mapping this new dataset in the gene-phenotype network allowed us to identify genes whose expression was robustly affected by water deprivation in both controlled and field conditions. The enrichment in genes correlated to relative water content and osmotic potential provides evidence of the importance of these traits in agronomical conditions. PMID:23056196
Buckner, Allen L.
Network-based management procedures serve as valuable aids in organizational management, achievement of objectives, problem solving, and decisionmaking. Network techniques especially applicable to educational management systems are the program evaluation and review technique (PERT) and the critical path method (CPM). Other network charting…
Network-based training can provide continuing medical education with methods, whose implementation by means of traditional training is difficult or practically impossible. By virtue of its chronological and geographical flexibility, educational application of the network may provide extra advantage for the trainee and the trainer. Implementation of network-based training is, however, demanding and laborious both technically and pedagogically, whereby organizations should strive for collaboration in organizing the training. In addition, the status of network-based continuing education in relation to the physician's working time should be clearly defined.
Grilo, M.; Fadigas, I. S.; Miranda, J. G. V.; Cunha, M. V.; Monteiro, R. L. S.; Pereira, H. B. B.
Here, we present a study on how the structure of semantic networks based on cliques (specifically, article titles) behaves when vertex removal strategies (i.e., random and uniform vertex removal - RUR, highest degree vertex removal - HDR, and highest intermediation centrality vertex removal - HICR) are applied to this type of network. We propose a method for calculation of the average size of the small components and we identify the existence of a fraction (fp) where the topological structure of the network changes. Semantic networks based on cliques maintain the small-world phenomenon when subjected to RUR, HDR and HICR for fractions of removed vertices less than or equal to fp.
Davis, Tiffany; Yoo, Seong-Moo; Pan, Wendi
The Alabama Learning Exchange (ALEX; www.alex.state.al.us) is a network-based education system designed and implemented to help improve education in Alabama. It accomplishes this goal by providing a single location for the state's K-12 educators to find information that will help improve their classroom effectiveness. The ALEX system includes…
A brief history of the field of neural networks research is given and some simple concepts are described. In addition, some neural network based avionics research and development programs are reviewed. The need for the United States Air Force and NASA to assume a leadership role in supporting this technology is stressed.
Bruce, Bertram C., Ed.; And Others
Exploring how new technologies and new pedagogies transform and are transformed by existing institutions, this book presents 14 essays that discuss network-based classrooms in which students use communications software on computer networks to converse in writing. The first part of the book discusses general themes and issues of the ENFI…
The overall objectives of this project are to investigate research issues pertaining to programming tools and efficiency issues in network based concurrent computing systems. The basis for these efforts is the PVM project that evolved during my visits to Oak Ridge Laboratories under the DOE Faculty Research Participation program; I continue to collaborate with researchers at Oak Ridge on some portions of the project.
... Inheritance; Heterozygous; Inheritance patterns; Heredity and disease; Heritable; Genetic markers ... The chromosomes are made up of strands of genetic information called DNA. Each chromosome contains sections of ...
The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms. PMID:27103885
Yu, Fei; Zeng, An; Gillard, Sébastien; Medo, Matúš
Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use-such as the possible influence of recommendation on the evolution of systems that use it-and finally discuss open research directions and challenges.
Carroll, Edward J.; Coleman, Norman P., Jr.; Reddy, G. N.
An overview is presented of the development of a neural network based speech recognition system. The two primary tasks involved were the development of a time invariant speech encoder and a pattern recognizer or detector. The speech encoder uses amplitude normalization and a Fast Fourier Transform to eliminate amplitude and frequency shifts of acoustic clues. The detector consists of a back-propagation network which accepts data from the encoder and identifies individual words. This use of neural networks offers two advantages over conventional algorithmic detectors: the detection time is no more than a few network time constants, and its recognition speed is independent of the number of the words in the vocabulary. The completed system has functioned as expected with high tolerance to input variation and with error rates comparable to a commercial system when used in a noisy environment.
Altintas, I.; Barreto, R.; Blondin, J. M.; Cheng, Z.; Critchlow, T.; Khan, A.; Klasky, Scott A; Ligon, J.; Ludaescher, B.; Mouallem, P. A.; Parker, S.; Podhorszki, Norbert; Shoshani, A.; Silva, C.; Vouk, M. A.
Comprehensive, end-to-end, data and workflow management solutions are needed to handle the increasing complexity of processes and data volumes associated with modern distributed scientific problem solving, such as ultra-scale simulations and high-throughput experiments. The key to the solution is an integrated network-based framework that is functional, dependable, fault-tolerant, and supports data and process provenance. Such a framework needs to make development and use of application workflows dramatically easier so that scientists' efforts can shift away from data management and utility software development to scientific research and discovery An integrated view of these activities is provided by the notion of scientific workflows - a series of structured activities and computations that arise in scientific problem-solving. An information technology framework that supports scientific workflows is the Ptolemy II based environment called Kepler. This paper discusses the issues associated with practical automation of scientific processes and workflows and illustrates this with workflows developed using the Kepler framework and tools.
It has already been shown that an artificial society based on the three relations of social configuration (market, communal, and obligatory relations) functioning in balance with each other formed a sustainable society which the social reproduction is possible. In this artificial society model, communal relations exist in a network-based community with alternating members rather than a conventional community with cooperative mutual assistance practiced in some agricultural communities. In this paper, using the comparison between network-based communities with alternating members and conventional communities with fixed members, the significance of a network-based community is considered. In concrete terms, the difference in appearance rate for sustainable society, economic activity and asset inequality between network-based communities and conventional communities is analyzed. The appearance rate for a sustainable society of network-based community is higher than that of conventional community. Moreover, most of network-based communities had a larger total number of trade volume than conventional communities. But, the value of Gini coefficient in conventional community is smaller than that of network-based community. These results show that communal relations based on a network-based community is significant for the social reproduction and economic efficiency. However, in such an artificial society, the inequality is sacrificed.
The genus Capsicum represents one of several well characterized Solanaceous genera. A wealth of classical and molecular genetics research is available for the genus. Information gleaned from its cultivated relatives, tomato and potato, provide further insight for basic and applied studies. Early ...
Maintaining genetic variation in wild populations of Arctic organisms is fundamental to the long-term persistence of high latitude biodiversity. Variability is important because it provides options for species to respond to changing environmental conditions and novel challenges such as emerging path...
Wang, Lui; Bayer, Steven
SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.
Cheung, Samson H.; Holst, Terry L. (Technical Monitor)
Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advance computational fluid dynamics codes, which is computationally expensive in mainframe supercomputer. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computer on a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package has been applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.
Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advanced computational fluid dynamics codes, which can be computationally expensive on mainframe supercomputers. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computing environment utilizing a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package is applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.
Wang, Xiaocheng; Song, Xiangli; Liu, Yuan; Tang, Yuling
This paper briefly introduces the design of control network based on OMRON PLC; and describes in detail step and setting of design based on three kinds of network: Ethernet, controller link and CompoBus/D. The design has been applied to lab construction. The practice shows that it is valuable for teaching and scientific research.
Koskimaa, Raine; Lehtonen, Miika; Heinonen, Ulla; Ruokamo, Heli; Tissari, Varpu; Vahtivuori-Hanninen, Sanna; Tella, Seppo
This paper discusses cultural conditions for networked-based mobile education. In our paper, we demonstrate how an Integrated Meta-Model that we have been developing in our MOMENTS project, i.e. Models and Methods for Future Knowledge Construction: Interdisciplinary Implementations with Mobile Technologies, can be used as a heuristic tool for…
The overall objectives of this project are to investigate research issues pertaining to programming tools and efficiency issues in network based concurrent computing systems. The basis for these efforts is the PVM project that evolved during my visits to Oak Ridge Laboratories under the DOE Faculty Research Participation program; I continue to collaborate with researchers at Oak Ridge on some portions of the project.
Goh, Wilson Wen Bin; Wong, Limsoon
Integrating biological networks with proteomics is a tantalizing option for system-level analysis; for example it can help remove false-positives from proteomics data and improve coverage by detecting false-negatives, as well as resolving inconsistent inter-sample protein expression due to biological heterogeneity. Yet, designing a robust network-based analysis strategy on proteomics data is nontrivial. The issues include dealing with test set bias caused by, for example, inappropriate normalization procedure, devising appropriate benchmarking criteria and formulating statistically robust feature-selection techniques. Given the increasing importance of proteomics in contemporary clinical studies, more powerful network-based approaches are needed. We provide some design principles and considerations that can help achieve this, while taking into account the idiosyncrasies of proteomics data.
Guney, Emre; Menche, Jörg; Vidal, Marc; Barábasi, Albert-László
The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy prediction. Here, we take advantage of our increasing understanding of the network-based origins of diseases to introduce a drug-disease proximity measure that quantifies the interplay between drugs targets and diseases. By correcting for the known biases of the interactome, proximity helps us uncover the therapeutic effect of drugs, as well as to distinguish palliative from effective treatments. Our analysis of 238 drugs used in 78 diseases indicates that the therapeutic effect of drugs is localized in a small network neighborhood of the disease genes and highlights efficacy issues for drugs used in Parkinson and several inflammatory disorders. Finally, network-based proximity allows us to predict novel drug-disease associations that offer unprecedented opportunities for drug repurposing and the detection of adverse effects. PMID:26831545
Networks, based upon informal relationships, have ensured that care was delivered to patients for many years. This informal organisation of care, based upon personal relationships, ensures that where the bureaucratic organisation fails the patient, health professionals' work together to network the resources the patient needs. Networks are not new. Formalising networks and recognising their potential to deliver seamless care is new. The NHS must ensure that networks are developed, allowing them freedom from bureaucracy to reach their potential. The Northern and Yorkshire Learning Alliance (NYLA) was established as part of the Northern and Yorkshire health community's efforts to radically improve care. The NYLA operates as a network with a small team of change experts working to develop change management and service improvement capacity across 10,000 square miles. As a network based organisation the team has learned many lessons, which may inform the development of clinical networks in England.
Lembeck, Michael F.
This paper identifies the requirements and describes an architectural framework for an artificial neural network-based system that is capable of fulfilling monitoring and control requirements of future aerospace missions. Incorporated into this framework are a newly developed training algorithm and the concept of cooperative network architectures. The feasibility of such an approach is demonstrated for its ability to identify faults in low frequency waveforms.
Lu, Zhe-Ming; Wu, Zhen; Luo, Hao; Wang, Hao-Xian
This paper proposes an improved community model for social networks based on social mobility. The relationship between the group distribution and the community size is investigated in terms of communication rate and turnover rate. The degree distributions, clustering coefficients, average distances and diameters of networks are analyzed. Experimental results demonstrate that the proposed model possesses the small-world property and can reproduce social networks effectively and efficiently.
Matis, S.; Xu, Y.; Shah, M.B.; Mural, R.J.; Einstein, J.R.; Uberbacher, E.C.
Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a gene localization and modeling system called GRAIL. GRAIL is a multiple sensor-neural network based system. It localizes genes in anonymous DNA sequence by recognizing gene features related to protein-coding slice sites, and then combines the recognized features using a neural network system. Localized coding regions are then optimally parsed into a gene mode. RNA polymerase II promoters can also be predicted. Through years of extensive testing, GRAIL consistently localizes about 90 percent of coding portions of test genes with a false positive rate of about 10 percent. A number of genes for major genetic diseases have been located through the use of GRAIL, and over 1000 research laboratories worldwide use GRAIL on regular bases for localization of genes on their newly sequenced DNA.
Bodruzzaman, M.; Essawy, M.A.
The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to
Ghiassian, Susan Dina
With the availability of large-scale data, it is now possible to systematically study the underlying interaction maps of many complex systems in multiple disciplines. Statistical physics has a long and successful history in modeling and characterizing systems with a large number of interacting individuals. Indeed, numerous approaches that were first developed in the context of statistical physics, such as the notion of random walks and diffusion processes, have been applied successfully to study and characterize complex systems in the context of network science. Based on these tools, network science has made important contributions to our understanding of many real-world, self-organizing systems, for example in computer science, sociology and economics. Biological systems are no exception. Indeed, recent studies reflect the necessity of applying statistical and network-based approaches in order to understand complex biological systems, such as cells. In these approaches, a cell is viewed as a complex network consisting of interactions among cellular components, such as genes and proteins. Given the cellular network as a platform, machinery, functionality and failure of a cell can be studied with network-based approaches, a field known as systems biology. Here, we apply network-based approaches to explore human diseases and their associated genes within the cellular network. This dissertation is divided in three parts: (i) A systematic analysis of the connectivity patterns among disease proteins within the cellular network. The quantification of these patterns inspires the design of an algorithm which predicts a disease-specific subnetwork containing yet unknown disease associated proteins. (ii) We apply the introduced algorithm to explore the common underlying mechanism of many complex diseases. We detect a subnetwork from which inflammatory processes initiate and result in many autoimmune diseases. (iii) The last chapter of this dissertation describes the
Chao, Tien-Hsin; Stoner, William W.
Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.
Sugunnasil, Prompong; Somhom, Samerkae
We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction.
Lin, Cheng-Jian; Lee, Chi-Yung
This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods.
Network-based parallel computing is emerging as a cost-effective alternative for solving many problems which require use of supercomputers or massively parallel computers. The primary objective of this project has been to conduct experimental research on performance evaluation for clustered parallel computing. First, a testbed was established by augmenting our existing SUNSPARCs' network with PVM (Parallel Virtual Machine) which is a software system for linking clusters of machines. Second, a set of three basic applications were selected. The applications consist of a parallel search, a parallel sort, a parallel matrix multiplication. These application programs were implemented in C programming language under PVM. Third, we conducted performance evaluation under various configurations and problem sizes. Alternative parallel computing models and workload allocations for application programs were explored. The performance metric was limited to elapsed time or response time which in the context of parallel computing can be expressed in terms of speedup. The results reveal that the overhead of communication latency between processes in many cases is the restricting factor to performance. That is, coarse-grain parallelism which requires less frequent communication between processes will result in higher performance in network-based computing. Finally, we are in the final stages of installing an Asynchronous Transfer Mode (ATM) switch and four ATM interfaces (each 155 Mbps) which will allow us to extend our study to newer applications, performance metrics, and configurations.
Liu, Guozhong; Deng, Wenyi; Yan, Bixi; Lv, Naiguang
Remote medical monitoring network for long-term monitoring of physiological variables would be helpful for recovery of patients as people are monitored at more comfortable conditions. Furthermore, long-term monitoring would be beneficial to investigate slowly developing deterioration in wellness status of a subject and provide medical treatment as soon as possible. The home monitor runs on an embedded microcomputer Rabbit3000 and interfaces with different medical monitoring module through serial ports. The network based on asymmetric digital subscriber line (ADSL) or local area network (LAN) is established and a client - server model, each embedded home medical monitor is client and the monitoring center is the server, is applied to the system design. The client is able to provide its information to the server when client's request of connection to the server is permitted. The monitoring center focuses on the management of the communications, the acquisition of medical data, and the visualization and analysis of the data, etc. Diagnosing model of sleep apnea syndrome is built basing on ECG, heart rate, respiration wave, blood pressure, oxygen saturation, air temperature of mouth cavity or nasal cavity, so sleep status can be analyzed by physiological data acquired as people in sleep. Remote medical monitoring network based on embedded micro Internetworking technology have advantages of lower price, convenience and feasibility, which have been tested by the prototype.
Zhang, Kunlin; Wang, Jing
Schizophrenia is a common psychiatric disorder with high heritability and complex genetic architecture. Genome-wide association studies (GWAS) have identified several significant loci associated with schizophrenia. However, the explained heritability is still low. Growing evidence has shown schizophrenia is attributable to multiple genes with moderate effects. In-depth mining and integration of GWAS data is urgently expected to uncover disease-related gene combination patterns. Network-based analysis is a promising strategy to better interpret GWAS to identify disease-related network modules. We performed a network-based analysis on three independent schizophrenia GWASs by using a refined analysis framework, which included a more accurate gene P-value calculation, dynamic network module searching algorithm and detailed functional analysis for the obtained modules genes. The result generated 79 modules including 238 genes, which form a highly connected subnetwork with more statistical significance than expected by chance. The result validated several reported disease genes, such as MAD1L1, MCC, SDCCAG8, VAT1L, MAPK14, MYH9 and FXYD6, and also obtained several novel candidate genes and gene-gene interactions. Pathway enrichment analysis of the module genes suggested they were enriched in several neural and immune system related pathways/GO terms, such as neurotrophin signaling pathway, synaptosome, regulation of protein ubiquitination, and antigen processing and presentation. Further crosstalk analysis revealed these pathways/GO terms were cooperated with each other, and identified several important genes, which might play vital roles to connect these functions. Our network-based analysis of schizophrenia GWASs will facilitate the understanding of genetic mechanisms of schizophrenia. PMID:26193471
Network-based drug discovery aims at harnessing the power of networks to investigate the mechanism of action of existing drugs, or new molecules, in order to identify innovative therapeutic treatments. In this review, we describe some of the most recent advances in the field of network pharmacology, starting with approaches relying on computational models of transcriptional networks, then moving to protein and signaling network models and concluding with “drug networks”. These networks are derived from different sources of experimental data, or literature-based analysis, and provide a complementary view of drug mode of action. Molecular and drug networks are powerful integrated computational and experimental approaches that will likely speed up and improve the drug discovery process, once fully integrated into the academic and industrial drug discovery pipeline. PMID:24330611
Li, Xiangyi; Qin, Guangrong; Yang, Qingmin
Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing. PMID:27891522
Hashem, S.; Keller, P.E.; Kouzes, R.T.; Kangas, L.J.
Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. In this paper, we examine the effectiveness of using artificial neural networks for real-time data analysis of a sensor array. Analyzing the sensor data in parallel may allow for rapid identification of contaminants in the field without requiring highly selective individual sensors. We use a prototype sensor array which consists of nine tin-oxide Taguchi-type sensors, a temperature sensor, and a humidity sensor. We illustrate that by using neural network based analysis of the sensor data, the selectivity of the sensor array may be significantly improved, especially when some (or all) the sensors are not highly selective.
Verbeke, Lieven P C; Van den Eynden, Jimmy; Fierro, Ana Carolina; Demeester, Piet; Fostier, Jan; Marchal, Kathleen
The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi)-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad-outcome patient group could
Zhang, Yanjun; Tao, Gang; Chen, Mou
This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.
He, Xuan; Zhao, Hai; Cai, Wei; Liu, Zheng; Si, Shuai-Zong
A new construction method of earthquake networks based on the theory of complex networks is presented in this paper. We propose a space-time influence domain for each earthquake to quantify the subsequence of earthquakes which are directly influenced by the former earthquake. The size of the domain is according to the magnitude of earthquake. In this way, the seismic data in the region of California are mapped to a topology of earthquake network. It is discovered that the earthquake networks in different time spans behave as scale-free networks. This result can be interpreted in terms of the Gutenberg-Richter law. Discovery of small-world characteristic is also reported on the earthquake network constructed by our method. The Omori law emerges as a feature of seismicity for the out-going links of the network. These characteristics highlight a novel aspect of seismicity as a complex phenomenon and will help us to reveal the internal mechanism of seismic system.
Lotfi Shahreza, Maryam; Ghadiri, Nasser; Mousavi, Sayed Rasoul; Varshosaz, Jaleh; Green, James R
Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.
Whalen, Andrew; Hoppitt, William J. E.
A number of recent studies have used Network Based Diffusion Analysis (NBDA) to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC) can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA). To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed. PMID:27092089
Huang, Shaobin; Lv, Tianyang; Zhang, Xizhe; Yang, Yange; Zheng, Weimin; Wen, Chao
It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn't comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role.
Wang, Rongcun; Huang, Rubing; Qu, Binbin
The object-oriented software systems frequently evolve to meet new change requirements. Understanding the characteristics of changes aids testers and system designers to improve the quality of softwares. Identifying important modules becomes a key issue in the process of evolution. In this context, a novel network-based approach is proposed to comprehensively investigate change distributions and the correlation between centrality measures and the scope of change propagation. First, software dependency networks are constructed at class level. And then, the number of times of cochanges among classes is minded from software repositories. According to the dependency relationships and the number of times of cochanges among classes, the scope of change propagation is calculated. Using Spearman rank correlation analyzes the correlation between centrality measures and the scope of change propagation. Three case studies on java open source software projects Findbugs, Hibernate, and Spring are conducted to research the characteristics of change propagation. Experimental results show that (i) change distribution is very uneven; (ii) PageRank, Degree, and CIRank are significantly correlated to the scope of change propagation. Particularly, CIRank shows higher correlation coefficient, which suggests it can be a more useful indicator for measuring the scope of change propagation of classes in object-oriented software system.
Torbati, Nima; Ayatollahi, Ahmad; Kermani, Ali
The aim of this research is to propose a new neural network based method for medical image segmentation. Firstly, a modified self-organizing map (SOM) network, named moving average SOM (MA-SOM), is utilized to segment medical images. After the initial segmentation stage, a merging process is designed to connect the objects of a joint cluster together. A two-dimensional (2D) discrete wavelet transform (DWT) is used to build the input feature space of the network. The experimental results show that MA-SOM is robust to noise and it determines the input image pattern properly. The segmentation results of breast ultrasound images (BUS) demonstrate that there is a significant correlation between the tumor region selected by a physician and the tumor region segmented by our proposed method. In addition, the proposed method segments X-ray computerized tomography (CT) and magnetic resonance (MR) head images much better than the incremental supervised neural network (ISNN) and SOM-based methods.
Todd Vollmer; Ondrej Linda; Milos Manic
Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.
Casanova, Dalcimar; Backes, André Ricardo; Martinez Bruno, Odemir
This work proposed a generalization of the method proposed by the authors: 'A complex network-based approach for boundary shape analysis'. Instead of modelling a contour into a graph and use complex networks rules to characterize it, here, we generalize the technique. This way, the work proposes a mathematical tool for characterization signals, curves and set of points. To evaluate the pattern description power of the proposal, an experiment of plat identification based on leaf veins image are conducted. Leaf vein is a taxon characteristic used to plant identification proposes, and one of its characteristics is that these structures are complex, and difficult to be represented as a signal or curves and this way to be analyzed in a classical pattern recognition approach. Here, we model the veins as a set of points and model as graphs. As features, we use the degree and joint degree measurements in a dynamic evolution. The results demonstrates that the technique has a good power of discrimination and can be used for plant identification, as well as other complex pattern recognition tasks.
Fujino, Yuichi; Fujimura, Kaori; Nomura, Shin-ichiro; Kawashima, Harumi; Tsuchikawa, Megumu; Matsumoto, Toru; Nagao, Kei-ichi; Uruma, Takahiro; Yamamoto, Shinji; Takizawa, Hotaka; Kuroda, Chikazumi; Nakayama, Tomio
This research aims to support chest computed tomography (CT) medical checkups to decrease the death rate by lung cancer. We have developed a remote cooperative reading system for lung cancer screening over the Internet, a secure transmission function, and a cooperative reading environment. It is called the Network-based Reading System. A telemedicine system involves many issues, such as network costs and data security if we use it over the Internet, which is an open network. In Japan, broadband access is widespread and its cost is the lowest in the world. We developed our system considering human machine interface and security. It consists of data entry terminals, a database server, a computer aided diagnosis (CAD) system, and some reading terminals. It uses a secure Digital Imaging and Communication in Medicine (DICOM) encrypting method and Public Key Infrastructure (PKI) based secure DICOM image data distribution. We carried out an experimental trial over the Japan Gigabit Network (JGN), which is the testbed for the Japanese next-generation network, and conducted verification experiments of secure screening image distribution, some kinds of data addition, and remote cooperative reading. We found that network bandwidth of about 1.5 Mbps enabled distribution of screening images and cooperative reading and that the encryption and image distribution methods we proposed were applicable to the encryption and distribution of general DICOM images via the Internet.
Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas
Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.
Mahfouz, Ahmed; Ziats, Mark N.; Rennert, Owen M.; Lelieveldt, Boudewijn P. F.; Reinders, Marcel J. T.
The human brain comprises systems of networks that span the molecular, cellular, anatomic and functional levels. Molecular studies of the developing brain have focused on elucidating networks among gene products that may drive cellular brain development by functioning together in biological pathways. On the other hand, studies of the brain connectome attempt to determine how anatomically distinct brain regions are connected to each other, either anatomically (diffusion tensor imaging) or functionally (functional MRI and EEG), and how they change over development. A global examination of the relationship between gene expression and connectivity in the developing human brain is necessary to understand how the genetic signature of different brain regions instructs connections to other regions. Furthermore, analyzing the development of connectivity networks based on the spatio-temporal dynamics of gene expression provides a new insight into the effect of neurodevelopmental disease genes on brain networks. In this work, we construct connectivity networks between brain regions based on the similarity of their gene expression signature, termed "Genomic Connectivity Networks" (GCNs). Genomic connectivity networks were constructed using data from the BrainSpan Transcriptional Atlas of the Developing Human Brain. Our goal was to understand how the genetic signatures of anatomically distinct brain regions relate to each other across development. We assessed the neurodevelopmental changes in connectivity patterns of brain regions when networks were constructed with genes implicated in the neurodevelopmental disorder autism (autism spectrum disorder; ASD). Using graph theory metrics to characterize the GCNs, we show that ASD-GCNs are relatively less connected later in development with the cerebellum showing a very distinct expression of ASD-associated genes compared to other brain regions.
Azpeitia, Eugenio; Benítez, Mariana; Padilla-Longoria, Pablo; Espinosa-Soto, Carlos; Alvarez-Buylla, Elena R.
In this article we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the inference of gene regulatory networks. Here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson, epistasis is defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus (herein, classical epistasis). Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct inference of gene interaction topologies is hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our article complements previous accounts, not only by focusing on the implications of the hierarchical and
Multiple sclerosis (MS) is an inflammatory CNS disease with a substantial genetic component, originally mapped to only the human leukocyte antigen (HLA) region. In the last 5 years, a total of seven genome-wide association studies and one meta-analysis successfully identified 57 non-HLA susceptibility loci. Here, we merged nominal statistical evidence of association and physical evidence of interaction to conduct a protein-interaction-network-based pathway analysis (PINBPA) on two large genetic MS studies comprising a total of 15,317 cases and 29,529 controls. The distribution of nominally significant loci at the gene level matched the patterns of extended linkage disequilibrium in regions of interest. We found that products of genome-wide significantly associated genes are more likely to interact physically and belong to the same or related pathways. We next searched for subnetworks (modules) of genes (and their encoded proteins) enriched with nominally associated loci within each study and identified those modules in common between the two studies. We demonstrate that these modules are more likely to contain genes with bona fide susceptibility variants and, in addition, identify several high-confidence candidates (including BCL10, CD48, REL, TRAF3, and TEC). PINBPA is a powerful approach to gaining further insights into the biology of associated genes and to prioritizing candidates for subsequent genetic studies of complex traits.
Cheng, Feixiong; Hong, Huixiao; Yang, Shengyong; Wei, Yuquan
Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.
Guo, Nancy Lan; Wan, Ying-Wooi
Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson’s correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson’s correlation networks when evaluated with MSigDB database. PMID:25392692
Rezvan, Abolfazl; Marashi, Sayed-Amir; Eslahchi, Changiz
A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.
Background The incidence of congenital heart disease (CHD) is continuously increasing among infants born alive nowadays, making it one of the leading causes of infant morbidity worldwide. Various studies suggest that both genetic and environmental factors lead to CHD, and therefore identifying its candidate genes and disease-markers has been one of the central topics in CHD research. By using the high-throughput genomic data of CHD which are available recently, network-based methods provide powerful alternatives of systematic analysis of complex diseases and identification of dysfunctional modules and candidate disease genes. Results In this paper, by modeling the information flow from source disease genes to targets of differentially expressed genes via a context-specific protein-protein interaction network, we extracted dysfunctional modules which were then validated by various types of measurements and independent datasets. Network topology analysis of these modules revealed major and auxiliary pathways and cellular processes in CHD, demonstrating the biological usefulness of the identified modules. We also prioritized a list of candidate CHD genes from these modules using a guilt-by-association approach, which are well supported by various kinds of literature and experimental evidence. Conclusions We provided a network-based analysis to detect dysfunctional modules and disease genes of CHD by modeling the information transmission from source disease genes to targets of differentially expressed genes. Our method resulted in 12 modules from the constructed CHD subnetwork. We further identified and prioritized candidate disease genes of CHD from these dysfunctional modules. In conclusion, module analysis not only revealed several important findings with regard to the underlying molecular mechanisms of CHD, but also suggested the distinct network properties of causal disease genes which lead to identification of candidate CHD genes. PMID:22136190
Xia, Youshen; Wang, Jun
This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction.
Leung, Elaine L; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua; Liu, Liang
Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple '-omics' databases. The newly developed algorithm- or network-based computational models can tightly integrate '-omics' databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various '-omics' platforms and computational tools would accelerate development of network-based drug discovery and network medicine.
Leung, Elaine L.; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua
Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple ‘-omics’ databases. The newly developed algorithm- or network-based computational models can tightly integrate ‘-omics’ databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various ‘-omics’ platforms and computational tools would accelerate development of network-based drug discovery and network medicine. PMID:22877768
Wu, Zheng-Guang; Shi, Peng; Su, Hongye; Chu, Jian
This paper investigates the problem of sampled-data stabilization for neural-network-based control systems with an optimal guaranteed cost. Using time-dependent Lyapunov functional approach, some novel conditions are proposed to guarantee the closed-loop systems exponentially stable, which fully use the available information about the actual sampling pattern. Based on the derived conditions, the design methods of the desired sampled-data three-layer fully connected feedforward neural-network-based controller are established to obtain the largest sampling interval and the smallest upper bound of the cost function. A practical example is provided to demonstrate the effectiveness and feasibility of the proposed techniques.
Yang, Minghong; Bai, Wei; Guo, Huiyong; Wen, Hongqiao; Yu, Haihu; Jiang, Desheng
This paper reviews the work on huge capacity fiber-optic sensing network based on ultra-weak draw tower gratings developed at the National Engineering Laboratory for Fiber Optic Sensing Technology (NEL-FOST), Wuhan University of Technology, China. A versatile drawing tower grating sensor network based on ultra-weak fiber Bragg gratings (FBGs) is firstly proposed and demonstrated. The sensing network is interrogated with time- and wavelength-division multiplexing method, which is very promising for the large-scale sensing network.
Pasluosta, Cristian F; Chiu, Alan W L
This chapter describes the implementation of a neural network-based predictive control system for driving a prosthetic hand. Nonlinearities associated with the electromechanical aspects of prosthetic devices present great challenges for precise control of this type of device. Model-based controllers may overcome this issue. Moreover, given the complexity of these kinds of electromechanical systems, neural network-based modeling arises as a good fit for modeling the fingers' dynamics. The results of simulations mimicking potential situations encountered during activities of daily living demonstrate the feasibility of this technique.
Describes a study conducted at the Naval Postgraduate School to determine student attitudes toward various aspects of network-based instruction. Discusses Internet technology; Web-based education; online learning; learning styles; and results from Kolb's Learning Style Inventory, the Hidden Figures Test, and a number of multivariate procedures.…
AN ARTIFICIAL NEURAL NETWORK-BASED DECISION-SUPPORT SYSTEM FOR INTEGRATED NETWORK SECURITY THESIS ...The views expressed in this thesis are those of the author and do not reflect the official policy or position of the United States Air Force... THESIS Presented to the Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force
Discussion of the need for training and lifelong learning in light of new information and communication technologies focuses on small businesses with few employees who need rapid and continuous training. Topics include communities of practice; network-based learning; distance education; enterprise training; mutual training; knowledge creation;…
Novel crosslinked thin polymer networks based on vegetable oil hydroxyfatty acids (HFAs) were prepared by UV photopolymerization and their mechanical properties were evaluated. Two raw materials, castor oil and 7,10-dihydroxy-8(E)-octadecenoic acid (DOD) were used as sources of mono- and di-HFAs, r...
NUMBER Andrew M . Smith , Major, USAF 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES...NETWORK-BASED APPROACH TO OPTIMIZE PERSONNEL RECOVERY FOR THE JOINT FORCE by Andrew M . Smith Major, USAF A paper submitted to the
Niu, Tianhua; Zhou, Yu; Zhang, Lan; Zeng, Yong; Zhu, Wei; Wang, Yu-ping; Deng, Hong-wen
Background Existing microarray studies of bone mineral density (BMD) have been critical for understanding the pathophysiology of osteoporosis, and have identified a number of candidate genes. However, these studies were limited by their relatively small sample sizes and were usually analyzed individually. Here, we propose a novel network-based meta-analysis approach that combines data across six microarray studies to identify functional modules from human protein-protein interaction (PPI) data, and highlight several differentially expressed genes (DEGs) and a functional module that may play an important role in BMD regulation in women. Methods Expression profiling studies were identified by searching PubMed, Gene Expression Omnibus (GEO) and ArrayExpress. Two meta-analysis methods were applied across different gene expression profiling studies. The first, a nonparametric Fisher’s method, combined p-values from individual experiments to identify genes with large effect sizes. The second method combined effect sizes from individual datasets into a meta-effect size to gain a higher precision of effect size estimation across all datasets. Genes with Q test’s p-values < 0.05 or I2 values > 50% were assessed by a random effects model and the remainder by a fixed effects model. Using Fisher’s combined p-values, functional modules were identified through an integrated analysis of microarray data in the context of large protein–protein interaction (PPI) networks. Two previously published meta-analysis studies of genome-wide association (GWA) datasets were used to determine whether these module genes were genetically associated with BMD. Pathway enrichment analysis was performed with a hypergeometric test. Results Six gene expression datasets were identified, which included a total of 249 (129 high BMD and 120 low BMD) female subjects. Using a network-based meta-analysis, a consensus module containing 58 genes (nodes) and 83 edges was detected. Pathway enrichment
De Maeyer, Dries; Weytjens, Bram; De Raedt, Luc; Marchal, Kathleen
In clonal systems, interpreting driver genes in terms of molecular networks helps understanding how these drivers elicit an adaptive phenotype. Obtaining such a network-based understanding depends on the correct identification of driver genes. In clonal systems, independent evolved lines can acquire a similar adaptive phenotype by affecting the same molecular pathways, a phenomenon referred to as parallelism at the molecular pathway level. This implies that successful driver identification depends on interpreting mutated genes in terms of molecular networks. Driver identification and obtaining a network-based understanding of the adaptive phenotype are thus confounded problems that ideally should be solved simultaneously. In this study, a network-based eQTL method is presented that solves both the driver identification and the network-based interpretation problem. As input the method uses coupled genotype-expression phenotype data (eQTL data) of independently evolved lines with similar adaptive phenotypes and an organism-specific genome-wide interaction network. The search for mutational consistency at pathway level is defined as a subnetwork inference problem, which consists of inferring a subnetwork from the genome-wide interaction network that best connects the genes containing mutations to differentially expressed genes. Based on their connectivity with the differentially expressed genes, mutated genes are prioritized as driver genes. Based on semisynthetic data and two publicly available data sets, we illustrate the potential of the network-based eQTL method to prioritize driver genes and to gain insights in the molecular mechanisms underlying an adaptive phenotype. The method is available at http://bioinformatics.intec.ugent.be/phenetic_eqtl/index.html.
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Smith, Merritt H.; Pallis, Jani M.
Continuing improvement in processing speed has made it feasible to solve the Reynolds-Averaged Navier-Stokes equations for simple three-dimensional flows on advanced workstations. Combining multiple workstations into a network-based heterogeneous parallel computer allows the application of programming principles learned on MIMD (Multiple Instruction Multiple Data) distributed memory parallel computers to the solution of larger problems. An overset-grid flow solution code has been developed which uses a cluster of workstations as a network-based parallel computer. Inter-process communication is provided by the Parallel Virtual Machine (PVM) software. Solution speed equivalent to one-third of a Cray-YMP processor has been achieved from a cluster of nine commonly used engineering workstation processors. Load imbalance and communication overhead are the principal impediments to parallel efficiency in this application.
Fang, Zhaoyuan; Tian, Weidong; Ji, Hongbin
Classical algorithms aiming at identifying biological pathways significantly related to studying conditions frequently reduced pathways to gene sets, with an obvious ignorance of the constitutive non-equivalence of various genes within a defined pathway. We here designed a network-based method to determine such non-equivalence in terms of gene weights. The gene weights determined are biologically consistent and robust to network perturbations. By integrating the gene weights into the classical gene set analysis, with a subsequent correction for the "over-counting" bias associated with multi-subunit proteins, we have developed a novel gene-weighed pathway analysis approach, as implemented in an R package called "Gene Associaqtion Network-based Pathway Analysis" (GANPA). Through analysis of several microarray datasets, including the p53 dataset, asthma dataset and three breast cancer datasets, we demonstrated that our approach is biologically reliable and reproducible, and therefore helpful for microarray data interpretation and hypothesis generation.
Lee, Byoung Jik; Lee, Hosin “David”
The previous neural network based on the proximity values was developed using rectangular pavement images. However, the proximity value derived from the rectangular image was biased towards transverse cracking. By sectioning the rectangular image into a set of square sub-images, the neural network based on the proximity value became more robust and consistent in determining a crack type. This paper presents an improved neural network to determine a crack type from a pavement surface image based on square sub-images over the neural network trained using rectangular pavement images. The advantage of using square sub-image is demonstrated by using sample images of transverse cracking, longitudinal cracking and alligator cracking.
Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong
Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.
Kelkar, Atul G.; Haley, Pamela J. (Technical Monitor)
This report provides a comprehensive summary of the research work performed over the entire duration of the co-operative research agreement between NASA Langley Research Center and Kansas State University. This summary briefly lists the findings and also suggests possible future directions for the continuation of the subject research in the area of Generalized Predictive Control (GPC) and Network Based Generalized Predictive Control (NGPC).
Chang, Yunfeng; Li, Yunting; Yang, Liu; Guo, Lu; Liu, Gaochao
The power and resistance of two-port complex resistor network based on NW small world network model are studied in this paper. Mainly, we study the dependence of the network power and resistance on the degree of port vertices, the connection probability and the shortest distance. Qualitative analysis and a simplified formula for network resistance are given out. Finally, we define a branching parameter and give out its physical meaning in the analysis of complex resistor network.
Chiarello, F.; Carelli, P.; Castellano, M. G.; Torrioli, G.
We propose a scheme for the realization of artificial neural networks based on superconducting quantum interference devices (SQUIDs). In order to demonstrate the operation of this scheme we designed and successfully tested a small network that implements an XOR gate and is trained by means of examples. The proposed scheme can be particularly convenient as support for superconducting applications such as detectors for astrophysics, high energy experiments, medicine imaging and so on.
Lee, Soobin; Ryu, Jun-Hyung; Hodge, Bri-Mathias; Lee, In-Beum
This paper presents a neural network-based forecasting framework for photovoltaic power (PV) generation as a decision-supporting tool to employ renewable energies in the process industry. The applicability of the proposed framework is illustrated by comparing its performance against other methodologies such as linear and nonlinear time series modelling approaches. A case study of an actual PV power plant in South Korea is presented.
Izzi, Claudia; Liut, Francesca; Dallera, Nadia; Mazza, Cinzia; Magistroni, Riccardo; Savoldi, Gianfranco; Scolari, Francesco
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most frequent genetic disease, characterized by progressive development of bilateral renal cysts. Two causative genes have been identified: PKD1 and PKD2. ADPKD phenotype is highly variable. Typically, ADPKD is an adult onset disease. However, occasionally, ADPKD manifests as very early onset disease. The phenotypic variability of ADPKD can be explained at three genetic levels: genic, allelic and gene modifier effects. Recent advances in molecular screening for PKD gene mutations and the introduction of the new next generation sequencing (NGS)- based genotyping approach have generated considerable improvement regarding the knowledge of genetic basis of ADPKD. The purpose of this article is to provide a comprehensive review of the genetics of ADPKD, focusing on new insights in genotype-phenotype correlation and exploring novel clinical approach to genetic testing. Evaluation of these new genetic information requires a multidisciplinary approach involving a nephrologist and a clinical geneticist.
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Nora, J.J.; Fraser, F.C.
This book presents a discussion of medical genetics for the practitioner treating or counseling patients with genetic disease. It includes a discussion of the relationship of heredity and diseases, the chromosomal basis for heredity, gene frequencies, and genetics of development and maldevelopment. The authors also focus on teratology, somatic cell genetics, genetics and cancer, genetics of behavior.
Ungvári, Ildikó; Hullám, Gábor; Antal, Péter; Kiszel, Petra Sz; Gézsi, András; Hadadi, Éva; Virág, Viktor; Hajós, Gergely; Millinghoffer, András; Nagy, Adrienne; Kiss, András; Semsei, Ágnes F; Temesi, Gergely; Melegh, Béla; Kisfali, Péter; Széll, Márta; Bikov, András; Gálffy, Gabriella; Tamási, Lilla; Falus, András; Szalai, Csaba
Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called bayesian network based bayesian multilevel analysis of relevance (BN-BMLA). This method uses bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated.With frequentist methods one SNP (rs3751464 in the FRMD6 gene) provided evidence for an association with asthma (OR = 1.43(1.2-1.8); p = 3×10(-4)). The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics.In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance.
Santiago, Jose A; Potashkin, Judith A
Environmental and genetic factors are likely to be involved in the pathogenesis of Parkinson's disease (PD), the second most prevalent neurodegenerative disease among the elderly. Network-based metaanalysis of four independent microarray studies identified the hepatocyte nuclear factor 4 alpha (HNF4A), a transcription factor associated with gluconeogenesis and diabetes, as a central regulatory hub gene up-regulated in blood of PD patients. In parallel, the polypyrimidine tract binding protein 1 (PTBP1), involved in the stabilization and mRNA translation of insulin, was identified as the most down-regulated gene. Quantitative PCR assays revealed that HNF4A and PTBP1 mRNAs were up- and down-regulated, respectively, in blood of 51 PD patients and 45 controls nested in the Diagnostic and Prognostic Biomarkers for Parkinson's Disease. These results were confirmed in blood of 50 PD patients compared with 46 healthy controls nested in the Harvard Biomarker Study. Relative abundance of HNF4A mRNA correlated with the Hoehn and Yahr stage at baseline, suggesting its clinical utility to monitor disease severity. Using both markers, PD patients were classified with 90% sensitivity and 80% specificity. Longitudinal performance analysis demonstrated that relative abundance of HNF4A and PTBP1 mRNAs significantly decreased and increased, respectively, in PD patients during the 3-y follow-up period. The inverse regulation of HNF4A and PTBP1 provides a molecular rationale for the altered insulin signaling observed in PD patients. The longitudinally dynamic biomarkers identified in this study may be useful for monitoring disease-modifying therapies for PD.
Mosca, Ettore; Milanesi, Luciano
Nowadays, computational and statistical methods focusing on integrated analysis of omics data are necessary. A few approaches have been recently described in the literature and a small number of software packages are available. We have developed a new method to generate networks of biological components that incorporate multi-omics information. The novelty of this method relies on using a multi-objective (MO) optimization procedure in order to drive the identification of networks that are enriched according to several statistical estimators. The network-based analysis of omics with MO optimization described in this work can be applied to different types of omics and biological interactions. By using this approach we found protein networks that participate in the establishment of the increased basal differentiation observed in breast tumors of BRCA1-mutation carriers. Additionally, we showed how MO optimization can be used to carry out a network-based comparison among several omic data sets: using transcriptomic data from two types of breast tumors and the corresponding epithelial cells from which tumors were generated, we found a protein network that shows a strong and coherent (the same direction) differential expression when comparing each tumor with its respective epithelial tissue. We have also compared the transcriptional variation detected in three different types of tumors originated in breast, colon and pancreas with the corresponding healthy tissues. Despite the global low correlation observed in the three pairs of tumors, we found more similar networks regulated in the same direction in colon and pancreas tumor cells. In conclusion, we propose the network-based analysis of omics with MO optimization as a valid tool for integrated analysis of omics data.
Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.
Devlin, Stephen; Treloar, Thomas
We consider an evolutionary prisoner's dilemma on a random network. We introduce a simple quantitative network-based parameter and show that it effectively predicts the success of cooperation in simulations on the network. The criterion is shown to be accurate on a variety of networks with degree distributions ranging from regular to Poisson to scale free. The parameter allows for comparisons of random networks regardless of their underlying topology. Finally, we draw analogies between the criterion for the success of cooperation introduced here and existing criteria in other contexts.
Bourdine, Anton V.; Bukashkin, Sergey A.; Buzov, Alexander V.; Kubanov, Victor P.; Praporshchikov, Denis E.; Tyazhev, Anatoly I.
This work is concerned on description of the concept of corporative wireless vehicle voice networks based on Radioover- Fiber (RoF) technology, which is integration of wireless and fiber optic networks. The concept of RoF means to transport data over optical fibers by modulating lightwave with radio frequency signal or at the intermediate frequency/baseband that provides to take advantage of the low loss and large bandwidth of an optical fiber together with immunity to electromagnetic influence, flexibility and transparence. A brief overview of key RoF techniques as well as comparative analysis and ability of its application for wireless vehicle voice network realization is presented.
Shafi, Imran; Ahmad, Jamil; Shah, SyedIsmail; Ikram, AtaulAziz; Ahmad Khan, Adnan; Bashir, Sajid
This paper describes the validity-guided fuzzy clustering evaluation for optimal training of localized neural networks (LNNs) used for reassigning time-frequency representations (TFRs). Our experiments show that the validity-guided fuzzy approach ameliorates the difficulty of choosing correct number of clusters and in conjunction with neural network-based processing technique utilizing a hybrid approach can effectively reduce the blur in the spectrograms. In the course of every partitioning problem the number of subsets must be given before the calculation, but it is rarely known apriori, in this case it must be searched also with using validity measures. Experimental results demonstrate the effectiveness of the approach.
Wei, Xiaodan; Liu, Lijun; Zhou, Wenshu
In this paper, we study the global stability and attractivity of the endemic equilibrium for a network-based SIS epidemic model with nonmonotone incidence rate. The model was introduced in Li (2015). We prove that the endemic equilibrium is globally asymptotically stable if α (a parameter of this model) is sufficiently large, and is globally attractive if the transmission rate λ satisfies λ/λc ∈(1 , 2 ] , where λc is the epidemic threshold. Some numerical experiments are also presented to illustrate the theoretical results.
Ahmed, Hassanein S.; Mohamed, Kamel
Artificial Neural Networks (ANNs) are excellent tools for controller design. ANNs have many advantages compared to traditional control methods. These advantages include simple architecture, training and generalization and distortion insensitivity to nonlinear approximations and nonexact input data. Induction motors have many excellent features, such as simple and rugged construction, high reliability, high robustness, low cost, minimum maintenance, high efficiency, and good self-starting capabilities. In this paper, we propose a neural-network-based inverse model for speed controllers for induction motors. Simulation results show that the ANNs have a high tracing capability.
Wang, Tingting; Dai, Weidi; Jiao, Pengfei; Wang, Wenjun
Many real-world data can be represented as dynamic networks which are the evolutionary networks with timestamps. Analyzing dynamic attributes is important to understanding the structures and functions of these complex networks. Especially, studying the influential nodes is significant to exploring and analyzing networks. In this paper, we propose a method to identify influential nodes in dynamic social networks based on identifying such nodes in the temporal communities which make up the dynamic networks. Firstly, we detect the community structures of all the snapshot networks based on the degree-corrected stochastic block model (DCBM). After getting the community structures, we capture the evolution of every community in the dynamic network by the extended Jaccard’s coefficient which is defined to map communities among all the snapshot networks. Then we obtain the initial influential nodes of the dynamic network and aggregate them based on three widely used centrality metrics. Experiments on real-world and synthetic datasets demonstrate that our method can identify influential nodes in dynamic networks accurately, at the same time, we also find some interesting phenomena and conclusions for those that have been validated in complex network or social science.
Bae, Tae-Suk; Grejner-Brzezinska, Dorota; Mader, Gerald; Dennis, Michael
New guidelines and procedures for real-time (RT) network-based solutions are required in order to support Global Navigation Satellite System (GNSS) derived heights. Two kinds of experiments were carried out to analyze the performance of the network-based real-time kinematic (RTK) solutions. New test marks were installed in different surrounding environments, and the existing GPS benchmarks were used for analyzing the effect of different factors, such as baseline lengths, antenna types, on the final accuracy and reliability of the height estimation. The RT solutions are categorized into three groups: single-base RTK, multiple-epoch network RTK (mRTN), and single-epoch network RTK (sRTN). The RTK solution can be biased up to 9 mm depending on the surrounding environment, but there was no notable bias for a longer reference base station (about 30 km) In addition, the occupation time for the network RTK was investigated in various cases. There is no explicit bias in the solution for different durations, but smoother results were obtained for longer durations. Further investigation is needed into the effect of changing the occupation time between solutions and into the possibility of using single-epoch solutions in precise determination of heights by GNSS.
Wu, Jian; Yuan, Shenfang; Zhou, Genyuan; Ji, Sai; Wang, Zilong; Wang, Yang
The verification of aerospace structures, including full-scale fatigue and static test programs, is essential for structure strength design and evaluation. However, the current overall ground strength testing systems employ a large number of wires for communication among sensors and data acquisition facilities. The centralized data processing makes test programs lack efficiency and intelligence. Wireless sensor network (WSN) technology might be expected to address the limitations of cable-based aeronautical ground testing systems. This paper presents a wireless sensor network based aircraft strength testing (AST) system design and its evaluation on a real aircraft specimen. In this paper, a miniature, high-precision, and shock-proof wireless sensor node is designed for multi-channel strain gauge signal conditioning and monitoring. A cluster-star network topology protocol and application layer interface are designed in detail. To verify the functionality of the designed wireless sensor network for strength testing capability, a multi-point WSN based AST system is developed for static testing of a real aircraft undercarriage. Based on the designed wireless sensor nodes, the wireless sensor network is deployed to gather, process, and transmit strain gauge signals and monitor results under different static test loads. This paper shows the efficiency of the wireless sensor network based AST system, compared to a conventional AST system.
Background Mitochondrial outer membrane permeabilization (MOMP) is one of the most important points in the majority of apoptotic signaling cascades and it is controlled by a network of interactions between the members of the Bcl-2 family. Methods To understand the role of individual members of this family within the MOMP regulation, we have constructed a Boolean network-based model of interactions between the Bcl-2 proteins. Results Computational simulations have revealed the existence of trapping states which, independently from the incoming stimuli, block the occurrence of MOMP. Our results emphasize the role of the antiapoptotic protein Mcl-1 in the majority of these configurations. We demonstrate here the importance of the Bid and Bim for activation of effectors Bax and Bak, and the irreversibility of this activation. The model further points to the antiapoptotic protein Bcl-w as a key factor preventing Bax activation. Conclusions In spite of relative simplicity, the Boolean network-based model provides useful insight into main functioning logic of the Bcl-2 switch, consistent with experimental findings. PMID:23767791
Wu, Jian; Yuan, Shenfang; Zhou, Genyuan; Ji, Sai; Wang, Zilong; Wang, Yang
The verification of aerospace structures, including full-scale fatigue and static test programs, is essential for structure strength design and evaluation. However, the current overall ground strength testing systems employ a large number of wires for communication among sensors and data acquisition facilities. The centralized data processing makes test programs lack efficiency and intelligence. Wireless sensor network (WSN) technology might be expected to address the limitations of cable-based aeronautical ground testing systems. This paper presents a wireless sensor network based aircraft strength testing (AST) system design and its evaluation on a real aircraft specimen. In this paper, a miniature, high-precision, and shock-proof wireless sensor node is designed for multi-channel strain gauge signal conditioning and monitoring. A cluster-star network topology protocol and application layer interface are designed in detail. To verify the functionality of the designed wireless sensor network for strength testing capability, a multi-point WSN based AST system is developed for static testing of a real aircraft undercarriage. Based on the designed wireless sensor nodes, the wireless sensor network is deployed to gather, process, and transmit strain gauge signals and monitor results under different static test loads. This paper shows the efficiency of the wireless sensor network based AST system, compared to a conventional AST system. PMID:22408521
Wang, Yueying; Shen, Hao; Duan, Dengping
This paper investigates the problem of stabilization of sampled-data neural-network-based systems with state quantization. Different with previous works, the communication limitation of state quantization is considered for the first time. More specifically, it is assumed that the sampled state measurements from sensor to the controller are quantized via a quantizer. To reduce conservativeness, a novel piecewise Lyapunov-Krasovskii functional (LKF) is constructed by introducing a line-integral type Lyapunov function and some useful terms that take full advantage of the available information about the actual sampling pattern. Based on the new LKF, much less conservative stabilization conditions are derived to obtain the maximal sampling period and the minimal guaranteed cost control performance. The desired quantized sampled-data three-layer fully connected feedforward neural-network-based controllers are designed by a linear matrix inequality approach. A search algorithm is given to find the optimal values of tuning parameters. The effectiveness and advantage of proposed method are demonstrated by the numerical simulation of an inverted pendulum.
Bae, Tae-Suk; Grejner-Brzezinska, Dorota; Mader, Gerald; Dennis, Michael
New guidelines and procedures for real-time (RT) network-based solutions are required in order to support Global Navigation Satellite System (GNSS) derived heights. Two kinds of experiments were carried out to analyze the performance of the network-based real-time kinematic (RTK) solutions. New test marks were installed in different surrounding environments, and the existing GPS benchmarks were used for analyzing the effect of different factors, such as baseline lengths, antenna types, on the final accuracy and reliability of the height estimation. The RT solutions are categorized into three groups: single-base RTK, multiple-epoch network RTK (mRTN), and single-epoch network RTK (sRTN). The RTK solution can be biased up to 9 mm depending on the surrounding environment, but there was no notable bias for a longer reference base station (about 30 km) In addition, the occupation time for the network RTK was investigated in various cases. There is no explicit bias in the solution for different durations, but smoother results were obtained for longer durations. Further investigation is needed into the effect of changing the occupation time between solutions and into the possibility of using single-epoch solutions in precise determination of heights by GNSS. PMID:26516856
Akay, Altug; Dragomir, Andrei; Erlandsson, Bjorn-Erik
Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users' forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from users' forum posts. We then introduced a novel network-based approach for modeling users' forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments.
Santaniello, Sabato; Granite, Stephen J; Sarma, Sridevi V; Winslow, Raimond L
Sepsis is a systemic deleterious host response to infection. It is a major healthcare problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by using static scores derived from bed-side measurements individually, i.e., without systematically accounting for potential interactions between these signals and their dynamics. In this study, we apply network-based data analysis to take into account interactions between bed-side physiological time series (PTS) data collected in ICU patients, and we investigate features to distinguish between sepsis and non-sepsis conditions. We treated each PTS source as a node on a graph and we retrieved the graph connectivity matrix over time by tracking the correlation between each pair of sources' signals over consecutive time windows. Then, for each connectivity matrix, we computed the eigenvalue decomposition. We found that, even though raw PTS measurements may have indistinguishable distributions in non-sepsis and early sepsis states, the median /I of the eigenvalues computed from the same data is statistically different (p <; 0.001) in the two states and the evolution of /I may reflect the disease progression. Although preliminary, these findings suggest that network-based features computed from continuous PTS data may be useful for early sepsis detection.
Jorde, L.B.; Carey, J.C.; White, R.L.
This book on the subject of medical genetics is a textbook aimed at a very broad audience: principally, medical students, nursing students, graduate, and undergraduate students. The book is actually a primer of general genetics as applied to humans and provides a well-balanced introduction to the scientific and clinical basis of human genetics. The twelve chapters include: Introduction, Basic Cell Biology, Genetic Variation, Autosomal Dominant and Recessive Inheritance, Sex-linked and Mitochondrial Inheritance, Clinical Cytogenetics, Gene Mapping, Immunogenetics, Cancer Genetics, Multifactorial Inheritance and Common Disease, Genetic Screening, Genetic Diagnosis and Gene Therapy, and Clinical Genetics and Genetic Counseling.
Wang, Lui; Bayer, Steven E.
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
Park, Y.M.; Choi, M.S.; Lee, K.Y.
A neural network-based Power System Stabilizer (Neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The uses of power flow dynamics provide a PSS for a wide range operation with reduced size neutral networks. The Neuro-PSS consists of two neutral networks: Neuro-Identifier and Neuro-Controller. The low-frequency oscillation is modeled by the Neuro-Identifier using the power flow dynamics, then a Generalized Backpropagation-Thorough-Time (GBTT) algorithm is developed to train the Neuro-Controller. The simulation results show that the Neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS.
Allen, Jenny; Weinrich, Mason; Hoppitt, Will; Rendell, Luke
We used network-based diffusion analysis to reveal the cultural spread of a naturally occurring foraging innovation, lobtail feeding, through a population of humpback whales (Megaptera novaeangliae) over a period of 27 years. Support for models with a social transmission component was 6 to 23 orders of magnitude greater than for models without. The spatial and temporal distribution of sand lance, a prey species, was also important in predicting the rate of acquisition. Our results, coupled with existing knowledge about song traditions, show that this species can maintain multiple independently evolving traditions in its populations. These insights strengthen the case that cetaceans represent a peak in the evolution of nonhuman culture, independent of the primate lineage.
Virtual communities provide numerous resources, immediate feedback, and information sharing, enabling people to rapidly acquire information and knowledge and supporting diverse applications that facilitate interpersonal interactions, communication, and sharing. Moreover, incorporating highly mobile and convenient devices into practice-based courses can be advantageous in learning situations. Therefore, in this study, a tablet PC and Google+ were introduced to a health education practice course to elucidate satisfaction of learning module and conditions and analyze the sequence and frequency of learning behaviors during the social-network-based learning process. According to the analytical results, social networks can improve interaction among peers and between educators and students, particularly when these networks are used to search for data, post articles, engage in discussions, and communicate. In addition, most nursing students and nursing educators expressed a positive attitude and satisfaction toward these innovative teaching methods, and looked forward to continuing the use of this learning approach.
Garcia, Joseph A.; Cheung, Samson; Holst, Terry L. (Technical Monitor)
A set of Computational Fluid Dynamics (CFD) routines and flow transition prediction tools are integrated into a network based parallel numerical optimization routine. Through this optimization routine, the design of a 2-D airfoil and an infinitely swept wing will be studied in order to advance the design cycle capability of supersonic laminar flow wings. The goal of advancing supersonic laminar flow wing design is achieved by wisely choosing the design variables used in the optimization routine. The design variables are represented by the theory of Fourier series and potential theory. These theories, combined with the parallel CFD flow routines and flow transition prediction tools, provide a design space for a global optimal point to be searched. Finally, the parallel optimization routine enables gradient evaluations to be performed in a fast and parallel fashion.
Hu, Yiming; Zhao, Hongyu
Measuring the similarity between genes is often the starting point for building gene regulatory networks. Most similarity measures used in practice only consider pairwise information with a few also consider network structure. Although theoretical properties of pairwise measures are well understood in the statistics literature, little is known about their statistical properties of those similarity measures based on network structure. In this article, we consider a new whole genome network-based similarity measure, called CCor, that makes use of information of all the genes in the network. We derive a concentration inequality of CCor and compare it with the commonly used Pearson correlation coefficient for inferring network modules. Both theoretical analysis and real data example demonstrate the advantages of CCor over existing measures for inferring gene modules.
Cheng, Yuhua; Chen, Kai; Bai, Libing; Dai, Meizhi
In this paper, the Back Propagation (BP) neural network based control strategy is proposed for the heating system of a polysilicon reduction furnace. It is applied to obtain the control signal I(d), which is used to adjust the heating power through operations of the silicon core temperature, furnace temperature, silicon core voltage, and resistance of the current control cycle. With the control signal I(d) the polycrystalline silicon can be heated from room temperature to the required temperature smoothly and steadily. The proposed BP network applied in this paper can obtain the accurate control signal I(d) and achieve the precise control purpose. This paper presents the principle of the BP network and demonstrates the effectiveness of the BP network in the heating system of a polysilicon reduction furnace by combining the simulation analysis with experimental results.
This paper studies the dynamics of a network-based SIS epidemic model with nonmonotone incidence rate. This type of nonlinear incidence can be used to describe the psychological effect of certain diseases spread in a contact network at high infective levels. We first find a threshold value for the transmission rate. This value completely determines the dynamics of the model and interestingly, the threshold is not dependent on the functional form of the nonlinear incidence rate. Furthermore, if the transmission rate is less than or equal to the threshold value, the disease will die out. Otherwise, it will be permanent. Numerical experiments are given to illustrate the theoretical results. We also consider the effect of the nonlinear incidence on the epidemic dynamics.
Yin, Tianshu; Chen, Shu; Wu, Xiaohui; Tian, Weidong
Here we describe GenePANDA, a novel network-based tool for prioritizing candidate disease genes. GenePANDA assesses whether a gene is likely a candidate disease gene based on its relative distance to known disease genes in a functional association network. A unique feature of GenePANDA is the introduction of adjusted network distance derived by normalizing the raw network distance between two genes with their respective mean raw network distance to all other genes in the network. The use of adjusted network distance significantly improves GenePANDA’s performance on prioritizing complex disease genes. GenePANDA achieves superior performance over five previously published algorithms for prioritizing disease genes. Finally, GenePANDA can assist in prioritizing functionally important SNPs identified by GWAS. PMID:28252032
Hu, Yiming; Zhao, Hongyu
Summary Measuring the similarity between genes is often the starting point for building gene regulatory networks. Most similarity measures used in practice only consider pairwise information with a few also consider network structure. Although theoretical properties of pairwise measures are well understood in the statistics literature, little is known about their statistical properties of those similarity measures based on network structure. In this article, we consider a new whole genome network-based similarity measure, called CCor, that makes use of information of all the genes in the network. We derive a concentration inequality of CCor and compare it with the commonly used Pearson correlation coe cient for inferring network modules. Both theoretical analysis and real data example demonstrate the advantages of CCor over existing measures for inferring gene modules. PMID:26953524
Farmer, B.; Bhat, V. S.; Sklenar, J.; Teipel, E.; Woods, J.; Ketterson, J. B.; Hastings, J. T.; De Long, L. E.
The static and dynamic magnetic responses of patterned ferromagnetic thin films are uniquely altered in the case of aperiodic patterns that retain long-range order (e.g., quasicrystals). We have fabricated permalloy wire networks based on periodic square antidot lattices (ADLs) distorted according to an aperiodic Fibonacci sequence applied to two lattice translations, d1 = 1618 nm and d2 = 1000 nm. The wire segment thickness is fixed at t = 25 nm, and the width W varies from 80 to 510 nm. We measured the DC magnetization between room temperature and 5 K. Room-temperature, narrow-band (9.7 GHz) ferromagnetic resonance (FMR) spectra were acquired for various directions of applied magnetic field. The DC magnetization curves exhibited pronounced step anomalies and plateaus that signal flux closure states. Although the Fibonacci distortion breaks the fourfold symmetry of a finite periodic square ADL, the FMR data exhibit fourfold rotational symmetry with respect to the applied DC magnetic field direction.
Núñez, Rubén Antón, Ignacio; Askins, Steve; Sala, Gabriel; Domínguez, César; Voarino, Philippe; Steiner, Marc; Siefer, Gerald; Fucci, Rafaelle; Roca, Franco; Minuto, Alessandro; Morabito, Paolo
In the frame of the European project SOPHIA, a spectral network based on component (also called isotypes) cells has been created. Among the members of this project, several spectral sensors based on component cells and collimating tubes, so-called spectroheliometers, were installed in the last years, allowing the collection of minute-resolution spectral data useful for CPV systems characterization across Europe. The use of spectroheliometers has been proved useful to establish the necessary spectral conditions to perform power rating of CPV modules and systems. If enough data in a given period of time is collected, ideally a year, it is possible to characterize spectrally the place where measurements are taken, in the same way that hours of annual irradiation can be estimated using a pyrheliometer.
Dong, Hui; Ling, Rongyao; Zhang, Dan
This paper is concerned with the network-based H∞ synchronization control for a class of discrete time-delay neural networks, and attention is focused on how to reduce the communication rate since the communication resource is limited. Techniques such as the measurement size reduction, signal quantization and stochastic signal transmission are introduced to achieve the above goal. An uncertain switched system model is first proposed to capture the above-networked uncertainties. Based on the switched system theory and Lyapunov stability approach, a sufficient condition is obtained such that the closed-loop synchronization system is exponentially stable in the mean-square sense with a prescribed H∞ performance level. The controller gains are determined by solving a set of linear matrix inequalities (LMIs). A numerical example is finally presented to show the effectiveness of the proposed design method.
Llosa, Jordi; Vilajosana, Ignasi; Vilajosana, Xavier; Navarro, Nacho; Suriñach, Emma; Marquès, Joan Manuel
In this paper, we take a hard look at the performance of REMOTE, a sensor network based application that provides a detailed picture of a boat movement, individual rower performance, or his/her performance compared with other crew members. The application analyzes data gathered with a WSN strategically deployed over a boat to obtain information on the boat and oar movements. Functionalities of REMOTE are compared to those of RowX  outdoor instrument, a commercial wired sensor instrument designed for similar purposes. This study demonstrates that with smart geometrical configuration of the sensors, rotation and translation of the oars and boat can be obtained. Three different tests are performed: laboratory calibration allows us to become familiar with the accelerometer readings and validate the theory, ergometer tests which help us to set the acquisition parameters, and on boat tests shows the application potential of this technologies in sports. PMID:22423204
Song, Seokil; Kwak, Yunsik; Lee, Seokhee
Data centric storages for sensor networks have been proposed to efficiently process multi-dimensional range queries as well as exact matches. Usually, a sensor network does not process only one type of the query, but processes various types of queries such as range queries, exact matches and skyline queries. Therefore, a sensor network based on a data centric storage for range queries and exact matches should process skyline queries efficiently. However, existing algorithms for skyline queries have not considered the features of data centric storages. Some of the data centric storages store similar data in sensor nodes that are placed on geographically similar locations. Consequently, all data are ordered in a sensor network. In this paper, we propose a new skyline query processing algorithm that exploits the above features of data centric storages. PMID:22346642
Fox Ramos, Alexander E; Alcover, Charlotte; Evanno, Laurent; Maciuk, Alexandre; Litaudon, Marc; Duplais, Christophe; Bernadat, Guillaume; Gallard, Jean-François; Jullian, Jean-Christophe; Mouray, Elisabeth; Grellier, Philippe; Loiseau, Philippe M; Pomel, Sébastien; Poupon, Erwan; Champy, Pierre; Beniddir, Mehdi A
Three new monoterpene indole alkaloids (1-3) have been isolated from the bark of Geissospermum laeve, together with the known alkaloids (-)-leuconolam (4), geissolosimine (5), and geissospermine (6). The structures of 1-3 were elucidated by analysis of their HRMS and NMR spectroscopic data. The absolute configuration of geissolaevine (1) was deduced from the comparison of experimental and theoretically calculated ECD spectra. The isolation workflow was guided by a molecular networking-based dereplication strategy using an in-house database of monoterpene indole alkaloids. In addition, five known compounds previously undescribed in the Geissospermum genus were dereplicated from the G. laeve alkaloid extract network and were assigned with various levels of identification confidence. The antiparasitic activities against Plasmodium falciparum and Leishmania donovani as well as the cytotoxic activity against the MRC-5 cell line were determined for compounds 1-5.
Triftaridou, Aggeliki I; Vamvakaki, Maria; Patrickios, Costas S
Eight isomeric networks based on equimolar terpolymers were synthesized using group transfer polymerization (GTP) and were characterized in terms of their swelling properties. Two hydrophilic monomers, the nonionic methoxy hexa(ethylene glycol) methacrylate (HEGMA) and the ionizable 2-(dimethylamino)ethyl methacrylate (DMAEMA), and a hydrophobic (nonionic) monomer, methyl methacrylate (MMA), were employed for the syntheses. 1,4-Bis(methoxytrimethylsiloxymethylene)cyclohexane (MTSMC) was used as the bifunctional GTP initiator, while ethylene glycol dimethacrylate (EGDMA) served as the cross-linker. Seven of the networks were model networks, six of which were based on the symmetrical pentablock terpolymers ABCBA, ACBCA, BACAB, BCACB, CBABC, and CABAC, whereas the seventh model network was based on the statistical terpolymer. The eighth network was a randomly cross-linked network based on the statistical terpolymer, prepared by the simultaneous quaterpolymerization of the three monomers and the cross-linker. The molecular weights and molecular weight distributions of the linear pentablock terpolymer precursors, as well as those of their homopolymer and ABA triblock copolymer precursors, were characterized by gel permeation chromatography (GPC) in tetrahydrofuran. The sol fraction of each network was measured and found to be relatively low. The aqueous degrees of swelling of all networks were found to increase at acidic pH due to the ionization of the DMAEMA tertiary amine units. The acidic degrees of swelling of the pentablock terpolymer networks were lower than those of their statistical counterparts due to microphase separation in the former type of networks, also confirmed by thermodynamic calculations and small-angle neutron scattering experiments.
Guo, T. H.; Musgrave, J.
In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using
Srinivasan, K.; Senthilkumar, D. V.; Raja Mohamed, I.; Murali, K.; Lakshmanan, M.; Kurths, J.
We construct a new RC phase shift network based Chua's circuit, which exhibits a period-doubling bifurcation route to chaos. Using coupled versions of such a phase-shift network based Chua's oscillators, we describe a new method for achieving complete synchronization (CS), approximate lag synchronization (LS), and approximate anticipating synchronization (AS) without delay or parameter mismatch. Employing the Pecora and Carroll approach, chaos synchronization is achieved in coupled chaotic oscillators, where the drive system variables control the response system. As a result, AS or LS or CS is demonstrated without using a variable delay line both experimentally and numerically.
Schweiger, Axel; Key, Jeff
This report summarizes the main accomplishments of the project. Specifics are provided in three journal papers which are enclosed with this report. Two of the journal articles are currently in press, one has already been published. Our work focused on two main areas: (1) RadNet. The main objective of the project was the development of a neural network-based method to compute downwelling shortwave and longwave fluxes directly from TOVS HIRS and MSU brightness temperatures. (2) FlaxNet. A second objective of the project involved the development of neural network-based method for the calculation of surface fluxes based on radiative transfer physics.
Reconstructing the phylogeny of Pyrus has been difficult due to the wide distribution of the genus and lack of informative data. In this study, we collected 110 accessions representing 25 Pyrus species and constructed both phylogenetic trees and phylogenetic networks based on multiple DNA sequence d...
Putnik, Goran; Costa, Eric; Alves, Cátia; Castro, Hélio; Varela, Leonilde; Shah, Vaibhav
Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment,…
... this page: //medlineplus.gov/ency/patientinstructions/000510.htm Genetic counseling To use the sharing features on this ... cystic fibrosis or Down syndrome. Who May Want Genetic Counseling? It is up to you whether or ...
... This can cause a medical condition called a genetic disorder. You can inherit a gene mutation from ... during your lifetime. There are three types of genetic disorders: Single-gene disorders, where a mutation affects ...
Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.
One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.
Zhang, Lianhai; Wu, Xiaojiang; Hu, Ying; Wang, Xiaohong; He, Zhonghu; Xie, Yuntao; Pan, Kaifeng; Wang, Ning; Dong, Zhihua; Zhang, Lei; Ji, Jiafu
There is a growing interest in integrating biomaterial repositories into larger infrastructures in order to meet research demands. However, even for a single hospital or institute, where both population-based and multiple disease-based biobanks have existed for a long time, the integration of existing separate biobanks into a virtual cancer biobank is still challenging. The guidelines and procedures for biobanking are varied and not universally enforced or followed in separate biobanks. Within the last 2 years, we initiated a project to establish a centralized biobank facility in a common storage environment. Analyzing the challenges and interests of stakeholders for the biobanks, a working group comprised of representatives from the central and separate banks, ethic committees, and research administration offices reached an agreement to implement a central facility by following the ISBER best practices for biobanking, and including regular project reviews by the ethical and scientific boards. Furthermore, by implementing a modified minimum information system with biobank data sharing, a network based intra-hospital virtual cancer bank was established to facilitate sharing information of samples held by separate banks. Meanwhile, this virtual biobank network, which has integrated patient information from hospital health care systems, will gradually integrate follow-up information from the cancer registry office and data from epidemiology studies, providing controlled access for sample providers and resource users. In the future, this infrastructure designed for a single hospital may be helpful for building a broader virtual network for data and specimen exchanges.
Yang, Hui; Cheng, Lei; Deng, Junni; Zhao, Yongli; Zhang, Jie; Lee, Young
The IP over optical transport network is a very promising networking architecture applied to the interconnection of geographically distributed data centers due to the performance guarantee of low delay, huge bandwidth and high reliability at a low cost. It can enable efficient resource utilization and support heterogeneous bandwidth demands in highly-available, cost-effective and energy-effective manner. In case of cross-layer link failure, to ensure a high-level quality of service (QoS) for user request after the failure becomes a research focus. In this paper, we propose a novel cross-layer restoration scheme for data center services with software defined networking based on IP over optical network. The cross-layer restoration scheme can enable joint optimization of IP network and optical network resources, and enhance the data center service restoration responsiveness to the dynamic end-to-end service demands. We quantitatively evaluate the feasibility and performances through the simulation under heavy traffic load scenario in terms of path blocking probability and path restoration latency. Numeric results show that the cross-layer restoration scheme improves the recovery success rate and minimizes the overall recovery time.
Ahvar, Ehsan; Lee, Gyu Myoung; Han, Son N; Crespi, Noel; Khan, Imran
User location is crucial context information for future smart homes where many location based services will be proposed. This location necessarily means that User Location Discovery (ULD) will play an important role in future smart homes. Concerns about privacy and the need to carry a mobile or a tag device within a smart home currently make conventional ULD systems uncomfortable for users. Future smart homes will need a ULD system to consider these challenges. This paper addresses the design of such a ULD system for context-aware services in future smart homes stressing the following challenges: (i) users' privacy; (ii) device-/tag-free; and (iii) fault tolerance and accuracy. On the other hand, emerging new technologies, such as the Internet of Things, embedded systems, intelligent devices and machine-to-machine communication, are penetrating into our daily life with more and more sensors available for use in our homes. Considering this opportunity, we propose a ULD system that is capitalizing on the prevalence of sensors for the home while satisfying the aforementioned challenges. The proposed sensor network-based and user-friendly ULD system relies on different types of inexpensive sensors, as well as a context broker with a fuzzy-based decision-maker. The context broker receives context information from different types of sensors and evaluates that data using the fuzzy set theory. We demonstrate the performance of the proposed system by illustrating a use case, utilizing both an analytical model and simulation.
Kim, Nac-Woo; Son, Seung-Chul; Lee, Byung-Tak
Recently, as effective demand for high-quality, large-capacity content such as three-dimensional (3D), multiangle, and gigabit-web has increased, a network infrastructure capable of satisfying future broadcast and communication service requirements is required. In this paper, we introduce a convergence service based on a gigabit network and then propose a technique for delivering gigabit 3D content. Here, the term 3D content delivery technique refers to an overlay-multicast-based distributed service platform that is comprised of a media relay agent and a management server. The service platform is designed to back up both live video and file-based video streaming. Using this platform, we can provide 3D remote education and 3D multiangle services via 3D-based video streaming between a service provider and service consumers dispersed at different locations. To evaluate our 3D content delivery technique, we run a series of trials of gigabit network-based 3D trial services to service subscribers. Then, we conduct a survey to measure user satisfaction with the 3D delivery service and simulated network and service quality. From experimental results, we confirm that this type of distributed service platform can be used as the delivery framework for applications such as realistic 3D-based seminars or 3D video conferences.
Cheng, Feixiong; Zhou, Yadi; Li, Weihua; Liu, Guixia; Tang, Yun
Chemical-protein interaction (CPI) is the central topic of target identification and drug discovery. However, large scale determination of CPI is a big challenge for in vitro or in vivo experiments, while in silico prediction shows great advantages due to low cost and high accuracy. On the basis of our previous drug-target interaction prediction via network-based inference (NBI) method, we further developed node- and edge-weighted NBI methods for CPI prediction here. Two comprehensive CPI bipartite networks extracted from ChEMBL database were used to evaluate the methods, one containing 17,111 CPI pairs between 4,741 compounds and 97 G protein-coupled receptors, the other including 13,648 CPI pairs between 2,827 compounds and 206 kinases. The range of the area under receiver operating characteristic curves was 0.73 to 0.83 for the external validation sets, which confirmed the reliability of the prediction. The weak-interaction hypothesis in CPI network was identified by the edge-weighted NBI method. Moreover, to validate the methods, several candidate targets were predicted for five approved drugs, namely imatinib, dasatinib, sertindole, olanzapine and ziprasidone. The molecular hypotheses and experimental evidence for these predictions were further provided. These results confirmed that our methods have potential values in understanding molecular basis of drug polypharmacology and would be helpful for drug repositioning. PMID:22815915
Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi
In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data.
Wu, Guanming; Dawson, Eric; Duong, Adrian; Haw, Robin; Stein, Lincoln
High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called "ReactomeFIViz", which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.
Galbraith, Byron V; Guenther, Frank H; Versace, Massimiliano
Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.
Xing, Lizhi; Ye, Qing; Guan, Jun
This paper analyzed the spreading effect of industrial sectors with complex network model under perspective of econophysics. Input-output analysis, as an important research tool, focuses more on static analysis. However, the fundamental aim of industry analysis is to figure out how interaction between different industries makes impacts on economic development, which turns out to be a dynamic process. Thus, industrial complex network based on input-output tables from WIOD is proposed to be a bridge connecting accurate static quantitative analysis and comparable dynamic one. With application of revised structural holes theory, flow betweenness and random walk centrality were respectively chosen to evaluate industrial sectors' long-term and short-term spreading effect process in this paper. It shows that industries with higher flow betweenness or random walk centrality would bring about more intensive industrial spreading effect to the industrial chains they stands in, because value stream transmission of industrial sectors depends on how many products or services it can get from the other ones, and they are regarded as brokers with bigger information superiority and more intermediate interests.
Yang, Dr. Li
The alerts produced by network-based intrusion detection systems, e.g. Snort, can be difficult for network administrators to efficiently review and respond to due to the enormous number of alerts generated in a short time frame. This work describes how the visualization of raw IDS alert data assists network administrators in understanding the current state of a network and quickens the process of reviewing and responding to intrusion attempts. The project presented in this work consists of three primary components. The first component provides a visual mapping of the network topology that allows the end-user to easily browse clustered alerts. The second component is based on the flocking behavior of birds such that birds tend to follow other birds with similar behaviors. This component allows the end-user to see the clustering process and provides an efficient means for reviewing alert data. The third component discovers and visualizes patterns of multistage attacks by profiling the attacker s behaviors.
Wu, Huai-Ning; Li, Han-Xiong
This paper presents a Galerkin/neural-network- based guaranteed cost control (GCC) design for a class of parabolic partial differential equation (PDE) systems with unknown nonlinearities. A parabolic PDE system typically involves a spatial differential operator with eigenspectrum that can be partitioned into a finite-dimensional slow one and an infinite-dimensional stable fast complement. Motivated by this, in the proposed control scheme, Galerkin method is initially applied to the PDE system to derive an ordinary differential equation (ODE) system with unknown nonlinearities, which accurately describes the dynamics of the dominant (slow) modes of the PDE system. The resulting nonlinear ODE system is subsequently parameterized by a multilayer neural network (MNN) with one-hidden layer and zero bias terms. Then, based on the neural model and a Lure-type Lyapunov function, a linear modal feedback controller is developed to stabilize the closed-loop PDE system and provide an upper bound for the quadratic cost function associated with the finite-dimensional slow system for all admissible approximation errors of the network. The outcome of the GCC problem is formulated as a linear matrix inequality (LMI) problem. Moreover, by using the existing LMI optimization technique, a suboptimal guaranteed cost controller in the sense of minimizing the cost bound is obtained. Finally, the proposed design method is applied to the control of the temperature profile of a catalytic rod.
Galbraith, Byron V.; Guenther, Frank H.; Versace, Massimiliano
Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or “learning by doing,” an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object. PMID:26257640
Luo, Shibo; Dong, Mianxiong; Ota, Kaoru; Wu, Jun; Li, Jianhua
Software-Defined Networking-based Mobile Networks (SDN-MNs) are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic nature and complexity, traditional network security assessment methodologies cannot be applied directly to SDN-MNs, and a novel security assessment methodology is needed. In this paper, an effective security assessment mechanism based on attack graphs and an Analytic Hierarchy Process (AHP) is proposed for SDN-MNs. Firstly, this paper discusses the security assessment problem of SDN-MNs and proposes a methodology using attack graphs and AHP. Secondly, to address the diversity and complexity of SDN-MNs, a novel attack graph definition and attack graph generation algorithm are proposed. In order to quantify security levels, the Node Minimal Effort (NME) is defined to quantify attack cost and derive system security levels based on NME. Thirdly, to calculate the NME of an attack graph that takes the dynamic factors of SDN-MN into consideration, we use AHP integrated with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) as the methodology. Finally, we offer a case study to validate the proposed methodology. The case study and evaluation show the advantages of the proposed security assessment mechanism. PMID:26694409
Cairns, Junmei; Ung, Choong Yong; da Rocha, Edroaldo Lummertz; Zhang, Cheng; Correia, Cristina; Weinshilboum, Richard; Wang, Liewei; Li, Hu
To better address the problem of drug resistance during cancer chemotherapy and explore the possibility of manipulating drug response phenotypes, we developed a network-based phenotype mapping approach (P-Map) to identify gene candidates that upon perturbed can alter sensitivity to drugs. We used basal transcriptomics data from a panel of human lymphoblastoid cell lines (LCL) to infer drug response networks (DRNs) that are responsible for conferring response phenotypes for anthracycline and taxane, two common anticancer agents use in clinics. We further tested selected gene candidates that interact with phenotypic differentially expressed genes (PDEGs), which are up-regulated genes in LCL for a given class of drug response phenotype in triple-negative breast cancer (TNBC) cells. Our results indicate that it is possible to manipulate a drug response phenotype, from resistant to sensitive or vice versa, by perturbing gene candidates in DRNs and suggest plausible mechanisms regulating directionality of drug response sensitivity. More important, the current work highlights a new way to formulate systems-based therapeutic design: supplementing therapeutics that aim to target disease culprits with phenotypic modulators capable of altering DRN properties with the goal to re-sensitize resistant phenotypes. PMID:27841317
Zhang, Ya; Zhao, Hai; He, Xuan; Pei, Fan-Dong; Li, Guang-Guang
Bayesian networks (BNs) are used to analyze the conditional dependencies among different events, which are expressed by conditional probability. Scientists have already investigated the seismic activities by using BNs. Recently, earthquake network is used as a novel methodology to analyze the relationships among the earthquake events. In this paper, we propose a way to predict earthquake from a new perspective. The BN is constructed after processing, which is derived from the earthquake network based on space-time influence domain. And then, the BN parameters are learnt by using the cases which are designed from the seismic data in the period between 00:00:00 on January 1, 1992 and 00:00:00 on January 1, 2012. At last, predictions are done for the data in the period between 00:00:00 on January 1, 2012 and 00:00:00 on January 1, 2015 combining the BN with the parameters. The results show that the success rate of the prediction including delayed prediction is about 65%. It is also discovered that the predictions for some nodes have high rate of accuracy under investigation.
Gebhart, Michael; Leitgeb, Erich; Birnbacher, Ulla; Schrotter, Peter
The satisfaction of all communication needs from single households and business companies over a single access infrastructure is probably the most challenging topic in communications technology today. But even though the so-called "Last Mile Access Bottleneck" is well known since more than ten years and many distribution technologies have been tried out, the optimal solution has not yet been found and paying commercial access networks offering all service classes are still rare today. Conventional services like telephone, radio and TV, as well as new and emerging services like email, web browsing, online-gaming, video conferences, business data transfer or external data storage can all be transmitted over the well known and cost effective Ethernet networking protocol standard. Key requirements for the deployment technology driven by the different services are high data rates to the single customer, security, moderate deployment costs and good scalability to number and density of users, quick and flexible deployment without legal impediments and high availability, referring to the properties of optical and wireless communication. We demonstrate all elements of an Ethernet Access Network based on Free Space Optic distribution technology. Main physical parts are Central Office, Distribution Network and Customer Equipment. Transmission of different services, as well as configuration, service upgrades and remote control of the network are handled by networking features over one FSO connection. All parts of the network are proven, the latest commercially available technology. The set up is flexible and can be adapted to any more specific need if required.
Wen, Tanya; Hsieh, Shulan
Preoccupation and compulsive use of the internet can have negative psychological effects, such that it is increasingly being recognized as a mental disorder. The present study employed network-based statistics to explore how whole-brain functional connections at rest is related to the extent of individual's level of internet addiction, indexed by a self-rated questionnaire. We identified two topologically significant networks, one with connections that are positively correlated with internet addiction tendency, and one with connections negatively correlated with internet addiction tendency. The two networks are interconnected mostly at frontal regions, which might reflect alterations in the frontal region for different aspects of cognitive control (i.e., for control of internet usage and gaming skills). Next, we categorized the brain into several large regional subgroupings, and found that the majority of proportions of connections in the two networks correspond to the cerebellar model of addiction which encompasses the four-circuit model. Lastly, we observed that the brain regions with the most inter-regional connections associated with internet addiction tendency replicate those often seen in addiction literature, and is corroborated by our meta-analysis of internet addiction studies. This research provides a better understanding of large-scale networks involved in internet addiction tendency and shows that pre-clinical levels of internet addiction are associated with similar regions and connections as clinical cases of addiction.
Seregni, M; Pella, A; Riboldi, M; Baroni, G
In radiotherapy, intra-fractional organ motion introduces uncertainties in target localization, leading to unacceptable inaccuracy in dose delivery. Especially in highly selective treatments, such as those delivered with particles beams instead of photons, organ motion may results in severe side effects and/or limited tumor control. Tumor tracking is a motion mitigation strategy that allows an almost continuous dose delivery while the beam is dynamically steered to match the position of the moving target in real-time. Currently, tumor tracking is applied clinically only in the CyberKnife system for photon radiotherapy, whereas neither clinical solutions nor dedicated methodologies are available for particle therapy. Consequently, the aim of the proposed study is to develop a neural networks-based prototypal tracking algorithm intended for particle therapy. We developed a method that exploits three independent neural networks to estimate the internal target position as a function of external surrogate signals. This method was tested on data relative to 20 patients treated with CyberKnife, whose performance was used as benchmark. Results show that the developed algorithm allows targeting error reduction with respect to the CyberKnife system, thus proving the potential value of artificial neural networks for the implementation of tumor tracking methodologies.
Cao, Xinwang; Wang, Xianfeng; Ding, Bin; Yu, Jianyong; Sun, Gang
Cellulose nanowhiskers as a kind of renewable and biocompatible nanomaterials evoke much interest because of its versatility in various applications. Herein, for the first time, a novel controllable fabrication of spider-web-like nanoporous networks based on jute cellulose nanowhiskers (JCNs) deposited on the electrospun (ES) nanofibrous membrane by simple directly immersion-drying method is reported. Jute cellulose nanowhiskers were extracted from jute fibers with a high yield (over 80%) via a 2,2,6,6-tetramethylpiperidine-1-oxyl radical (TEMPO)/NaBr/NaClO system selective oxidization combined with mechanical homogenization. The morphology of JCNs nanoporous networks/ES nanofibrous membrane architecture, including coverage rate, pore-width and layer-by-layer packing structure of the nanoporous networks, can be finely controlled by regulating the JCNs dispersions properties and drying conditions. The versatile nanoporous network composites based on jute cellulose nanowhiskers with ultrathin diameters (3-10 nm) and nanofibrous membrane supports with diameters of 100-300 nm, would be particularly useful for filter applications.
Al-Harazi, Olfat; Al Insaif, Sadiq; Al-Ajlan, Monirah A; Kaya, Namik; Dzimiri, Nduna; Colak, Dilek
A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.
Han, Bing; Taha, Tarek M
There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.
Liang, Y. C.; Lin, W. Z.; Lee, H. P.; Lim, S. P.; Lee, K. H.; Feng, D. P.
This paper proposes a neuro-network-based method for model reduction that combines the generalized Hebbian algorithm (GHA) with the Galerkin procedure to perform the dynamic simulation and analysis of nonlinear microelectromechanical systems (MEMS). An unsupervised neural network is adopted to find the principal eigenvectors of a correlation matrix of snapshots. It has been shown that the extensive computer results of the principal component analysis using the neural network of GHA can extract an empirical basis from numerical or experimental data, which can be used to convert the original system into a lumped low-order macromodel. The macromodel can be employed to carry out the dynamic simulation of the original system resulting in a dramatic reduction of computation time while not losing flexibility and accuracy. Compared with other existing model reduction methods for the dynamic simulation of MEMS, the present method does not need to compute the input correlation matrix in advance. It needs only to find very few required basis functions, which can be learned directly from the input data, and this means that the method possesses potential advantages when the measured data are large. The method is evaluated to simulate the pull-in dynamics of a doubly-clamped microbeam subjected to different input voltage spectra of electrostatic actuation. The efficiency and the flexibility of the proposed method are examined by comparing the results with those of the fully meshed finite-difference method.
Pong, Ting-Chuen; Lee, Chung-Mong; Slagle, James
The image correspondence problem is considered the most difficult step in both stereo and motion analysis. Stereo vision is useful in determining the 3-D positions of points on visible surface in a scene. Motion analysis is useful in determining the spatial and temporal relationships of objects in an environment. Besides stereo and motion analysis, there is the image correspondence problem. Most of this work is based on point or local area properties of the observed gray level values in 2-D images. A global and general approach to this problem is described by using a knowledge-based system. The knowledge it uses consists of both physical properties and spatial relationships of the edges and regions extracted from the given images. The physical component depends on features of the edge or region) in isolation. The spatial component involves the set of edges and regions adjacent to a given edge (or region) of the first image and the set of edges and regions adjacent to each potentially matching edge (or region) of the second image; thus the spatial context of each edge or region is considered. A computational network is used to represent this knowledge, it allows the computation of the likelihood of matching two edges or regions with logical and heuristic operators. An expert system shell called AGNESS (A Generalized Network-based Expert System Shell) is used to build a prototype system.
Li, Caiyan; Wei, Zhi; Li, Hongzhe
Empirical Bayes methods are widely used in the analysis of microarray gene expression data in order to identify the differentially expressed genes or genes that are associated with other general phenotypes. Available methods often assume that genes are independent. However, genes are expected to function interactively and to form molecular modules to affect the phenotypes. In order to account for regulatory dependency among genes, we propose in this paper a network-based empirical Bayes method for analyzing genomic data in the framework of linear models, where the dependency of genes is modeled by a discrete Markov random field defined on a predefined biological network. This method provides a statistical framework for integrating the known biological network information into the analysis of genomic data. We present an iterated conditional mode algorithm for parameter estimation and for estimating the posterior probabilities using Gibbs sampling. We demonstrate the application of the proposed methods using simulations and analysis of a human brain aging microarray gene expression data set.
Ding, Chao; Yao, Hong; Du, Jun; Peng, Xingzhao; Wang, Zhe; Zhao, Jingbo
Recently the robustness of coupled network under cascading failure has attracted a lot of attention. In this paper, we investigate the cascading failure of the interconnected weighted networks based on the state of link. The load on one link is defined by a function of the strength of the two nodes at the ends of that link, using four intentional attack strategies, we study the invulnerability of the interconnected weighted networks when cascading failure occurs. Our studies show that when the link with highest load is attacked, the damage to the network will be more serious by attacking the inner-link with highest load than that caused by attacking the coupling link with highest load, and no matter how the coupling links distribute, there are two thresholds. In addition, we find that the larger the weight increment in the model or the smaller the network’s mean clustering coefficient, the stronger the ability of the network to resist cascading failure when the inner-link with highest load is attacked, while the weaker the ability of the network to suppress the cascading failure when the inner-link with lowest load is attacked.
Pani, Ajaya Kumar; Vadlamudi, Vamsi Krishna; Mohanta, Hare Krishna
The online estimation of process outputs mostly related to quality, as opposed to their belated measurement by means of hardware measuring devices and laboratory analysis, represents the most valuable feature of soft sensors. As of now there have been very few attempts for soft sensing of cement clinker quality which is mostly done by offline laboratory analysis resulting at times in low quality clinker. In the present work three different neural network based soft sensors have been developed for online estimation of cement clinker properties. Different input and output data for a rotary cement kiln were collected from a cement plant producing 10,000 tons of clinker per day. The raw data were pre-processed to remove the outliers and the resulting missing data were imputed. The processed data were then used to develop a back propagation neural network model, a radial basis network model and a regression network model to estimate the clinker quality online. A comparison of the estimation capabilities of the three models has been done by simulation of the developed models. It was observed that radial basis network model produced better estimation capabilities than the back propagation and regression network models.
Shi, Kai; Gao, Lin; Wang, Bingbo
Although a lot of methods have been proposed to identify driver genes, how to separate the driver mutations from the passenger mutations is still a challenging problem in cancer genomics. The detection of driver genes with rare mutation and low accuracy is unsolved better. In this study, we present an integrated network-based approach to locate potential driver genes in a cohort of patients. The approach is composed of two steps including a network diffusion step and an aggregated ranking step, which fuses the correlation between the gene mutations and gene expression, the relationship between the mutated genes and the heterogeneous characteristic of the patient mutation. We analyze three cancer datasets including Glioblastoma multiforme, Ovarian cancer and Breast cancer. Our method has not only identified the known driver genes with high-frequency mutations, but also discovered the potential driver genes with a rare mutation. At the same time, validation by literature search and functional enrichment analysis reveal that the predicted genes are obviously related to these three kinds of cancers.
Vesperini, Fabio; Schuller, Björn
In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F-measure over the three databases. PMID:28182121
Williams-Hayes, Peggy S.
The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.
Ye, Qing; Guan, Jun
This paper analyzed the spreading effect of industrial sectors with complex network model under perspective of econophysics. Input-output analysis, as an important research tool, focuses more on static analysis. However, the fundamental aim of industry analysis is to figure out how interaction between different industries makes impacts on economic development, which turns out to be a dynamic process. Thus, industrial complex network based on input-output tables from WIOD is proposed to be a bridge connecting accurate static quantitative analysis and comparable dynamic one. With application of revised structural holes theory, flow betweenness and random walk centrality were respectively chosen to evaluate industrial sectors’ long-term and short-term spreading effect process in this paper. It shows that industries with higher flow betweenness or random walk centrality would bring about more intensive industrial spreading effect to the industrial chains they stands in, because value stream transmission of industrial sectors depends on how many products or services it can get from the other ones, and they are regarded as brokers with bigger information superiority and more intermediate interests. PMID:27218468
Luo, Shibo; Dong, Mianxiong; Ota, Kaoru; Wu, Jun; Li, Jianhua
Software-Defined Networking-based Mobile Networks (SDN-MNs) are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic nature and complexity, traditional network security assessment methodologies cannot be applied directly to SDN-MNs, and a novel security assessment methodology is needed. In this paper, an effective security assessment mechanism based on attack graphs and an Analytic Hierarchy Process (AHP) is proposed for SDN-MNs. Firstly, this paper discusses the security assessment problem of SDN-MNs and proposes a methodology using attack graphs and AHP. Secondly, to address the diversity and complexity of SDN-MNs, a novel attack graph definition and attack graph generation algorithm are proposed. In order to quantify security levels, the Node Minimal Effort (NME) is defined to quantify attack cost and derive system security levels based on NME. Thirdly, to calculate the NME of an attack graph that takes the dynamic factors of SDN-MN into consideration, we use AHP integrated with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) as the methodology. Finally, we offer a case study to validate the proposed methodology. The case study and evaluation show the advantages of the proposed security assessment mechanism.
Nair, Aditya; Taira, Kunihiko
We construct a network-based representation of energy pathways in unsteady separated flows using a POD-Galerkin projection model. In this formulation, we regard the POD modes as the network nodes and the energy transfer between the modes as the network edges. Based on the energy transfer analysis performed by Noack et al. (2008), edge weights are characterized on the interaction graph. As an example, we examine the energy transfer within the two-dimensional incompressible flow over a circular cylinder. In particular, we analyze the energy pathways involved in flow transition from the unstable symmetric steady state to periodic shedding cycle. The growth of perturbation energy over the network is examined to highlight key features of flow physics and to determine how the energy transfer can be influenced. Furthermore, we implement closed-loop flow control on the POD-Galerkin model to alter the energy interaction path and modify the global behavior of the wake dynamics. The insights gained will be used to perform further network analysis on fluid flows with added complexity. Work supported by US Army Research Office (W911NF-14-1-0386) and US Air Force Office of Scientific Research (YIP: FA9550-13-1-0183).
Wen, Tanya; Hsieh, Shulan
Preoccupation and compulsive use of the internet can have negative psychological effects, such that it is increasingly being recognized as a mental disorder. The present study employed network-based statistics to explore how whole-brain functional connections at rest is related to the extent of individual’s level of internet addiction, indexed by a self-rated questionnaire. We identified two topologically significant networks, one with connections that are positively correlated with internet addiction tendency, and one with connections negatively correlated with internet addiction tendency. The two networks are interconnected mostly at frontal regions, which might reflect alterations in the frontal region for different aspects of cognitive control (i.e., for control of internet usage and gaming skills). Next, we categorized the brain into several large regional subgroupings, and found that the majority of proportions of connections in the two networks correspond to the cerebellar model of addiction which encompasses the four-circuit model. Lastly, we observed that the brain regions with the most inter-regional connections associated with internet addiction tendency replicate those often seen in addiction literature, and is corroborated by our meta-analysis of internet addiction studies. This research provides a better understanding of large-scale networks involved in internet addiction tendency and shows that pre-clinical levels of internet addiction are associated with similar regions and connections as clinical cases of addiction. PMID:26869896
Neural network relationships between the full-scale, experimental hub accelerations and the corresponding pilot floor vertical vibration are studied. The present physics-based, quantitative effort represents an initial systematic study on the UH-60A Black Hawk hub accelerations. The NASA/Army UH-60A Airloads Program flight test database was used. A 'maneuver-effect-factor (MEF)', derived using the roll-angle and the pitch-rate, was used. Three neural network based representation-cases were considered. The pilot floor vertical vibration was considered in the first case and the hub accelerations were separately considered in the second case. The third case considered both the hub accelerations and the pilot floor vertical vibration. Neither the advance ratio nor the gross weight alone could be used to predict the pilot floor vertical vibration. However, the advance ratio and the gross weight together could be used to predict the pilot floor vertical vibration over the entire flight envelope. The hub accelerations data were modeled and found to be of very acceptable quality. The hub accelerations alone could not be used to predict the pilot floor vertical vibration. Thus, the hub accelerations alone do not drive the pilot floor vertical vibration. However, the hub accelerations, along with either the advance ratio or the gross weight or both, could be used to satisfactorily predict the pilot floor vertical vibration. The hub accelerations are clearly a factor in determining the pilot floor vertical vibration.
Ahvar, Ehsan; Lee, Gyu Myoung; Han, Son N.; Crespi, Noel; Khan, Imran
User location is crucial context information for future smart homes where many location based services will be proposed. This location necessarily means that User Location Discovery (ULD) will play an important role in future smart homes. Concerns about privacy and the need to carry a mobile or a tag device within a smart home currently make conventional ULD systems uncomfortable for users. Future smart homes will need a ULD system to consider these challenges. This paper addresses the design of such a ULD system for context-aware services in future smart homes stressing the following challenges: (i) users’ privacy; (ii) device-/tag-free; and (iii) fault tolerance and accuracy. On the other hand, emerging new technologies, such as the Internet of Things, embedded systems, intelligent devices and machine-to-machine communication, are penetrating into our daily life with more and more sensors available for use in our homes. Considering this opportunity, we propose a ULD system that is capitalizing on the prevalence of sensors for the home while satisfying the aforementioned challenges. The proposed sensor network-based and user-friendly ULD system relies on different types of inexpensive sensors, as well as a context broker with a fuzzy-based decision-maker. The context broker receives context information from different types of sensors and evaluates that data using the fuzzy set theory. We demonstrate the performance of the proposed system by illustrating a use case, utilizing both an analytical model and simulation. PMID:27355951
Resnik, David B; Vorhaus, Daniel B
In this article we examine four objections to the genetic modification of human beings: the freedom argument, the giftedness argument, the authenticity argument, and the uniqueness argument. We then demonstrate that each of these arguments against genetic modification assumes a strong version of genetic determinism. Since these strong deterministic assumptions are false, the arguments against genetic modification, which assume and depend upon these assumptions, are therefore unsound. Serious discussion of the morality of genetic modification, and the development of sound science policy, should be driven by arguments that address the actual consequences of genetic modification for individuals and society, not by ones propped up by false or misleading biological assumptions.
Resnik, David B; Vorhaus, Daniel B
In this article we examine four objections to the genetic modification of human beings: the freedom argument, the giftedness argument, the authenticity argument, and the uniqueness argument. We then demonstrate that each of these arguments against genetic modification assumes a strong version of genetic determinism. Since these strong deterministic assumptions are false, the arguments against genetic modification, which assume and depend upon these assumptions, are therefore unsound. Serious discussion of the morality of genetic modification, and the development of sound science policy, should be driven by arguments that address the actual consequences of genetic modification for individuals and society, not by ones propped up by false or misleading biological assumptions. PMID:16800884
Munoz, Karen E.; Hyde, Luke W.; Hariri, Ahmad R.
Imaging genetics is an experimental strategy that integrates molecular genetics and neuroimaging technology to examine biological mechanisms that mediate differences in behavior and the risks for psychiatric disorder. The basic principles in imaging genetics and the development of the field are discussed.
Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.
Boyanova, Desislava; Nilla, Santosh; Klau, Gunnar W; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus
The continuously evolving field of proteomics produces increasing amounts of data while improving the quality of protein identifications. Albeit quantitative measurements are becoming more popular, many proteomic studies are still based on non-quantitative methods for protein identification. These studies result in potentially large sets of identified proteins, where the biological interpretation of proteins can be challenging. Systems biology develops innovative network-based methods, which allow an integrated analysis of these data. Here we present a novel approach, which combines prior knowledge of protein-protein interactions (PPI) with proteomics data using functional similarity measurements of interacting proteins. This integrated network analysis exactly identifies network modules with a maximal consistent functional similarity reflecting biological processes of the investigated cells. We validated our approach on small (H9N2 virus-infected gastric cells) and large (blood constituents) proteomic data sets. Using this novel algorithm, we identified characteristic functional modules in virus-infected cells, comprising key signaling proteins (e.g. the stress-related kinase RAF1) and demonstrate that this method allows a module-based functional characterization of cell types. Analysis of a large proteome data set of blood constituents resulted in clear separation of blood cells according to their developmental origin. A detailed investigation of the T-cell proteome further illustrates how the algorithm partitions large networks into functional subnetworks each representing specific cellular functions. These results demonstrate that the integrated network approach not only allows a detailed analysis of proteome networks but also yields a functional decomposition of complex proteomic data sets and thereby provides deeper insights into the underlying cellular processes of the investigated system.
Rubaai, Ahmed; Kotaru, Raj
In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.
A neural network-based scheme to do a multivariate analysis for forecasting the occurrence and intensity of a meteo event is presented. Many sounding-derived indices are combined together to build a short-term forecast of thunderstorm and rainfall events, in the plain of the Friuli Venezia Giulia region (hereafter FVG, NE Italy). For thunderstorm forecasting, sounding, lightning strikes and mesonet station data (rain and wind) from April to November of the years 1995-2002 have been used to train and validate the artificial neural network (hereafter ANN), while the 2003 and 2004 data have been used as an independent test sample. Two kind of ANNs have been developed: the first is a "classification model" ANN and is built for forecasting the thunderstorm occurrence. If this first ANN predicts convective activity, then a second ANN, built as a "regression model", is used for forecasting the thunderstorm intensity, as defined in a previous article. The classification performances are evaluated with the ROC diagram and some indices derived from the Table of Contingency (like KSS, FAR, Odds Ratio). The regression performances are evaluated using the Mean Square Error and the linear cross correlation coefficient R. A similar approach is applied to the problem of 6 h rainfall forecast in the Friuli Venezia Giulia plain, but in this second case the data cover the period from 1992 to 2004. Also the forecasts of binary events (defined as the occurrence of 5, 20 or 40 mm of maximum rain), made by classification and regression ANN, were compared. Particular emphasis is given to the sounding-derived indices which are chosen in the first places by the predictor forward selection algorithm.
Pilosof, Shai; Morand, Serge; Krasnov, Boris R.; Nunn, Charles L.
Epidemiological networks are commonly used to explore dynamics of parasite transmission among individuals in a population of a given host species. However, many parasites infect multiple host species, and thus multi-host networks may offer a better framework for investigating parasite dynamics. We investigated the factors that influence parasite sharing – and thus potential transmission pathways – among rodent hosts in Southeast Asia. We focused on differences between networks of a single host species and networks that involve multiple host species. In host-parasite networks, modularity (the extent to which the network is divided into subgroups of rodents that interact with similar parasites) was higher in the multi-species than in the single-species networks. This suggests that phylogeny affects patterns of parasite sharing, which was confirmed in analyses showing that it predicted affiliation of individuals to modules. We then constructed “potential transmission networks” based on the host-parasite networks, in which edges depict the similarity between a pair of individuals in the parasites they share. The centrality of individuals in these networks differed between multi- and single-species networks, with species identity and individual characteristics influencing their position in the networks. Simulations further revealed that parasite dynamics differed between multi- and single-species networks. We conclude that multi-host networks based on parasite sharing can provide new insights into the potential for transmission among hosts in an ecological community. In addition, the factors that determine the nature of parasite sharing (i.e. structure of the host-parasite network) may impact transmission patterns. PMID:25748947
Prezioso, M.; Merrikh-Bayat, F.; Hoskins, B. D.; Adam, G. C.; Likharev, K. K.; Strukov, D. B.
Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 1014 synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks based on circuits combining complementary metal-oxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one or several crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits, including first demonstrations of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks. Very recently, such experiments have been extended to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors, whose nonlinear current-voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm to perform the perfect classification of 3 × 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.
Williams, Peggy S.
The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team is to develop and flight-test control systems that use neural network technology to optimize the performance of the aircraft under nominal conditions as well as stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to the baseline aerodynamic derivatives in flight. This set of open-loop flight tests was performed in preparation for a future phase of flights in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed a pitch frequency sweep and an automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. An examination of flight data shows that addition of the flight-identified aerodynamic derivative increments into the simulation improved the pitch handling qualities of the aircraft.
Prezioso, M; Merrikh-Bayat, F; Hoskins, B D; Adam, G C; Likharev, K K; Strukov, D B
Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 10(14) synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks based on circuits combining complementary metal-oxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one or several crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits, including first demonstrations of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks. Very recently, such experiments have been extended to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors, whose nonlinear current-voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm to perform the perfect classification of 3 × 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.
Li, Ling; Jin, Zhen-Lan; Li, Bin
Rhythm of brain activities represents oscillations of postsynaptic potentials in neocortex, therefore it can serve as an indicator of the brain activity state. In order to check the connectivity of brain rhythm, this paper develops a new method of constructing functional network based on phase synchronization. Electroencephalogram (EEG) data were collected while subjects looking at a green cross in two states, performing an attention task and relaxing with eyes-open. The EEG from these two states was filtered by three band-pass filters to obtain signals of theta (4-7 Hz), alpha (8-13 Hz) and beta (14-30 Hz) bands. Mean resultant length was used to estimate strength of phase synchronization in three bands to construct networks of both states, and mean degree K and cluster coefficient C of networks were calculated as a function of threshold. The result shows higher cluster coefficient in the attention state than in the eyes-open state in all three bands, suggesting that cluster coefficient reflects brain state. In addition, an obvious fronto-parietal network is found in the attention state, which is a well-known attention network. These results indicate that attention modulates the fronto-parietal connectivity in different modes as compared with the eyes-open state. Taken together this method is an objective and important tool to study the properties of neural networks of brain rhythm. Project supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 30800242). yCorresponding author. E-mail: email@example.com
Horvát, Emőke-Ágnes; Zhang, Jitao David; Uhlmann, Stefan; Sahin, Özgür; Zweig, Katharina Anna
Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis.
Völker, Uwe; Völzke, Henry; Kroemer, Heyo; Nauck, Matthias; Wallaschofski, Henri
Background The additional clinical value of clustering cardiovascular risk factors to define the metabolic syndrome (MetS) is still under debate. However, it is unclear which cardiovascular risk factors tend to cluster predominately and how individual risk factor states change over time. Methods & Results We used data from 3,187 individuals aged 20–79 years from the population-based Study of Health in Pomerania for a network-based approach to visualize clustered MetS risk factor states and their change over a five-year follow-up period. MetS was defined by harmonized Adult Treatment Panel III criteria, and each individual's risk factor burden was classified according to the five MetS components at baseline and follow-up. We used the map generator to depict 32 (25) different states and highlight the most important transitions between the 1,024 (322) possible states in the weighted directed network. At baseline, we found the largest fraction (19.3%) of all individuals free of any MetS risk factors and identified hypertension (15.4%) and central obesity (6.3%), as well as their combination (19.0%), as the most common MetS risk factors. Analyzing risk factor flow over the five-year follow-up, we found that most individuals remained in their risk factor state and that low high-density lipoprotein cholesterol (HDL) (6.3%) was the most prominent additional risk factor beyond hypertension and central obesity. Also among individuals without any MetS risk factor at baseline, low HDL (3.5%), hypertension (2.1%), and central obesity (1.6%) were the first risk factors to manifest during follow-up. Conclusions We identified hypertension and central obesity as the predominant MetS risk factor cluster and low HDL concentrations as the most prominent new onset risk factor. PMID:22724019
Wang, Rui-Sheng; Oldham, William M.; Loscalzo, Joseph
Molecular oxygen is indispensable for cellular viability and function. Hypoxia is a stress condition in which oxygen demand exceeds supply. Low cellular oxygen content induces a number of molecular changes to activate regulatory pathways responsible for increasing the oxygen supply and optimizing cellular metabolism under limited oxygen conditions. Hypoxia plays critical roles in the pathobiology of many diseases, such as cancer, heart failure, myocardial ischemia, stroke, and chronic lung diseases. Although the complicated associations between hypoxia and cardiovascular (and cerebrovascular) diseases (CVD) have been recognized for some time, there are few studies that investigate their biological link from a systems biology perspective. In this study, we integrate hypoxia genes, CVD genes, and the human protein interactome in order to explore the relationship between hypoxia and cardiovascular diseases at a systems level. We show that hypoxia genes are much closer to CVD genes in the human protein interactome than that expected by chance. We also find that hypoxia genes play significant bridging roles in connecting different cardiovascular diseases. We construct a hypoxia-CVD bipartite network and find several interesting hypoxia-CVD modules with significant gene ontology similarity. Finally, we show that hypoxia genes tend to have more CVD interactors in the human interactome than in random networks of matching topology. Based on these observations, we can predict novel genes that may be associated with CVD. This network-based association study gives us a broad view of the relationships between hypoxia and cardiovascular diseases and provides new insights into the role of hypoxia in cardiovascular biology.
Otis, Jeremy R.; Berman, Dustin; Butts, Jonathan; Lopez, Juan
Industrial Control Systems (ICS) monitor and control operations associated with the national critical infrastructure (e.g., electric power grid, oil and gas pipelines and water treatment facilities). These systems rely on technologies and architectures that were designed for system reliability and availability. Security associated with ICS was never an inherent concern, primarily due to the protections afforded by network isolation. However, a trend in ICS operations is to migrate to commercial networks via TCP/IP in order to leverage commodity benefits and cost savings. As a result, system vulnerabilities are now exposed to the online community. Indeed, recent research has demonstrated that many exposed ICS devices are being discovered using readily available applications (e.g., ShodanHQ search engine and Google-esque queries). Due to the lack of security and logging capabilities for ICS, most knowledge about attacks are derived from real world incidents after an attack has already been carried out and the damage has been done. This research provides a method for introducing sensors into the ICS environment that collect information about network-based attacks. The sensors are developed using an inexpensive Gumstix platform that can be deployed and incorporated with production systems. Data obtained from the sensors provide insight into attack tactics (e.g., port scans, Nessus scans, Metasploit modules, and zero-day exploits) and characteristics (e.g., attack origin, frequency, and level of persistence). Findings enable security professionals to draw an accurate, real-time awareness of the threats against ICS devices and help shift the security posture from reactionary to preventative.
Ruffalo, Matthew; Koyutürk, Mehmet; Sharan, Roded
Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA) generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these "silent players". For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.
Prabahar, Archana; Natarajan, Jeyakumar
MicroRNAs are a class of small non-coding regulatory RNA molecules that modulate the expression of several genes at post-transcriptional level and play a vital role in disease pathogenesis. Recent research shows that a range of miRNAs are involved in the regulation of immunity and its deregulation results in immune mediated diseases such as cancer, inflammation and autoimmune diseases. Computational discovery of these immune miRNAs using a set of specific features is highly desirable. In the current investigation, we present a SVM based classification system which uses a set of novel network based topological and motif features in addition to the baseline sequential and structural features to predict immune specific miRNAs from other non-immune miRNAs. The classifier was trained and tested on a balanced set of equal number of positive and negative examples to show the discriminative power of our network features. Experimental results show that our approach achieves an accuracy of 90.2% and outperforms the classification accuracy of 63.2% reported using the traditional miRNA sequential and structural features. The proposed classifier was further validated with two immune disease sub-class datasets related to multiple sclerosis microarray data and psoriasis RNA-seq data with higher accuracy. These results indicate that our classifier which uses network and motif features along with sequential and structural features will lead to significant improvement in classifying immune miRNAs and hence can be applied to identify other specific classes of miRNAs as an extensible miRNA classification system.
Bian, Zhong-Rui; Yin, Juan; Sun, Wen; Lin, Dian-Jie
Diagnose of active tuberculosis (TB) is challenging and treatment response is also difficult to efficiently monitor. The aim of this study was to use an integrated analysis of microarray and network-based method to the samples from publically available datasets to obtain a diagnostic module set and pathways in active TB. Towards this goal, background protein-protein interactions (PPI) network was generated based on global PPI information and gene expression data, following by identification of differential expression network (DEN) from the background PPI network. Then, ego genes were extracted according to the degree features in DEN. Next, module collection was conducted by ego gene expansion based on EgoNet algorithm. After that, differential expression of modules between active TB and controls was evaluated using random permutation test. Finally, biological significance of differential modules was detected by pathways enrichment analysis based on Reactome database, and Fisher's exact test was implemented to extract differential pathways for active TB. Totally, 47 ego genes and 47 candidate modules were identified from the DEN. By setting the cutoff-criteria of gene size >5 and classification accuracy ≥0.9, 7 ego modules (Module 4, Module 7, Module 9, Module 19, Module 25, Module 38 and Module 43) were extracted, and all of them had the statistical significance between active TB and controls. Then, Fisher's exact test was conducted to capture differential pathways for active TB. Interestingly, genes in Module 4, Module 25, Module 38, and Module 43 were enriched in the same pathway, formation of a pool of free 40S subunits. Significant pathway for Module 7 and Module 9 was eukaryotic translation termination, and for Module 19 was nonsense mediated decay enhanced by the exon junction complex (EJC). Accordingly, differential modules and pathways might be potential biomarkers for treating active TB, and provide valuable clues for better understanding of molecular
Rak, Steven J.; Kolodzy, Paul J.
Object recognition in laser radar sensor imagery is a challenging application of neural networks. The task involves recognition of objects at a variety of distances and aspects with significant levels of sensor noise. These variables are related to sensor parameters such as sensor signal strength and angular resolution, as well as object range and viewing aspect. The effect of these parameters on a fixed recognition system based on log-polar mapped features and an unsupervised neural network classifier are investigated. This work is an attempt to quantify the design parameters of a laser radar measurement system with respect to classifying and/or identifying objects by the shape of their silhouettes. Experiments with vehicle silhouettes rotated through 90 deg-of-view angle from broadside to head-on ('out-of-plane' rotation) have been used to quantify the performance of a log-polar map/neural-network based 3-D object recognition system. These experiments investigated several key issues such as category stability, category memory compression, image fidelity, and viewing aspect. Initial results indicate a compression from 720 possible categories (8 vehicles X 90 out-of-plane rotations) to a classifier memory with approximately 30 stable recognition categories. These results parallel the human experience of studying an object from several viewing angles yet recognizing it through a wide range of viewing angles. Results are presented illustrating category formation for an eight vehicle dataset as a function of several sensor parameters. These include: (1) sensor noise, as a function of carrier-to-noise ratio; (2) pixels on the vehicle, related to angular resolution and target range; and (3) viewing aspect, as related to sensor-to-platform depression angle. This work contributes to the formation of a three- dimensional object recognition system.
Arrasmith, William W.
The advancement of neural network methods and technologies is finding applications in many fields and disciplines of interest to the defense, intelligence, and homeland security communities. Rapidly reconfigurable sensors for real or near-real time signal or image processing can be used for multi-functional purposes such as image compression, target tracking, image fusion, edge detection, thresholding, pattern recognition, and atmospheric turbulence compensation to name a few. A neural network based smart sensor is described that can accomplish these tasks individually or in combination, in real-time or near real-time. As a computationally intensive example, the case of optical imaging through volume turbulence is addressed. For imaging systems in the visible and near infrared part of the electromagnetic spectrum, the atmosphere is often the dominant factor in reducing the imaging system's resolution and image quality. The neural network approach described in this paper is shown to present a viable means for implementing turbulence compensation techniques for near-field and distributed turbulence scenarios. Representative high-speed neural network hardware is presented. Existing 2-D cellular neural network (CNN) hardware is capable of 3 trillion operations per second with peta-operations per second possible using current 3-D manufacturing processes. This hardware can be used for high-speed applications that require fast convolutions and de-convolutions. Existing 3-D artificial neural network technology is capable of peta-operations per second and can be used for fast array processing operations. Methods for optical imaging through distributed turbulence are discussed, simulation results are presented and computational and performance assessments are provided.
Li, Jie; Lei, Kecheng; Wu, Zengrui; Li, Weihua; Liu, Guixia; Liu, Jianwen; Cheng, Feixiong; Tang, Yun
As the recent development of high-throughput technologies in cancer pharmacogenomics, there is an urgent need to develop new computational approaches for comprehensive identification of new pharmacogenomic biomarkers, such as microRNAs (miRNAs). In this study, a network-based framework, namely the SMiR-NBI model, was developed to prioritize miRNAs as potential biomarkers characterizing treatment responses of anticancer drugs on the basis of a heterogeneous network connecting drugs, miRNAs and genes. A high area under the receiver operating characteristic curve of 0.820 ± 0.013 was yielded during 10-fold cross validation. In addition, high performance was further validated in identifying new anticancer mechanism-of-action for natural products and non-steroidal anti-inflammatory drugs. Finally, the newly predicted miRNAs for tamoxifen and metformin were experimentally validated in MCF-7 and MDA-MB-231 breast cancer cell lines via qRT-PCR assays. High success rates of 60% and 65% were yielded for tamoxifen and metformin, respectively. Specifically, 11 oncomiRNAs (e.g. miR-20a-5p, miR-27a-3p, miR-29a-3p, and miR-146a-5p) from the top 20 predicted miRNAs were experimentally verified as new pharmacogenomic biomarkers for metformin in MCF-7 or MDA-MB-231 cell lines. In summary, the SMiR-NBI model would provide a powerful tool to identify potential pharmacogenomic biomarkers characterized by miRNAs in the emerging field of precision cancer medicine, which is available at http://lmmd.ecust.edu.cn/database/smir-nbi/. PMID:27329603
Chen, Yang; Zhang, Xiang; Zhang, Guo-qiang; Xu, Rong
Systems approaches to analyzing disease phenotype networks in combination with protein functional interaction networks have great potential in illuminating disease pathophysiological mechanisms. While many genetic networks are readily available, disease phenotype networks remain largely incomplete. In this study, we built a large-scale Disease Manifestation Network (DMN) from 50,543 highly accurate disease-manifestation semantic relationships in the United Medical Language System (UMLS). Our new phenotype network contains 2305 nodes and 373,527 weighted edges to represent the disease phenotypic similarities. We first compared DMN with the networks representing genetic relationships among diseases, and demonstrated that the phenotype clustering in DMN reflects common disease genetics. Then we compared DMN with a widely-used disease phenotype network in previous gene discovery studies, called mimMiner, which was extracted from the textual descriptions in Online Mendelian Inheritance in Man (OMIM). We demonstrated that DMN contains different knowledge from the existing phenotype data source. Finally, a case study on Marfan syndrome further proved that DMN contains useful information and can provide leads to discover unknown disease causes. Integrating DMN in systems approaches with mimMiner and other data offers the opportunities to predict novel disease genetics. We made DMN publicly available at nlp/case.edu/public/data/DMN. PMID:25277758
Weier, Heinz -Ulrich G
Herein are described multicolor FISH probe sets termed "genetic barcodes" targeting several cancer or disease-related loci to assess gene rearrangements and copy number changes in tumor cells. Two, three or more different fluorophores are used to detect the genetic barcode sections thus permitting unique labeling and multilocus analysis in individual cell nuclei. Gene specific barcodes can be generated and combined to provide both numerical and structural genetic information for these and other pertinent disease associated genes.
Gomez Tejeda Zanudo, Jorge
In order to understand how the interactions of molecular components inside cells give rise to cellular function, creating models that incorporate the current biological knowledge while also making testable predictions that guide experimental work is of utmost importance. Creating such models is a challenging task in complex diseases such as cancer, in which numerous components are known to play an important role. To model the dynamics of the networks underlying complex diseases I use network-based models with discrete dynamics, which have been shown to reproduce the qualitative dynamics of a multitude of cellular systems while requiring only the combinatorial nature of the interactions and qualitative information on the desired/undesired states. I developed analytical and computational tools based on a type of function-dependent subnetwork that stabilizes in a steady state regardless of the state of the rest of the network, and which I termed stable motif. Based on the concept of stable motif, I proposed a method to identify a model's dynamical attractors, which have been found to be identifiable with the cell fates and cell behaviors of modeled organisms. I also proposed a stable-motif-based control method that identifies targets whose manipulation ensures the convergence of the model towards an attract or of interest. The identified control targets can be single or multiple nodes, are proven to always drive any initial condition to the desired attractor, and need to be applied only transiently to be effective. I illustrated the potential of these methods by collaborating with wet-lab cancer biologists to construct and analyze a model for a process involved in the spread of cancer cells (epithelial-mesenchymal transition), and also applied them to several published models for complex diseases, such as a type of white blood cell cancer (T-LGL leukemia). These methods allowed me to find attractors of larger models than what was previously possible, identify the
Decker, Arthur J.
A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation
Molnos, Sonja; Mamdouh, Tarek; Petri, Stefan; Nocke, Thomas; Weinkauf, Tino; Coumou, Dim
The polar and subtropical jet streams are strong upper-level winds with a crucial influence on weather throughout the Northern Hemisphere midlatitudes. In particular, the polar jet is located between cold arctic air to the north and warmer subtropical air to the south. Strongly meandering states therefore often lead to extreme surface weather. Some algorithms exist which can detect the 2-D (latitude and longitude) jets' core around the hemisphere, but all of them use a minimal threshold to determine the subtropical and polar jet stream. This is particularly problematic for the polar jet stream, whose wind velocities can change rapidly from very weak to very high values and vice versa. We develop a network-based scheme using Dijkstra's shortest-path algorithm to detect the polar and subtropical jet stream core. This algorithm not only considers the commonly used wind strength for core detection but also takes wind direction and climatological latitudinal position into account. Furthermore, it distinguishes between polar and subtropical jet, and between separate and merged jet states. The parameter values of the detection scheme are optimized using simulated annealing and a skill function that accounts for the zonal-mean jet stream position (Rikus, 2015). After the successful optimization process, we apply our scheme to reanalysis data covering 1979-2015 and calculate seasonal-mean probabilistic maps and trends in wind strength and position of jet streams. We present longitudinally defined probability distributions of the positions for both jets for all on the Northern Hemisphere seasons. This shows that winter is characterized by two well-separated jets over Europe and Asia (ca. 20° W to 140° E). In contrast, summer normally has a single merged jet over the western hemisphere but can have both merged and separated jet states in the eastern hemisphere. With this algorithm it is possible to investigate the position of the jets' cores around the hemisphere and it is
Presents a review of genetic engineering, in which the genotypes of plants and animals (including human genotypes) may be manipulated for the benefit of the human species. Discusses associated problems and solutions and provides an extensive bibliography of literature relating to genetic engineering. (JR)
Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Tabatabaei, Seyyed Mohammad; Namaki, Saeed
Background The involvement of multiple genes and missing heritability, which are dominant in complex diseases such as multiple sclerosis (MS), entail using network biology to better elucidate their molecular basis and genetic factors. We therefore aimed to integrate interactome (protein–protein interaction (PPI)) and transcriptomes data to construct and analyze PPI networks for MS disease. Methods Gene expression profiles in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from MS patients, sampled in relapse or remission and controls, were analyzed. Differentially expressed genes which determined only in CSF (MS vs. control) and PBMCs (relapse vs. remission) separately integrated with PPI data to construct the Query-Query PPI (QQPPI) networks. The networks were further analyzed to investigate more central genes, functional modules and complexes involved in MS progression. Results The networks were analyzed and high centrality genes were identified. Exploration of functional modules and complexes showed that the majority of high centrality genes incorporated in biological pathways driving MS pathogenesis. Proteasome and spliceosome were also noticeable in enriched pathways in PBMCs (relapse vs. remission) which were identified by both modularity and clique analyses. Finally, STK4, RB1, CDKN1A, CDK1, RAC1, EZH2, SDCBP genes in CSF (MS vs. control) and CDC37, MAP3K3, MYC genes in PBMCs (relapse vs. remission) were identified as potential candidate genes for MS, which were the more central genes involved in biological pathways. Discussion This study showed that network-based analysis could explicate the complex interplay between biological processes underlying MS. Furthermore, an experimental validation of candidate genes can lead to identification of potential therapeutic targets. PMID:28028462
Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar
The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.
Ollonqvist, Kirsi; Aaltonen, Tuula; Karppi, Sirkka-Liisa; Hinkka, Katariina; Pöntinen, Seppo
The AGE study is a national randomised, long-term, multicentre research project aimed at comparing a new network-based rehabilitation programme with the use of standard health and social services. The use of home help services is associated with increasing age, living alone and having difficulties with activities of daily living. During a rehabilitation intervention the elderly participants' need for care can be assessed. The focus of this paper is to investigate the possible effects of the network-based rehabilitation programme on the use of informal and formal support among home-dwelling elderly at a high risk of long-term institutionalisation. The randomised controlled trial with a 12-month follow-up was implemented in 7 rehabilitation centres and 41 municipalities in Finland. The participants were recruited between January and October 2002. A total of 708 home-dwelling persons aged 65 years or older with progressively decreasing functional capacity and at the risk of being institutionalised within 2 years participated. Persons with acute or progressive diseases or poor cognitive capacity (Mini Mental State Examination<18 points), and those who had participated in any inpatient rehabilitation during the preceding 5 years, were excluded. Participants were randomly allocated to the intervention group (n=343) or to the control group (n=365). The intervention consisted of a network-based rehabilitation programme specifically designed for frail elderly people. Main outcome measures included the help received from relatives and municipal or private services. The use of municipal services increased more in the intervention group (P<0.05) than in the control group. Support from relatives decreased in the control group. The rehabilitees' ability to manage with daily activities decreased and they received additional help; hence, in this respect the rehabilitation model seems successful. A longer follow-up within the still ongoing AGE study is needed to verify whether the
Despite its prominence for characterization of complex mixtures, LC–MS/MS frequently fails to identify many proteins. Network-based analysis methods, based on protein–protein interaction networks (PPINs), biological pathways, and protein complexes, are useful for recovering non-detected proteins, thereby enhancing analytical resolution. However, network-based analysis methods do come in varied flavors for which the respective efficacies are largely unknown. We compare the recovery performance and functional insights from three distinct instances of PPIN-based approaches, viz., Proteomics Expansion Pipeline (PEP), Functional Class Scoring (FCS), and Maxlink, in a test scenario of valproic acid (VPA)-treated mice. We find that the most comprehensive functional insights, as well as best non-detected protein recovery performance, are derived from FCS utilizing real biological complexes. This outstrips other network-based methods such as Maxlink or Proteomics Expansion Pipeline (PEP). From FCS, we identified known biological complexes involved in epigenetic modifications, neuronal system development, and cytoskeletal rearrangements. This is congruent with the observed phenotype where adult mice showed an increase in dendritic branching to allow the rewiring of visual cortical circuitry and an improvement in their visual acuity when tested behaviorally. In addition, PEP also identified a novel complex, comprising YWHAB, NR1, NR2B, ACTB, and TJP1, which is functionally related to the observed phenotype. Although our results suggest different network analysis methods can produce different results, on the whole, the findings are mutually supportive. More critically, the non-overlapping information each provides can provide greater holistic understanding of complex phenotypes. PMID:23557376
... all things Genetic Alliance, Expecting Health and more... Co-Creating A Healthy Future See all the photos, videos, slideshows and more that we co-created at our 30th Anniversary conference. BioTrust BioTrust ...
... chromosomes to their child, 22 autosomal and 1 sex chromosome. The inheritance of genetic diseases, abnormalities, or traits ... chromosome the abnormal gene resides on (autosomal or sex chromosome), and by whether the gene itself is dominant ...
Reid, Kathryn J.; Sakati, Nadia; Prichard, Lorraine L.; Schneiderman, Lawrence J.; Jones, Oliver W.; Dixson, Barbara K.
The geographic distribution of County Health Department clinic facilities in the state of California has made it readily possible to establish a regionalized program for genetic counseling services, using public health nurses as a major source of case-finding. From both consumer and health professional standpoints, regionalized satellite genetic counseling clinics have been successful, and in particular, the effectiveness of public health nurses in identifying clinical genetic problems is readily apparent. Long-term follow-up reinforcement of genetic counseling appears to be an important conclusion from these studies. It is our suggestion that reinforcement of counseling would best be accomplished through the health team member (physician, nurse and so forth) following the patient or family rather than through the consulting geneticist. PMID:946335
Fariselli, P; Casadio, R
In this paper we describe a microcomputer program (HTP) for predicting the location and orientation of alpha-helical transmembrane segments in integral membrane proteins. HTP is a neural network-based tool which gives as output the protein membrane topology based on the statistical propensity of residues to be located in external and internal loops. This method, which uses single protein sequences as input to the network system, correctly predicts the topology of 71 out of 92 membrane proteins of putative membrane orientation, independently of the protein source.
Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing
In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time t. The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.
Sun, Yixiang; Du, Haifeng; Gong, Maoguo; Ma, Lijia; Wang, Shanfeng
Structural balance is a large area of study in signed networks, and it is intrinsically a global property of the whole network. Computing global structural balance in signed networks, which has attracted some attention in recent years, is to measure how unbalanced a signed network is and it is a nondeterministic polynomial-time hard problem. Many approaches are developed to compute global balance. However, the results obtained by them are partial and unsatisfactory. In this study, the computation of global structural balance is solved as an optimization problem by using the Memetic Algorithm. The optimization algorithm, named Meme-SB, is proposed to optimize an evaluation function, energy function, which is used to compute a distance to exact balance. Our proposed algorithm combines Genetic Algorithm and a greedy strategy as the local search procedure. Experiments on social and biological networks show the excellent effectiveness and efficiency of the proposed method.
Cosgrove, Elissa J.; Zhou, Yingchun; Gardner, Timothy S.; Kolaczyk, Eric D.
Motivation: DNA microarrays are routinely applied to study diseased or drug-treated cell populations. A critical challenge is distinguishing the genes directly affected by these perturbations from the hundreds of genes that are indirectly affected. Here, we developed a sparse simultaneous equation model (SSEM) of mRNA expression data and applied Lasso regression to estimate the model parameters, thus constructing a network model of gene interaction effects. This inferred network model was then used to filter data from a given experimental condition of interest and predict the genes directly targeted by that perturbation. Results: Our proposed SSEM–Lasso method demonstrated substantial improvement in sensitivity compared with other tested methods for predicting the targets of perturbations in both simulated datasets and microarray compendia. In simulated data, for two different network types, and over a wide range of signal-to-noise ratios, our algorithm demonstrated a 167% increase in sensitivity on average for the top 100 ranked genes, compared with the next best method. Our method also performed well in identifying targets of genetic perturbations in microarray compendia, with up to a 24% improvement in sensitivity on average for the top 100 ranked genes. The overall performance of our network-filtering method shows promise for identifying the direct targets of genetic dysregulation in cancer and disease from expression profiles. Availability: Microarray data are available at the Many Microbe Microarrays Database (M3D, http://m3d.bu.edu). Algorithm scripts are available at the Gardner Lab website (http://gardnerlab.bu.edu/SSEMLasso). Contact: firstname.lastname@example.org Supplementary information: Supplementary Data are available at Bioinformatics on line. PMID:18779235
Andermann, Anne; Blancquaert, Ingeborg
Abstract OBJECTIVE To provide a primer for primary care professionals who are increasingly called upon to discuss the growing number of genetic screening services available and to help patients make informed decisions about whether to participate in genetic screening, how to interpret results, and which interventions are most appropriate. QUALITY OF EVIDENCE As part of a larger research program, a wide literature relating to genetic screening was reviewed. PubMed and Internet searches were conducted using broad search terms. Effort was also made to identify the gray literature. MAIN MESSAGE Genetic screening is a type of public health program that is systematically offered to a specified population of asymptomatic individuals with the aim of providing those identified as high risk with prevention, early treatment, or reproductive options. Ensuring an added benefit from screening, as compared with standard clinical care, and preventing unintended harms, such as undue anxiety or stigmatization, depends on the design and implementation of screening programs, including the recruitment methods, education and counseling provided, timing of screening, predictive value of tests, interventions available, and presence of oversight mechanisms and safeguards. There is therefore growing apprehension that economic interests might lead to a market-driven approach to introducing and expanding screening before program effectiveness, acceptability, and feasibility have been demonstrated. As with any medical intervention, there is a moral imperative for genetic screening to do more good than harm, not only from the perspective of individuals and families, but also for the target population and society as a whole. CONCLUSION Primary care professionals have an important role to play in helping their patients navigate the rapidly changing terrain of genetic screening services by informing them about the benefits and risks of new genetic and genomic technologies and empowering them to
Lin, Che-Wei; Yang, Ya-Ting C; Wang, Jeen-Shing; Yang, Yi-Ching
This paper presents a wearable module and neural-network-based activity classification algorithm for energy expenditure estimation. The purpose of our design is first to categorize physical activities with similar intensity levels, and then to construct energy expenditure regression (EER) models using neural networks in order to optimize the estimation performance. The classification of physical activities for EER model construction is based on the acceleration and ECG signal data collected by wearable sensor modules developed by our research lab. The proposed algorithm consists of procedures for data collection, data preprocessing, activity classification, feature selection, and construction of EER models using neural networks. In order to reduce the computational load and achieve satisfactory estimation performance, we employed sequential forward and backward search strategies for feature selection. Two representative neural networks, a radial basis function network (RBFN) and a generalized regression neural network (GRNN), were employed as EER models for performance comparisons. Our experimental results have successfully validated the effectiveness of our wearable sensor module and its neural-network-based activity classification algorithm for energy expenditure estimation. In addition, our results demonstrate the superior performance of GRNN as compared to RBFN.
Li, Jun; Roebuck, Paul; Grünewald, Stefan; Liang, Han
An important task in biomedical research is identifying biomarkers that correlate with patient clinical data, and these biomarkers then provide a critical foundation for the diagnosis and treatment of disease. Conventionally, such an analysis is based on individual genes, but the results are often noisy and difficult to interpret. Using a biological network as the searching platform, network-based biomarkers are expected to be more robust and provide deep insights into the molecular mechanisms of disease. We have developed a novel bioinformatics web server for identifying network-based biomarkers that most correlate with patient survival data, SurvNet. The web server takes three input files: one biological network file, representing a gene regulatory or protein interaction network; one molecular profiling file, containing any type of gene- or protein-centred high-throughput biological data (e.g. microarray expression data or DNA methylation data); and one patient survival data file (e.g. patients' progression-free survival data). Given user-defined parameters, SurvNet will automatically search for subnetworks that most correlate with the observed patient survival data. As the output, SurvNet will generate a list of network biomarkers and display them through a user-friendly interface. SurvNet can be accessed at http://bioinformatics.mdanderson.org/main/SurvNet.
Arrasmith, William W.; Sullivan, Sean F.
Phase diversity imaging methods work well in removing atmospheric turbulence and some system effects from predominantly near-field imaging systems. However, phase diversity approaches can be computationally intensive and slow. We present a recently adapted, high-speed phase diversity method using a conventional, software-based neural network paradigm. This phase-diversity method has the advantage of eliminating many time consuming, computationally heavy calculations and directly estimates the optical transfer function from the entrance pupil phases or phase differences. Additionally, this method is more accurate than conventional Zernike-based, phase diversity approaches and lends itself to implementation on parallel software or hardware architectures. We use computer simulation to demonstrate how this high-speed, phase diverse imaging method can be implemented on a parallel, highspeed, neural network-based architecture-specifically the Cellular Neural Network (CNN). The CNN architecture was chosen as a representative, neural network-based processing environment because 1) the CNN can be implemented in 2-D or 3-D processing schemes, 2) it can be implemented in hardware or software, 3) recent 2-D implementations of CNN technology have shown a 3 orders of magnitude superiority in speed, area, or power over equivalent digital representations, and 4) a complete development environment exists. We also provide a short discussion on processing speed.
... seeing a genetic counselor? Q. What is a genetic counselor? A. Genetic counselors are healthcare professionals with ... and serve as patient advocates. Q. What is genetic counseling? A. Genetic counseling is the process of ...
... of Genetic Terms Definitions for genetic terms Specific Genetic Disorders Many human diseases have a genetic component. ... Condition in an Adult The Undiagnosed Diseases Program Genetic Disorders Achondroplasia Alpha-1 Antitrypsin Deficiency Antiphospholipid Syndrome ...
Catalogna, Merav; Cohen, Eyal; Fishman, Sigal; Halpern, Zamir; Nevo, Uri; Ben-Jacob, Eshel
Insulin resistance (IR) is one of the most widespread health problems in modern times. The gold standard for quantification of IR is the hyperinsulinemic-euglycemic glucose clamp technique. During the test, a regulated glucose infusion is delivered intravenously to maintain a constant blood glucose concentration. Current control algorithms for regulating this glucose infusion are based on feedback control. These models require frequent sampling of blood, and can only partly capture the complexity associated with regulation of glucose. Here we present an improved clamp control algorithm which is motivated by the stochastic nature of glucose kinetics, while using the minimal need in blood samples required for evaluation of IR. A glucose pump control algorithm, based on artificial neural networks model was developed. The system was trained with a data base collected from 62 rat model experiments, using a back-propagation Levenberg-Marquardt optimization. Genetic algorithm was used to optimize network topology and learning features. The predictive value of the proposed algorithm during the temporal period of interest was significantly improved relative to a feedback control applied at an equivalent low sampling interval. Robustness to noise analysis demonstrates the applicability of the algorithm in realistic situations. PMID:22952998
This paper uses the recently proposed H(infinity)-learning method, for updating the parameter of the radial basis function neural network (RBFNN) used as a control scheme for the unified power flow controller (UPFC) to improve the transient stability performance of a multimachine power system. The RBFNN uses a single neuron architecture whose input is proportional to the difference in error and the updating of its parameters is carried via a proportional value of the error. Also, the coefficients of the difference of error, error, and auxiliary signal used for improving damping performance are depicted by a genetic algorithm. The performance of the newly designed controller is evaluated in a four-machine power system subjected to different types of disturbances. The newly designed single-neuron RBFNN-based UPFC exhibits better damping performance compared to the conventional PID as well as the extended Kalman filter (EKF) updating-based RBFNN scheme, making the unstable cases stable. Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation. Also, all the machines are being equipped with the conventional power system stabilizer (PSS) to study the coordinated effect of UPFC and PSS in the system.
We aim to develop design principles for enhancing functional robustness of engineered cells using gene-network topology. We observed the effect of genetic regulation types (inhibition and activation) on robustness. Inhibition was much more stable than activation in E. coli. In the case of activation, if the upstream activator expression is shutdown by mutation, then its downstream expression is shut down as well. Without activation, the activator shutdown due to mutation will make its downstream expression ``remains`` turned off. Thus, the change in the metabolic load is higher in the activation case. Therefore, the stronger activation, the less robust the circuits are. In the inhibition case, we found that the story becomes opposite. When an inhibitor expression is shut down by mutation, the downstream expression turns on because the inhibitor is not expressed. This compensates changes in the metabolic load that might have been decreased without the inhibition. This result presents potential significant roles of network topology on the robustness of engineered cellular networks. This also emphasizes that the concept of fitness landscape, where the local slope corresponds to the fitness difference between different genotypes, can be useful to design robust gene circuits. We acknowledge the support of the NSF (MCB Award # 1515280).
Lin, Yi-Kuei; Yeh, Cheng-Ta
From the perspective of supply chain management, the selected carrier plays an important role in freight delivery. This article proposes a new criterion of multi-commodity reliability and optimises the carrier selection based on such a criterion for logistics networks with routes and nodes, over which multiple commodities are delivered. Carrier selection concerns the selection of exactly one carrier to deliver freight on each route. The capacity of each carrier has several available values associated with a probability distribution, since some of a carrier's capacity may be reserved for various orders. Therefore, the logistics network, given any carrier selection, is a multi-commodity multi-state logistics network. Multi-commodity reliability is defined as a probability that the logistics network can satisfy a customer's demand for various commodities, and is a performance indicator for freight delivery. To solve this problem, this study proposes an optimisation algorithm that integrates genetic algorithm, minimal paths and Recursive Sum of Disjoint Products. A practical example in which multi-sized LCD monitors are delivered from China to Germany is considered to illustrate the solution procedure.
Ferreira, Pedro M.; Gomes, João M.; Martins, Igor A. C.; Ruano, António E.
Accurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature. PMID:23202230
Ferreira, Pedro M; Gomes, João M; Martins, Igor A C; Ruano, António E
Accurate measurements of global solar radiation and atmospheric temperature,as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature.
The Boolean network paradigm is a simple and effective way to interpret genomic systems, but discovering the structure of these networks remains a difficult task. The minimum description length (MDL) principle has already been used for inferring genetic regulatory networks from time-series expression data and has proven useful for recovering the directed connections in Boolean networks. However, the existing method uses an ad hoc measure of description length that necessitates a tuning parameter for artificially balancing the model and error costs and, as a result, directly conflicts with the MDL principle's implied universality. In order to surpass this difficulty, we propose a novel MDL-based method in which the description length is a theoretical measure derived from a universal normalized maximum likelihood model. The search space is reduced by applying an implementable analogue of Kolmogorov's structure function. The performance of the proposed method is demonstrated on random synthetic networks, for which it is shown to improve upon previously published network inference algorithms with respect to both speed and accuracy. Finally, it is applied to time-series Drosophila gene expression measurements. PMID:18437238
Lu, Fei; Lipka, Alexander E.; Glaubitz, Jeff; Elshire, Rob; Cherney, Jerome H.; Casler, Michael D.; Buckler, Edward S.; Costich, Denise E.
Switchgrass (Panicum virgatum L.) is a perennial grass that has been designated as an herbaceous model biofuel crop for the United States of America. To facilitate accelerated breeding programs of switchgrass, we developed both an association panel and linkage populations for genome-wide association study (GWAS) and genomic selection (GS). All of the 840 individuals were then genotyped using genotyping by sequencing (GBS), generating 350 GB of sequence in total. As a highly heterozygous polyploid (tetraploid and octoploid) species lacking a reference genome, switchgrass is highly intractable with earlier methodologies of single nucleotide polymorphism (SNP) discovery. To access the genetic diversity of species like switchgrass, we developed a SNP discovery pipeline based on a network approach called the Universal Network-Enabled Analysis Kit (UNEAK). Complexities that hinder single nucleotide polymorphism discovery, such as repeats, paralogs, and sequencing errors, are easily resolved with UNEAK. Here, 1.2 million putative SNPs were discovered in a diverse collection of primarily upland, northern-adapted switchgrass populations. Further analysis of this data set revealed the fundamentally diploid nature of tetraploid switchgrass. Taking advantage of the high conservation of genome structure between switchgrass and foxtail millet (Setaria italica (L.) P. Beauv.), two parent-specific, synteny-based, ultra high-density linkage maps containing a total of 88,217 SNPs were constructed. Also, our results showed clear patterns of isolation-by-distance and isolation-by-ploidy in natural populations of switchgrass. Phylogenetic analysis supported a general south-to-north migration path of switchgrass. In addition, this analysis suggested that upland tetraploid arose from upland octoploid. All together, this study provides unparalleled insights into the diversity, genomic complexity, population structure, phylogeny, phylogeography, ploidy, and evolutionary dynamics of
Background Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes. Results The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle. Conclusions We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases’ common biology, and in the elaboration of diagnosis and treatments. PMID:24460644
Domingo, E. ); Holland, J.J. . Dept. of Biology); Ahlquist, P. . Dept. of Plant Pathology)
This book contains the proceedings on RNA genetics: RNA-directed virus replication Volume 1. Topics covered include: Replication of the poliovirus genome; Influenza viral RNA transcription and replication; and Relication of the reoviridal: Information derived from gene cloning and expression.
Whitehouse, H. L. K.
Discusses the mechanisms of genetic recombination with particular emphasis on the study of the fungus Sordaria brevicollis. The study of recombination is facilitated by the use of mutants of this fungus in which the color of the ascospores is affected. (JR)
Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.
The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.
Nayar, Priya; Singh, Bhim; Mishra, Sukumar
An artificial intelligence based control algorithm is used in solving power quality problems of a diesel engine driven synchronous generator with automatic voltage regulator and governor based standalone system. A voltage source converter integrated with a battery energy storage system is employed to mitigate the power quality problems. An adaptive neural network based signed regressor control algorithm is used for the estimation of the fundamental component of load currents for control of a standalone system with load leveling as an integral feature. The developed model of the system performs accurately under varying load conditions and provides good dynamic response to the step changes in loads. The real time performance is achieved using MATLAB along with simulink/simpower system toolboxes and results adhere to an IEEE-519 standard for power quality enhancement.
Canelon, Jose I.; Shieh, Leang S.; Song, Gangbing
This article presents a new neural network-based approach for self-tuning control of nonlinear single-input single-output (SISO) discrete-time dynamic systems. According to the approach, a neural network ARMAX (NN-ARMAX) model of the system is identified and continuously updated, using an online training algorithm. Control design is accomplished by solving an optimal discrete-time linear quadratic tracking problem using an observer-type linear state-space Kalman innovation model, which is built from the parameters of a local linear version of the NN-ARMAX model. The state-feedback control law is implemented using the Kalman state, which is calculated without estimating the noise covariance properties. The proposed control approach is shown to be very effective and outperforms the self-tuning control approach based on a linear ARMAX model on two simulation examples.
Cuadra, Lucas; Alexandre, Enrique; Gil-Pita, Roberto; Vicen-Bueno, Raúl; Álvarez, Lorena
Sound classifiers embedded in digital hearing aids are usually designed by using sound databases that do not include the distortions associated to the feedback that often occurs when these devices have to work at high gain and low gain margin to oscillation. The consequence is that the classifier learns inappropriate sound patterns. In this paper we explore the feasibility of using different sound databases (generated according to 18 configurations of real patients), and a variety of learning strategies for neural networks in the effort of reducing the probability of erroneous classification. The experimental work basically points out that the proposed methods assist the neural network-based classifier in reducing its error probability in more than 18%. This helps enhance the elderly user's comfort: the hearing aid automatically selects, with higher success probability, the program that is best adapted to the changing acoustic environment the user is facing.
Valdes, A.; Khorasani, K.
The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for the Pulsed Plasma Thrusters (PPTs) that are used in the Attitude Control Subsystem (ACS) of satellites that are tasked to perform a formation flying mission. By using data collected from the relative attitudes of the formation flying satellites our proposed "High Level" FDI scheme can detect the pair of thrusters which is faulty, however fault isolation cannot be accomplished. Based on the "High Level" FDI scheme and the DNN-based "Low Level" FDI scheme developed earlier by the authors, an "Integrated" DNN-based FDI scheme is then proposed. To demonstrate the FDI capabilities of the proposed schemes various fault scenarios are simulated.
Peng, Hsiao-Chun; Lu, Hai-Han; Li, Chung-Yi; Su, Heng-Sheng; Hsu, Chin-Tai
An integration of fiber-to-the-home (FTTH) and graded-index plastic optical fiber (GI-POF) in-house networks based on injection-locked vertical cavity surface emitting lasers (VCSELs) and direct-detection technique is proposed and experimentally demonstrated. Sufficient low bit error rate (BER) values were obtained over a combination of 20-km single-mode fiber (SMF) and 50-m GI-POF links. Signal qualities satisfy the worldwide interoperability for microwave access (WiMAX) requirement with data signals of 20 Mbps/5.8 GHz and 70 Mbps/10 GHz, respectively. Since our proposed network does not use sophisticated and expensive RF devices in premises, it reveals a prominent one with simpler and more economic advantages. Our proposed architecture is suitable for the SMF-based primary and GI-POF-based in-house networks.
Aarabi, Mohammad Hadi; Kamalian, Aida; Mohajer, Bahram; Shandiz, Mahdi Shirin; Eqlimi, Ehsan; Shojaei, Ahmad; Safabakhsh, Hamidreza
Parkinson's Disease (PD) is a progressive neurodegenerative disorder assumed to involve different areas of CNS and PNS. Thus, Diffusion Tensor Imaging (DTI) is used to examine the areas engaged in PD neurodegeneration. In the present study, we computed average tract length and fiber volume as a measure of white matter integrity and adopted Network Based Statistics (NBS) to conduct group analyses between age- and gender-matched PD patients and healthy control connectivity matrices. NBS is a powerful statistical tool that utilizes the presence of every link in connectivity matrices and controls family wise error rates (in weak sense). The major regions with significantly reduced interconnecting fiber volume or average tract length were cingulum, temporal lobe, frontal lobe, parahippocampus, hippocampus, olfactory lobe, and occipital lobe.
Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour
In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance.
This text provides full and balanced coverage of the concepts requisite for a thorough understanding of human genetics. Applications to both the individual and society are integrated throughout the lively and personal narrative, and the essential principles of heredity are clearly presented to prepare students for informed participation in public controversies. High-interest, controversial topics, including recombinant DNA technology, oncogenes, embryo transfer, environmental mutagens and carcinogens, IQ testing, and eugenics encourage understanding of important social issues.
Chinnery, Patrick Francis; Hudson, Gavin
Introduction In the last 10 years the field of mitochondrial genetics has widened, shifting the focus from rare sporadic, metabolic disease to the effects of mitochondrial DNA (mtDNA) variation in a growing spectrum of human disease. The aim of this review is to guide the reader through some key concepts regarding mitochondria before introducing both classic and emerging mitochondrial disorders. Sources of data In this article, a review of the current mitochondrial genetics literature was conducted using PubMed (http://www.ncbi.nlm.nih.gov/pubmed/). In addition, this review makes use of a growing number of publically available databases including MITOMAP, a human mitochondrial genome database (www.mitomap.org), the Human DNA polymerase Gamma Mutation Database (http://tools.niehs.nih.gov/polg/) and PhyloTree.org (www.phylotree.org), a repository of global mtDNA variation. Areas of agreement The disruption in cellular energy, resulting from defects in mtDNA or defects in the nuclear-encoded genes responsible for mitochondrial maintenance, manifests in a growing number of human diseases. Areas of controversy The exact mechanisms which govern the inheritance of mtDNA are hotly debated. Growing points Although still in the early stages, the development of in vitro genetic manipulation could see an end to the inheritance of the most severe mtDNA disease. PMID:23704099
... Old Feeding Your 1- to 2-Year-Old Genetic Testing KidsHealth > For Parents > Genetic Testing Print A ... blood, skin, bone, or other tissue is needed. Genetic Testing During Pregnancy For genetic testing before birth, ...
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... En Español: Regulación de pruebas genéticas Regulation of Genetic Tests Overview of Genetic Testing Introduction to Genetic ... Statements Congressional Activity Genetic Testing Resources Overview of Genetic Testing As the science of genomics advances, genetic ...
Fernández-Alemán, José Luis; López-González, Laura; González-Sequeros, Ofelia; Jayne, Chrisina; López-Jiménez, Juan José; Carrillo-de-Gea, Juan Manuel; Toval, Ambrosio
This paper presents an empirical study of a formative neural network-based assessment approach by using mobile technology to provide pharmacy students with intelligent diagnostic feedback. An unsupervised learning algorithm was integrated with an audience response system called SIDRA in order to generate states that collect some commonality in responses to questions and add diagnostic feedback for guided learning. A total of 89 pharmacy students enrolled on a Human Anatomy course were taught using two different teaching methods. Forty-four students employed intelligent SIDRA (i-SIDRA), whereas 45 students received the same training but without using i-SIDRA. A statistically significant difference was found between the experimental group (i-SIDRA) and the control group (traditional learning methodology), with T (87) = 6.598, p < 0.001. In four MCQs tests, the difference between the number of correct answers in the first attempt and in the last attempt was also studied. A global effect size of 0.644 was achieved in the meta-analysis carried out. The students expressed satisfaction with the content provided by i-SIDRA and the methodology used during the process of learning anatomy (M = 4.59). The new empirical contribution presented in this paper allows instructors to perform post hoc analyses of each particular student's progress to ensure appropriate training.
Chen, Xing; Yan, Chenggang Clarence; Luo, Cai; Ji, Wen; Zhang, Yongdong; Dai, Qionghai
Increasing evidence has indicated that plenty of lncRNAs play important roles in many critical biological processes. Developing powerful computational models to construct lncRNA functional similarity network based on heterogeneous biological datasets is one of the most important and popular topics in the fields of both lncRNAs and complex diseases. Functional similarity network construction could benefit the model development for both lncRNA function inference and lncRNA-disease association identification. However, little effort has been attempted to analysis and calculate lncRNA functional similarity on a large scale. In this study, based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases, we developed two novel lncRNA functional similarity calculation models (LNCSIM). LNCSIM was evaluated by introducing similarity scores into the model of Laplacian Regularized Least Squares for LncRNA-Disease Association (LRLSLDA) for lncRNA-disease association prediction. As a result, new predictive models improved the performance of LRLSLDA in the leave-one-out cross validation of various known lncRNA-disease associations datasets. Furthermore, some of the predictive results for colorectal cancer and lung cancer were verified by independent biological experimental studies. It is anticipated that LNCSIM could be a useful and important biological tool for human disease diagnosis, treatment, and prevention.
Liu, Derong; Wang, Ding; Wang, Fei-Yue; Li, Hongliang; Yang, Xiong
In this paper, the infinite horizon optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems is investigated using neural-network-based online solution of Hamilton-Jacobi-Bellman (HJB) equation. By establishing an appropriate bounded function and defining a modified cost function, the optimal robust guaranteed cost control problem is transformed into an optimal control problem. It can be observed that the optimal cost function of the nominal system is nothing but the optimal guaranteed cost of the original uncertain system. A critic neural network is constructed to facilitate the solution of the modified HJB equation corresponding to the nominal system. More importantly, an additional stabilizing term is introduced for helping to verify the stability, which reinforces the updating process of the weight vector and reduces the requirement of an initial stabilizing control. The uniform ultimate boundedness of the closed-loop system is analyzed by using the Lyapunov approach as well. Two simulation examples are provided to verify the effectiveness of the present control approach.
Hu, Songlin; Yue, Dong; Xie, Xiangpeng; Ma, Yong; Yin, Xiuxia
This paper focuses on a problem of event-triggered stabilization for a class of nonuniformly sampled neural-network-based control systems (NNBCSs). First, a new event-triggered data transmission mechanism is designed based on the nonperiodic sampled data. Different from the previous works, the proposed triggering scheme enables the NNBCSs design to enjoy the advantages of both nonuniform and event-triggered sampling schemes. Second, under the nonperiodic event-triggered data transmission scheme, the nonperiodic sampled-data three-layer fully connected feedforward neural-network (TLFCFFNN)-based event-triggered controller is constructed, and the resulting closed-loop TLFCFFNN-based event-triggered control system is modeled as a state delay system based on time-delay system modeling approach. Then, the stability criteria for the closed-loop system is formulated using Lyapunov-Krasovskii functional approach. Third, the sufficient conditions for the codesign of the TLFCFFNN-based controller and triggering parameters are given in terms of solvability of matrix inequalities to guarantee the asymptotical stability of the closed-loop system and an upper bound on the given cost function while reducing the updates of the controller. Finally, three numerical examples are provided to illustrate the effectiveness and benefits of the proposed results.
Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L.; Hong, Huixiao
Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system. PMID:27558848
Huang, Da; Yang, Zhi; Li, Xiaolin; Zhang, Liling; Hu, Jing; Su, Yanjie; Hu, Nantao; Yin, Guilin; He, Dannong; Zhang, Yafei
Graphene is an ideal candidate for gas sensing due to its excellent conductivity and large specific surface areas. However, it usually suffers from sheet stacking, which seriously debilitates its sensing performance. Herein, we demonstrate a three-dimensional conductive network based on stacked SiO2@graphene core-shell hybrid frameworks for enhanced gas sensing. SiO2 spheres are uniformly encapsulated by graphene oxide (GO) through an electrostatic self-assembly approach to form SiO2@GO core-shell hybrid frameworks, which are reduced through thermal annealing to establish three-dimensional (3D) conductive sensing networks. The SiO2 supported 3D conductive graphene frameworks reveal superior sensing performance to bare reduced graphene oxide (RGO) films, which can be attributed to their less agglomeration and larger surface area. The response value of the 3D framework based sensor for 50 ppm NH3 and 50 ppm NO2 increased 8 times and 5 times, respectively. Additionally, the sensing performance degradation caused by the stacking of the sensing materials is significantly suppressed because the graphene layers are separated by the SiO2 spheres. The sensing performance decays by 92% for the bare RGO films when the concentration of the sensing material increases 8 times, while there is only a decay of 25% for that of the SiO2@graphene core-shell hybrid frameworks. This work provides an insight into 3D frameworks of hybrid materials for effectively improving gas sensing performance.
Trawinski, Krzysztof; Chica, Manuel; Pancho, David P; Damas, Sergio; Cordon, Oscar
An appropriate visualization of multiobjective nondominated solutions is a valuable asset for decision making. Although there are methods for visualizing the solutions in the design space, they do not provide any information about their relationship. In this paper, we propose a novel methodology that allows the visualization of the nondominated solutions in the design space and their relationships by means of a network. The nodes represent the solutions in the objective space while the edges show the relationships among the solutions in the design space. Our proposal (called moGrams) thus provides a joint visualization of both objective and design spaces. It aims at helping the decision maker to get more understanding of the problem so that (s)he can choose the most appropriate and flexible final solution. moGrams can be applied to any multicriteria problem in which the solutions are related by a similarity metric. Besides, the decision maker interaction is facilitated by modifying the network based on the current preferences to obtain a clearer view. An exhaustive experimental study is performed using four multiobjective problems with a variable number of objectives to show both usefulness and versatility of moGrams. The results exhibit interesting characteristics of our methodology for visualizing and analyzing solutions of multiobjective problems.
Kammoun, Malek; Picard, Brigitte; Henry-Berger, Joëlle; Cassar-Malek, Isabelle
Thanks to genomics, we have previously identified markers of beef tenderness, and computed a bioinformatic analysis that enabled us to build an interactome in which we found Hsp27 at a crucial node. Here, we have used a network-based approach for understanding the contribution of Hsp27 to tenderness through the prediction of its interactors related to tenderness. We have revealed the direct interactors of Hsp27. The predicted partners of Hsp27 included proteins involved in different functions, e.g. members of Hsp families (Hsp20, Cryab, Hsp70a1a, and Hsp90aa1), regulators of apoptosis (Fas, Chuk, and caspase-3), translation factors (Eif4E, and Eif4G1), cytoskeletal proteins (Desmin) and antioxidants (Sod1). The abundances of 15 proteins were quantified by Western blotting in two muscles of HspB1-null mice and their controls. We observed changes in the amount of most of the Hsp27 predicted targets in mice devoid of Hsp27 mainly in the most oxidative muscle. Our study demonstrates the functional links between Hsp27 and its predicted targets. It suggests that Hsp status, apoptotic processes and protection against oxidative stress are crucial for post-mortem muscle metabolism, subsequent proteolysis, and therefore for beef tenderness. PMID:24688716
Tan, Huaqiao; Chen, Weilin; Li, Yang-Guang; Liu, Ding; Chen, Limin; Wang, Enbo
In this paper, three pure inorganic eight-connected self-catenated networks based on the Silverton-type polyoxometalate [CeMo 12O 42] 8- with lanthanide, transition metal and alkali metal cations as linkers: [Li(H 2O) 4] 2Co(H 2O) 4Ce(H 2O) 3[CeMo 12O 42]·3H 2O (1), H 0.5[Li(H 2O) 4] 2.5[Ni(H 2O) 4] 0.5Ce(H 2O) 3[CeMo 12O 42]·3H 2O (2) and H[Li(H 2O) 4] 3Ce(H 2O) 3[CeMo 12O 42]·3H 2O (3) have been successfully synthesized and characterized by elemental analysis, IR spectroscopy, thermal gravimetric analysis, X-ray photoelectron spectroscopy, electrochemical analyses and single crystal X-ray diffraction. The single crystal X-ray diffraction analyses reveal that compounds 1- 3 are isostructural. The [Ce IVMo 12O 42] 8- polyoxoanions are connected by Ce 4+ to form the infinite 1D chains. And then the parallel stacking chains linked by transition metal cations and lithium ions construct to an eight-connected self-catenated 4 2456 3 topology framework.
Tang, Shihuan; Chen, Yan; Wen, Shaoxin; Zhang, Hongchun; Liu, Xi; Chao, Enxiang
Traditional Chinese medicine (TCM) has shown significant efficacy in the treatment of cough variant asthma (CVA), a special type of asthma. However, there is shortage of explanations for relevant mechanism of treatment. As Zhengs differentiation is a critical concept in TCM, it is necessary to explain the mechanism of treatment of Zhengs. Based on TCM clinical cases, this study illustrated the mechanism of the treatment of three remarkably relevant Zhengs for CVA: “FengXieFanFei,” “FeiQiShiXuan”, and “QiDaoLuanJi.” To achieve this goal, five steps were carried out: (1) determining feature Zhengs and corresponding key herbs of CVA by analyses of clinical cases; (2) finding out potential targets of the key herbs and clustering them based on their functional annotations; (3) constructing an ingredient-herb network and an ingredient network; (4) identifying modules of the ingredient network; (5) illustrating the mechanism of the treatment by further mining the latent biological implications within each module. The systematic study reveals that the treatment of “FengXieFanFei,” “FeiQiShiXuan,” and “QiDaoLuanJi” has effects on the regulation of multiple bioprocesses by herbs containing different ingredients with functions of steroid metabolism regulation, airway inflammation, and ion conduction and transportation. This network-based systematic study will be a good way to boost the scientific understanding of mechanism of the treatment of Zhengs. PMID:24348708
Hotta, Hirohisa; Murahashi, Yoshimitsu; Doki, Shinji; Okuma, Shigeru
In order to ride on the strength of paralell operation a feature of neural network, it is preferable that all neuron is implemented on hardware. Formerly, we combine Neural Network and ΔΣ modulation, which is a method of converting to 1bit pulsed signal. Then we succeeded to configurate “a Pulsed Neural Network based on ΔΣ modulation(DSM-PNN)", which keep the circuit scale as same as to operate precisely. In last paper, we proposed hardware implementation methods of DSM-PNN with GHA learning rule and show its availability in linear operation. However, since neural networks are characterized by nonlinear map, signals needs to be treated with sufficient precision, also in nonlinear operation. In this paper, in order to shows that the 1-bit signal processing by DSM-PNN can be available, even when it includes nonlinear operation, we proposed the technique of realizing algorithm of ICA including nonlinear operation in DSM-PNN and confirm the performance of it.
Guo, Lei; Wang, Yao; Yu, Hongli; Yin, Ning; Li, Ying
Acupuncture is based on the theory of traditional Chinese medicine. Its therapeutic effectiveness has been proved by clinical practice. However, its mechanism of action is still unclear. Magnetic stimulation at acupuncture point provides a new means for studying the theory of acupuncture. Based on the Graph Theory, the construction and analysis method of complex network can help to investigate the topology of brain functional network and understand the working mechanism of brain. In this study, magnetic stimulation was used to stimulate Neiguan (PC6) acupoint and the EEG (Electroencephalograph) signal was recorded. Using non-linear method (Sample Entropy) and complex network theory, brain functional network based on EEG signal under magnetic stimulation at PC6 acupoint was constructed and analyzed. In addition, the features of complex network were comparatively analyzed between the quiescent and stimulated states. Our experimental results show the topology of the network is changed, the connection of the network is enhanced, the efficiency of information transmission is improved and the small-world property is strengthened through stimulating the PC6 acupoint.
Zhang, Jie; Yang, Hui; Zhao, Yongli; Ji, Yuefeng; Li, Hui; Lin, Yi; Li, Gang; Han, Jianrui; Lee, Young; Ma, Teng
Due to the high burstiness and high-bandwidth characteristics of the applications, data center interconnection by elastic optical networks have attracted much attention of network operators and service providers. Many data center applications require lower delay and higher availability with the end-to-end guaranteed quality of service. In this paper, we propose and implement a novel elastic optical network based on enhanced software defined networking (eSDN) architecture for data center application, by introducing a transport-aware cross stratum optimization (TA-CSO) strategy. eSDN can enable cross stratum optimization of application and elastic optical network stratum resources and provide the elastic physical layer parameter adjustment, e.g., modulation format and bandwidth. We have designed and verified experimentally software defined path provisioning on our testbed with four real OpenFlow-enabled elastic optical nodes for data center application. The overall feasibility and efficiency of the proposed architecture is also experimentally demonstrated and compared with individual CSO and physical layer adjustment strategies in terms of path setup/release/adjustment latency, blocking probability and resource occupation rate.
Zhang, Jisheng; Jia, Limin; Niu, Shuyun; Zhang, Fan; Tong, Lu; Zhou, Xuesong
It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs) start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network- based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs’ route planning for small and medium-scale networks. PMID:26076404
Objective Clinical and Experimental Reproductive Medicine (CERM) converted its language to English only beginning with the first issue of 2011. From that point in time, one of the goals of the journal has been to become a truly international journal. This paper aims to identify the position of CERM in its scholarly journal network based on the journal's metrics. Methods The journal's metrics, including citations, countries of author affiliation, and countries of citing authors, Hirsch index, and proportion of funded articles, were gathered from Web of Science and analyzed. Results The two-year impact factor of 2013 was calculated at 0.971 excluding self-citation, which corresponds to a Journal Citation Reports ranking of 85.9% in the category of obstetrics and gynecology. In 2012, 2013, and 2014, the total citations were 17, 68, and 85, respectively. Authors from nine countries contributed to CERM. Researchers from 25 countries cited CERM in their articles. The Hirsch index was six. Out of 88 original articles, 35 studies received funds (39.8%). Conclusion Based on the journal metrics, changing the journal language to English was found to be successful in promoting CERM to international journal status. PMID:25599036
Zhang, Jisheng; Jia, Limin; Niu, Shuyun; Zhang, Fan; Tong, Lu; Zhou, Xuesong
It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs) start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network- based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs' route planning for small and medium-scale networks.
Zhan, Yichun; Ji, Meng; Yu, Shaohua
Resilient Packet Ring (RPR) has been standardized in the IEEE 802.17 working group. In multi-ring networks, similarly with other ring-based technology, intra-ring traffic demand is protected against single node and span failures within 50 ms by the "steering" and "wrapping" protection. Inter-ring traffic demand, however, is susceptible to failures at nodes or links where the traffic demand transits from one ring to another. Normally, the survivability of interconnecting node or link failure has to be provided by other technologies, such as MPLS and Spanning Tree Protocol. Unfortunately, most schemes cannot provide a cost-effective solution with guaranteeing the restoration within the 50 ms timeframe. In this paper we proposed a cost-effective and fast Recovery Mechanism for Multi-ring Interconnection Networks Based on RPR. Differential from Spanning Tree Protocol (STP) and other protection technologies, this mechanism has the ability of sub-50ms protection provisioning and scalability based on the bridging function in RPR. Particular with enhanced bridging support, this mechanism can provide efficient bandwidth spatial reuse on multi-ring RPR networks. The proposed novel mechanism has been implemented on our 10Gbps network processor (NP) based multi-service provisioning platform. All experimental results presented in this paper come from actual testing on the network test bed and show that the all the inter-ring traffic are given the sub-50ms recovery guarantee as intra-ring traffic in normal case.
Jin, Nana; Wu, Deng; Gong, Yonghui; Bi, Xiaoman; Jiang, Hong; Li, Kongning; Wang, Qianghu
An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches. PMID:25243127
Djukanovic, M.B.; Calovic, M.S.; Vesovic, B.V.; Sobajic, D.J.
This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.
Ansari, Nirwan; Liu, Dequan
A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.
Li, Wan; Chen, Lina; He, Weiming; Li, Weiguo; Qu, Xiaoli; Liang, Binhua; Gao, Qianping; Feng, Chenchen; Jia, Xu; Lv, Yana; Zhang, Siya; Li, Xia
The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial). Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on "guilt by association" analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on "guilt by association" analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.
Hoi, K. I.; Yuen, K. V.; Mok, K. M.
In this study the neural network based air quality prediction model was tested in a typical coastal city, Macau, with Latitude 22° 10'N and Longitude 113° 34'E. By using five years of air quality and meteorological data recorded at an ambient air quality monitoring station between 2001 and 2005, it was found that the performance of the ANN model was generally improved by increasing the number of hidden neurons in the training phase. However, the performance of the ANN model was not sensitive to the change in the number of hidden neurons during the prediction phase. Therefore, the improvement in the error statistics for a complex ANN model in the training phase may be only caused by the overfitting of the data. In addition, the posterior PDF of the parameter vector conditional on the training dataset was investigated for different number of hidden neurons. It was found that the parametric space for a simple ANN model was globally identifiable and the Levenberg-Marquardt backpropagation algorithm was able to locate the optimal parameter vector. However, the parameter vector might contain redundant parameters and the parametric space was not globally identifiable when the model class became complex. In addition, the Levenberg-Marquardt backpropagation algorithm was unable to locate the most optimal parameter vector in this situation. Finally, it was concluded that the a more complex MLP model, that fits the data better, is not necessarily better than a simple one.
Metadata Standard and Data Exchange Specifications to Describe, Model, and Integrate Complex and Diverse High-Throughput Screening Data from the Library of Integrated Network-based Cellular Signatures (LINCS).
Vempati, Uma D; Chung, Caty; Mader, Chris; Koleti, Amar; Datar, Nakul; Vidović, Dušica; Wrobel, David; Erickson, Sean; Muhlich, Jeremy L; Berriz, Gabriel; Benes, Cyril H; Subramanian, Aravind; Pillai, Ajay; Shamu, Caroline E; Schürer, Stephan C
The National Institutes of Health Library of Integrated Network-based Cellular Signatures (LINCS) program is generating extensive multidimensional data sets, including biochemical, genome-wide transcriptional, and phenotypic cellular response signatures to a variety of small-molecule and genetic perturbations with the goal of creating a sustainable, widely applicable, and readily accessible systems biology knowledge resource. Integration and analysis of diverse LINCS data sets depend on the availability of sufficient metadata to describe the assays and screening results and on their syntactic, structural, and semantic consistency. Here we report metadata specifications for the most important molecular and cellular components and recommend them for adoption beyond the LINCS project. We focus on the minimum required information to model LINCS assays and results based on a number of use cases, and we recommend controlled terminologies and ontologies to annotate assays with syntactic consistency and semantic integrity. We also report specifications for a simple annotation format (SAF) to describe assays and screening results based on our metadata specifications with explicit controlled vocabularies. SAF specifically serves to programmatically access and exchange LINCS data as a prerequisite for a distributed information management infrastructure. We applied the metadata specifications to annotate large numbers of LINCS cell lines, proteins, and small molecules. The resources generated and presented here are freely available.
Gero, J S; Kazakov, V
We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful genetic material. Compared to the standard GA, it provides some computational advantages as well as a tool for automatic generation of hierarchical genetic representations specifically tailored to suit certain classes of problems.
Shi, Mingguang; He, Jianmin
Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was to establish a computational approach for making personalized estimates of the survival benefit from adjuvant CTX. We developed Sub-Network based Random Forest classifier for predicting Chemotherapy Benefit (SNRFCB) based gene expression datasets of lung cancer. The SNRFCB approach was then validated in independent test cohorts for identifying chemotherapy responder cohorts and chemotherapy non-responder cohorts. SNRFCB involved the pre-selection of gene sub-network signatures based on the mutations and on protein-protein interaction data as well as the application of the random forest algorithm to gene expression datasets. Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer patients in the chemotherapy responder group (P = 0.008), but it was not beneficial to patients in the chemotherapy non-responder group (P = 0.657). Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer squamous cell carcinoma (SQCC) subtype patients in the chemotherapy responder cohorts (P = 0.024), but it was not beneficial to patients in the chemotherapy non-responder cohorts (P = 0.383). SNRFCB improved prediction performance as compared to the machine learning method, support vector machine (SVM). To test the general applicability of the predictive model, we further applied the SNRFCB approach to human breast cancer datasets and also observed superior performance. SNRFCB could provide recurrent probability for individual patients and identify which patients may benefit from adjuvant CTX in clinical trials.
Cho, Yongrae; Kim, Minsung
The volatility and uncertainty in the process of technological developments are growing faster than ever due to rapid technological innovations. Such phenomena result in integration among disparate technology fields. At this point, it is a critical research issue to understand the different roles and the propensity of each element technology for technological convergence. In particular, the network-based approach provides a holistic view in terms of technological linkage structures. Furthermore, the development of new indicators based on network visualization can reveal the dynamic patterns among disparate technologies in the process of technological convergence and provide insights for future technological developments. This research attempts to analyze and discover the patterns of the international patent classification codes of the United States Patent and Trademark Office's patent data in printed electronics, which is a representative technology in the technological convergence process. To this end, we apply the physical idea as a new methodological approach to interpret technological convergence. More specifically, the concepts of entropy and gravity are applied to measure the activities among patent citations and the binding forces among heterogeneous technologies during technological convergence. By applying the entropy and gravity indexes, we could distinguish the characteristic role of each technology in printed electronics. At the technological convergence stage, each technology exhibits idiosyncratic dynamics which tend to decrease technological differences and heterogeneity. Furthermore, through nonlinear regression analysis, we have found the decreasing patterns of disparity over a given total period in the evolution of technological convergence. This research has discovered the specific role of each element technology field and has consequently identified the co-evolutionary patterns of technological convergence. These new findings on the evolutionary
YUE, HONG; YANG, BO; YANG, FANG; HU, XIAO-LI; KONG, FAN-BIN
Recent progress in bioinformatics has facilitated the clarification of biological processes associated with complex diseases. Numerous methods of co-expression analysis have been proposed for use in the study of pairwise relationships among genes. In the present study, a combined network based on gene pairs was constructed following the conversion and combination of gene pair score values using a novel algorithm across multiple approaches. Three hippocampal expression profiles of patients with Alzheimer's disease (AD) and normal controls were extracted from the ArrayExpress database, and a total of 144 differentially expressed (DE) genes across multiple studies were identified by a rank product (RP) method. Five groups of co-expression gene pairs and five networks were identified and constructed using four existing methods [weighted gene co-expression network analysis (WGCNA), empirical Bayesian (EB), differentially co-expressed genes and links (DCGL), search tool for the retrieval of interacting genes/proteins database (STRING)] and a novel rank-based algorithm with combined score, respectively. Topological analysis indicated that the co-expression network constructed by the WGCNA method had the tendency to exhibit small-world characteristics, and the combined co-expression network was confirmed to be a scale-free network. Functional analysis of the co-expression gene pairs was conducted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The co-expression gene pairs were mostly enriched in five pathways, namely proteasome, oxidative phosphorylation, Parkinson's disease, Huntington's disease and AD. This study provides a new perspective to co-expression analysis. Since different methods of analysis often present varying abilities, the novel combination algorithm may provide a more credible and robust outcome, and could be used to complement to traditional co-expression analysis. PMID:27168792
Bonthala, Venkata Suresh; Mayes, Katie; Moreton, Joanna; Blythe, Martin; Wright, Victoria; May, Sean Tobias; Massawe, Festo; Mayes, Sean; Twycross, Jamie
Bambara groundnut (Vigna subterranea (L.) Verdc.) is an African legume and is a promising underutilized crop with good seed nutritional values. Low temperature stress in a number of African countries at night, such as Botswana, can effect the growth and development of bambara groundnut, leading to losses in potential crop yield. Therefore, in this study we developed a computational pipeline to identify and analyze the genes and gene modules associated with low temperature stress responses in bambara groundnut using the cross-species microarray technique (as bambara groundnut has no microarray chip) coupled with network-based analysis. Analyses of the bambara groundnut transcriptome using cross-species gene expression data resulted in the identification of 375 and 659 differentially expressed genes (p<0.01) under the sub-optimal (23°C) and very sub-optimal (18°C) temperatures, respectively, of which 110 genes are commonly shared between the two stress conditions. The construction of a Highest Reciprocal Rank-based gene co-expression network, followed by its partition using a Heuristic Cluster Chiseling Algorithm resulted in 6 and 7 gene modules in sub-optimal and very sub-optimal temperature stresses being identified, respectively. Modules of sub-optimal temperature stress are principally enriched with carbohydrate and lipid metabolic processes, while most of the modules of very sub-optimal temperature stress are significantly enriched with responses to stimuli and various metabolic processes. Several transcription factors (from MYB, NAC, WRKY, WHIRLY & GATA classes) that may regulate the downstream genes involved in response to stimulus in order for the plant to withstand very sub-optimal temperature stress were highlighted. The identified gene modules could be useful in breeding for low-temperature stress tolerant bambara groundnut varieties. PMID:26859686
... Conditions Diagnosis & Management Genetic Testing (1 link) Genetic Testing Registry: Beaded hair General Information from MedlinePlus (5 links) Diagnostic Tests Drug Therapy Genetic Counseling Palliative Care Surgery and Rehabilitation ...
... including clouding of the lens of the eye ( cataract ) and a narrowed opening of the eye (narrowed ... Genetic Testing Registry: Anophthalmia/Microphthalmia Genetic Testing Registry: Cataract, congenital, with microphthalmia Genetic Testing Registry: Cataract, microphthalmia ...
New developments in the prediction and treatment of genetic diseases are presented. Genetic counseling and the role of the counselor, and rights of individuals to reproduce versus societal impact of genetic disorders, are discussed. (RW)
... MENU Toggle navigation Home Page Search Share: Email Facebook Twitter Home Health Conditions Genes Chromosomes & mtDNA Resources Help Me Understand Genetics Genetics Home Reference provides consumer-friendly information about the effects of genetic variation ...
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... Education & Events Advocacy For Patients About ACOG Prenatal Genetic Diagnostic Tests Home For Patients Search FAQs Prenatal ... Pamphlets - Spanish FAQ164, September 2016 PDF Format Prenatal Genetic Diagnostic Tests Pregnancy What is prenatal genetic testing? ...
Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible.
NUDTSNA at TREC 2015 Microblog Track: A Live Retrieval System Framework for Social Network based on Semantic Expansion and Quality Model Xiang Zhu...attracted increasing attention with develop- ment of social network services. To explore user’s interests and boost retrieval and recommendation...classified into a topic are ranked by our key word bool logistic model. 1. Introduction Information retrieval and recommendation in online so- cial network has
Guney, Emre; Oliva, Baldo
Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO) analysis highlighted the role of functional diversity for such diseases.
1. Origins 9 2. Why Network-Based Instruction? ; 9 3. Educational Uses of the Internet 11 4. Learning Styles 13 5. Cost Effectiveness 15 H...for organizations to train and educate their personnel anytime and anywhere. [Ref. 6] 4. Learning Styles Distance learning may extend access...the conventional classroom will not benefit students. To create options that enhance learning, we must consider different learning styles in the
Hublin, Christer; Kaprio, Jaakko
Parasomnias are undesirable phenomena associated with sleep. Many of them run in families, and genetic factors have been long suggested to be involved in their occurrence. This article reviews the present knowledge of the genetics of the major classical behavioral parasomnias as well as present results from genetic epidemiological studies. The level and type of evidence for genetic effects varies much from parasomnia to parasomnia. The genetic factors are best established in enuresis, with several linkages to chromosomal loci, but their functions are not so far known. Environmental causes and gene-environment interactions are most probably also of great importance in the origin of complex traits or disorders such as parasomnias.
Zhang, Dawei; Han, Qing-Long; Jia, Xinchun
This paper investigates network-based output tracking control for a T-S fuzzy system that can not be stabilized by a nondelayed fuzzy static output feedback controller, but can be stabilized by a delayed fuzzy static output feedback controller. By intentionally introducing a communication network that produces proper network-induced delays in the feedback control loop, a stable and satisfactory tracking control can be ensured for the T-S fuzzy system. Due to the presence of network-induced delays, the fuzzy system and the fuzzy tracking controller operate in an asynchronous way. Taking the asynchronous operation and network-induced delays into consideration, the network-based tracking control system is modeled as an asynchronous T-S fuzzy system with an interval time-varying delay. A new delay-dependent criterion for L2 -gain tracking performance is derived by using the deviation bounds of asynchronous normalized membership functions and a complete Lyapunov-Krasovskii functional. Applying a particle swarm optimization technique with the feasibility of the derived criterion, a novel design algorithm is presented to determine the minimum L2 -gain tracking performance and control gains simultaneously. The effectiveness of the proposed method is illustrated by performing network-based output tracking control of a Duffing-Van der Pol's oscillator.
Mekanik, F.; Imteaz, M. A.; Talei, A.
Accurate seasonal rainfall forecasting is an important step in the development of reliable runoff forecast models. The large scale climate modes affecting rainfall in Australia have recently been proven useful in rainfall prediction problems. In this study, adaptive network-based fuzzy inference systems (ANFIS) models are developed for the first time for southeast Australia in order to forecast spring rainfall. The models are applied in east, center and west Victoria as case studies. Large scale climate signals comprising El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Inter-decadal Pacific Ocean (IPO) are selected as rainfall predictors. Eight models are developed based on single climate modes (ENSO, IOD, and IPO) and combined climate modes (ENSO-IPO and ENSO-IOD). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (r) and root mean square error in probability (RMSEP) skill score are used to evaluate the performance of the proposed models. The predictions demonstrate that ANFIS models based on individual IOD index perform superior in terms of RMSE, MAE and r to the models based on individual ENSO indices. It is further discovered that IPO is not an effective predictor for the region and the combined ENSO-IOD and ENSO-IPO predictors did not improve the predictions. In order to evaluate the effectiveness of the proposed models a comparison is conducted between ANFIS models and the conventional Artificial Neural Network (ANN), the Predictive Ocean Atmosphere Model for Australia (POAMA) and climatology forecasts. POAMA is the official dynamic model used by the Australian Bureau of Meteorology. The ANFIS predictions certify a superior performance for most of the region compared to ANN and climatology forecasts. POAMA performs better in regards to RMSE and MAE in east and part of central Victoria, however, compared to ANFIS it shows weaker results in west Victoria in terms of prediction errors and RMSEP skill
Wisconsin Univ., Madison. Dept. of Curriculum and Instruction.
The Interactive Genetics Tutorial (IGT) project and the Intelligent Tutoring System for the IGT project named MENDEL supplement genetics instruction in biology courses by providing students with experience in designing, conducting, and evaluating genetics experiments. The MENDEL software is designed to: (1) simulate genetics experiments that…
Lazzaro, Brian P; Schneider, David S
In this commentary, Brian P. Lazzaro and David S. Schneider examine the topic of the Genetics of Immunity as explored in this month's issues of GENETICS and G3: Genes|Genomes|Genetics. These inaugural articles are part of a joint Genetics of Immunity collection (ongoing) in the GSA journals.
Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K.
The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process. PMID:26989410
Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K
The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.
Bogaard, Kali; Johnson, Marlene
Genetics is playing an increasingly important role in the diagnosis, monitoring and treatment of diseases, and the expansion of genetics into health care has generated the field of genomic medicine. Health care delivery is shifting away from general diagnostic evaluation toward a generation of therapeutics based on a patient's genetic makeup. Meanwhile, the scientific community debates how best to incorporate genetics and genomic medicine into practice. While obstacles remain, the ultimate goal is to use information generated from the study of human genetics to improve disease treatment, cure and prevention. As the use of genetics in medical diagnosis and treatment increases, health care workers will require an understanding of genetics and genomic medicine.
... Testing What is genetic ancestry testing? What is genetic ancestry testing? Genetic ancestry testing, or genetic genealogy, ... mixed with other groups. For more information about genetic ancestry testing: The University of Utah provides video ...
... Consultation How are genetic conditions diagnosed? How are genetic conditions diagnosed? A doctor may suspect a diagnosis ... and advocacy resources. For more information about diagnosing genetic conditions: Genetics Home Reference provides information about genetic ...
Barker, G. R.
Various topics on the biochemistry of genetic manipulation are discussed. These include genetic transformation and DNA; genetic expression; DNA replication, repair, and mutation; technology of genetic manipulation; and applications of genetic manipulation. Other techniques employed are also considered. (JN)
Background Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm. Results PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets. Conclusions The ANN
Murphy, M.A.; Dezzani, R.; Pilliod, D.S.; Storfer, A.
Explaining functional connectivity among occupied habitats is crucial for understanding metapopulation dynamics and species ecology. Landscape genetics has primarily focused on elucidating how ecological features between observations influence gene flow. Functional connectivity, however, may be the result of both these between-site (landscape resistance) landscape characteristics and at-site (patch quality) landscape processes that can be captured using network based models. We test hypotheses of functional connectivity that include both between-site and at-site landscape processes in metapopulations of Columbia spotted frogs (Rana luteiventris) by employing a novel justification of gravity models for landscape genetics (eight microsatellite loci, 37 sites, n = 441). Primarily used in transportation and economic geography, gravity models are a unique approach as flow (e.g. gene flow) is explained as a function of three basic components: distance between sites, production/attraction (e.g. at-site landscape process) and resistance (e.g. between-site landscape process). The study system contains a network of nutrient poor high mountain lakes where we hypothesized a short growing season and complex topography between sites limit R. luteiventris gene flow. In addition, we hypothesized production of offspring is limited by breeding site characteristics such as the introduction of predatory fish and inherent site productivity. We found that R. luteiventris connectivity was negatively correlated with distance between sites, presence of predatory fish (at-site) and topographic complexity (between-site). Conversely, site productivity (as measured by heat load index, at-site) and growing season (as measured by frost-free period between-sites) were positively correlated with gene flow. The negative effect of predation and positive effect of site productivity, in concert with bottleneck tests, support the presence of source-sink dynamics. In conclusion, gravity models provide a
Murphy, Melanie A; Dezzani, R; Pilliod, D S; Storfer, A
Explaining functional connectivity among occupied habitats is crucial for understanding metapopulation dynamics and species ecology. Landscape genetics has primarily focused on elucidating how ecological features between observations influence gene flow. Functional connectivity, however, may be the result of both these between-site (landscape resistance) landscape characteristics and at-site (patch quality) landscape processes that can be captured using network based models. We test hypotheses of functional connectivity that include both between-site and at-site landscape processes in metapopulations of Columbia spotted frogs (Rana luteiventris) by employing a novel justification of gravity models for landscape genetics (eight microsatellite loci, 37 sites, n = 441). Primarily used in transportation and economic geography, gravity models are a unique approach as flow (e.g. gene flow) is explained as a function of three basic components: distance between sites, production/attraction (e.g. at-site landscape process) and resistance (e.g. between-site landscape process). The study system contains a network of nutrient poor high mountain lakes where we hypothesized a short growing season and complex topography between sites limit R. luteiventris gene flow. In addition, we hypothesized production of offspring is limited by breeding site characteristics such as the introduction of predatory fish and inherent site productivity. We found that R. luteiventris connectivity was negatively correlated with distance between sites, presence of predatory fish (at-site) and topographic complexity (between-site). Conversely, site productivity (as measured by heat load index, at-site) and growing season (as measured by frost-free period between-sites) were positively correlated with gene flow. The negative effect of predation and positive effect of site productivity, in concert with bottleneck tests, support the presence of source-sink dynamics. In conclusion, gravity models provide a
Koen, Erin L.; Bowman, Jeff; Garroway, Colin J.; Wilson, Paul J.
Landscape genetic analyses assess the influence of landscape structure on genetic differentiation. It is rarely possible to collect genetic samples from all individuals on the landscape and thus it is important to assess the sensitivity of landscape genetic analyses to the effects of unsampled and under-sampled sites. Network-based measures of genetic distance, such as conditional genetic distance (cGD), might be particularly sensitive to sampling intensity because pairwise estimates are relative to the entire network. We addressed this question by subsampling microsatellite data from two empirical datasets. We found that pairwise estimates of cGD were sensitive to both unsampled and under-sampled sites, and FST, Dest, and deucl were more sensitive to under-sampled than unsampled sites. We found that the rank order of cGD was also sensitive to unsampled and under-sampled sites, but not enough to affect the outcome of Mantel tests for isolation by distance. We simulated isolation by resistance and found that although cGD estimates were sensitive to unsampled sites, by increasing the number of sites sampled the accuracy of conclusions drawn from landscape genetic analyses increased, a feature that is not possible with pairwise estimates of genetic differentiation such as FST, Dest, and deucl. We suggest that users of cGD assess the sensitivity of this measure by subsampling within their own network and use caution when making extrapolations beyond their sampled network. PMID:23409155
Kogelman, Lisette J. A.; Pant, Sameer D.; Fredholm, Merete; Kadarmideen, Haja N.
Obesity is a complex condition with world-wide exponentially rising prevalence rates, linked with severe diseases like Type 2 Diabetes. Economic and welfare consequences have led to a raised interest in a better understanding of the biological and genetic background. To date, whole genome investigations focusing on single genetic variants have achieved limited success, and the importance of including genetic interactions is becoming evident. Here, the aim was to perform an integrative genomic analysis in an F2 pig resource population that was constructed with an aim to maximize genetic variation of obesity-related phenotypes and genotyped using the 60K SNP chip. Firstly, Genome Wide Association (GWA) analysis was performed on the Obesity Index to locate candidate genomic regions that were further validated using combined Linkage Disequilibrium Linkage Analysis and investigated by evaluation of haplotype blocks. We built Weighted Interaction SNP Hub (WISH) and differentially wired (DW) networks using genotypic correlations amongst obesity-associated SNPs resulting from GWA analysis. GWA results and SNP modules detected by WISH and DW analyses were further investigated by functional enrichment analyses. The functional annotation of SNPs revealed several genes associated with obesity, e.g., NPC2 and OR4D10. Moreover, gene enrichment analyses identified several significantly associated pathways, over and above the GWA study results, that may influence obesity and obesity related diseases, e.g., metabolic processes. WISH networks based on genotypic correlations allowed further identification of various gene ontology terms and pathways related to obesity and related traits, which were not identified by the GWA study. In conclusion, this is the first study to develop a (genetic) obesity index and employ systems genetics in a porcine model to provide important insights into the complex genetic architecture associated with obesity and many biological pathways that underlie
By comparing strategies of genetic alterations introduced in genetic engineering with spontaneously occurring genetic variation, we have come to conclude that both processes depend on several distinct and specific molecular mechanisms. These mechanisms can be attributed, with regard to their evolutionary impact, to three different strategies of genetic variation. These are local nucleotide sequence changes, intragenomic rearrangement of DNA segments and the acquisition of a foreign DNA segment by horizontal gene transfer. Both the strategies followed in genetic engineering and the amounts of DNA sequences thereby involved are identical to, or at least very comparable with, those involved in natural genetic variation. Therefore, conjectural risks of genetic engineering must be of the same order as those for natural biological evolution and for conventional breeding methods. These risks are known to be quite low. There is no scientific reason to assume special long-term risks for GM crops. For future agricultural developments, a road map is designed that can be expected to lead, by a combination of genetic engineering and conventional plant breeding, to crops that can insure food security and eliminate malnutrition and hunger for the entire human population on our planet. Public-private partnerships should be formed with the mission to reach the set goals in the coming decades.
... genetic conditions treated or managed? What is genetic testing? How can I find a genetics professional in my area? ... Manual Consumer Version: How Blood Clots Orphanet: Familial afibrinogenemia ...
Wang, Lui; Bayer, Steve E.
SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.
A genetic brain disorder is caused by a variation or a mutation in a gene. A variation is a different form ... mutation is a change in a gene. Genetic brain disorders affect the development and function of the ...
Lewis, Jenny; Wood-Robinson, Colin
Presents the results of investigations into young people's awareness of, and attitudes toward, genetics and DNA technology. Summarizes survey results and explores the process by which students form an opinion of new technology in the field of genetics. (DDR)
... PDF Open All Close All Description SADDAN (severe achondroplasia with developmental delay and acanthosis nigricans) is a ... Genetic Testing (1 link) Genetic Testing Registry: Severe achondroplasia with developmental delay and acanthosis nigricans Other Diagnosis ...
... Current Issue Past Issues Feature: Vision Latest Research: Genetic Links Past Issues / Summer 2008 Table of Contents ... laboratories is one way the NEI is expanding genetic testing of eye diseases. Photo courtesy of National ...
... Calendar of Events Fundraising Events Conferences Press Releases Genetics of FTD After receiving a diagnosis of FTD ... that recent advances in science have brought the genetics of FTD into much better focus. In 2012, ...
... in Latin America Information For... Media Policy Makers Genetics of Hearing Loss Language: English Español (Spanish) Recommend ... of hearing loss in babies is due to genetic causes. There are also a number of things ...
... View All Articles | Inside Life Science Home Page Genetics by the Numbers By Chelsea Toledo and Kirstie ... June 11, 2012 Scholars have been studying modern genetics since the mid-19th century, but even today ...
... Education & Events Advocacy For Patients About ACOG Prenatal Genetic Testing Chart (Infographic) Home For Patients Search FAQs Prenatal Genetic Testing Chart (Infographic) PFSI010 ››› Weeks 1–4 Weeks ...
... Find us on YouTube Follow us on Instagram Genetics and the Brain by Carl Sherman September 10, ... effects that may be responsible. How Much Is Genetic? [x] , [xi] , [xii] , [xiii] A basic question in ...
... Newly Diagnosed Patients There are over 6,000 genetic disorders that can be passed down through the ... mission to help prevent, manage and treat inherited genetic diseases. View our latest News Brief here . You ...
This book begins with an overview of the current principles of genetics and molecular genetics. Over this foundation, it adds detailed and specialized information: a description of the translation, transcription, expression and regulation of DNA and RNA; a description of the manipulation of genetic material via promoters, enhancers, and gene splicing; and a description of cloning techniques, especially those for blood group genes. The last chapter looks to the impact of molecular genetics on transfusion medicine.
Mahil, Satveer K; Capon, Francesca; Barker, Jonathan N
Psoriasis is a common and debilitating immune-mediated skin disease with a complex genetic basis. Genetic studies have provided critical insights into the pathogenesis of disease. This article focuses on the results of genetic association studies, which provide evidence that psoriasis susceptibility genes are involved in innate and adaptive immunity and skin barrier functions. The potential for disease stratification and the development of more effective treatments with fewer side effects using genetic data are highlighted.
Boughter, John D; Bachmanov, Alexander A
This review focuses on behavioral genetic studies of sweet, umami, bitter and salt taste responses in mammals. Studies involving mouse inbred strain comparisons and genetic analyses, and their impact on elucidation of taste receptors and transduction mechanisms are discussed. Finally, the effect of genetic variation in taste responsiveness on complex traits such as drug intake is considered. Recent advances in development of genomic resources make behavioral genetics a powerful approach for understanding mechanisms of taste. PMID:17903279
Shendure, Jay; Fields, Stanley
Human genetics has historically depended on the identification of individuals whose natural genetic variation underlies an observable trait or disease risk. Here we argue that new technologies now augment this historical approach by allowing the use of massively parallel assays in model systems to measure the functional effects of genetic variation in many human genes. These studies will help establish the disease risk of both observed and potential genetic variants and to overcome the problem of "variants of uncertain significance."
Oh, Min; Ahn, Jaegyoon; Yoon, Youngmi
The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug) was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively) in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer's disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer's disease.
Charles, Abigail Sheena
This study investigated the knowledge and skills that biology students may need to help them understand statistics/mathematics as it applies to genetics. The data are based on analyses of current representative genetics texts, practicing genetics professors' perspectives, and more directly, students' perceptions of, and performance in, doing…
After agreeing to host over 200 students on a daylong genetics field trip, the author needed an easy-to-prepare genetics experiment to accompany the DNA-necklace and gel-electrophoresis activities already planned. One of the student's mothers is a pediatric physician at the local hospital, and she suggested exploring genetic-disease screening…
Lyons, Leslie A.
DNA testing for domestic cat diseases and appearance traits is a rapidly growing asset for veterinary medicine. Approximately thirty-three genes contain fifty mutations that cause feline health problems or alterations in the cat’s appearance. A variety of commercial laboratories can now perform cat genetic diagnostics, allowing both the veterinary clinician and the private owner to obtain DNA test results. DNA is easily obtained from a cat via a buccal swab using a standard cotton bud or cytological brush, allowing DNA samples to be easily sent to any laboratory in the world. The DNA test results identify carriers of the traits, predict the incidence of traits from breeding programs, and influence medical prognoses and treatments. An overall goal of identifying these genetic mutations is the correction of the defect via gene therapies and designer drug therapies. Thus, genetic testing is an effective preventative medicine and a potential ultimate cure. However, genetic diagnostic tests may still be novel for many veterinary practitioners and their application in the clinical setting needs to have the same scrutiny as any other diagnostic procedure. This article will review the genetic tests for the domestic cat, potential sources of error for genetic testing, and the pros and cons of DNA results in veterinary medicine. Highlighted are genetic tests specific to the individual cat, which are a part of the cat’s internal genome. PMID:21147473
Lyons, Leslie A
DNA testing for domestic cat diseases and appearance traits is a rapidly growing asset for veterinary medicine. Approximately 33 genes contain 50 mutations that cause feline health problems or alterations in the cat's appearance. A variety of commercial laboratories can now perform cat genetic diagnostics, allowing both the veterinary clinician and the private owner to obtain DNA test results. DNA is easily obtained from a cat via a buccal swab with a standard cotton bud or cytological brush, allowing DNA samples to be easily sent to any laboratory in the world. The DNA test results identify carriers of the traits, predict the incidence of traits from breeding programs, and influence medical prognoses and treatments. An overall goal of identifying these genetic mutations is the correction of the defect via gene therapies and designer drug therapies. Thus, genetic testing is an effective preventative medicine and a potential ultimate cure. However, genetic diagnostic tests may still be novel for many veterinary practitioners and their application in the clinical setting needs to have the same scrutiny as any other diagnostic procedure. This article will review the genetic tests for the domestic cat, potential sources of error for genetic testing, and the pros and cons of DNA results in veterinary medicine. Highlighted are genetic tests specific to the individual cat, which are a part of the cat's internal genome.
Zheng, Junyu; Che, Wenwei; Wang, Xuemei; Louie, Peter; Zhong, Liuju
Gridded air pollutant emission inventories are prerequisites for using air quality models to assess air pollution control strategies and predict air quality. A precise gridded emission inventory will help improve the accuracy of air quality simulation. Mobile source emissions are one of the major contributors to volatile organic compound (VOC) and nitrogen oxide (NOx) pollutants, the precursors of ozone formation. However, because of the complexity of road networks and variations in traffic flows at different road types and locations, spatial allocation of emissions from mobile sources into grid cells is challenging. This paper proposes a new methodological framework, named as "the road-network-based approach," for spatially allocating regional mobile source emission inventories. The new approach utilizes the Geographic Information System (GIS)-based road network information and road-types-based traffic flow data to provide spatial surrogates for allocating Pearl River Delta (PRD) regional mobile source emission inventories. The results show that the new approach provides reasonable spatial distributions of mobile source emissions, and the distributions are in good agreement with PRD regional on-road emission line sources. Comparisons between using the population-based and the new road-network-based approaches are made. The air quality modeling results indicate that the new approach can obviously improve model predictions with increasing accuracy in mobile source emission allocations. Means of choosing appropriate approaches for spatially allocating regional mobile source emissions are discussed.
Hu, Liang; Wang, Qin; Qin, Zhen; Su, Kaiqi; Huang, Liquan; Hu, Ning; Wang, Ping
5-hydroxytryptamine (5-HT) is an important neurotransmitter in regulating emotions and related behaviors in mammals. To detect and monitor the 5-HT, effective and convenient methods are demanded in investigation of neuronal network. In this study, hippocampal neuronal networks (HNNs) endogenously expressing 5-HT receptors were employed as sensing elements to build an in vitro neuronal network-based biosensor. The electrophysiological characteristics were analyzed in both neuron and network levels. The firing rates and amplitudes were derived from signal to determine the biosensor response characteristics. The experimental results demonstrate a dose-dependent inhibitory effect of 5-HT on hippocampal neuron activities, indicating the effectiveness of this hybrid biosensor in detecting 5-HT with a response range from 0.01μmol/L to 10μmol/L. In addition, the cross-correlation analysis of HNNs activities suggests 5-HT could weaken HNN connectivity reversibly, providing more specificity of this biosensor in detecting 5-HT. Moreover, 5-HT induced spatiotemporal firing pattern alterations could be monitored in neuron and network levels simultaneously by this hybrid biosensor in a convenient and direct way. With those merits, this neuronal network-based biosensor will be promising to be a valuable and utility platform for the study of neurotransmitter in vitro.
Frankel, M. S.
Issues concerning the use of genetic technology are discussed. Some areas discussed include treating genetic disease, prenatal diagnosis and selective abortion, screening for genetic disease, and genetic counseling. Policy issues stemming from these capabilities are considered.
... Testing How is genetic testing done? How is genetic testing done? Once a person decides to proceed ... is called informed consent . For more information about genetic testing procedures: The National Society of Genetic Counselors ...
... Your 1- to 2-Year-Old All About Genetics KidsHealth > For Parents > All About Genetics Print A ... way they pick up special laboratory dyes. continue Genetic Problems Errors in the genetic code or "gene ...
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Genetics is becoming increasingly integrated into peoples' lives. Different measures have been taken to try and better genetics education. This thesis examined undergraduate students at the University of North Texas not majoring in the life sciences interest in genetic concepts through the means of a Likert style survey. ANOVA analysis showed there was variation amongst the interest level in different genetic concepts. In addition age and lecture were also analyzed as contributing factors to students' interest. Both age and lecture were evaluated to see if they contributed to the interest of students in genetic concepts and neither showed statistical significance. The Genetic Interest Assessment (GIA) serves to help mediate the gap between genetic curriculum and students' interest.
Shearer, A. Eliot; Smith, Richard J.H.
Purpose of review To provide an update on recently discovered human deafness genes and to describe advances in comprehensive genetic testing platforms for deafness, both of which have been enabled by new massively parallel sequencing technologies. Recent findings Over the review period, three syndromic and six nonsyndromic deafness genes have been discovered, bringing the total number of nonsyndromic deafness genes to 64. Four studies have shown the utility of massively parallel sequencing for comprehensive genetic testing for deafness. Three of these platforms have been released on a clinical or commercial basis. Summary Deafness is the most common sensory deficit in humans. Genetic diagnosis has traditionally been difficult due to extreme genetic heterogeneity and a lack of phenotypic variability. For these reasons, comprehensive genetic screening platforms have been developed with the use of massively parallel sequencing. These technologies are also accelerating the pace of gene discovery for deafness. Because genetic diagnosis is the basis for molecular therapies, these advances lay the foundation for the clinical care of deaf and hard-of-hearing persons in the future. PMID:23042251
Blackburn, H D
For 100s of years, livestock producers have employed various types of selection to alter livestock populations. Current selection strategies are little different, except our technologies for selection have become more powerful. Genetic resources at the breed level have been in and out of favour over time. These resources are the raw materials used to manipulate populations, and therefore, they are critical to the past and future success of the livestock sector. With increasing ability to rapidly change genetic composition of livestock populations, the conservation of these genetic resources becomes more critical. Globally, awareness of the need to steward genetic resources has increased. A growing number of countries have embarked on large scale conservation efforts by using in situ, ex situ (gene banking), or both approaches. Gene banking efforts have substantially increased and data suggest that gene banks are successfully capturing genetic diversity for research or industry use. It is also noteworthy that both industry and the research community are utilizing gene bank holdings. As pressures grow to meet consumer demands and potential changes in production systems, the linkage between selection goals and genetic conservation will increase as a mechanism to facilitate continued livestock sector development.
He, Min; Li, Wei
In April 2005, with the voluntary involvement of more than 50 worldwide genetic counselors or medical geneticists, we developed a website for online genetic counseling and genetic education on common genetic disease throughout China (URL: http://www.gcnet.org.cn). This website is offering professional online genetic counseling, as well as providing information about common genetic diseases which is a resource for genetic counselors and online genetic counselees. Online genetic counseling is an alternative method to the widely accepted face-to-face counseling. The data warehouse of China Genetic Counseling Network (CGCN) will be a unique supplement to current status of Clinical Genetics and healthcare system in China.
Casillas, Sònia; Barbadilla, Antonio
Molecular population genetics aims to explain genetic variation and molecular evolution from population genetics principles. The field was born 50 years ago with the first measures of genetic variation in allozyme loci, continued with the nucleotide sequencing era, and is currently in the era of population genomics. During this period, molecular population genetics has been revolutionized by progress in data acquisition and theoretical developments. The conceptual elegance of the neutral theory of molecular evolution or the footprint carved by natural selection on the patterns of genetic variation are two examples of the vast number of inspiring findings of population genetics research. Since the inception of the field, Drosophila has been the prominent model species: molecular variation in populations was first described in Drosophila and most of the population genetics hypotheses were tested in Drosophila species. In this review, we describe the main concepts, methods, and landmarks of molecular population genetics, using the Drosophila model as a reference. We describe the different genetic data sets made available by advances in molecular technologies, and the theoretical developments fostered by these data. Finally, we review the results and new insights provided by the population genomics approach, and conclude by enumerating challenges and new lines of inquiry posed by increasingly large population scale sequence data. PMID:28270526
Casillas, Sònia; Barbadilla, Antonio
Molecular population genetics aims to explain genetic variation and molecular evolution from population genetics principles. The field was born 50 years ago with the first measures of genetic variation in allozyme loci, continued with the nucleotide sequencing era, and is currently in the era of population genomics. During this period, molecular population genetics has been revolutionized by progress in data acquisition and theoretical developments. The conceptual elegance of the neutral theory of molecular evolution or the footprint carved by natural selection on the patterns of genetic variation are two examples of the vast number of inspiring findings of population genetics research. Since the inception of the field, Drosophila has been the prominent model species: molecular variation in populations was first described in Drosophila and most of the population genetics hypotheses were tested in Drosophila species. In this review, we describe the main concepts, methods, and landmarks of molecular population genetics, using the Drosophila model as a reference. We describe the different genetic data sets made available by advances in molecular technologies, and the theoretical developments fostered by these data. Finally, we review the results and new insights provided by the population genomics approach, and conclude by enumerating challenges and new lines of inquiry posed by increasingly large population scale sequence data.
VanderWaal, Kimberly L; Atwill, Edward R; Isbell, Lynne A; McCowan, Brenda
Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in
Schreiner, Matthew; Forsyth, Jennifer K; Karlsgodt, Katherine H; Anderson, Ariana E; Hirsh, Nurit; Kushan, Leila; Uddin, Lucina Q; Mattiacio, Leah; Coman, Ioana L; Kates, Wendy R; Bearden, Carrie E
22q11.2 Deletion syndrome (22q11DS) is a genetic disorder associated with numerous phenotypic consequences and is one of the greatest known risk factors for psychosis. We investigated intrinsic-connectivity-networks (ICNs) as potential biomarkers for patient and psychosis-risk status in 2 independent cohorts, UCLA (33 22q11DS-participants, 33 demographically matched controls), and Syracuse (28 22q11DS, 28 controls). After assessing group connectivity differences, ICNs from the UCLA cohort were used to train classifiers to distinguish cases from controls, and to predict psychosis risk status within 22q11DS; classifiers were subsequently tested on the Syracuse cohort. In both cohorts we observed significant hypoconnectivity in 22q11DS relative to controls within anterior cingulate (ACC)/precuneus, executive, default mode (DMN), posterior DMN, and salience networks. Of 12 ICN-derived classifiers tested in the Syracuse replication-cohort, the ACC/precuneus, DMN, and posterior DMN classifiers accurately distinguished between 22q11DS and controls. Within 22q11DS subjects, connectivity alterations within 4 networks predicted psychosis risk status for a given individual in both cohorts: the ACC/precuneus, DMN, left executive, and salience networks. Widespread within-network-hypoconnectivity in large-scale networks implicated in higher-order cognition may be a defining characteristic of 22q11DS during adolescence and early adulthood; furthermore, loss of coherence within these networks may be a valuable biomarker for individual prediction of psychosis-risk in 22q11DS.
Lind, Mackenzie J.; Gehrman, Philip R.
This review summarizes current research on the genetics of insomnia, as genetic contributions are thought to be important for insomnia etiology. We begin by providing an overview of genetic methods (both quantitative and measured gene), followed by a discussion of the insomnia genetics literature with regard to each of the following common methodologies: twin and family studies, candidate gene studies, and genome-wide association studies (GWAS). Next, we summarize the most recent gene identification efforts (primarily GWAS results) and propose several potential mechanisms through which identified genes may contribute to the disorder. Finally, we discuss new genetic approaches and how these may prove useful for insomnia, proposing an agenda for future insomnia genetics research. PMID:27999387
Smith, Richard J H; Hone, Stephen
Genetic testing for deafness has become a reality. It has changed the paradigm for evaluating deaf and hard-of-hearing persons and will be used by physicians for diagnostic purposes and as a basis for treatment and management options. Although mutation screening is currently available for only a limited number of genes, in these specific instances, diagnosis, carrier detection, and reproductive risk counseling can be provided. In the coming years there will be an expansion of the role of genetic testing and counseling will not be limited to reproductive issues. Treatment and management decisions will be made based on specific genetic diagnoses. Although genetic testing may be a confusing service for the practicing otolaryngologist, it is an important part of medical care. New discoveries and technologies will expand and increase the complexity of genetic testing options and it will become the responsibility of otolaryngologists to familiarize themselves with current discoveries and accepted protocols for genetic testing.
Holderegger, Rolf; Buehler, Dominique; Gugerli, Felix; Manel, Stéphanie
Landscape genetics is the amalgamation of landscape ecology and population genetics to help with understanding microevolutionary processes such as gene flow and adaptation. In this review, we examine why landscape genetics of plants lags behind that of animals, both in number of studies and consideration of landscape elements. The classical landscape distance/resistance approach to study gene flow is challenging in plants, whereas boundary detection and the assessment of contemporary gene flow are more feasible. By contrast, the new field of landscape genetics of adaptive genetic variation, establishing the relationship between adaptive genomic regions and environmental factors in natural populations, is prominent in plant studies. Landscape genetics is ideally suited to study processes such as migration and adaptation under global change.
ABSTRACT Complementary to traditional gene mapping approaches used to identify the hereditary components of complex diseases, integrative genomics and systems genetics have emerged as powerful strategies to decipher the key genetic drivers of molecular pathways that underlie disease. Broadly speaking, integrative genomics aims to link cellular-level traits (such as mRNA expression) to the genome to identify their genetic determinants. With the characterization of several cellular-level traits within the same system, the integrative genomics approach evolved into a more comprehensive study design, called systems genetics, which aims to unravel the complex biological networks and pathways involved in disease, and in turn map their genetic control points. The first fully integrated systems genetics study was carried out in rats, and the results, which revealed conserved trans-acting genetic regulation of a pro-inflammatory network relevant to type 1 diabetes, were translated to humans. Many studies using different organisms subsequently stemmed from this example. The aim of this Review is to describe the most recent advances in the fields of integrative genomics and systems genetics applied in the rat, with a focus on studies of complex diseases ranging from inflammatory to cardiometabolic disorders. We aim to provide the genetics community with a comprehensive insight into how the systems genetics approach came to life, starting from the first integrative genomics strategies [such as expression quantitative trait loci (eQTLs) mapping] and concluding with the most sophisticated gene network-based analyses in multiple systems and disease states. Although not limited to studies that have been directly translated to humans, we will focus particularly on the successful investigations in the rat that have led to primary discoveries of genes and pathways relevant to human disease. PMID:27736746
Young, Robert R
Genetic toxicology is the scientific discipline dealing with the effects of chemical, physical and biological agents on the heredity of living organisms. The Internet offers a wide range of online digital resources for the field of Genetic Toxicology. The history of genetic toxicology and electronic data collections are reviewed. Web-based resources at US National Library of Medicine (NLM), including MEDLINE, PUBMED, Gateway, Entrez, and TOXNET, are discussed. Search strategies and Medical Subject Headings (MeSH) are reviewed in the context of genetic toxicology. The TOXNET group of databases are discussed with emphasis on those databases with genetic toxicology content including GENE-TOX, TOXLINE, Hazardous Substances Data Bank, Integrated Risk Information System, and Chemical Carcinogenesis Research Information System. Location of chemical information including chemical structure and linkage to health and regulatory information using CHEMIDPLUS at NLM and other databases is reviewed. Various government agencies have active genetic toxicology research programs or use genetic toxicology data to assist fulfilling the agency's mission. Online resources at the US Food and Drug Administration (FDA), the US Environmental Protection Agency (EPA), the National Institutes of Environmental Health Sciences, and the National Toxicology Program (NTP) are outlined. Much of the genetic toxicology for pharmaceuticals, industrial chemicals and pesticides that is performed in the world is regulatory-driven. Regulatory web resources are presented for the laws mandating testing, guidelines on study design, Good Laboratory Practice (GLP) regulations, and requirements for electronic data collection and reporting. The Internet provides a range of other supporting resources to the field of genetic toxicology. The web links for key professional societies and journals in genetic toxicology are listed. Distance education, educational media resources, and job placement services are also
Morse, Stephen J
Some believe that genetics threatens privacy and autonomy and will eviscerate the concept of human nature. Despite the astonishing research advances, however, none of these dire predictions and no radical transformation of the law have occurred. Advocates have tried to use genetic evidence to affect judgments of criminal responsibility. At present, however genetic research can provide little aid to assessments of criminal responsibility and it does not suggest a radical critique of responsibility.
Since the introduction in the mid-1980s of analyses of minisatellites for DNA analyses, a revolution has taken place in forensic genetics. The subsequent invention of the PCR made it possible to develop forensic genetics tools that allow both very informative routine investigations and still more and more advanced, special investigations in cases concerning crime, paternity, relationship, disaster victim identification etc. The present review gives an update on the use of DNA investigations in forensic genetics.
Some argue that genetic enhancements and environmental enhancements are not importantly different: environmental enhancements such as private schools and chess lessons are simply the old-school way to have a designer baby. I argue that there is an important distinction between the two practices--a distinction that makes state restrictions on genetic enhancements more justifiable than state restrictions on environmental enhancements. The difference is that parents have no settled expectations about genetic enhancements.
Chen, Chia-Yi; Lin, Ying-Pyng; Lu, Hai-Han; Wu, Po-Yi; Lin, Huang-Chang; Wu, Hsiao-Wen
An in-building network based on cable television (CATV) integration with quadrature phase-shift keying (QPSK) orthogonal frequency-division multiplexing (OFDM) transport over a combination of single-mode fibers (SMF) and perfluorinated graded-index plastic optical fibers (GI-POF) is proposed and experimentally demonstrated. In this system, a 1558.5 nm optical signal is directly transmitted along two fiber spans (20 km SMF + 25 m GI-POF). An optimum guard band is carefully established to ensure that no very substantial signal interference is induced between the CATV and QPSK OFDM bands. Error free transmission with sufficiently low bit error rate values was achieved for 1.25 Gbps/771.5 MHz QPSK OFDM signals; also, acceptable carrier-to-noise ratio, composite second-order, and composite triple-beat performances were obtained for CATV signals. This proposed network is significant because it is economical and convenient to install.
Zhang, Nian; Wunsch, Donald C., II
The applications of non-standard logic device are increasing fast in the industry. Many of these applications require high speed, low power, functionality and flexibility, which cannot be obtained by standard logic device. These special logic cells can be constructed by the topology design strategy automatically or manually. However, the need arises for the topology design verification. The layout versus schematic (LVS) analysis is an essential part of topology design verification, and subcircuit extraction is one of the operations in the LVS testing. In this paper, we first provided an efficient decision tree approach to the graph isomorphism problem, and then effectively applied it to the subcircuit extraction problem based on the solution to the graph isomorphism problem. To evaluate its performance, we compare it with the neural networks based heuristic dynamic programming algorithm (SubHDP) which is by far one of the fastest algorithms for subcircuit extraction problem.
Acernese, F.; Barone, F.; de Rosa, M.; De Rosa, R.; Eleuteri, A.; Milano, L.; Tagliaferri, R.
In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis.
Zhang, Junwen; Wang, Jing; Xu, Yuming; Xu, Mu; Lu, Feng; Cheng, Lin; Yu, Jianjun; Chang, Gee-Kung
We propose and experimentally demonstrate a novel fiber-wireless integrated mobile backhaul network based on a hybrid millimeter-wave (MMW) and free-space-optics (FSO) architecture using an adaptive combining technique. Both 60 GHz MMW and FSO links are demonstrated and fully integrated with optical fibers in a scalable and cost-effective backhaul system setup. Joint signal processing with an adaptive diversity combining technique (ADCT) is utilized at the receiver side based on a maximum ratio combining algorithm. Mobile backhaul transportation of 4-Gb/s 16 quadrature amplitude modulation frequency-division multiplexing (QAM-OFDM) data is experimentally demonstrated and tested under various weather conditions synthesized in the lab. Performance improvement in terms of reduced error vector magnitude (EVM) and enhanced link reliability are validated under fog, rain, and turbulence conditions.
Adasme-Carreño, Francisco; Muñoz-Gutierrez, Camila; Caballero, Julio; Alzate-Morales, Jans H
A conformational selection method, based on hydrogen bond (Hbond) network analysis, has been designed in order to rationalize the configurations sampled using molecular dynamics (MD), which are commonly used in the estimation of the relative binding free energy of ligands to macromolecules through the MM/GBSA or MM/PBSA method. This approach makes use of protein-ligand complexes obtained from X-ray crystallographic data, as well as from molecular docking calculations. The combination of several computational approaches, like long MD simulations on protein-ligand complexes, Hbond network-based selection by scripting techniques and finally MM/GBSA, provides better statistical correlations against experimental binding data than previous similar reported studies. This approach has been successfully applied in the ranking of several protein kinase inhibitors (CDK2, Aurora A and p38), which present both diverse and related chemical structures.
Kasiviswanathan, K.; Sudheer, K.
Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived
This book provides a conceptual understanding of the biology of genes and also gives current events and controversies in the field. Basic transmission genetics, molecular genetics, and population genetics are covered, with additional discussions relating to such topics as agriculture, aging, forensic science, genetic counseling, gene splicing, and recombinant DNA. Low level radiation and its effects, drugs and heredity, IQ, heredity and racial variation, and creationism versus evolution are also described. ''Billboard'' style diagrams visually explain important concepts. Boldfaced key terms are defined within the text and in a comprehensive glossary. Selected readings, discussion questions and problems, and excellent chapter summaries further aid study.
Moresco, Eva Marie Y.; Li, Xiaohong; Beutler, Bruce
Forward genetic analysis is an unbiased approach for identifying genes essential to defined biological phenomena. When applied to mice, it is one of the most powerful methods to facilitate understanding of the genetic basis of human biology and disease. The speed at which disease-causing mutations can be identified in mutagenized mice has been markedly increased by recent advances in DNA sequencing technology. Creating and analyzing mutant phenotypes may therefore become rate-limiting in forward genetic experimentation. We review the forward genetic approach and its future in the context of recent technological advances, in particular massively parallel DNA sequencing, induced pluripotent stem cells, and haploid embryonic stem cells. PMID:23608223
... Genetic Testing Registry: Congenital aniridia Other Diagnosis and Management Resources (1 link) GeneReview: Aniridia General Information from MedlinePlus (5 links) Diagnostic Tests Drug Therapy ...
Nowak, Dorota M.; Gajecka, Marzena
Keratoconus (KTCN) is non-inflammatory thinning and anterior protrusion of the cornea that results in steepening and distortion of the cornea, altered refractive error, and decreased vision. Keratoconus is a complex condition of multifactorial etiology. Both genetic and environmental factors are associated with KTCN. Evidence of genetic etiology includes familial inheritance, discordance between dizygotic twins, and association with other known genetic disorders. Several loci responsible for a familial form of KTCN have been mapped; however, no mutations in any genes have been identified for any of these loci. This article focuses on the genetic aspects. In addition, bioinformatics methods applied in KTCN gene identification process are discussed. PMID:21572727
Chenoweth, Peter J
Genetic sperm defects are specific sperm defects, which have been shown to have a genetic mode of transmission. Such genetic linkage, either direct or indirect, has been associated with a number of sperm defects in different species, with this number increasing with improved diagnostic capabilities. A number of sperm defects, which have proven or suspected genetic modes of transmission are discussed herein, with particular emphasis on cattle. These include: 1. Acrosome defects (knobbed, ruffled and incomplete); 2. Head defects (abnormal condensation, decapitated, round head, rolled head, nuclear crest); 3. Midpiece abnormalities ("Dag" defect, "corkscrew" defect, "pseudo-droplet" defect); 4. Tail defects ("tail stump" defect, primary ciliary dyskinesia).
Stern, David L.; Orgogozo, Virginie
Ever since the integration of Mendelian genetics into evolutionary biology in the early 20th century, evolutionary geneticists have for the most part treated genes and mutations as generic entities. However, recent observations indicate that all genes are not equal in the eyes of evolution. Evolutionarily relevant mutations tend to accumulate in hotspot genes and at specific positions within genes. Genetic evolution is constrained by gene function, the structure of genetic networks, and population biology. The genetic basis of evolution may be predictable to some extent, and further understanding of this predictability requires incorporation of the specific functions and characteristics of genes into evolutionary theory. PMID:19197055
Duffy, David J; Krstic, Aleksandar; Halasz, Melinda; Schwarzl, Thomas; Fey, Dirk; Iljin, Kristiina; Mehta, Jai Prakash; Killick, Kate; Whilde, Jenny; Turriziani, Benedetta; Haapa-Paananen, Saija; Fey, Vidal; Fischer, Matthias; Westermann, Frank; Henrich, Kai-Oliver; Bannert, Steffen; Higgins, Desmond G; Kolch, Walter
Despite intensive study, many mysteries remain about the MYCN oncogene's functions. Here we focus on MYCN's role in neuroblastoma, the most common extracranial childhood cancer. MYCN gene amplification occurs in 20% of cases, but other recurrent somatic mutations are rare. This scarcity of tractable targets has hampered efforts to develop new therapeutic options. We employed a multi-level omics approach to examine MYCN functioning and identify novel therapeutic targets for this largely un-druggable oncogene. We used systems medicine based computational network reconstruction and analysis to integrate a range of omic techniques: sequencing-based transcriptomics, genome-wide chromatin immunoprecipitation, siRNA screening and interaction proteomics, revealing that MYCN controls highly connected networks, with MYCN primarily supressing the activity of network components. MYCN's oncogenic functions are likely independent of its classical heterodimerisation partner, MAX. In particular, MYCN controls its own protein interaction network by transcriptionally regulating its binding partners.Our network-based approach identified vulnerable therapeutically targetable nodes that function as critical regulators or effectors of MYCN in neuroblastoma. These were validated by siRNA knockdown screens, functional studies and patient data. We identified β-estradiol and MAPK/ERK as having functional cross-talk with MYCN and being novel targetable vulnerabilities of MYCN-amplified neuroblastoma. These results reveal surprising differences between the functioning of endogenous, overexpressed and amplified MYCN, and rationalise how different MYCN dosages can orchestrate cell fate decisions and cancerous outcomes. Importantly, this work describes a systems-level approach to systematically uncovering network based vulnerabilities and therapeutic targets for multifactorial diseases by integrating disparate omic data types.
Genetic screening, gene therapy and other applications of genetic engineering are permissible in Judaism when used for the treatment, cure, or prevention of disease. Such genetic manipulation is not considered to be a violation of God's natural law, but a legitimate implementation of the biblical mandate to heal. If Tay-Sachs disease, diabetes, hemophilia, cystic fibrosis, Huntington's disease or other genetic diseases can be cured or prevented by "gene surgery," then it is certainly permitted in Jewish law. Genetic premarital screening is encouraged in Judaism for the purpose of discouraging at-risk marriages for a fatal illness such as Tay-Sachs disease. Neonatal screening for treatable conditions such as phenylketonuria is certainly desirable and perhaps required in Jewish law. Preimplantation screening and the implantation of only "healthy" zygotes into the mother's womb to prevent the birth of an affected child are probably sanctioned in Jewish law. Whether or not these assisted reproduction techniques may be used to choose the sex of one's offspring, to prevent the birth of a child with a sex-linked disease such as hemophilia, has not yet been ruled on by modern rabbinic decisions. Prenatal screening with the specific intent of aborting an affected fetus is not allowed according to most rabbinic authorities, although a minority view permits it "for great need." Not to have children if both parents are carriers of genetic diseases such as Tay-Sachs is not a Jewish option. Preimplantation screening is preferable. All screening test results must remain confidential. Judaism does not permit the alteration or manipulation of physical traits and characteristics such as height, eye and hair color, facial features and the like, when such change provides no useful benefit to mankind. On the other hand, it is permissible to clone organisms and microorganisms to facilitate the production of insulin, growth hormone, and other agents intended to benefit mankind and to
Czeizel, Andrew E.
The beginning of human genetics and its medical part:
Coyle, Heather; Drell, Dan
Various: (1)TriState 2000 Genetics in the Courts (2) Growing impact of the new genetics on the courts (3)Human testing (4) Legal analysis - in re G.C. (5) Legal analysis - GM ''peanots'', and (6) Legal analysis for State vs Miller
Sloan, Chantel D.; Sayarath, Vicki; Moore, Jason H.
Alcoholism is a common disease resulting from the complex interaction of genetic, social, and environmental factors. Interest in the high heritability of alcoholism has resulted in many studies of how single genes, as well as an individual’s entire genetic content (i.e., genome) and the proteins expressed by the genome, influence alcoholism risk. The use of large-scale methods to identify and characterize genetic material (i.e., high-throughput technologies) for data gathering and analysis recently has made it possible to investigate the complexity of the genetic architecture of susceptibility to common diseases such as alcoholism on a systems level. Systems genetics is the study of all genetic variations, their interactions with each other (i.e., epistasis), their interactions with the environment (i.e., plastic reaction norms), their relationship with interindividual variation in traits that are influenced by many genes and contribute to disease susceptibility (i.e., intermediate quantitative traits or endophenotypes1) defined at different levels of hierarchical biochemical and physiological systems, and their relationship with health and disease. The goal of systems genetics is to provide an understanding of the complex relationship between the genome and disease by investigating intermediate biological processes. After investigating main effects, the first step in a systems genetics approach, as described here, is to search for gene–gene (i.e., epistatic) reactions. PMID:23584748
One of the major changes in developmental psychology during the past 50 years has been the acceptance of the important role of nature (genetics) as well as nurture (environment). Past research consisting of twin and adoption studies has shown that genetic influence is substantial for most domains of developmental psychology. Present research…
Paaby, Annalise B; Gibson, Greg
Evolutionary developmental genetics has traditionally been conducted by two groups: Molecular evolutionists who emphasize divergence between species or higher taxa, and quantitative geneticists who study variation within species. Neither approach really comes to grips with the complexities of evolutionary transitions, particularly in light of the realization from genome-wide association studies that most complex traits fit an infinitesimal architecture, being influenced by thousands of loci. This paper discusses robustness, plasticity and lability, phenomena that we argue potentiate major evolutionary changes and provide a bridge between the conceptual treatments of macro- and micro-evolution. We offer cryptic genetic variation and conditional neutrality as mechanisms by which standing genetic variation can lead to developmental system drift and, sheltered within canalized processes, may facilitate developmental transitions and the evolution of novelty. Synthesis of the two dominant perspectives will require recognition that adaptation, divergence, drift and stability all depend on similar underlying quantitative genetic processes-processes that cannot be fully observed in continuously varying visible traits.
Paaby, Annalise B.; Gibson, Greg
Evolutionary developmental genetics has traditionally been conducted by two groups: Molecular evolutionists who emphasize divergence between species or higher taxa, and quantitative geneticists who study variation within species. Neither approach really comes to grips with the complexities of evolutionary transitions, particularly in light of the realization from genome-wide association studies that most complex traits fit an infinitesimal architecture, being influenced by thousands of loci. This paper discusses robustness, plasticity and lability, phenomena that we argue potentiate major evolutionary changes and provide a bridge between the conceptual treatments of macro- and micro-evolution. We offer cryptic genetic variation and conditional neutrality as mechanisms by which standing genetic variation can lead to developmental system drift and, sheltered within canalized processes, may facilitate developmental transitions and the evolution of novelty. Synthesis of the two dominant perspectives will require recognition that adaptation, divergence, drift and stability all depend on similar underlying quantitative genetic processes—processes that cannot be fully observed in continuously varying visible traits. PMID:27304973
Zietsch, Brendan P.; de Candia, Teresa R; Keller, Matthew C.
We describe the scientific enterprise at the intersection of evolutionary psychology and behavioral genetics—a field that could be termed Evolutionary Behavioral Genetics—and how modern genetic data is revolutionizing our ability to test questions in this field. We first explain how genetically informative data and designs can be used to investigate questions about the evolution of human behavior, and describe some of the findings arising from these approaches. Second, we explain how evolutionary theory can be applied to the investigation of behavioral genetic variation. We give examples of how new data and methods provide insight into the genetic architecture of behavioral variation and what this tells us about the evolutionary processes that acted on the underlying causal genetic variants. PMID:25587556
Smith, Shelley D.; Pennington, Bruce F.
A discussion of basic genetic principles is followed by a review of selected genetic syndromes involving learning disabilites (such as Noonan Syndrome, Neurofibromatosis, Pheuylketonuria, and cleft lip and palate). Guidelines for securing a genetic evaluation are given. (CL)
Roberts, D.F.; De Stefano, G.F.
This book contains several papers divided among three sections. The section titles are: Genetic Diversity--Its Dimensions; Genetic Diversity--Its Origin and Maintenance; and Genetic Diversity--Applications and Problems of Complex Characters.
... for the genetic terms used on this page Genetics, Disease Prevention and Treatment Overview How can learning ... gov] Top of page How can knowing about genetics help treat disease? Every year, more than two ...
McInerney, Joseph D.
Describes the contribution made to the quality of human life by the study of genetics. Presents a description of the current status of genetics education. Suggests changes in genetics education necessary to keep up with new developments. (39 references) (CW)
Vidal Gallardo, Mercedes
The continuous advances in our society in the last decades have allowed us to get to know the personal genetic data. Although this discovery has important benefits, it also causes a great paradox, since the genetic information can be an element of social stigma, and its inappropriate use can damage the fundamental rights. It is obvious that there are cases in which the genetic risk, that is, the predisposition of a person to suffer some illnesses, can be a discriminatory element, especially in the contractual field.
Zhou, Ruanbao (Inventor); Gibbons, William (Inventor)
The disclosed embodiments provide cyanobacteria spp. that have been genetically engineered to have increased production of carbon-based products of interest. These genetically engineered hosts efficiently convert carbon dioxide and light into carbon-based products of interest such as long chained hydrocarbons. Several constructs containing polynucleotides encoding enzymes active in the metabolic pathways of cyanobacteria are disclosed. In many instances, the cyanobacteria strains have been further genetically modified to optimize production of the carbon-based products of interest. The optimization includes both up-regulation and down-regulation of particular genes.
Charles, Abigail Sheena
This study investigated the knowledge and skills that biology students may need to help them understand statistics/mathematics as it applies to genetics. The data are based on analyses of current representative genetics texts, practicing genetics professors' perspectives, and more directly, students' perceptions of, and performance in, doing statistically-based genetics problems. This issue is at the emerging edge of modern college-level genetics instruction, and this study attempts to identify key theoretical components for creating a specialized biological statistics curriculum. The goal of this curriculum will be to prepare biology students with the skills for assimilating quantitatively-based genetic processes, increasingly at the forefront of modern genetics. To fulfill this, two college level classes at two universities were surveyed. One university was located in the northeastern US and the other in the West Indies. There was a sample size of 42 students and a supplementary interview was administered to a select 9 students. Interviews were also administered to professors in the field in order to gain insight into the teaching of statistics in genetics. Key findings indicated that students had very little to no background in statistics (55%). Although students did perform well on exams with 60% of the population receiving an A or B grade, 77% of them did not offer good explanations on a probability question associated with the normal distribution provided in the survey. The scope and presentation of the applicable statistics/mathematics in some of the most used textbooks in genetics teaching, as well as genetics syllabi used by instructors do not help the issue. It was found that the text books, often times, either did not give effective explanations for students, or completely left out certain topics. The omission of certain statistical/mathematical oriented topics was seen to be also true with the genetics syllabi reviewed for this study. Nonetheless
This commentary article reviews a recent meta-analysis of genetic influences on antisocial behavior by Rhee and Waldman (2002). The authors combined the results of 51 twin and adoption studies and concluded that antisocial behavior has an important genetic component. However, twin and adoption studies contain several methodological flaws and are subject to the confounding influence of environmental factors. Therefore, Rhee and Waldman's conclusions in favor of genetic influences are not supported by the evidence. Two additional topics are Rhee and Waldman's incorrect description of the heritability concept and their failure to discuss several German criminal twin studies published during the Nazi era.
Komoto, Satoshi; Taniguchi, Koki
The rotavirus genome is composed of 11 gene segments of dsRNA. A recent breakthrough in the field of rotaviruses is the development of a reverse genetics system for generating recombinant rotaviruses possessing a gene segment derived from cloned cDNA. Although this approach is a helper virus-driven system that is technically limited and gives low levels of recombinant viruses, it allows alteration of the rotavirus genome, thus contributing to our understanding of these medically important viruses. So far, this approach has successfully been applied to three of the 11 viral segments in our laboratory and others, and the efficiency of recovery of recombinant viruses has been improved. However, we are still waiting for the development of a helper virus-free reverse genetics system for generating an infectious rotavirus entirely from cDNAs, as has been achieved for other members of the Reoviridae family.
Wu, Jian; Su, Zhong; Li, Zuofeng
Our purpose was to develop a neural network-based registration quality evaluator (RQE) that can improve the 2D/3D image registration robustness for pediatric patient setup in external beam radiotherapy. Orthogonal daily setup X-ray images of six pediatric patients with brain tumors receiving proton therapy treatments were retrospectively registered with their treatment planning computed tomography (CT) images. A neural network-based pattern classifier was used to determine whether a registration solution was successful based on geometric features of the similarity measure values near the point-of-solution. Supervised training and test datasets were generated by rigidly registering a pair of orthogonal daily setup X-ray images to the treatment planning CT. The best solution for each registration task was selected from 50 optimizing attempts that differed only by the randomly generated initial transformation parameters. The distance from each individual solution to the best solution in the normalized parametrical space was compared to a user-defined error tolerance to determine whether that solution was acceptable. A supervised training was then used to train the RQE. Performance of the RQE was evaluated using test dataset consisting of registration results that were not used in training. The RQE was integrated with our in-house 2D/3D registration system and its performance was evaluated using the same patient dataset. With an optimized sampling step size (i.e., 5 mm) in the feature space, the RQE has the sensitivity and the specificity in the ranges of 0.865-0.964 and 0.797-0.990, respectively, when used to detect registration error with mean voxel displacement (MVD) greater than 1 mm. The trial-to-acceptance ratio of the integrated 2D/3D registration system, for all patients, is equal to 1.48. The final acceptance ratio is 92.4%. The proposed RQE can potentially be used in a 2D/3D rigid image registration system to improve the overall robustness by rejecting
Goh, Wilson Wen Bin; Lee, Yie Hou; Ramdzan, Zubaidah M.; Chung, Maxey C.M.; Wong, Limsoon; Sergot, Marek J.
Hepatocellular Carcinoma (HCC) ranks among the deadliest of cancers and has a complex etiology. Proteomics analysis using iTRAQ provides a direct way to analyze perturbations in protein expression during HCC progression from early- to late-stage but suffers from consistency and coverage issues. Appropriate use of network-based analytical methods can help to overcome these issues. We built an integrated and comprehensive protein-protein interaction network (PPIN) by merging several major databases. Additionally, the network was filtered for GO coherent edges. Significantly differential genes (seeds) were selected from iTRAQ data and mapped onto this network. Undetected proteins linked to seeds (linked proteins) were identified and functionally characterized. The process of network cleaning provides a list of higher quality linked proteins, which are highly enriched for similar biological process Gene Ontology terms. Linked proteins are also enriched for known cancer genes and are linked to many well-established cancer processes such as apoptosis and immune response. We found that there is an increased propensity for known cancer genes to be found in highly linked proteins. Three highly-linked proteins were identified that may play an important role in driving HCC progression—the G-protein coupled receptor signaling proteins, ARRB1/2 and the structural protein beta-actin, ACTB. Interestingly, both ARRB proteins evaded detection in the iTRAQ screen. ACTB was not detected in the original dataset derived from Mascot but was found to be strongly supported when we re-ran analysis using another protein detection database (Paragon). Identification of linked proteins helps to partially overcome the coverage issue in shotgun proteomics analysis. The set of linked proteins are found to be enriched for cancer-specific processes, and more likely so if they are more highly linked. Additionally, a higher quality linked set is derived if network-cleaning is performed prior. This
... Encyclopedia: Semen Analysis MedlinePlus Health Topic: Assisted Reproductive Technology General Information from MedlinePlus (5 links) Diagnostic Tests Drug Therapy Genetic Counseling Palliative Care Surgery and ...
... Encyclopedia: Semen Analysis MedlinePlus Health Topic: Assisted Reproductive Technology General Information from MedlinePlus (5 links) Diagnostic Tests Drug Therapy Genetic Counseling Palliative Care Surgery and ...
Ridge, Perry G.; Ebbert, Mark T. W.; Kauwe, John S. K.
Alzheimer's disease is the most common form of dementia and is the only top 10 cause of death in the United States that lacks disease-altering treatments. It is a complex disorder with environmental and genetic components. There are two major types of Alzheimer's disease, early onset and the more common late onset. The genetics of early-onset Alzheimer's disease are largely understood with variants in three different genes leading to disease. In contrast, while several common alleles associated with late-onset Alzheimer's disease, including APOE, have been identified using association studies, the genetics of late-onset Alzheimer's disease are not fully understood. Here we review the known genetics of early- and late-onset Alzheimer's disease. PMID:23984328
Rodriguez-Fontenla, Cristina; Gonzalez, Antonio
Osteoarthritis (OA) is a complex disease caused by the interaction of multiple genetic and environmental factors. This review focuses on the studies that have contributed to the discovery of genetic susceptibility factors in OA. The most relevant associations discovered until now are discussed in detail: GDF-5, 7q22 locus, MCF2L, DOT1L, NCOA3 and also some important findings from the arcOGEN study. Moreover, the different approaches that can be used to minimize the specific problems of the study of OA genetics are discussed. These include the study of microsatellites, phenotype standardization and other methods such as meta-analysis of GWAS and gene-based analysis. It is expected that these new approaches contribute to finding new susceptibility genetic factors for OA.
OHAMA, Takeshi; INAGAKI, Yuji; BESSHO, Yoshitaka; OSAWA, Syozo
In 1985, we reported that a bacterium, Mycoplasma capricolum, used a deviant genetic code, namely UGA, a “universal” stop codon, was read as tryptophan. This finding, together with the deviant nuclear genetic codes in not a few organisms and a number of mitochondria, shows that the genetic code is not universal, and is in a state of evolution. To account for the changes in codon meanings, we proposed the codon capture theory stating that all the code changes are non-disruptive without accompanied changes of amino acid sequences of proteins. Supporting evidence for the theory is presented in this review. A possible evolutionary process from the ancient to the present-day genetic code is also discussed. PMID:18941287
Sadagopan, Karthikeyan A; Capasso, Jenina; Levin, Alex V
The eye has played a major role in human genomics including gene therapy. It is the fourth most common organ system after integument (skin, hair and nails), nervous system, and musculoskeletal system to be involved in genetic disorders. The eye is involved in single gene disorders and those caused by multifactorial etiology. Retinoblastoma was the first human cancer gene to be cloned. Leber hereditary optic neuropathy was the first mitochondrial disorder described. X-Linked red-green color deficiency was the first X-linked disorder described. The eye, unlike any other body organ, allows directly visualization of genetic phenomena such as skewed X-inactivation in the fundus of a female carrier of ocular albinism. Basic concepts of genetics and their application to clinical ophthalmological practice are important not only in making a precise diagnosis and appropriate referral, but also in management and genetic counseling.
... abnormal swelling caused by a buildup of fluid (lymphedema) and a condition called anhydrotic ectodermal dysplasia that ... Registry: Ectodermal dysplasia, anhidrotic, with immunodeficiency, osteopetrosis, and lymphedema Genetic Testing Registry: Osteopetrosis and infantile neuroaxonal dystrophy ...
For the last 20 years the concepts of identity and identification have been subject to much interest in the humanities and social sciences. However, the implications of genetics for identity and identification have been largely neglected. In this paper, I distinguish various conceptions of identity (as continuity over time, as basic kind of being, as unique set of properties, and as social role) and identification (as subjective experience of identity in various senses and as social ascription of identity in various senses), and investigate systematically genetic perspectives on each of these conceptions. I stress the importance of taking the genetic perspectives seriously but also their limitations. In particular, I pinpoint conceptual problems that arise when a genetic approach to identity is adopted.
... homogentisate oxidase. This enzyme helps break down the amino acids phenylalanine and tyrosine, which are important building blocks ... Resources MedlinePlus (2 links) Encyclopedia: Alkaptonuria Health Topic: Amino Acid Metabolism Disorders Genetic and Rare Diseases Information Center ( ...
... condition characterized by elevated blood levels of the amino acid histidine, a building block of most proteins. Histidinemia ... Additional Information & Resources MedlinePlus (2 links) Health Topic: Amino Acid Metabolism Disorders Health Topic: Newborn Screening Genetic and ...
... condition characterized by elevated blood levels of the amino acid lysine, a building block of most proteins. Hyperlysinemia ... Additional Information & Resources MedlinePlus (2 links) Health Topic: Amino Acid Metabolism Disorders Health Topic: Newborn Screening Genetic and ...
... in my area? Other Names for This Condition French type sialuria Sialuria, French type Related Information How are genetic conditions and ... and Rare Diseases Information Center (1 link) Sialuria, French type Educational Resources (5 links) Cincinnati Children's Hospital ...
... Information & Resources MedlinePlus (4 links) Encyclopedia: Phenylketonuria Encyclopedia: Serum Phenylalanine Screening Health Topic: Newborn Screening Health Topic: Phenylketonuria Genetic and Rare Diseases Information Center (1 link) Phenylketonuria Additional NIH Resources ( ...
Dishotsky, Norman I.; And Others
Reviews studies of the effects of lysergic acid diethylamide (LSD) on man and other organisms. Concludes that pure LSD injected in moderate doses does not cause chromosome or detectable genetic damage and is not a teratogen or carcinogen. (JM)
Wells, Kevin D
Historically, genetic engineering for mammalian reproductive questions has been accomplished primarily in the mouse. However, all the genetic manipulations that can be done in the mouse can now be accomplished in most domesticated mammals. Random integration of transgenes, homologous recombination and gene editing are now routine for several mammalian species. For livestock, queries related to fertility can be asked directly for the species in question, without a need for a mouse model. For human clinical concerns, the most appropriate model should be selected based on physiology, anatomy, or even size. The mouse will continue to be a useful genetically engineered model. However, other species are now amenable to the full range of genetic manipulations and should be considered as possible models for human conditions when appropriate.
... A A A Listen En Español Genetics of Diabetes You've probably wondered how you developed diabetes. ... to develop diabetes than others. What Leads to Diabetes? Type 1 and type 2 diabetes have different ...
Steinlein, Ortrud K.
The term “epilepsy” describes a heterogeneous group of disorders, most of them caused by interactions between several or even many genes and environmental factors. Much rarer are the genetic epilepsies that are due to single-gene mutations or defined structural chromosomal aberrations, such as microdeletions. The discovery of several of the genes underlying these rare genetic epilepsies has already considerably contributed to our understanding of the basic mechanisms epileptogenesis. The progress made in the last 15 years in the genetics of epilepsy is providing new possibilities for diagnosis and therapy. Here, different genetic epilepsies are reviewed as examples, to demonstrate the various pathways that can lead from genes to seizures. PMID:18472482
... particular ethnic groups? Genetic Changes Mutations in the CAT gene can cause acatalasemia . This gene provides instructions ... DNA, proteins, and cell membranes. Mutations in the CAT gene greatly reduce the activity of catalase. A ...
This report is taken from the April 1992 draft of the DOE Human Genome 1991--1992 Program Report, which is expected to be published in May 1992. The primer is intended to be an introduction to basic principles of molecular genetics pertaining to the genome project. The material contained herein is not final and may be incomplete. Techniques of genetic mapping and DNA sequencing are described.
Kolettis, Peter N
Genetic diseases that do not primarily affect the genitourinary tract may have urologic manifestations. These urologic manifestations range from benign and malignant renal disease to infertility. Thus, the practicing urologist may be involved in the care of these patients and should have knowledge of these diseases. Continued improvements in the diagnosis and treatment of these genetic diseases will likely result in improved survival and will increase the number of patients who may develop urologic manifestations of these diseases.
Allen, Carolyn M; Founds, Sandra A
Although the etiology of preterm birth is incompletely understood, phenotype classifications combined with recent technologies such as genome-wide association studies and next-generation sequencing could lead to discovering genotypes associated with preterm birth. Identifying genetic contributions will allow for genetic screening tests to predict or detect pregnancies with potential for preterm birth. In this article we discuss current knowledge regarding phenotype classifications, genotypes, and their associations with preterm birth.
Bilen, Ozlem; Pokharel, Yashashwi; Ballantyne, Christie M
Hereditary dyslipidemias are often underdiagnosed and undertreated, yet with significant health implications, most importantly causing preventable premature cardiovascular diseases. The commonly used clinical criteria to diagnose hereditary lipid disorders are specific but are not very sensitive. Genetic testing may be of value in making accurate diagnosis and improving cascade screening of family members, and potentially, in risk assessment and choice of therapy. This review focuses on using genetic testing in the clinical setting for lipid disorders, particularly familial hypercholesterolemia.
Bilen, Ozlem; Pokharel, Yashashwi; Ballantyne, Christie M
Hereditary dyslipidemias are often underdiagnosed and undertreated, yet with significant health implications, most importantly causing preventable premature cardiovascular diseases. The commonly used clinical criteria to diagnose hereditary lipid disorders are specific but are not very sensitive. Genetic testing may be of value in making accurate diagnosis and improving cascade screening of family members, and potentially, in risk assessment and choice of therapy. This review focuses on using genetic testing in the clinical setting for lipid disorders, particularly familial hypercholesterolemia.
Vadlamudi, Lata; Milne, Roger L.; Lawrence, Kate; Heron, Sarah E.; Eckhaus, Jazmin; Keay, Deborah; Connellan, Mary; Torn-Broers, Yvonne; Howell, R. Anne; Mulley, John C.; Scheffer, Ingrid E.; Dibbens, Leanne M.; Hopper, John L.
Objective: Analysis of twins with epilepsy to explore the genetic architecture of specific epilepsies, to evaluate the applicability of the 2010 International League Against Epilepsy (ILAE) organization of epilepsy syndromes, and to integrate molecular genetics with phenotypic analyses. Methods: A total of 558 twin pairs suspected to have epilepsy were ascertained from twin registries (69%) or referral (31%). Casewise concordance estimates were calculated for epilepsy syndromes. Epilepsies were then grouped according to the 2010 ILAE organizational scheme. Molecular genetic information was utilized where applicable. Results: Of 558 twin pairs, 418 had confirmed seizures. A total of 534 twin individuals were affected. There were higher twin concordance estimates for monozygotic (MZ) than for dizygotic (DZ) twins for idiopathic generalized epilepsies (MZ = 0.77; DZ = 0.35), genetic epilepsy with febrile seizures plus (MZ = 0.85; DZ = 0.25), and focal epilepsies (MZ = 0.40; DZ = 0.03). Utilizing the 2010 ILAE scheme, the twin data clearly demonstrated genetic influences in the syndromes designated as genetic. Of the 384 tested twin individuals, 10.9% had mutations of large effect in known epilepsy genes or carried validated susceptibility alleles. Conclusions: Twin studies confirm clear genetic influences for specific epilepsies. Analysis of the twin sample using the 2010 ILAE scheme strongly supported the validity of grouping the “genetic” syndromes together and shows this organizational scheme to be a more flexible and biologically meaningful system than previous classifications. Successful selected molecular testing applied to this cohort is the prelude to future large-scale next-generation sequencing of epilepsy research cohorts. Insights into genetic architecture provided by twin studies provide essential data for optimizing such approaches. PMID:25107880
Moreno García, M; Gómez Rodríguez, M J; Barreiro Miranda, E
Congenital heart malformations are the most common of all birth defects, affecting 0.5-1% of all live births. Some of these malformations are due to genetic anomalies. Patterns of autosomal dominant, autosomal recessive and X-linked inheritance have been described. Mitochondrial inheritance and chromosomal anomalies can also be responsible for congenital heart malformations. Several genes for congenital heart defects have been identified. We review current knowledge on the genetic etiology of congenital heart disease.
Salk, Rachel H.; Hyde, Janet S.
Over the past century, much of genetics was deterministic, and feminist researchers framed justified criticisms of genetics research. However, over the past two decades, genetics research has evolved remarkably and has moved far from earlier deterministic approaches. Our article provides a brief primer on modern genetics, emphasizing contemporary…
This paper provides an overview of the ethical issues pertaining to the use of genetic insights and techniques in sport. Initially, it considers a range of scientific findings that have stimulated debate about the ethical issues associated with genetics applied to sport. It also outlines some of the early policy responses to these discoveries from world leading sports organizations, along with knowledge about actual use of gene technologies in sport. Subsequently, it considers the challenges with distinguishing between therapeutic use and human enhancement within genetic science, which is a particularly important issue for the world of sport. Next, particular attention is given to the use of genetic information, which raises questions about the legitimacy and reliability of genetic tests, along with the potential public value of having DNA databanks to economize in health care. Finally, the ethics of gene transfer are considered, inviting questions into the values of sport and humanity. It argues that, while gene modification may seem conceptually similar to other forms of doping, the requirements upon athletes are such that new forms of enhancement become increasingly necessary to discover. Insofar as genetic science is able to create safer, more effective techniques of human modification, then it may be an appealing route through which to modify athletes to safeguard the future of elite sports as enterprises of human excellence.
Schellenberg, Gerard D
The genetics community working on Alzheimer's disease and related dementias has made remarkable progress in the past 20 years. The cumulative efforts by multiple groups have lead to the identification of three autosomal dominant genes for early onset AD. These are the amyloid-beta protein precursor gene (APP), and the genes encoding presenilin1 and 2. The knowledge derived from this work has firmly established Abeta as a critical disease molecule and lead to candidate drugs currently in treatment trials. Work on a related disease, frontotemporal dementia with parkinsonism - chromosome 17 type has also added to our understanding of pathogenesis by revealing that tau, the protein component of neurofibrillary tangles, is also a critical molecule in neurodegeneration. Lessons learned that still influence work on human genetics include the need to recognize and deal with genetic heterogeneity, a feature common to many genetic disorders. Genetic heterogeneity, if recognized, can be source of information. Another critical lesson is that clinical, molecular, and statistical scientists need to work closely on disease projects to succeed in solving the complex problems of common genetic disorders.
Zhou, Tianshou; Zhang, Jiajun; Yuan, Zhanjiang; Chen, Luonan
Synchronization of genetic or cellular oscillators is a central topic in understanding the rhythmicity of living organisms at both molecular and cellular levels. Here, we show how a collective rhythm across a population of genetic oscillators through synchronization-induced intercellular communication is achieved, and how an ensemble of independent genetic oscillators is synchronized by a common noisy signaling molecule. Our main purpose is to elucidate various synchronization mechanisms from the viewpoint of dynamics, by investigating the effects of various biologically plausible couplings, several kinds of noise, and external stimuli. To have a comprehensive understanding on the synchronization of genetic oscillators, we consider three classes of genetic oscillators: smooth oscillators (exhibiting sine-like oscillations), relaxation oscillators (displaying jump dynamics), and stochastic oscillators (noise-induced oscillation). For every class, we further study two cases: with intercellular communication (including phase-attractive and repulsive coupling) and without communication between cells. We find that an ensemble of smooth oscillators has different synchronization phenomena from those in the case of relaxation oscillators, where noise plays a different but key role in synchronization. To show differences in synchronization between them, we make comparisons in many aspects. We also show that a population of genetic stochastic oscillators have their own synchronization mechanisms. In addition, we present interesting phenomena, e.g., for relaxation-type stochastic oscillators coupled to a quorum-sensing mechanism, different noise intensities can induce different periodic motions (i.e., inhomogeneous limit cycles).
Schuckit, M A; Li, T K; Cloninger, C R; Deitrich, R A
Great progress has been made by research on the contribution genetic factors make to a vulnerability toward alcoholism. Animal studies have demonstrated the importance of genetics in ethanol preference and levels of consumption, and human family, twin, and adoption research have revealed a 4-fold higher risk for offspring of alcoholics, even if they were adopted out at birth. The work presented in this symposium reviews the ongoing search for genetic trait markers of a vulnerability toward alcoholism. Dr. Li has used both animal and human research to demonstrate the possible importance of the genetic control of enzymes involved in ethanol metabolism and has worked to help develop an animal model of alcoholism. The possible importance of subgroups with different levels of predisposition toward alcoholism is emphasized by Dr. Cloninger. An overview of the studies of sons of alcoholics, given by Dr. Schuckit, reveals the potential importance of a decreased intensity of reaction to ethanol as part of a predisposition toward alcoholism and discusses the possible impact of some brain waves and ethanol metabolites to an alcoholism vulnerability. Dr. Deitrich reviews interrelationships between studies of animals and humans in the search for factors involved in a genetic vulnerability toward alcoholism. Taken together, these presentations underscore the importance of genetic factors in alcoholism, review animal and human research attempting to identify markers of a vulnerability, and reveal the high level of interaction between human and animal research.
McLaughlin, Russell L.; Heverin, Mark; Thorpe, Owen; Abrahams, Sharon; Al-Chalabi, Ammar; Hardiman, Orla
Objective: To determine the degree of consensus among clinicians on the clinical use of genetic testing in amyotrophic lateral sclerosis (ALS) and the factors that determine decision-making. Methods: ALS researchers worldwide were invited to participate in a detailed online survey to determine their attitudes and practices relating to genetic testing. Results: Responses from 167 clinicians from 21 different countries were analyzed. The majority of respondents (73.3%) do not consider that there is a consensus definition of familial ALS (FALS). Fifty-seven percent consider a family history of frontotemporal dementia and 48.5% the presence of a known ALS genetic mutation as sufficient for a diagnosis of FALS. Most respondents (90.2%) offer genetic testing to patients they define as having FALS and 49.4% to patients with sporadic ALS. Four main genes (SOD1, C9orf72, TARDBP, and FUS) are commonly tested. A total of 55.2% of respondents would seek genetic testing if they had personally received a diagnosis of ALS. Forty-two percent never offer presymptomatic testing to family members of patients with FALS. Responses varied between ALS specialists and nonspecialists and based on the number of new patients seen per year. Conclusions: There is a lack of consensus among clinicians as to the definition of FALS. Substantial variation exists in attitude and practices related to genetic testing of patients and presymptomatic testing of their relatives across geographic regions and between experienced specialists in ALS and nonspecialists. PMID:28159885
Talebi, H A; Khorasani, K; Tafazoli, S
This paper presents a robust fault detection and isolation (FDI) scheme for a general class of nonlinear systems using a neural-network-based observer strategy. Both actuator and sensor faults are considered. The nonlinear system considered is subject to both state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults, respectively. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods developed in the literature, our proposed FDI scheme does not rely on availability of full state measurements. The stability of the overall FDI scheme in presence of unknown sensor and actuator faults as well as plant and sensor noise and uncertainties is shown by using the Lyapunov's direct method. The stability analysis developed requires no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer-type actuators and magnetometer-type sensors that are commonly employed in the attitude control subsystem (ACS) of low-Earth orbit (LEO) satellites for attitude determination and control are considered in our case studies. The effectiveness and capabilities of our proposed fault diagnosis strategy are demonstrated and validated through extensive simulation studies.
Cohen, Laurie D.; Demartini, Andrea; Amato, Angela; Eterno, Vincenzo; Zambelli, Alberto; Bellazzi, Riccardo
The integration of data and knowledge from heterogeneous sources can be a key success factor in drug design, drug repurposing and multi-target therapies. In this context, biological networks provide a useful instrument to highlight the relationships and to model the phenomena underlying therapeutic action in cancer. In our work, we applied network-based modeling within a novel bioinformatics pipeline to identify promising multi-target drugs. Given a certain tumor type/subtype, we derive a disease-specific Protein-Protein Interaction (PPI) network by combining different data-bases and knowledge repositories. Next, the application of suitable graph-based algorithms allows selecting a set of potentially interesting combinations of drug targets. A list of drug candidates is then extracted by applying a recent data fusion approach based on matrix tri-factorization. Available knowledge about selected drugs mechanisms of action is finally exploited to identify the most promising candidates for planning in vitro studies. We applied this approach to the case of Triple Negative Breast Cancer (TNBC), a subtype of breast cancer whose biology is poorly understood and that lacks of specific molecular targets. Our “in-silico” findings have been confirmed by a number of in vitro experiments, whose results demonstrated the ability of the method to select candidates for drug repurposing. PMID:27632168
He, Weiming; Li, Weiguo; Qu, Xiaoli; Liang, Binhua; Gao, Qianping; Feng, Chenchen; Jia, Xu; Lv, Yana; Zhang, Siya; Li, Xia
The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial). Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on “guilt by association” analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on “guilt by association” analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way. PMID:23940716
Wagenknecht, Gudrun; Kaiser, Hans-Juergen; Obladen, Thorsten; Sabri, Osama; Buell, Udalrich
Individual region-of-interest atlas extraction consists of two main parts: T1-weighted MRI grayscale images are classified into brain tissues types (gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), scalp/bone (SB), background (BG)), followed by class image analysis to define automatically meaningful ROIs (e.g., cerebellum, cerebral lobes, etc.). The purpose of this algorithm is the automatic detection of training points for neural network-based classification of brain tissue types. One transaxial slice of the patient data set is analyzed. Background separation is done by simple region growing. A random generator extracts spatially uniformly distributed training points of class BG from that region. For WM training point extraction (TPE), the homogeneity operator is the most important. The most homogeneous voxels define the region for WM TPE. They are extracted by analyzing the cumulative histogram of the homogeneity operator response. Assuming a Gaussian gray value distribution in WM, a random number is used as a probabilistic threshold for TPE. Similarly, non-white matter and non-background regions are analyzed for GM and CSF training points. For SB TPE, the distance from the BG region is an additional feature. Simulated and real 3D MRI images are analyzed and error rates for TPE and classification calculated.
Lee, Patrick Kia Ming; Goh, Wilson Wen Bin; Sng, Judy Chia Ghee
The brain adapts to dynamic environmental conditions by altering its epigenetic state, thereby influencing neuronal transcriptional programs. An example of an epigenetic modification is protein methylation, catalyzed by protein arginine methyltransferases (PRMT). One member, Prmt8, is selectively expressed in the central nervous system during a crucial phase of early development, but little else is known regarding its function. We hypothesize Prmt8 plays a role in synaptic maturation during development. To evaluate this, we used a proteome-wide approach to characterize the synaptic proteome of Prmt8 knockout versus wild-type mice. Through comparative network-based analyses, proteins and functional clusters related to neurite development were identified to be differentially regulated between the two genotypes. One interesting protein that was differentially regulated was tenascin-R (TNR). Chromatin immunoprecipitation demonstrated binding of PRMT8 to the tenascin-r (Tnr) promoter. TNR, a component of perineuronal nets, preserves structural integrity of synaptic connections within neuronal networks during the development of visual-somatosensory cortices. On closer inspection, Prmt8 removal increased net formation and decreased inhibitory parvalbumin-positive (PV+) puncta on pyramidal neurons, thereby hindering the maturation of circuits. Consequently, visual acuity of the knockout mice was reduced. Our results demonstrated Prmt8's involvement in synaptic maturation and its prospect as an epigenetic modulator of developmental neuroplasticity by regulating structural elements such as the perineuronal nets.
Scheibe, Timothy D.; Johnson, Gary E.; Perkins, Bill
The goal of this project was to help develop technology and a unified structure to access and disseminate information related to the Bonneville Power Administration's fish and wildlife responsibility in the Pacific Northwest. BPA desires to increase access to, and exchange of, information produced by the Environment Fish, and Wildlife Group in concert with regional partners. Historically, data and information have been managed through numerous centralized, controlled information systems. Fisheries information has been fragmented and not widely exchanged. Where exchange has occurred, it often is not timely enough to allow resource managers to effectively use the information to guide planning and decision making. This project (and related projects) have successfully developed and piloted a network-based infrastructure that will serve as a vehicle to transparently connect existing information systems in a manner that makes information exchange efficient and inexpensive. This project was designed to provide a mechanism to help BPA address measures in the Northwest Power Planning Council's (NPPC) Fish and Wildlife program: 3.2H Disseminate Research and Monitoring Information and 5.1A.5 manage water supplies in accordance with the Annual Implementation Work Plan. This project also provided resources that can be used to assist monitoring and evaluation of the Program.
Background Inference of gene-regulatory networks (GRNs) is important for understanding behaviour and potential treatment of biological systems. Knowledge about GRNs gained from transcriptome analysis can be increased by multiple experiments and/or multiple stimuli. Since GRNs are complex and dynamical, appropriate methods and algorithms are needed for constructing models describing these dynamics. Algorithms based on heuristic approaches reduce the effort in parameter identification and computation time. Results The NetGenerator V2.0 algorithm, a heuristic for network inference, is proposed and described. It automatically generates a system of differential equations modelling structure and dynamics of the network based on time-resolved gene expression data. In contrast to a previous version, the inference considers multi-stimuli multi-experiment data and contains different methods for integrating prior knowledge. The resulting significant changes in the algorithmic procedures are explained in detail. NetGenerator is applied to relevant benchmark examples evaluating the inference for data from experiments with different stimuli. Also, the underlying GRN of chondrogenic differentiation, a real-world multi-stimulus problem, is inferred and analysed. Conclusions NetGenerator is able to determine the structure and parameters of GRNs and their dynamics. The new features of the algorithm extend the range of possible experimental set-ups, results and biological interpretations. Based upon benchmarks, the algorithm provides good results in terms of specificity, sensitivity, efficiency and model fit. PMID:23280066
Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei
Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915measuredsamples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rateand heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08. PMID:26624613
Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei
Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915 measured samples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rate and heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.
Discusses such high points of human genetics as the study of chromosomes, somatic cell hybrids, the population formula: the Hardy-Weinberg Law, biochemical genetics, the single-active X Theory, behavioral genetics and finally how genetics can serve humanity. (BR)
Morton, N E
Starting from a broad definition of genetic epidemiology, current developments in association, segregation, and linkage analysis of complex inheritance are considered together with integration of genetic and physical maps and resolution of genetic heterogeneity. Mitochondrial inheritance, imprinting, uniparental disomy, pregressive amplification, and gonadal mosaicism are some of the novel mechanisms discussed, with speculation about the future of genetic epidemiology.
Tetushkin, E Iu
The supplementary historical discipline genealogy is also a supplementary genetic discipline. In its formation, genetics borrowed from genealogy some methods of pedigree analysis. In the 21th century, it started receiving contribution from computer-aided genealogy and genetic (molecular) genealogy. The former provides novel tools for genetics, while the latter, which employing genetic methods, enriches genetics with new evidence. Genealogists formulated three main laws ofgenealogy: the law of three generations, the law of doubling the ancestry number, and the law of declining ancestry. The significance and meaning of these laws can be fully understood only in light of genetics. For instance, a controversy between the exponential growth of the number of ancestors of an individual, i.e., the law of doubling the ancestry number, and the limited number of the humankind is explained by the presence of weak inbreeding because of sibs' interference; the latter causes the pedigrees' collapse, i.e., explains also the law of diminishing ancestry number. Mathematic modeling of pedigrees' collapse presented in a number of studies showed that the number of ancestors of each individual attains maximum in a particular generation termed ancestry saturated generation. All representatives of this and preceding generation that left progeny are common ancestors of all current members of the population. In subdivided populations, these generations are more ancient than in panmictic ones, whereas in small isolates and social strata with limited numbers of partners, they are younger. The genealogical law of three generations, according to which each hundred years contain on average three generation intervals, holds for generation lengths for Y-chromosomal DNA, typically equal to 31-32 years; for autosomal and mtDNA, this time is somewhat shorter. Moving along ascending lineas, the number of genetically effective ancestors transmitting their DNA fragment to descendants increases far
Van Tintelen, J Peter; Pieper, Petronella G; Van Spaendonck-Zwarts, Karin Y; Van Den Berg, Maarten P
Although familial forms of cardiomyopathy such as hypertrophic or dilated cardiomyopathy have been recognized for decades, it is only recently that much of the genetic basis of these inherited cardiomyopathies has been elucidated. This has provided important insights into the pathophysiological mechanisms underlying the disease phenotype. This increased knowledge and the availability of genetic testing has resulted in increasing numbers of mutation carriers who are being monitored, including many who are now of child-bearing age. Pregnancy is generally well tolerated in asymptomatic patients or mutation carriers with inherited cardiomyopathies. However, since pregnancy leads to major physiological changes in the cardiovascular system, in women with genetic cardiomyopathies or who carry a mutation pre-disposing to a genetic cardiomyopathy, pregnancy entails a risk of developing heart failure and/or arrhythmias. This deterioration of cardiac function may occur despite optimal medical treatment. Advanced left ventricular dysfunction, poor functional class (NYHA class III or IV), or prior cardiac events appear to increase the risk of maternal cardiac complications. However, there are no large series of cardiomyopathy patients who are regularly evaluated for cardiac complications during pregnancy and for certain types of inherited cardiomyopathy, only case reports on individual pregnancies are available. Pre-conception cardiologic evaluation and genetic counselling are important for every woman with a cardiomyopathy or a cardiomyopathy-related mutation who is considering having a family. In this article, we give an overview of the basic clinical aspects, genetics, and pregnancy outcome in women with different types of inherited cardiomyopathies. We also discuss the genetic aspects of pregnancy-associated cardiomyopathy, including peripartum cardiomyopathy.
Pichi, Francesco; Carrai, Paola; Srivastava, Sunil K; Lowder, Careen Y; Nucci, Paolo; Neri, Piergiorgio
Immune-mediated uveitis may be associated with a systemic disease or may be localized to the eye. T-cell-dependent immunological events are increasingly being regarded as extremely important in the pathogenesis of uveitis. Several studies have also shown that macrophages are major effectors of tissue damage in uveitis. Uveitis phenotypes can differ substantially, and most uveitis diseases are considered polygenic with complex inheritance patterns. This review attempts to present the current state of knowledge from in vitro and in vivo research on the role of genetics in the development and clinical course of uveitis. A review of the literature in the PubMed, MEDLINE, and Cochrane databases was conducted to identify clinical trials, comparative studies, case series, and case reports describing host genetic factors as well as immune imbalance which contribute to the development of uveitis. The search was limited to primary reports published in English with human subjects from 1990 to the present, yielding 3590 manuscripts. In addition, referenced articles from the initial searches were hand searched to identify additional relevant reports. After title and abstract selection, duplicate elimination, and manual search, 55 papers were selected for analysis and reviewed by the authors for inclusion in this review. Studies have demonstrated associations between various genetic factors and the development and clinical course of intraocular inflammatory conditions. Genes involved included genes expressing interleukins, chemokines, chemokine receptors, and tumor necrosis factor and genes involved in complement system. When considering the genetics of uveitis, common threads can be identified. Genome-wide scans and other genetic methods are becoming increasingly successful in identifying genetic loci and candidate genes in many inflammatory disorders that have a uveitic component. It will be important to test these findings as uveitis-specific genetic factors. Therefore, the
Zucker, A; Patriquin, D
Gene therapy, pre-natal diagnosis, genetically altered bacteria, patenting new life forms: these are all outgrowths from the development of genetics. Our focus will be on the moral issues engendered by some of the genetic techniques which are now so well integrated into clinical medicine. The section on genetic counseling is meant to show the most frequent moral problems encountered as they might really occur. Genetic screening is presented as a mix of preventive medicine and aid for genetic counseling. Genetic engineering is discussed in the context of evolution and human needs and desires.
Morton, Newton E
Genetic epidemiology developed in the middle of the last century, focused on inherited causes of disease but with methods and results applicable to other traits and even forensics. Early success with linkage led to the localization of genes contributing to disease, and ultimately to the Human Genome Project. The discovery of millions of DNA markers has encouraged more efficient positional cloning by linkage disequilibrium (LD), using LD maps and haplotypes in ways that are rapidly evolving. This has led to large international programmes, some promising and others alarming, with laws about DNA patenting and ethical guidelines for responsible research still struggling to be born. PMID:14561327
... Handouts Genetic counseling (Medical Encyclopedia) Also in Spanish Topic Image MedlinePlus Email Updates Get Genetic Counseling updates ... GO GO MEDICAL ENCYCLOPEDIA Genetic counseling Related Health Topics Birth Defects Family History Genetic Disorders Genetic Testing ...
... sobre las pruebas genéticas Frequently Asked Questions About Genetic Testing What is genetic testing? What can I ... find more information about genetic testing? What is genetic testing? Genetic testing uses laboratory methods to look ...
... of genetic tests? What are the types of genetic tests? Genetic testing can provide information about a ... paternity). For more information about the uses of genetic testing: A Brief Primer on Genetic Testing , which ...
McKenna, Dan; Pulvermacher, R.; Davis, D. R.
We have developed and are currently testing an autonomous 2 channel photometer designed to measure the night sky brightness in the visual wavelengths over a multi-year campaign. The photometer uses a robust silicon sensor filtered with Hoya CM500 glass. The Sky brightness is measured every minute at two elevation angles typically zenith and 20 degrees to monitor brightness and transparency. The Sky Brightness monitor consists of two units, the remote photometer and a network interface. Currently these devices use 2.4 Ghz transceivers with a free space range of 100 meters. The remote unit is battery powered with day time recharging using a solar panel. Data received by the network interface transmits data via standard POP Email protocol. A second version is under development for radio sensitive areas using an optical fiber for data transmission. We will present the current comparison with the National Park Service sky monitoring camera. We will also discuss the calibration methods used for standardization and temperature compensation. This system is expected to be deployed in the next year and be operated by the International Dark Sky Association SKYMONITOR project.
Janikow, Cezary Z.
Genetic programming refers to a class of genetic algorithms utilizing generic representation in the form of program trees. For a particular application, one needs to provide the set of functions, whose compositions determine the space of program structures being evolved, and the set of terminals, which determine the space of specific instances of those programs. The algorithm searches the space for the best program for a given problem, applying evolutionary mechanisms borrowed from nature. Genetic algorithms have shown great capabilities in approximately solving optimization problems which could not be approximated or solved with other methods. Genetic programming extends their capabilities to deal with a broader variety of problems. However, it also extends the size of the search space, which often becomes too large to be effectively searched even by evolutionary methods. Therefore, our objective is to utilize problem constraints, if such can be identified, to restrict this space. In this publication, we propose a generic constraint specification language, powerful enough for a broad class of problem constraints. This language has two elements -- one reduces only the number of program instances, the other reduces both the space of program structures as well as their instances. With this language, we define the minimal set of complete constraints, and a set of operators guaranteeing offspring validity from valid parents. We also show that these operators are not less efficient than the standard genetic programming operators if one preprocesses the constraints - the necessary mechanisms are identified.
Luquetti, Daniela V; Heike, Carrie L; Hing, Anne V; Cunningham, Michael L; Cox, Timothy C
Microtia is a congenital anomaly of the ear that ranges in severity from mild structural abnormalities to complete absence of the ear, and can occur as an isolated birth defect or as part of a spectrum of anomalies or a syndrome. Microtia is often associated with hearing loss and patients typically require treatment for hearing impairment and surgical ear reconstruction. The reported prevalence varies among regions, from 0.83 to 17.4 per 10,000 births, and the prevalence is considered to be higher in Hispanics, Asians, Native Americans, and Andeans. The etiology of microtia and the cause of this wide variability in prevalence are poorly understood. Strong evidence supports the role of environmental and genetic causes for microtia. Although some studies have identified candidate genetic variants for microtia, no causal genetic mutation has been confirmed. The application of novel strategies in developmental biology and genetics has facilitated elucidation of mechanisms controlling craniofacial development. In this paper we review current knowledge of the epidemiology and genetics of microtia, including potential candidate genes supported by evidence from human syndromes and animal models. We also discuss the possible etiopathogenesis in light of the hypotheses formulated to date: Neural crest cells disturbance, vascular disruption, and altitude.
Peralta-Romero, José de Jesús; Gómez-Zamudio, Jaime Héctor; Estrada-Velasco, Bárbara; Karam-Araujo, Roberto; Cruz-López, Miguel
Obesity is a major health problem around the globe. The statistics of overweight and obesity at early ages have reached alarming levels and placed our country in the first place in regard to childhood obesity. In the development of obesity two major factors take part, one genetic and the other one environmental. From the perspective of environmental changes both overweight and obesity result from the imbalance in the energy balance: people ingest more energy than they expend. Despite people live in the same obesogenic environment not all of them develop obesity; it requires genetic factors for this to happen. This review focuses on the description of the main methodologies to find genetic markers, as well as the main loci in candidate genes, whose single nucleotide polymorphisms (SNPs) are associated with obesity and its comorbidities in children, highlighting the association of these genes in the Mexican population. Knowledge of the genetic markers associated with obesity will help to understand the molecular and physiological mechanisms, the genetic background and changes in body mass index in the Mexican population. This information is useful for the planning of new hypotheses in the search for new biomarkers that can be used in a predictive and preventive way, as well as for the development of new therapeutic strategies.
Skibola, Christine F.; Curry, John D.; Nieters, Alexandra
BACKGROUND Genetic susceptibility studies of lymphoma may serve to identify at risk populations and to elucidate important disease mechanisms. METHODS This review considered all studies published through October 2006 on the contribution of genetic polymorphisms in the risk of lymphoma. RESULTS Numerous studies implicate the role of genetic variants that promote B-cell survival and growth with increased risk of lymphoma. Several reports including a large pooled study by InterLymph, an international consortium of non-Hodgkin lymphoma (NHL) case-control studies, found positive associations between variant alleles in TNF -308G>A and IL10 -3575T>A genes and risk of diffuse large B-cell lymphoma. Four studies reported positive associations between a GSTT1 deletion and risk of Hodgkin and non-Hodgkin lymphoma. Genetic studies of folate-metabolizing genes implicate folate in NHL risk, but further studies that include folate and alcohol assessments are needed. Links between NHL and genes involved in energy regulation and hormone production and metabolism may provide insights into novel mechanisms implicating neuro- and endocrine-immune cross-talk with lymphomagenesis, but will need replication in larger populations. CONCLUSIONS Numerous studies suggest that common genetic variants with low penetrance influence lymphoma risk, though replication studies will be needed to eliminate false positive associations. PMID:17606447
Loukola, Anu; Hällfors, Jenni; Korhonen, Tellervo; Kaprio, Jaakko
Regular smoking is the major risk factor for cardiovascular disease and cancers, and thus is one of the most preventable causes of morbidity and mortality worldwide. Intake of nicotine, its central nervous system effects, and its metabolism are regulated by biological pathways; some of these are well known, but others are not. Genetic studies offer a method for developing insights into the genes contributing to those pathways. In recent years, large genome-wide association study (GWAS) meta-analyses have consistently revealed that the strongest genetic contribution to smoking-related traits comes from variation in the nicotinic receptor subunit genes. Many other genes, including those coding for enzymes involved in nicotine metabolism, also have been implicated. However, the proportion of phenotypic variance explained by the identified genetic variants is very modest. This review intends to cover progress made in genetics and genetic epidemiology of smoking behavior in recent years, and focuses on studies revealing the nicotinic receptor gene cluster on chromosome 15q25. Evidence supporting the involvement of a novel pathway in the shared pathophysiology of nicotine dependence and schizophrenia is also briefly reviewed. A summary of the current knowledge on gene–environment interactions involved in smoking behavior is included. PMID:24778978
Ninoa, F.; Ilaria, M.; Noviello, C.; Santoro, L.; Rätsch, I.M.; Martino, A.; Cobellis, G.
Vesicoureteral reflux (VUR) is the retrograde passage of urine from the bladder to the upper urinary tract. It is the most common congenital urological anomaly affecting 1-2% of children and 30-40% of patients with urinary tract infections. VUR is a major risk factor for pyelonephritic scarring and chronic renal failure in children. It is the result of a shortened intravesical ureter with an enlarged or malpositioned ureteric orifice. An ectopic embryonal ureteric budding development is implicated in the pathogenesis of VUR, which is a complex genetic developmental disorder. Many genes are involved in the ureteric budding formation and subsequently in the urinary tract and kidney development. Previous studies demonstrate an heterogeneous genetic pattern of VUR. In fact no single major locus or gene for primary VUR has been identified. It is likely that different forms of VUR with different genetic determinantes are present. Moreover genetic studies of syndromes with associated VUR have revealed several possible candidate genes involved in the pathogenesis of VUR and related urinary tract malformations. Mutations in genes essential for urinary tract morphogenesis are linked to numerous congenital syndromes, and in most of those VUR is a feature. The Authors provide an overview of the developmental processes leading to the VUR. The different genes and signaling pathways controlling the embryonal urinary tract development are analyzed. A better understanding of VUR genetic bases could improve the management of this condition in children. PMID:27013925
Fernández-Moreno, Mercedes; Rego, Ignacio; Carreira-Garcia, Vanessa; Blanco, Francisco J
Osteoarthritis is a degenerative articular disease with complex pathogeny because diverse factors interact causing a process of deterioration of the cartilage. Despite the multifactorial nature of this pathology, from the 50’s it´s known that certain forms of osteoarthritis are related to a strong genetic component. The genetic bases of this disease do not follow the typical patterns of mendelian inheritance and probably they are related to alterations in multiple genes. The identification of a high number of candidate genes to confer susceptibility to the development of the osteoarthritis shows the complex nature of this disease. At the moment, the genetic mechanisms of this disease are not known, however, which seems clear is that expression levels of several genes are altered, and that the inheritance will become a substantial factor in future considerations of diagnosis and treatment of the osteoarthritis. PMID:19516961
DOBBS, Matthew B; GURNETT, Christina A
Modern advances in genetics have allowed investigators to begin to identify the complex etiology of clubfoot. It has become increasingly apparent that clubfoot is a heterogeneous disorder with a polygenetic threshold model explaining its inheritance patterns. Several recent genetics studies have identified a key developmental pathway, the PITX1-TBX4 transriptional pathway, as being important in clubfoot etiology. Both PITX1 and TBX4 are uniquely expressed in the hindlimb which helps explain the foot phenotype seen with mutations in these transcription factors. Future studies are needed to develop animal models to determine the exact mechanisms by which these genetic abnormalities cause clubfoot and to test other hypotheses of clubfoot pathogenesis. PMID:21817922
This paper provides part of an analysis of the use of the Maori term whakapapa in a study designed to test the compatibility and commensurability of views of members of the indigenous culture of New Zealand with other views of genetic technologies extant in the country. It is concerned with the narrow sense of whakapapa as denoting biological ancestry, leaving the wider sense of whakapapa as denoting cultural identity for discussion elsewhere. The phenomenon of genetic curiosity is employed to facilitate this comparison. Four levels of curiosity are identified, in the Maori data, which penetrate more or less deeply into the psyche of individuals, affecting their health and wellbeing. These phenomena are compared with non-Maori experiences and considerable commonalities are discovered together with a point of marked difference. The results raise important questions for the ethical application of genetic technologies.
Escamilla, Michael A; Zavala, Juan M
Bipolar disorder especially the most severe type (type I), has a strong genetic component. Family studies suggest that a small number of genes of modest effect are involved in this disorder. Family-based studies have identified a number of chromosomal regions linked to bipolar disorder, and progress is currently being made in identifying positional candidate genes within those regions. A number of candidate genes have also shown evidence of association with bipolar disorder, and genome-wide association studies are now under way, using dense genetic maps. Replication studies in larger or combined datasets are needed to definitively assign a role for specific genes in this disorder. This review covers our current knowledge of the genetics of bipolar disorder, and provides a commentary on current approaches used to identify the genes involved in this complex behavioral disorder.
Buskila, Dan; Neumann, Lily
The pathogenesis of fibromyalgia (FM) and related conditions is not entirely understood. Recent evidence suggests that these syndromes may share heritable pathophysiologic features. Familial studies suggest that genetic and familial factors may play a role in the etiopathogenesis of these conditions. There is evidence that polymorphisms of genes in the serotoninergic and catecholaminergic systems are linked to the pathophysiology of FM and related conditions and are associated with personality traits. The precise role of genetic factors in the etiopathology of FM remains unknown, but it is likely that several genes are operating together to initiate this syndrome. Larger longitudinal studies are needed to better clarify the role of genetics in the development of FM.
He, Kan; Zhou, Tao; Shao, Jiaofang; Ren, Xiaoliang; Zhao, Zhongying; Liu, Dahai
Numerous genetic targets and some individual pathways associated with aging have been identified using the worm model. However, less is known about the genetic mechanisms of aging in genome wide, particularly at the level of multiple pathways as well as the regulatory networks during aging. Here, we employed the gene expression datasets of three time points during aging in Caenorhabditis elegans (C. elegans) and performed the approach of gene set enrichment analysis (GSEA) on each dataset between adjacent stages. As a result, multiple genetic pathways and targets were identified as significantly down- or up-regulated. Among them, 5 truly aging-dependent signaling pathways including MAPK signaling pathway, mTOR signaling pathway, Wnt signaling pathway, TGF-beta signaling pathway and ErbB signaling pathway as well as 12 significantly associated genes were identified with dynamic expression pattern during aging. On the other hand, the continued declines in the regulation of several metabolic pathways have been demonstrated to display age-related changes. Furthermore, the reconstructed regulatory networks based on three of aging related Chromatin immunoprecipitation experiments followed by sequencing (ChIP-seq) datasets and the expression matrices of 154 involved genes in above signaling pathways provide new insights into aging at the multiple pathways level. The combination of multiple genetic pathways and targets needs to be taken into consideration in future studies of aging, in which the dynamic regulation would be uncovered.
Sauzède, R.; Claustre, H.; Uitz, J.; Jamet, C.; Dall'Olmo, G.; D'Ortenzio, F.; Gentili, B.; Poteau, A.; Schmechtig, C.
The present study proposes a novel method that merges satellite ocean color bio-optical products with Argo temperature-salinity profiles to infer the vertical distribution of the particulate backscattering coefficient (bbp). This neural network-based method (SOCA-BBP for Satellite Ocean-Color merged with Argo data to infer the vertical distribution of the Particulate Backscattering coefficient) uses three main input components: (1) satellite-based surface estimates of bbp and chlorophyll a concentration matched up in space and time with (2) depth-resolved physical properties derived from temperature-salinity profiles measured by Argo profiling floats and (3) the day of the year of the considered satellite-Argo matchup. The neural network is trained and validated using a database including 4725 simultaneous profiles of temperature-salinity and bio-optical properties collected by Bio-Argo floats, with concomitant satellite-derived products. The Bio-Argo profiles are representative of the global open-ocean in terms of oceanographic conditions, making the proposed method applicable to most open-ocean environments. SOCA-BBP is validated using 20% of the entire database (global error of 21%). We present additional validation results based on two other independent data sets acquired (1) by four Bio-Argo floats deployed in major oceanic basins, not represented in the database used to train the method; and (2) during an AMT (Atlantic Meridional Transect) field cruise in 2009. These validation tests based on two fully independent data sets indicate the robustness of the predicted vertical distribution of bbp. To illustrate the potential of the method, we merged monthly climatological Argo profiles with ocean color products to produce a depth-resolved climatology of bbp for the global ocean.
Parisi, Federico; Ferrari, Gianluigi; Giuberti, Matteo; Contin, Laura; Cimolin, Veronica; Azzaro, Corrado; Albani, Giovanni; Mauro, Alessandro
Recently, we have proposed a body-sensor-network-based approach, composed of a few body-worn wireless inertial nodes, for automatic assignment of Unified Parkinson's Disease Rating Scale (UPDRS) scores in the following tasks: Leg agility (LA), Sit-to-Stand (S2S), and Gait (G). Unlike our previous works and the majority of the published studies, where UPDRS tasks were the sole focus, in this paper, we carry out a comparative investigation of the LA, S2S, and G tasks. In particular, after providing an accurate description of the features identified for the kinematic characterization of the three tasks, we comment on the correlation between the most relevant kinematic parameters and the UPDRS scoring. We analyzed the performance achieved by the automatic UPDRS scoring system and compared the estimated UPDRS evaluation with the one performed by neurologists, showing that the proposed system compares favorably with typical interrater variability. We then investigated the correlations between the UPDRS scores assigned to the various tasks by both the neurologists and the automatic system. The results, based on a limited number of subjects with Parkinson's disease (PD) (34 patients, 47 clinical trials), show poor-to-moderate correlations between the UPDRS scores of different tasks, highlighting that the patients' motor performance may vary significantly from one task to another, since different tasks relate to different aspects of the disease. An aggregate UPDRS score is also considered as a concise parameter, which can provide additional information on the overall level of the motor impairments of a Parkinson's patient. Finally, we discuss a possible implementation of a practical e-health application for the remote monitoring of PD patients.
Kaushal, Mayank; Oni-Orisan, Akinwunmi; Chen, Gang; Li, Wenjun; Leschke, Jack; Ward, B Douglas; Kalinosky, Benjamin; Budde, Matthew D; Schmit, Brian D; Li, Shi-Jiang; Muqeet, Vaishnavi; Kurpad, Shekar N
Large-scale network analysis characterizes the brain as a complex network of nodes and edges to evaluate functional connectivity patterns. The utility of graph-based techniques has been demonstrated in an increasing number of resting-state functional MRI (rs-fMRI) studies in the normal and diseased brain. However, to our knowledge, graph theory has not been used to study the reorganization pattern of resting-state brain networks in patients with traumatic complete spinal cord injury (SCI). In the present analysis, we applied a graph-theoretical approach to explore changes to global brain network architecture as a result of SCI. Fifteen subjects with chronic (> 2 years) complete (American Spinal Injury Association [ASIA] A) cervical SCI and 15 neurologically intact controls were scanned using rs-fMRI. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI) or nodes. The average time series was extracted at each node, and correlation analysis was performed between every pair of nodes. A functional connectivity matrix for each subject was then generated. Subsequently, the matrices were averaged across groups, and network changes were evaluated between groups using the network-based statistic (NBS) method. Our results showed decreased connectivity in a subnetwork of the whole brain in SCI compared with control subjects. Upon further examination, increased connectivity was observed in a subnetwork of the sensorimotor cortex and cerebellum network in SCI. In conclusion, our findings emphasize the applicability of NBS to study functional connectivity architecture in diseased brain states. Further, we show reorganization of large-scale resting-state brain networks in traumatic SCI, with potential prognostic and therapeutic implications.
De, Suvranu; Deo, Dhannanjay; Sankaranarayanan, Ganesh; Arikatla, Venkata S
BACKGROUND: While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. METHODS: In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. RESULTS: We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. CONCLUSIONS: A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal
Agarwal, Smriti; Bisht, Amit Singh; Singh, Dharmendra; Pathak, Nagendra Prasad
Millimetre wave imaging (MMW) is gaining tremendous interest among researchers, which has potential applications for security check, standoff personal screening, automotive collision-avoidance, and lot more. Current state-of-art imaging techniques viz. microwave and X-ray imaging suffers from lower resolution and harmful ionizing radiation, respectively. In contrast, MMW imaging operates at lower power and is non-ionizing, hence, medically safe. Despite these favourable attributes, MMW imaging encounters various challenges as; still it is very less explored area and lacks suitable imaging methodology for extracting complete target information. Keeping in view of these challenges, a MMW active imaging radar system at 60 GHz was designed for standoff imaging application. A C-scan (horizontal and vertical scanning) methodology was developed that provides cross-range resolution of 8.59 mm. The paper further details a suitable target identification and classification methodology. For identification of regular shape targets: mean-standard deviation based segmentation technique was formulated and further validated using a different target shape. For classification: probability density function based target material discrimination methodology was proposed and further validated on different dataset. Lastly, a novel artificial neural network based scale and rotation invariant, image reconstruction methodology has been proposed to counter the distortions in the image caused due to noise, rotation or scale variations. The designed neural network once trained with sample images, automatically takes care of these deformations and successfully reconstructs the corrected image for the test targets. Techniques developed in this paper are tested and validated using four different regular shapes viz. rectangle, square, triangle and circle.
This chapter describes the resources held at the Maize Genetics Cooperation • Stock Center in detail and also provides some information about the North Central Regional Plant Introduction Station (NCRPIS) in Ames, IA, Centro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT) in Mexico, and the N...
Radford, A.; Bird-Stewart, J. A.
Discusses the genetics content in secondary school curricula, suggesting possible revisions to current A- and 0-level syllabi. Present teaching methods, textbooks, and General Certificate of Education (GCE) examination questions are reviewed, problems identified, and suggestions made regarding possible improvements. (Author/JN)
Knowing the location and make-up of each of the 50,000 to 100,000 human genes will revolutionize the practice of medicine. This knowledge will lead to tailor-made therapies not only for treating disease but also for preventing it - in short, to a new concept of patient care. The Human Genome Project, a 15-year, $3 billion quest to determine the nucleotide sequence of the entire human genome, will make this possible. In The New Genetics, Leon Jaroff recounts the long path of discovery thatt has led to this huge new scientific venture - from the theory of heredity put forth by Aristotle more than 2,000 years ago to the current attempts to treat adenosine deaminase (ADA) deficiency and malignant melanoma via gene therapy. Against this background, the geneticists, molecular biologists, clinicians, and ethicists involved in the Human Genome Project describe their work and how it will provide physicians with ever more precise and effective tools to treat human disease. Jaroff also reveals the other, more problematic side of the story. Patients with an undesirable genetic profile may be subject to discrimination by private insurers. Physicians who fail to recommend genetic screening may find themselves victims of malpractice or wrongful-life suits. Indeed, these issues and others have already begun to affect physicians. The New Genetics makes it abundantly clear tha a revolution has arrived, and that physicians must be prepared to cope with the new order.
Aznar, Mercedes Martinez; Orcajo, Teresa Ibanez
A teaching unit on genetics and human inheritance using problem-solving methodology was undertaken with fourth-level Spanish Secondary Education students (15 year olds). The goal was to study certain aspects of the students' learning process (concepts, procedures and attitude) when using this methodology in the school environment. The change…
Mallipatna, Ashwin; Marino, Meghan; Singh, Arun D
Retinoblastoma is a malignant retinal tumor that affects young children. Mutations in the RB1 gene cause retinoblastoma. Mutations in both RB1 alleles within the precursor retinal cell are essential, with one mutation that may be germline or somatic and the second one that is always somatic. Identification of the RB1 germline status of a patient allows differentiation between sporadic and heritable retinoblastoma variants. Application of this knowledge is crucial for assessing short-term (risk of additional tumors in the same eye and other eye) and long-term (risk of nonocular malignant tumors) prognosis and offering cost-effective surveillance strategies. Genetic testing and genetic counseling are therefore essential components of care for all children diagnosed with retinoblastoma. The American Joint Committee on Cancer has acknowledged the importance of detecting this heritable trait and has introduced the letter "H" to denote a heritable trait of all cancers, starting with retinoblastoma (in publication). In this article, we discuss the clinically relevant aspects of genetic testing and genetic counseling for a child with retinoblastoma.
Stančáková, Alena; Laakso, Markku
Metabolic syndrome (MetS) is a cluster of metabolic traits associated with an increased risk of cardiovascular disease and type 2 diabetes mellitus. Central obesity and insulin resistance are thought to play key roles in the pathogenesis of the MetS. The MetS has a significant genetic component, and therefore linkage analysis, candidate gene approach, and genome-wide association (GWA) studies have been applied in the search of gene variants for the MetS. A few variants have been identified, located mostly in or near genes regulating lipid metabolism. GWA studies for the individual components of the MetS have reported several loci having pleiotropic effects on multiple MetS-related traits. Genetic studies have provided so far only limited evidence for a common genetic background of the MetS. Epigenetic factors (DNA methylation and histone modification) are likely to play important roles in the pathogenesis of the MetS, and they might mediate the effects of environmental exposures on the risk of the MetS. Further research is needed to clarify the role of genetic variation and epigenetic mechanisms in the development of the MetS.
Corn breeding has been historically remarkably successful. Much research has investigated optimal breeding procedures, which are detailed here. A smaller effort has been put into identifying useful genetic resources for maize and how to best use them, but results from long-term base broadening effor...
Snyder, Alexandra; Makarov, Vladimir; Hellmann, Matthew; Rizvi, Naiyer; Merghoub, Taha; Wolchok, Jedd D; Chan, Timothy A
Immune checkpoint blockade therapy is changing oncology by improving the outcome of patients with advanced malignancies. Our research has revealed the genetic features of tumors present in patients who initiate a successful antitumor immune response and derive clinical benefit from immune checkpoint blockade therapy versus non-responders. PMID:26451299
The "Central Dogma" of genetics states that one gene, located in a DNA molecule, is ultimately translated into one protein. As important as this idea is, many teachers shy away from teaching the actual mechanism of gene translation, and many students find the concepts abstract and inaccessible. This article describes a unit, called Genetics…
McGillivray, Barbara C.
Genetic concerns and indications for prenatal diagnosis are first recognized by the family physician. Review of personal, pregnancy and family history may indicate concerns beyond that of advanced maternal age. Amniocentesis is still the most frequently used modality for prenatal diagnosis, but detailed ultrasound is valuable for structural abnormalities, and chorionic villus sampling is now being tested as an alternative to amniocentesis. PMID:21267316
Rosellini, D.; Veronesi, F.
The application of genetic engineering to plants has provided genetically modified plants (GMPs, or transgenic plants) that are cultivated worldwide on increasing areas. The most widespread GMPs are herbicide-resistant soybean and canola and insect-resistant corn and cotton. New GMPs that produce vaccines, pharmaceutical or industrial proteins, and fortified food are approaching the market. The techniques employed to introduce foreign genes into plants allow a quite good degree of predictability of the results, and their genome is minimally modified. However, some aspects of GMPs have raised concern: (a) control of the insertion site of the introduced DNA sequences into the plant genome and of its mutagenic effect; (b) presence of selectable marker genes conferring resistance to an antibiotic or an herbicide, linked to the useful gene; (c) insertion of undesired bacterial plasmid sequences; and (d) gene flow from transgenic plants to non-transgenic crops or wild plants. In response to public concerns, genetic engineering techniques are continuously being improved. Techniques to direct foreign gene integration into chosen genomic sites, to avoid the use of selectable genes or to remove them from the cultivated plants, to reduce the transfer of undesired bacterial sequences, and make use of alternative, safer selectable genes, are all fields of active research. In our laboratory, some of these new techniques are applied to alfalfa, an important forage plant. These emerging methods for plant genetic engineering are briefly reviewed in this work.
Genetic studies have helped us gain basic knowledge of the Tamarix invasion. We now have a better understanding of the species identities involved in the invasion, their evolutionary relationships, and the contribution of hybridization to the invasion. This information can be used to enhance the eff...
Good progress is being made on genetics and genomics of sugar beet, however it is in process and the tools are now being generated and some results are being analyzed. The GABI BeetSeq project released a first draft of the sugar beet genome of KWS2320, a dihaploid (see http://bvseq.molgen.mpg.de/Gen...
Atkins, Thomas; Roderick, Joyce
In order for students to understand genetics and evolution, they must first understand the structure of the DNA molecule. The function of DNA proceeds from its unique structure, a structure beautifully adapted for information storage, transcription, translation into amino acid sequences, replication, and time travel. The activity described in this…
Seager, Robert D.
In learning genetics, many students misunderstand and misinterpret what "dominance" means. Understanding is easier if students realize that dominance is not a mechanism, but rather a consequence of underlying cellular processes. For example, metabolic pathways are often little affected by changes in enzyme concentration. This means that…
Genetic research of the sunflower research unit, USDA-ARS, in Fargo, ND, was discussed in a presentation to a group of Canadian producers, industry representatives, and scientists. Because this was an international audience, I introduced the audience to ARS and the structure of the sunflower unit, a...
MacClintic, Scott D.; Nelson, Genevieve M.
Bacterial transformation is a commonly used technique in genetic engineering that involves transferring a gene of interest into a bacterial host so that the bacteria can be used to produce large quantities of the gene product. Although several kits are available for performing bacterial transformation in the classroom, students do not always…
A genetic variability analysis involving 45 accessions of Macadamia including four species, M. integrifolia, M. tetraphylla, M. ternifolia, and M. hildebrandii and a wild relative, Hicksbeachia pinnatifolia was performed usingeight enzyme systems encoded by 16 loci (Gpi-1 and 2, Idh-1 and 2, Lap, Md...
Edenberg, Howard J; Foroud, Tatiana
Multiple lines of evidence strongly indicate that genetic factors contribute to the risk for alcohol use disorders (AUD). There is substantial heterogeneity in AUD, which complicates studies seeking to identify specific genetic factors. To identify these genetic effects, several different alcohol-related phenotypes have been analyzed, including diagnosis and quantitative measures related to AUDs. Study designs have used candidate gene analyses, genetic linkage studies, genomewide association studies (GWAS), and analyses of rare variants. Two genes that encode enzymes of alcohol metabolism have the strongest effect on AUD: aldehyde dehydrogenase 2 and alcohol dehydrogenase 1B each has strongly protective variants that reduce risk, with odds ratios approximately 0.2-0.4. A number of other genes important in AUD have been identified and replicated, including GABRA2 and alcohol dehydrogenases 1B and 4. GWAS have identified additional candidates. Rare variants are likely also to play a role; studies of these are just beginning. A multifaceted approach to gene identification, targeting both rare and common variations and assembling much larger datasets for meta-analyses, is critical for identifying the key genes and pathways important in AUD.
Nance, Walter E
Deafness is an etiologically heterogeneous trait with many known genetic and environmental causes. Genetic factors account for at least half of all cases of profound congenital deafness, and can be classified by the mode of inheritance and the presence or absence of characteristic clinical features that may permit the diagnosis of a specific form of syndromic deafness. The identification of more than 120 independent genes for deafness has provided profound new insights into the pathophysiology of hearing, as well as many unexpected surprises. Although a large number of genes can clearly cause deafness, recessive mutations at a single locus, GJB2 or Connexin 26, account for more than half of all genetic cases in some, but not all populations. The high frequency may well be related to the greatly improved social, educational, and economic circumstances of the deaf that began with the introduction of sign language 300-400 years ago, along with a high frequency of marriages among the deaf in many countries. Similar mechanisms may account for the rapid fixation of genes for speech after the first mutations appeared 50,000-100,000 years ago. Molecular studies have shown that mutations involving several different loci may be the cause for the same form of syndromic deafness. Even within a single locus, different mutations can have profoundly different effects, leading to a different pattern of inheritance in some cases, or isolated hearing loss without the characteristic syndromic features in others. Most cases of genetic deafness result from mutations at a single locus, but an increasing number of examples are being recognized in which recessive mutations at two loci are involved. For example, digenic interactions are now known to be an important cause of deafness in individuals who carry a single mutation at the Connexin 26 locus along with a deletion involving the functionally related Connexin 30 locus. This mechanism complicates genetic evaluation and counseling, but
Chen, Chi-Hua; Fiecas, Mark; Gutiérrez, E. D.; Panizzon, Matthew S.; Eyler, Lisa T.; Vuoksimaa, Eero; Thompson, Wesley K.; Fennema-Notestine, Christine; Hagler, Donald J.; Jernigan, Terry L.; Neale, Michael C.; Franz, Carol E.; Lyons, Michael J.; Fischl, Bruce; Tsuang, Ming T.; Dale, Anders M.; Kremen, William S.
Animal data show that cortical development is initially patterned by genetic gradients largely along three orthogonal axes. We previously reported differences in genetic influences on cortical surface area along an anterior-posterior axis using neuroimaging data of adult human twins. Here, we demonstrate differences in genetic influences on cortical thickness along a dorsal-ventral axis in the same cohort. The phenomenon of orthogonal gradations in cortical organization evident in different structural and functional properties may originate from genetic gradients. Another emerging theme of cortical patterning is that patterns of genetic influences recapitulate the spatial topography of the cortex within hemispheres. The genetic patterning of both cortical thickness and surface area corresponds to cortical functional specializations. Intriguingly, in contrast to broad similarities in genetic patterning, two sets of analyses distinguish cortical thickness and surface area genetically. First, genetic contributions to cortical thickness and surface area are largely distinct; there is very little genetic correlation (i.e., shared genetic influences) between them. Second, organizing principles among genetically defined regions differ between thickness and surface area. Examining the structure of the genetic similarity matrix among clusters revealed that, whereas surface area clusters showed great genetic proximity with clusters from the same lobe, thickness clusters appear to have close genetic relatedness with clusters that have similar maturational timing. The discrepancies are in line with evidence that the two traits follow different mechanisms in neurodevelopment. Our findings highlight the complexity of genetic influences on cortical morphology and provide a glimpse into emerging principles of genetic organization of the cortex. PMID:24082094
Discusses the claims for a brave new world of genetic manipulation" and concludes that if we could agree upon applying genetic (or any other effective) remedies to global problems we probably would need no rescourse to them. Suggests that effective methods of preventing genetic disease are prevention of mutations and detection and…
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... a combination of genetic variations and environmental factors influence the development of this complex condition. Asperger syndrome ... healthcare professional . About Genetics Home Reference Site Map Customer Support Selection Criteria for Links USA.gov Copyright ...
... Share: Email Facebook Twitter Home Health Conditions Genes Chromosomes & mtDNA Resources Help Me Understand Genetics Home Health ... with a rearrangement (translocation) of genetic material between chromosomes 17 and 22. This translocation, written as t( ...
Kottow, Miguel H
Genetics research has shown enormous developments in recent decades, although as yet with only limited clinical application. Bioethical analysis has been unable to deal with the vast problems of genetics because emphasis has been put on the principlism applied to both clinical and research bioethics. Genetics nevertheless poses its most complex moral dilemmas at the public level, where a social brand of ethics ought to supersede the essentially interpersonal perspective of principlism. A more social understanding of ethics in genetics is required to unravel issues such as research and clinical explorations, ownership and patents, genetic manipulation, and allocation of resources. All these issues require reflection based on the requirements of citizenry, consideration of common assets, and definition of public policies in regulating genetic endeavors and protecting the society as a whole Bioethics has privileged the approach to individual ethical issues derived from genetic intervention, thereby neglecting the more salient aspects of genetics and social ethics.
Rapid technological advances in genetic analysis have revealed the genetic background of various diseases. Elucidation of the genes responsible for a disease enables better clinical management of the disease and helps to develop targeted drugs. Also, early diagnosis and management of at-risk family members can be made by identification of a genetic disease in the proband. On the other hand, genetic issues often cause psychological distress to the family. To perform genetic testing appropriately and to protect patients and family members from any harm, guidelines for genetic testing were released from the alliance of Japanese genetics-related academic societies in 2003. As genetic testing is becoming incorporated into clinical practice more broadly, the guideline was revised and released by the Japanese Society of Medical Sciences in 2011. All medical professionals in Japan are expected to follow this guideline.
... more common in particular ethnic groups? Genetic Changes Mutations in the IKBKG gene cause incontinentia pigmenti . The ... About 80 percent of affected individuals have a mutation that deletes some genetic material from the IKBKG ...
... Brunner HG. Feingold syndrome: clinical review and genetic mapping. Am J Med Genet A. 2003 Nov 1; ... Brunner HG. MYCN haploinsufficiency is associated with reduced brain size and intestinal atresias in Feingold syndrome. Nat ...
Miller, Charles J J; Matute, Daniel R
Our understanding of how genetic changes underlie the evolution of traits is growing fast. Two new studies now show that changes in the same genetic loci can drive the evolution of the same trait in multiple Drosophila species.
Miller, P S
Author argues that the Americans with Disabilities Act prohibits discrimination against workers based on their genetic makeup. He also examines state legislation and recently proposed federal legislation prohibiting genetic discrimination.
... MKS Related Information How are genetic conditions and genes named? Additional Information & Resources MedlinePlus (3 links) Health Topic: Brain Malformations Health Topic: Kidney Cysts Health Topic: Neural Tube Defects Genetic and Rare Diseases Information Center (1 link) ...
Mertens, Thomas R.; Robinson, Sandra K.
Describes different sources of readings for understanding issues and concepts of genetic engineering. Broad categories of reading materials are: concerns about genetic engineering; its background; procedures; and social, ethical and legal issues. References are listed. (PS)
The need for education of nurses in genetics was articulated more than 25 years ago. This article reviews the knowledge of practicing nurses about genetics as well as the content of genetics in nursing curricula. Implementation of federal legislation that mandated increased availability of genetic services and genetics education provided support for the examination of genetics content in curricula for health professionals, including nurses, and for the development of model programs to expand this content. Recent efforts to begin to develop a pool of nurse faculty who are well prepared in genetics will be described, as well as programs to provide the necessary content through continuing-education efforts. These efforts are expected to substantially improve the capability of nurses to contribute more effectively in the delivery of genetic services. PMID:3177390
Oftedal, Gry; Parkkinen, Veli-Pekka
Synthetic biology research is often described in terms of programming cells through the introduction of synthetic genes. Genetic material is seemingly attributed with a high level of causal responsibility. We discuss genetic causation in synthetic biology and distinguish three gene concepts differing in their assumptions of genetic control. We argue that synthetic biology generally employs a difference-making approach to establishing genetic causes, and that this approach does not commit to a specific notion of genetic program or genetic control. Still, we suggest that a strong program concept of genetic material can be used as a successful heuristic in certain areas of synthetic biology. Its application requires control of causal context, and may stand in need of a modular decomposition of the target system. We relate different modularity concepts to the discussion of genetic causation and point to possible advantages of and important limitations to seeking modularity in synthetic biology systems.
... other types of holoprosencephaly caused by genetic syndromes, chromosome abnormalities, or substances that cause birth defects (teratogens). The ... Some people do not have apparent structural brain abnormalities but have some of the facial features ... deletion syndrome Genetic Testing Registry: Holoprosencephaly ...
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... Y Z Test Your Knowledge Talking Glossary of Genetic Terms Designed to help learners at any level ... in a reference paper. The Talking Glossary of Genetic Terms The Human Genome Defined by Professionals at ...
... Facebook Share on Twitter Your Guide to Understanding Genetic Conditions Search MENU Toggle navigation Home Page Search ... Conditions Genes Chromosomes & mtDNA Resources Help Me Understand Genetics Home Health Conditions Friedreich ataxia Friedreich ataxia Enable ...
... a History of Eye Disease, Do You Need Genetic Testing? Mar. 23, 2012 Thanks to news coverage, ... of breast or ovarian cancer. Physicians now use genetic tests to decide on treatment for some types ...
... 84 Alcohol Alert Number 84 Print Version The Genetics of Alcoholism Why can some people have a ... to an increased risk of alcoholism. Cutting-Edge Genetic Research in Alcoholism Although researchers already have made ...
A dictionary of more than 150 genetics-related terms written for healthcare professionals, developed to support the comprehensive, evidence-based, peer-reviewed PDQ cancer genetics information summaries.
The task of the genetic counselor who identifies genetic causes of mental retardation and assists families to understand risk of recurrence is described. Considered are chromosomal genetic disorders such as Down's syndrome, inherited disorders such as Tay-Sachs disease, identification by testing the amniotic fluid cells (amniocentresis) in time…
Caspari, E.W.; Scandalios, J.G.
This book presents articles on genetics and the advances made in this field. Topics covered include the following: recovery, repair, and mutagenesis in Schizosaccharomyces pombe; gene transfer in fungi; Y chromosome function and spermatogenesis in Drosophila hydei; recent developments in population genetics; and genetics, cytology and evolution of Gossypium.
Teaches genetics and inheritance using the characters from Sesame Street. Asks students to create a gene map of their favorite character and determine those genes passing to the next generation. Includes a genetics activity sheet and genetic information on the characters. (YDS)
de Souza, P V
This article analyses the Brasilia criminal regulation on genetic. Act No. 8.974/95 is examined because it regulates some criminal typologies on genetic engineering and assisted reproduction. Moreover, it presents information about the Act Project No. 149/97, on genetic discrimination.
Stewart, J. Bird
Claims that most instruction dealing with genetics is limited to sex education and personal hygiene. Suggests that the biology curriculum should begin to deal with other issues related to genetics, including genetic normality, prenatal diagnoses, race, and intelligence. Predicts these topics will begin to appear in British examination programs.…
... 23(3):157-65. Review. Citation on PubMed Bird TD. Genetic aspects of Alzheimer disease. Genet Med. ... on PubMed or Free article on PubMed Central Bird TD. Genetic factors in Alzheimer's disease. N Engl ...